%0 Journal Article %J J Alzheimers Dis %D 2022 %T Analysis and Identification Genetic Effect of SARS-CoV-2 Infections to Alzheimer's Disease Patients by Integrated Bioinformatics. %A Wang, Fang %A Xu, Jia %A Xu, Shu-Jun %A Guo, Jie-Jie %A Wang, Feiming %A Wang, Qin-Wen %K Alzheimer Disease %K Computational Biology %K COVID-19 %K Databases, Genetic %K Gene Expression Profiling %K Humans %K Protein Interaction Maps %K SARS-CoV-2 %X

BACKGROUND: COVID-19 pandemic is a global crisis which results in millions of deaths and causes long-term neurological sequelae, such as Alzheimer's disease (AD).

OBJECTIVE: We aimed to explore the interaction between COVID-19 and AD by integrating bioinformatics to find the biomarkers which lead to AD occurrence and development with COVID-19 and provide early intervention.

METHODS: The differential expressed genes (DEGs) were found by GSE147507 and GSE132903, respectively. The common genes between COVID-19 and AD were identified. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interactions (PPI) network analysis were carried out. Hub genes were found by cytoscape. A multivariate logistic regression model was constructed. NetworkAnalyst was used for the analysis of TF-gene interactions, TF-miRNA coregulatory network, and Protein-chemical Interactions.

RESULTS: Forty common DEGs for AD and COVID-19 were found. GO and KEGG analysis indicated that the DEGs were enriched in the calcium signal pathway and other pathways. A PPI network was constructed, and 5 hub genes were identified (ITPR1, ITPR3, ITPKB, RAPGEF3, MFGE8). Four hub genes (ITPR1, ITPR3, ITPKB, RAPGEF3) which were considered as important factors in the development of AD that were affected by COVID-19 were shown by nomogram. Utilizing NetworkAnalyst, the interaction network of 4 hub genes and TF, miRNA, common AD risk genes, and known compounds is displayed, respectively.

CONCLUSION: COVID-19 patients are at high risk of developing AD. Vaccination is required. Four hub genes can be considered as biomarkers for prediction and treatment of AD development caused by COVID-19. Compounds with neuroprotective effects can be used as adjuvant therapy for COVID-19 patients.

%B J Alzheimers Dis %V 85 %P 729-744 %8 2022 %G eng %N 2 %1 https://www.ncbi.nlm.nih.gov/pubmed/34776447?dopt=Abstract %R 10.3233/JAD-215086 %0 Journal Article %J J Alzheimers Dis %D 2018 %T Epigenetic Drug Repositioning for Alzheimer's Disease Based on Epigenetic Targets in Human Interactome. %A Chatterjee, Paulami %A Roy, Debjani %A Rathi, Nitin %K Alzheimer Disease %K Antipsychotic Agents %K Computational Biology %K Drug Repositioning %K Epigenesis, Genetic %K Epigenomics %K Humans %K MicroRNAs %K Protein Interaction Maps %K PubMed %X

BACKGROUND: Epigenetics has emerged as an important field in drug discovery. Alzheimer's disease (AD), the leading neurodegenerative disorder throughout the world, is shown to have an epigenetic basis. Currently, there are very few effective epigenetic drugs available for AD.

OBJECTIVE: In this work, for the first time we have proposed 14 AD repositioning epigenetic drugs and identified their targets from extensive human interactome.

METHODS: Interacting partners of the AD epigenetic proteins were identified from the extensive human interactome to construct Epigenetic Protein-Protein Interaction Network (EP-PPIN). Epigenetic Drug-Target Network (EP-DTN) was constructed with the drugs associated with the proteins of EP-PPIN. Regulation of non-coding RNAs associated with the target proteins of these drugs was also studied. AD related target proteins, epigenetic targets, enriched pathways, and functional categories of the proposed repositioning drugs were also studied.

RESULTS: The proposed 14 AD epigenetic repositioning drugs have overlapping targets and miRs with known AD epigenetic targets and miRs. Furthermore, several shared functional categories and enriched pathways were obtained for these drugs with FDA approved epigenetic drugs and known AD drugs.

CONCLUSIONS: The findings of our work might provide insight into future AD epigenetic-therapeutics.

%B J Alzheimers Dis %V 61 %P 53-65 %8 2018 %G eng %N 1 %1 http://www.ncbi.nlm.nih.gov/pubmed/29199645?dopt=Abstract %R 10.3233/JAD-161104 %0 Journal Article %J J Alzheimers Dis %D 2016 %T Discovering New Genes in the Pathways of Common Sporadic Neurodegenerative Diseases: A Bioinformatics Approach. %A Kim, Yong Hwan %A Beak, Seung Han %A Charidimou, Andreas %A Song, Min %K Computational Biology %K Female %K Gene Regulatory Networks %K Genetic Predisposition to Disease %K Humans %K Male %K MEDLINE %K Neurodegenerative Diseases %X

Late onset Alzheimer's disease (AD) and Parkinson's disease (PD) are mostly "sporadic" age-related neurodegenerative disorders, but with a clear genetic component. However, their genetic architecture is complex and heterogeneous, largely remaining a conundrum, with only a handful of well-established genetic risk factors consistently associated with these diseases. It is possible that numerous, yet undiscovered, AD and PD related genes might exist. We focused on the 'gene' as a mediator to find new potential genes that might have a relationship with both disorders using bio-literature mining techniques. Based on Entrez Gene, we extracted the genes and directional gene-gene relation in the entire MEDLINE records and then constructed a directional gene-gene network. We identified common genes associated with two different but related diseases by performing shortest path analysis on the network. With our approach, we were able to identify and map already known genes that have a direct relationship with PD and AD. In addition, we identified 7 genes previously unknown to be a bridge between these two disorders. We confirmed 4 genes, ROS1, FMN1, ATP8A2, and SNORD12C, by biomedical literature and further checked 3 genes, ERVK-10, PRS, and C7orf49, that might have a high possibility to be related with both diseases. Additional experiments were performed to demonstrate the effectiveness of our proposed method. Comparing to the co-occurrence approach, our approach detected 25% more candidate genes and verified 10% more genes that have the relationship between both diseases than the co-occurrence approach did.

%B J Alzheimers Dis %V 51 %P 293-312 %8 2016 %G eng %N 1 %1 http://www.ncbi.nlm.nih.gov/pubmed/26836166?dopt=Abstract %R 10.3233/JAD-150769 %0 Journal Article %J J Alzheimers Dis %D 2016 %T Shared Genetic Etiology between Type 2 Diabetes and Alzheimer's Disease Identified by Bioinformatics Analysis. %A Gao, Lei %A Cui, Zhen %A Shen, Liang %A Ji, Hong-Fang %K Alzheimer Disease %K Apolipoprotein C-I %K Computational Biology %K Databases, Factual %K Diabetes Mellitus, Type 2 %K Female %K Genetic Predisposition to Disease %K Genome-Wide Association Study %K Humans %K Lipid Metabolism %K Male %K Polymorphism, Single Nucleotide %X

Type 2 diabetes (T2D) and Alzheimer's disease (AD) are two major health issues, and increasing evidence in recent years supports the close connection between these two diseases. The present study aimed to explore the shared genetic etiology underlying T2D and AD based on the available genome wide association studies (GWAS) data collected through August 2014. We performed bioinformatics analyses based on GWAS data of T2D and AD on single nucleotide polymorphisms (SNPs), gene, and pathway levels, respectively. Six SNPs (rs111789331, rs12721046, rs12721051, rs4420638, rs56131196, and rs66626994) were identified for the first time to be shared genetic factors between T2D and AD. Further functional enrichment analysis found lipid metabolism related pathways to be common between these two disorders. The findings may have important implications for future mechanistic and interventional studies for T2D and AD.

%B J Alzheimers Dis %V 50 %P 13-7 %8 2016 %G eng %N 1 %1 http://www.ncbi.nlm.nih.gov/pubmed/26639962?dopt=Abstract %R 10.3233/JAD-150580