Identification of the pathogenic relationship between Long COVID and Alzheimer's disease by bioinformatics methods

Abstract

Abstract Background The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused an unprecedented global health crisis. Although many Corona Virus Disease 2019 (COVID-19) patients have recovered, the long-term consequences of SARS-CoV-2 infection are unclear. Several independent epidemiological surveys and clinical studies have found that SARS-CoV-2 infection and Long COVID are closely related to Alzheimer's disease (AD). This could lead to long-term medical challenges and social burdens following this health crisis. However, the mechanism between Long COVID and AD is unknown. Methods Genes associated with Long COVID were collected from the database. Two sets of AD-related clinical sample datasets were collected in the Gene Expression Omnibus (GEO) database by limiting screening conditions. After identifying the differentially expressed genes (DEGs) of AD, the significant overlapping genes of AD and Long COVID were obtained by taking the intersection. Then, four kinds of analyses were performed, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment analysis, protein-protein interaction (PPI) analysis, identification of hub genes, hub gene verification and transcription factors (TFs) prediction. Results A total of 197 common genes were selected for subsequent analysis. GO and KEGG enrichment analysis showed that these genes were mainly enriched in multiple neurodegenerative disease related pathways. In addition, 20 important hub genes were identified using cytoHubba. At the same time, these hub genes were verified in another data set, where 19 hub gene expressions were significantly different in the two diseases and 6 hub genes were significantly different in AD patients of different genders. Finally, we collected 9 TFs that may regulate the expression of these hub genes in the Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRUSST) database and verified them in the current data set. Conclusion This work reveals the common pathways and hub genes of AD and Long COVID, providing new ideas for the pathogenic relationship between these two diseases and further mechanism research.