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. 2023 Jun 7:10:1151046.
doi: 10.3389/fmed.2023.1151046. eCollection 2023.

Molecular crosstalk between COVID-19 and Alzheimer's disease using microarray and RNA-seq datasets: A system biology approach

T Premkumar  1 , S Sajitha Lulu  1
Affiliations

Molecular crosstalk between COVID-19 and Alzheimer's disease using microarray and RNA-seq datasets: A system biology approach

T Premkumar et al. Front Med (Lausanne). .

Abstract

Objective: Coronavirus disease 2019 (COVID-19) is an infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). The clinical and epidemiological analysis reported the association between SARS-CoV-2 and neurological diseases. Among neurological diseases, Alzheimer's disease (AD) has developed as a crucial comorbidity of SARS-CoV-2. This study aimed to understand the common transcriptional signatures between SARS-CoV-2 and AD.

Materials and methods: System biology approaches were used to compare the datasets of AD and COVID-19 to identify the genetic association. For this, we have integrated three human whole transcriptomic datasets for COVID-19 and five microarray datasets for AD. We have identified differentially expressed genes for all the datasets and constructed a protein-protein interaction (PPI) network. Hub genes were identified from the PPI network, and hub genes-associated regulatory molecules (transcription factors and miRNAs) were identified for further validation.

Results: A total of 9,500 differentially expressed genes (DEGs) were identified for AD and 7,000 DEGs for COVID-19. Gene ontology analysis resulted in 37 molecular functions, 79 cellular components, and 129 biological processes were found to be commonly enriched in AD and COVID-19. We identified 26 hub genes which includes AKT1, ALB, BDNF, CD4, CDH1, DLG4, EGF, EGFR, FN1, GAPDH, INS, ITGB1, ACTB, SRC, TP53, CDC42, RUNX2, HSPA8, PSMD2, GFAP, VAMP2, MAPK8, CAV1, GNB1, RBX1, and ITGA2B. Specific miRNA targets associated with Alzheimer's disease and COVID-19 were identified through miRNA target prediction. In addition, we found hub genes-transcription factor and hub genes-drugs interaction. We also performed pathway analysis for the hub genes and found that several cell signaling pathways are enriched, such as PI3K-AKT, Neurotrophin, Rap1, Ras, and JAK-STAT.

Conclusion: Our results suggest that the identified hub genes could be diagnostic biomarkers and potential therapeutic drug targets for COVID-19 patients with AD comorbidity.

Keywords: Alzheimer’s disease; COVID-19; biomarkers; comorbidity; regulatory networks.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
A schematic diagram of the workflow adopted in the study depicting the major steps of preprocessing of microarray and RNA-Seq data followed by identification of differentially expressed genes using R packages and gene ontology and hub gene analysis. Further, the hub genes were exposed to pathway analysis, miRNAs, and transcription factor prediction.
Figure 2
Figure 2
The multiple volcano plot showing differentially expressed genes of COVID-19 and AD (upregulated genes in red and downregulated genes in blue).The x-axis depicts the log fold change in gene expression between different samples and the y-axis depicts FDR-adjusted p values.
Figure 3
Figure 3
Venn diagram of shared differentially expressed genes, where each ellipse represents AD-PBMC, AD-Tissue, COVID-19-PBMC, and COVID-19-Tissue with Nine (HST6, POLR3G, SLC6A20, ITGA2B, HOMER3, GMPR, AGBL1, CRABP2, OLFML2B) genes common among the four groups.
Figure 4
Figure 4
Network of protein–protein interaction and detected hub genes (from genes common among AD-PBMC, AD-Tissue, COVID-19-PBMC, and COVID-19-Tissue, module 1). (A) The up-regulated and down-regulated genes in red and green colors and hub genes in aqua. (B) Venn diagram representing the genes commonly shared among the topological features of MCC, Betweenness, Closeness, and Degree.
Figure 5
Figure 5
(A) Network constructed to represent the common genes shared by ontology terms of Alzheimer’s disease and COVID-19 gene ontology terms (module 2). Purple diamonds represent the hub genes of this network. (B) Venn diagram showing the genes commonly shared among the topological features of MCC, Betweenness, Closeness, and Degree.
Figure 6
Figure 6
Hub Genes-miRNAs Network (A) miRNAs interacting with more than three hub genes, aqua color squares representing the hub genes and the maroon color diamonds representing the miRNAs. (B) Predicted hub miRNAs using four topological features of CytoHubba including Betweenness, Closeness, Degree, and Stress.
Figure 7
Figure 7
Hub Genes-Transcription Factors network (red color diamond designates the hub genes and the green color circulars designate the Transcription Factors). The edges between the two genes indicates the interaction between TFs and hub genes.
Figure 8
Figure 8
Drug-Hub Gene Network (aqua color indicating the hub genes and red color indicating the drugs).
Figure 9
Figure 9
Top 20 gene ontology terms of hub genes (The x-axis label represents the gene ratio and the y-axis label represents gene ontology terms).
Figure 10
Figure 10
Pathway-Hub Gene Network (aqua color indicating the hub genes and the red color indicating the signal pathways).
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References

    1. Hu B, Guo H, Zhou P, Shi ZL. Characteristics of SARS-CoV-2 and COVID-19. Nat Rev Microbiol. (2020) 19:141–54. doi: 10.1038/s41579-020-00459-7 - DOI - PMC - PubMed
    1. Holmes EC, Goldstein SA, Rasmussen AL, Robertson DL, Crits-Christoph A, Wertheim JO, et al. . The origins of SARS-CoV-2: a critical review. Cells. (2021) 184:4848–56. doi: 10.1016/j.cell.2021.08.017, PMID: - DOI - PMC - PubMed
    1. Li MY, Li L, Zhang Y, Wang XS. Expression of the SARS-CoV-2 cell receptor gene ACE2 in a wide variety of human tissues. Infect Dis Poverty. (2020) 9:45. doi: 10.1186/s40249-020-00662-x, PMID: - DOI - PMC - PubMed
    1. Ferini-Strambi L, Salsone M. COVID-19 and neurological disorders: are neurodegenerative or neuroimmunological diseases more vulnerable? J Neurol. (2021) 268:409–19. doi: 10.1007/s00415-020-10070-8 - DOI - PMC - PubMed
    1. Wouk J, Rechenchoski DZ, Rodrigues BCD, Ribelato EV, Faccin-Galhardi LC. Viral infections and their relationship to neurological disorders. Arch Virol. (2021) 166:733–53. doi: 10.1007/s00705-021-04959-6 - DOI - PMC - PubMed