Abstract
Introduction: Coronavirus disease-2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. It was initially detected in Wuhan, China, in December 2019. In March 2020, the World Health Organization (WHO) declared COVID-19 a global pandemic. Compared to healthy individuals, patients with IgA nephropathy (IgAN) are at a higher risk of SARS-CoV-2 infection. However, the potential mechanisms remain unclear. This study explores the underlying molecular mechanisms and therapeutic agents for the management of IgAN and COVID-19 using the bioinformatics and system biology way. Methods: We first downloaded GSE73953 and GSE164805 from the Gene Expression Omnibus (GEO) database to obtain common differentially expressed genes (DEGs). Then, we performed the functional enrichment analysis, pathway analysis, protein-protein interaction (PPI) analysis, gene regulatory networks analysis, and potential drug analysis on these common DEGs. Results: We acquired 312 common DEGs from the IgAN and COVID-19 datasets and used various bioinformatics tools and statistical analyses to construct the PPI network to extract hub genes. Besides, we performed gene ontology (GO) and pathway analyses to reveal the common correlation between IgAN and COVID-19. Finally, on the basis of common DEGs, we determined the interactions between DEGs-miRNAs, the transcription factor-genes (TFs-genes), protein-drug, and gene-disease networks. Conclusion: We successfully identified hub genes that may act as biomarkers of COVID-19 and IgAN and also screened out some potential drugs to provide new ideas for COVID-19 and IgAN treatment.
Introduction
The acute respiratory disorder, namely COVID-19, is triggered by an RNA virus known as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) [1]. In December 2019 [2], an outbreak of COVID-19 began in Wuhan, China, which rapidly progressed as an epidemic all over the world, garnering the attention of persons from every field. Subsequently, the World Health Organization (WHO) announced the coronavirus disease-2019 (COVID-19) as a global public health emergency [3]. As of June 27, 2022, the WHO (https://covid19.who.int) confirmed 540,923,532 COVID-19 cases. Of which 6,325,785 patients died. The most frequent clinical signs of COVID-19 include sore throat, fever, and cough [4]. People suffering from various underlying diseases, such as renal disorder, diabetes, hypertension, and cardiovascular disease, are susceptible to COVID-19 infection, which may lead to severe infection combined with a higher mortality rate [5]. Moreover, COVID-19 may also be accompanied by certain autoimmune diseases (e.g., Guillain-Barre’s syndrome [6], autoimmune thrombocytopenic purpura [7], and autoimmune hemolytic anemia [8]). In this study, we hypothesized that the novel coronavirus could lead to the progression of autoimmune diseases by inducing autoimmune dysfunction, resulting in a serious adverse prognosis.
IgA nephropathy, the most normal glomerular disease worldwide [9], is also an autoimmune disease. The main pathological process involves the abnormal immune function of the mucosa, followed by respiratory or gastrointestinal infections, which increases IgA and corresponding IgG levels [10]. Next, the IgA and IgG can form immune complexes and get deposited in the glomerular mesangium, leading to mesangial cell proliferation and increased matrix. Finally, the complement system is activated, and several inflammatory factors are released, damaging the kidney. This suggests a significant role of mucosal immunity in IgA nephropathy [11]. SARS-CoV-2 is a kind of mucosal target virus that induces a strong mucosal immune response in the body, resulting in the secretion of large amounts of IgA [12]. Case was reported that patient developed IgA nephropathy after COVID-19 infection [13], which was characterized by markedly elevated blood creatinine levels and severe proteinuria during the infection period, which lasted up to 7 months even after infection. Hence, SARS-CoV-2 infection may accelerate the progression of IgA nephropathy (IgAN) to end-stage renal disease, which may lead to a poor prognosis. Besides, the current treatment for IgA nephropathy includes hormonal or immunosuppressive drugs, which may further decrease the body’s immunity against a variety of infections. Since patients with IgA nephropathy are more likely to be infected with the new coronavirus than healthy individuals, it is of great significance to further explore the influence of COVID-19 on IgAN and screen out potential therapeutic agents. Therefore, we used bioinformatics and system biology ways to explore the molecular mechanisms and uncover the treatment compounds of COVID-19 and IgA nephropathy. Our entire research process is shown in Figure 1.
Materials and Methods
Data Collection from COVID-19 and IgAN Databases
We revealed the association between IgAN and COVID-19 at the transcriptional level using microarray data from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) available in National Center for Biotechnology Information (NCBI) [14]. Both GSE164805 and GSE73953 were loaded from the GEO database. The platforms for both microarrays include GPL26963 (Agilent-085982 Arraystar human lncRNA V5 microarray [15]) and GPL4133 (Agilent-014850 Whole Human Genome Microarray 4 × 44K G4112F [16]) separately. GSE164805, a COVID-19 microarray dataset, consists of sequencing results from the blood samples of ten COVID-19 patients and five healthy donors, while GSE73953 consists of sequencing results from peripheral blood samples of 15 IgA nephropathy patients, two healthy controls, and 8 membranous nephropathy patients. In our study, we used IgA nephropathy patients and healthy controls for our analyses. The information on the two databases is listed in Table 1.
A list of microarray datasets from gene expression omnibus (GEO) database
Series . | Platform . | GeneChip . | Samples . |
---|---|---|---|
GSE164805 | GPL26963 | Agilent-085982 arraystar human lncRNA V5 microarray | 15 |
GSE73953 | GPL4133 | Agilent-014850 whole human genome microarray 4 × 44K G4112F | 17 |
Series . | Platform . | GeneChip . | Samples . |
---|---|---|---|
GSE164805 | GPL26963 | Agilent-085982 arraystar human lncRNA V5 microarray | 15 |
GSE73953 | GPL4133 | Agilent-014850 whole human genome microarray 4 × 44K G4112F | 17 |
Identification of Differentially Expressed Genes and Common Differentially Expressed Genes
The main aim of this work was to identify the common differentially expressed genes (DEGs) between IgAN and COVID-19. First, we used the GEO.2R [17] web tool to acquire the DEGs, where |logFC|≥2.4 and p value < 0.05. Next, the common DEGs were determined using the Venn diagram tool [18] (https://bioinformatics.psb.ugent.be/webtools/Venn/).
Gene Ontology Enrichment and Signaling Pathway Analysis
Gene Set Enrichment Analysis (GSEA) is an important analytical method focusing on the exploration of the share chromosomes locations, biological processes or regulation of gene sets associated with different interconnected diseases [19]. GSEA includes both gene ontology (GO) analysis and signaling pathway analysis. In gene set enrichment, p < 0.05 was set as the standard. To deeply comprehend the biological roles and signaling pathways of common DEGs, we used the Enrichr [20] tool (https://maayanlab.cloud/Enrichr/) to carry out the GO function (cellular component, biological processes, and molecular functions) and pathway enrichment analyses. Furthermore, to identify shared pathways between IgAN and COVID-19, we used five databases (KEGG 2019 Human, WikiPathways, Reactome, BioCarta, and BioPlanet) as the origin of the pathway classification.
Protein-Protein Interaction Network Analysis and Hub Genes Extraction
Protein-protein interaction (PPI) networks are significant for providing biological insights into the machinery of disease, functioning of cells, and designing/repositioning of drugs [21]. All the predicted and known correlations among proteins, including functional and physiological associations, were collected from the STRING database (http://string-db.org) [22]. To reveal the relationship between IgA nephropathy and COVID-19 at the protein level, we constructed the PPI networks by the STRING database. Here, the medium confidence of the PPI networks was set to 0.4. Hub genes of PPI networks were extracted by the CytoHubba [23] plugins in Cytoscape software [24] (v.3.9.1).
Gene Regulatory Network Analysis
Here, we identified the transcription factor (TF)-genes and genes-microRNA (miRNA) interaction networks upon the hub genes. The networks were constructed using NetworkAnalyst platform [25] (https://www.networkanalyst.ca/), while the interaction networks (TF-gene and gene-miRNA) were determined using the databases JASPAR [26] and miRTarBase [27] v.8.0, respectively.
Gene-Disease Association Analysis
DisGeNET (http://www.disgenet.org/) is a comprehensive platform, which integrates information on both variants and genes associated with human disorders from multiple databases [28]. According to the recently updated data, the platform includes more than 17,000 genes, 117,000 genetic variants, and 24,000 diseases and traits [29]. This platform has enhanced our understanding of gene-related diseases. In this study, the gene-disease correlation analysis of several hub genes was performed using the NetworkAnalyst platform, which revealed potential diseases and their chronic complications.
Evaluation of the Applicant Drugs
Protein-drug interaction prediction (PDI) or drug molecule identification is one of the crucial aspects of this study. We focused on identifying drug molecules based on the common DEGs between IgA nephropathy and COVID-19 using the Drug Signatures database (DSigDB) and Enrichr. The Enrichr was first released in 2013 and is an integrated resource for gene sets analysis [20]. It has 180,184 annotations for gene sets collected from 102 gene set libraries. DSigDB is a database involving genes-associated drugs or compounds [30]. It contains 22,527 gene sets, 19,531 genes, and 17,389 unique compounds [29]. The DSigDB resource is available via the Enrichr’s Diseases/Drugs module.
Results
Identification of DEGs and Common DEGs between IgAN and COVID-19
To determine the interactive correlation and impact of IgAN and COVID-19, we browsed the GEO’s microarray dataset to identify DEGs of GSE73953 and GSE164805 via the GEO2R. GSE73953 is an IgAN microarray dataset, which contains 5,760 DEGs, while GSE164805, a COVID-19 microarray dataset, contains 2,382 DEGs. Of these, 312 common DEGs were identified using the Venn diagram, as shown in Figure 2.
The research contains two microarray datasets, IgAN (GSE73953) and COVID-19 (GSE164805). A number of 312 common DEGs were identified between the two microarray datasets.
The research contains two microarray datasets, IgAN (GSE73953) and COVID-19 (GSE164805). A number of 312 common DEGs were identified between the two microarray datasets.
GO and Pathway Enrichment Analysis
The biological meaning of common DEGs was further explored by performing the GSEA [31]. The most important parts of GSEA include GO enrichment and signaling pathway analysis. GO is a comprehensive database, which provides biological knowledge on the gene function and GO terms [32], and involves three perspectives (biological process, cell composition, and molecular function). As shown in Table 2, the top five items belonging to these three categories were revealed by Enrichr. The corresponding bar graph is shown in Figure 3.
Gene ontological enrichment analysis of common DEGs between COVID-19 and IgAN
Category . | GO ID . | Term . | p values . | Genes . |
---|---|---|---|---|
Biologicalprocess | GO:0046632 | Alpha-beta T cell differentiation | 2.35E-03 | ITK/RSAD2 |
GO:0042439 | Ethanolamine-containing compound metabolic process | 2.35E-03 | NAAA/NAPEPLD | |
GO:0070291 | N-acylethanolamine metabolic process | 2.35E-03 | NAAA/NAPEPLD | |
GO:0070163 | Regulation of adiponectin secretion | 3.49E-03 | C1QTNF3/IL1B | |
GO:2001188 | Regulation of T cell activation via T cell receptor contact with antigen bound to MHC molecule on antigen presenting cell | 3.49E-03 | HLA-DMB/HAVCR2 | |
Cellularcomponent | GO:0072357 | PTW/PP1 phosphatase complex | 4.84E-03 | PPP1R12A/PPP1CA |
GO:0008287 | Protein serine/threonine phosphatase complex | 6.38E-03 | PPP1R12A/PPP1CA | |
GO:0042613 | MHC class II protein complex | 9.58E-04 | HLA-DMB/HLA-DPA1/HLA-DQB1 | |
GO:0005687 | U4 snRNP | 8.12E-03 | PRPF31/SNRPB | |
GO:0097025 | MPP7-DLG1-LIN7 complex | 7.56E-02 | LIN7A | |
Molecularfunction | GO:0015166 | Polyol transmembrane transporter activity | 2.35E-03 | AQP9/AQP1 |
GO:0015168 | Glycerol transmembrane transporter activity | 4.84E-03 | AQP9/AQP1 | |
GO:0015293 | Symporter activity | 1.00E-02 | SLC5A6/MFSD2A | |
GO:0032395 | MHC class II receptor activity | 1.00E-02 | HLA-DPA1/HLA-DQB1 | |
GO:0005436 | Sodium: phosphate symporter activity | 1.22E-02 | SLC20A1/MFSD2A |
Category . | GO ID . | Term . | p values . | Genes . |
---|---|---|---|---|
Biologicalprocess | GO:0046632 | Alpha-beta T cell differentiation | 2.35E-03 | ITK/RSAD2 |
GO:0042439 | Ethanolamine-containing compound metabolic process | 2.35E-03 | NAAA/NAPEPLD | |
GO:0070291 | N-acylethanolamine metabolic process | 2.35E-03 | NAAA/NAPEPLD | |
GO:0070163 | Regulation of adiponectin secretion | 3.49E-03 | C1QTNF3/IL1B | |
GO:2001188 | Regulation of T cell activation via T cell receptor contact with antigen bound to MHC molecule on antigen presenting cell | 3.49E-03 | HLA-DMB/HAVCR2 | |
Cellularcomponent | GO:0072357 | PTW/PP1 phosphatase complex | 4.84E-03 | PPP1R12A/PPP1CA |
GO:0008287 | Protein serine/threonine phosphatase complex | 6.38E-03 | PPP1R12A/PPP1CA | |
GO:0042613 | MHC class II protein complex | 9.58E-04 | HLA-DMB/HLA-DPA1/HLA-DQB1 | |
GO:0005687 | U4 snRNP | 8.12E-03 | PRPF31/SNRPB | |
GO:0097025 | MPP7-DLG1-LIN7 complex | 7.56E-02 | LIN7A | |
Molecularfunction | GO:0015166 | Polyol transmembrane transporter activity | 2.35E-03 | AQP9/AQP1 |
GO:0015168 | Glycerol transmembrane transporter activity | 4.84E-03 | AQP9/AQP1 | |
GO:0015293 | Symporter activity | 1.00E-02 | SLC5A6/MFSD2A | |
GO:0032395 | MHC class II receptor activity | 1.00E-02 | HLA-DPA1/HLA-DQB1 | |
GO:0005436 | Sodium: phosphate symporter activity | 1.22E-02 | SLC20A1/MFSD2A |
Top 5 terms of each category are listed.
Bar graph of the gene ontological enrichment analysis based on the common DEGs between COVID-19 and IgAN.
Bar graph of the gene ontological enrichment analysis based on the common DEGs between COVID-19 and IgAN.
Pathway analysis helps explore the relationship between genes and pathways. In this study, we used five databases, including KEGG 2019 human, WikiPathways, Reactome, BioCarta, and BioPlanet, to identify the most influential pathway between IgAN and COVID-19. The top pathways from each database are displayed in Table 3, while the bar graphs presenting the pathway analysis results are shown in Figures 4 and 5.
Signaling pathway analysis of common DEGs between COVID-19 and IgAN
Category . | Pathways . | p values . | Genes . |
---|---|---|---|
BioCarta | The 4-1BB-dependent immune response Homo sapiens h 41bbPathway | 9.58E-04 | MAP2K3/IFNG/RELA |
Chaperones modulate interferon signaling pathway Homo sapiens h tidPathway | 2.58E-03 | IFNG/LIN7A/RELA | |
The PRC2 complex sets long-term gene silencing through modification of histone tails Homo sapiens h prc2Pathway | 1.95E-02 | YY1/RBBP4 | |
Human cytomegalovirus and map kinase pathways Homo sapiens h hcmvPathway | 2.52E-02 | MAP2K3/RELA | |
Skeletal muscle hypertrophy is regulated via AKT/mTOR pathway Homo sapiens h igf1mtorpathway | 6.71E-03 | GSK3B/IGF1/EIF4G1 | |
Toll-like receptor pathway Homo sapiens h tollPathway | 2.08E-03 | MAP2K3/TAB1/ELK1/RELA | |
Signal transduction through IL1R Homo sapiens h il1rPathway | 2.31E-03 | MAP2K3/IL1B/TAB1/RELA | |
GATA3 participates in activating the Th2 cytokine genes expression Homo sapiens h GATA3pathway | 3.48E-02 | MAP2K3/GNAS | |
NFkB activation by nontypeable Hemophilus influenzae Homo sapiens h nthiPathway | 1.02E-02 | MAP2K3/IL1B/RELA | |
fMLP induced chemokine gene expression in HMC-1 cells Homo sapiens h fMLPpathway | 1.12E-02 | MAP2K3/ELK1/RELA | |
BioPlanet | Interleukin-1 processing | 4.84E-03 | IL1B/RELA |
Nef-mediated CD8 downregulation | 4.84E-03 | ATP6V1H/AP2M1 | |
Osteopontin signaling | 5.65E-04 | SPP1/MMP9/RELA | |
S6K1 signaling | 6.38E-03 | EIF4B/EIF4G1 | |
Binding of RNA by insulin-like growth factor 2 mRNA binding proteins (IGF2BPs/IMPs/VICKZs) | 6.38E-03 | ACTB/CD44 | |
Shuttle for transfer of acetyl groups from mitochondria to the cytosol | 6.38E-03 | CS/SLC25A1 | |
Nef-mediated CD4 downregulation | 8.12E-03 | ATP6V1H/AP2M1 | |
Ethanol oxidation | 1.00E-02 | ACSS2/ALDH2 | |
Passive transport by aquaporins | 1.22E-02 | AQP9/AQP1 | |
Mammalian target of rapamycin complex 1 (mTORC1)-mediated signaling | 1.22E-02 | EIF4B/EIF4G1 | |
KEGG 2019 human | Citrate cycle (TCA cycle) | 1.16E-03 | CS/IDH3B/SDHA/PCK2 |
Leishmaniasis | 3.22E-07 | MARCKSL1/IFNG/FCGR3B/IL1B/HLA-DMB/TAB1/ELK1/RELA/HLA-DPA1/HLA-DQB1 | |
Vitamin digestion and absorption | 5.97E-03 | SLC5A6/TCN2/SCARB1 | |
Graft-versus-host disease | 4.74E-04 | HLA-DMB/IFNG/IL1B/HLA-DPA1/HLA-DQB1 | |
Type I diabetes mellitus | 5.30E-04 | HLA-DMB/IFNG/IL1B/HLA-DPA1/HLA-DQB1 | |
Allograft rejection | 2.82E-03 | HLA-DMB/IFNG/HLA-DPA1/HLA-DQB1 | |
Asthma | 1.22E-02 | HLA-DMB/HLA-DPA1/HLA-DQB1 | |
Pantothenate and CoA biosynthesis | 4.19E-02 | VNN1/ALDH2 | |
Inflammatory bowel disease | 5.24E-04 | HLA-DMB/IFNG/IL1B/RELA/HLA-DPA1/HLA-DQB1 | |
Arginine biosynthesis | 4.56E-02 | ARG1/GOT2 | |
Reactome | CLEC7A/inflammasome pathway Homo sapiens R-HSA-5660668 | 3.49E-03 | IL1B/RELA |
Nef-mediated CD8 downregulation Homo sapiens R-HSA-182218 | 4.84E-03 | ATP6V1H/AP2M1 | |
Interleukin-1 processing Homo sapiens R-HSA-448706 | 4.84E-03 | IL1B/RELA | |
Insulin-like growth factor-2 mRNA binding proteins (IGF2BPs/IMPs/VICKZs) bind RNA Homo sapiens R-HSA-428359 | 6.38E-03 | ACTB/CD44 | |
Nef-mediated CD4 downregulation Homo sapiens R-HSA-167590 | 8.12E-03 | ATP6V1H/AP2M1 | |
Passive transport by aquaporins Homo sapiens R-HSA-432047 | 1.44E-02 | AQP9/AQP1 | |
Ethanol oxidation Homo sapiens R-HSA-71384 | 1.44E-02 | ACSS2/ALDH2 | |
Citric acid cycle (TCA cycle) Homo sapiens R-HSA-71403 | 3.03E-03 | CS/IDH3B/SDHA | |
DEx/H-box helicases activate type I IFN and inflammatory cytokines production Homo sapiens R-HSA-3134963 | 1.69E-02 | S100b/RELA | |
Synthesis of very long-chain fatty-acyl-CoAs Homo sapiens R-HSA-75876 | 4.06E-03 | ACSBG1/ELOVL7/PPT2 | |
WikiPathways | Fluoroacetic acid toxicity WP4966 | 7.26E-05 | CS/ACSS2/ALDH2 |
Activation of NLRP3 inflammasome by SARS-CoV-2 WP4876 | 4.84E-03 | IL1B/RELA | |
Osteopontin signaling WP1434 | 9.58E-04 | SPP1/MMP9/RELA | |
Suppression of HMGB1 mediated inflammation by THBD WP4479 | 8.12E-03 | THBD/RELA | |
TCA Cycle (aka Krebs or citric acid cycle) WP78 | 2.58E-03 | CS/IDH3B/SDHA | |
Inhibition of exosome biogenesis and secretion by Manumycin A in CRPC cells WP4301 | 2.58E-03 | MRAS/ARAF/RAB27A | |
Serotonin receptor 4/6/7 and NR3C Signaling WP734 | 3.03E-03 | HTR7/GNAS/ELK1 | |
Extracellular vesicles in the crosstalk of cardiac cells WP4300 | 3.03E-03 | SPP1/IGF1/MMP9 | |
Genes targeted by miRNAs in adipocytes WP1992 | 1.69E-02 | HDAC4/IGF1 | |
COVID-19 adverse outcome pathway WP4891 | 2.23E-02 | IL1B/CCL3 |
Category . | Pathways . | p values . | Genes . |
---|---|---|---|
BioCarta | The 4-1BB-dependent immune response Homo sapiens h 41bbPathway | 9.58E-04 | MAP2K3/IFNG/RELA |
Chaperones modulate interferon signaling pathway Homo sapiens h tidPathway | 2.58E-03 | IFNG/LIN7A/RELA | |
The PRC2 complex sets long-term gene silencing through modification of histone tails Homo sapiens h prc2Pathway | 1.95E-02 | YY1/RBBP4 | |
Human cytomegalovirus and map kinase pathways Homo sapiens h hcmvPathway | 2.52E-02 | MAP2K3/RELA | |
Skeletal muscle hypertrophy is regulated via AKT/mTOR pathway Homo sapiens h igf1mtorpathway | 6.71E-03 | GSK3B/IGF1/EIF4G1 | |
Toll-like receptor pathway Homo sapiens h tollPathway | 2.08E-03 | MAP2K3/TAB1/ELK1/RELA | |
Signal transduction through IL1R Homo sapiens h il1rPathway | 2.31E-03 | MAP2K3/IL1B/TAB1/RELA | |
GATA3 participates in activating the Th2 cytokine genes expression Homo sapiens h GATA3pathway | 3.48E-02 | MAP2K3/GNAS | |
NFkB activation by nontypeable Hemophilus influenzae Homo sapiens h nthiPathway | 1.02E-02 | MAP2K3/IL1B/RELA | |
fMLP induced chemokine gene expression in HMC-1 cells Homo sapiens h fMLPpathway | 1.12E-02 | MAP2K3/ELK1/RELA | |
BioPlanet | Interleukin-1 processing | 4.84E-03 | IL1B/RELA |
Nef-mediated CD8 downregulation | 4.84E-03 | ATP6V1H/AP2M1 | |
Osteopontin signaling | 5.65E-04 | SPP1/MMP9/RELA | |
S6K1 signaling | 6.38E-03 | EIF4B/EIF4G1 | |
Binding of RNA by insulin-like growth factor 2 mRNA binding proteins (IGF2BPs/IMPs/VICKZs) | 6.38E-03 | ACTB/CD44 | |
Shuttle for transfer of acetyl groups from mitochondria to the cytosol | 6.38E-03 | CS/SLC25A1 | |
Nef-mediated CD4 downregulation | 8.12E-03 | ATP6V1H/AP2M1 | |
Ethanol oxidation | 1.00E-02 | ACSS2/ALDH2 | |
Passive transport by aquaporins | 1.22E-02 | AQP9/AQP1 | |
Mammalian target of rapamycin complex 1 (mTORC1)-mediated signaling | 1.22E-02 | EIF4B/EIF4G1 | |
KEGG 2019 human | Citrate cycle (TCA cycle) | 1.16E-03 | CS/IDH3B/SDHA/PCK2 |
Leishmaniasis | 3.22E-07 | MARCKSL1/IFNG/FCGR3B/IL1B/HLA-DMB/TAB1/ELK1/RELA/HLA-DPA1/HLA-DQB1 | |
Vitamin digestion and absorption | 5.97E-03 | SLC5A6/TCN2/SCARB1 | |
Graft-versus-host disease | 4.74E-04 | HLA-DMB/IFNG/IL1B/HLA-DPA1/HLA-DQB1 | |
Type I diabetes mellitus | 5.30E-04 | HLA-DMB/IFNG/IL1B/HLA-DPA1/HLA-DQB1 | |
Allograft rejection | 2.82E-03 | HLA-DMB/IFNG/HLA-DPA1/HLA-DQB1 | |
Asthma | 1.22E-02 | HLA-DMB/HLA-DPA1/HLA-DQB1 | |
Pantothenate and CoA biosynthesis | 4.19E-02 | VNN1/ALDH2 | |
Inflammatory bowel disease | 5.24E-04 | HLA-DMB/IFNG/IL1B/RELA/HLA-DPA1/HLA-DQB1 | |
Arginine biosynthesis | 4.56E-02 | ARG1/GOT2 | |
Reactome | CLEC7A/inflammasome pathway Homo sapiens R-HSA-5660668 | 3.49E-03 | IL1B/RELA |
Nef-mediated CD8 downregulation Homo sapiens R-HSA-182218 | 4.84E-03 | ATP6V1H/AP2M1 | |
Interleukin-1 processing Homo sapiens R-HSA-448706 | 4.84E-03 | IL1B/RELA | |
Insulin-like growth factor-2 mRNA binding proteins (IGF2BPs/IMPs/VICKZs) bind RNA Homo sapiens R-HSA-428359 | 6.38E-03 | ACTB/CD44 | |
Nef-mediated CD4 downregulation Homo sapiens R-HSA-167590 | 8.12E-03 | ATP6V1H/AP2M1 | |
Passive transport by aquaporins Homo sapiens R-HSA-432047 | 1.44E-02 | AQP9/AQP1 | |
Ethanol oxidation Homo sapiens R-HSA-71384 | 1.44E-02 | ACSS2/ALDH2 | |
Citric acid cycle (TCA cycle) Homo sapiens R-HSA-71403 | 3.03E-03 | CS/IDH3B/SDHA | |
DEx/H-box helicases activate type I IFN and inflammatory cytokines production Homo sapiens R-HSA-3134963 | 1.69E-02 | S100b/RELA | |
Synthesis of very long-chain fatty-acyl-CoAs Homo sapiens R-HSA-75876 | 4.06E-03 | ACSBG1/ELOVL7/PPT2 | |
WikiPathways | Fluoroacetic acid toxicity WP4966 | 7.26E-05 | CS/ACSS2/ALDH2 |
Activation of NLRP3 inflammasome by SARS-CoV-2 WP4876 | 4.84E-03 | IL1B/RELA | |
Osteopontin signaling WP1434 | 9.58E-04 | SPP1/MMP9/RELA | |
Suppression of HMGB1 mediated inflammation by THBD WP4479 | 8.12E-03 | THBD/RELA | |
TCA Cycle (aka Krebs or citric acid cycle) WP78 | 2.58E-03 | CS/IDH3B/SDHA | |
Inhibition of exosome biogenesis and secretion by Manumycin A in CRPC cells WP4301 | 2.58E-03 | MRAS/ARAF/RAB27A | |
Serotonin receptor 4/6/7 and NR3C Signaling WP734 | 3.03E-03 | HTR7/GNAS/ELK1 | |
Extracellular vesicles in the crosstalk of cardiac cells WP4300 | 3.03E-03 | SPP1/IGF1/MMP9 | |
Genes targeted by miRNAs in adipocytes WP1992 | 1.69E-02 | HDAC4/IGF1 | |
COVID-19 adverse outcome pathway WP4891 | 2.23E-02 | IL1B/CCL3 |
Top 10 terms of each pathway enrichment analysis are listed.
Charts of the signaling pathway enrichment analysis based on the common DEGs by the Enricher. a Biocarta pathway. b Bioplanet pathway. c Reactome pathway. d WikiPathway.
Charts of the signaling pathway enrichment analysis based on the common DEGs by the Enricher. a Biocarta pathway. b Bioplanet pathway. c Reactome pathway. d WikiPathway.
The KEGG 2019 human pathway enrichment analysis performed by the Enricher according to shared DEGs between COVID-19 and IgAN. a The top ten annotations of KEGG 2019 human pathway classified by p value. b The visualized analysis of KEGG 2019 human pathway in Cytoscape.
The KEGG 2019 human pathway enrichment analysis performed by the Enricher according to shared DEGs between COVID-19 and IgAN. a The top ten annotations of KEGG 2019 human pathway classified by p value. b The visualized analysis of KEGG 2019 human pathway in Cytoscape.
PPI Network Analysis and Hub Genes Extraction
Based on the common DEGs between IgAN and COVID-19, the STRING database was applied to acquire the PPI network, while the Cytoscape software was employed for its visual analysis. The PPI network consisted of 228 nodes and 1,140 edges, as shown in Figure 6. CytoHubba plugin was used to obtain the hub genes, including IL1B, IFNG, CCL3, ACTB, MMP9, CD44, FCGR3B, IGF1, SPP1, and CD69 (shown in Fig. 7). These hub genes may act as the biomarkers of IgAN and COVID-19 and provide new idea to discover relevant targeted drugs subsequently.
The protein-protein interaction network constructed by the STRING database using common DEGs between IgAN and COVID-19. In the graph, the color of nodes gradually deepens and the shape gradually enlarges according to degree.
The protein-protein interaction network constructed by the STRING database using common DEGs between IgAN and COVID-19. In the graph, the color of nodes gradually deepens and the shape gradually enlarges according to degree.
Identification of 10 hub genes from the protein-protein interaction network by the plugin of Cytosacpe, Cytohubba. Here, the top ten hub genes are represented by the colored nodes. The gray nodes stand for the genes interacted with hub genes.
Identification of 10 hub genes from the protein-protein interaction network by the plugin of Cytosacpe, Cytohubba. Here, the top ten hub genes are represented by the colored nodes. The gray nodes stand for the genes interacted with hub genes.
Gene Regulatory Network Analysis
To investigate the variations at the transcriptional level and acquire a detailed knowledge of regulatory molecules or shared DEGs of the pivotal proteins, a web tool was used to decipher the regulatory miRNAs and TFs. The TFs-gene interaction networks are presented in Figure 8, while the gene-miRNA interaction networks are shown in Figure 9. A total of 284 miRNAs and 143 TFs were found associated with hub genes, indicating a strong relationship between them.
The interaction network of 143 transcription factors and 10 hub genes constructed by the NetworkAnalyst tool. Hub genes are shown as the orange nodes, and TFs are shown as purple nodes.
The interaction network of 143 transcription factors and 10 hub genes constructed by the NetworkAnalyst tool. Hub genes are shown as the orange nodes, and TFs are shown as purple nodes.
Genes-miRNA interaction network. Herein, there are total of 285 miRNAs represented by the triangle nodes and 9 gene symbols indicated by circle nodes.
Genes-miRNA interaction network. Herein, there are total of 285 miRNAs represented by the triangle nodes and 9 gene symbols indicated by circle nodes.
Identification of the Gene-Disease Association
Different diseases with one or more shared genes may be related to each other in some way. Targeted therapies provide a new option for the management of a kind of disease. To explore possible targeted therapeutic options for diseases, we need comprehensive information on the interaction between genes and diseases. Hence, according to hub genes, we constructed the gene-disease correlation network using the DisGeNET database and the Network Analyst platform. As a result, dermatitis, allergic contact, lung neoplasms, pneumonia, mammary neoplasms, chemical and drug-induced liver injury, hypersensitivity, pulmonary fibrosis, FCGR3B, malignant mesothelioma, and calcinosis had been found to be a strong association with hub genes, even with IgAN and COVID-19. Figure 10 presents the gene-disease associations.
The DGEs-diseases relationship network reveals the association between DEGs and disease. The gene names depicted by the circle nodes and there are 157 corresponding disorders indicated by the octagon nodes.
The DGEs-diseases relationship network reveals the association between DEGs and disease. The gene names depicted by the circle nodes and there are 157 corresponding disorders indicated by the octagon nodes.
Identification of the Candidate Drugs
The common DEGs between IgAN and COVID-19 were used as a basis for candidate therapeutic drugs. Enrichr was applied to extract the top ten drug molecules from the DSigDB database based on the p value. These identified potential drugs may be used in the treatment of both diseases and are listed in Table 4.
Discussion
IgA nephropathy, as the most common primary glomerulonephritis worldwide, is also a systemic inflammatory and autoimmune disorder [33]. The pathological manifestation of IgA nephropathy is characterized by the deposition of IgA in the glomeruli mesangium, indicating a crucial role of IgA in IgA nephropathy. Approximately, 30–40% of patients can progress to end-stage renal disease within 20–30 years of diagnosis [9, 34]. COVID-19, resulting from SARS-COV-2, is an acute respiratory disease with high mortality and infectivity [35]. SARS-COV-2, a mucosal targeted virus, may trigger the secretion of IgA by inducing strong mucosal immunity [12]. During the disease course, several chemokines, granulocyte colony-stimulating factors, and pro-inflammatory cytokines are released, accompanying immune dysfunction [36]. Therefore, SARS-COV-2 infection may accelerate the progress of IgA nephropathy. Moreover, even a healthy individual is susceptible to SARS-COV-2 infection, not just a patient with IgA nephropathy.
In this study, we explored the gene expression profiling of two RNA-seq datasets, i.e., IgAN and COVID-19, using a web tool, which helped reveal the hub genes that may play the role of candidate biomarkers in the investigated diseases [36]. Currently, high-throughput sequencing is widely used to predict biological markers of the diseases, making it an important method for diseases diagnosis [37]. First, we obtained the DEGs by downloading the datasets GSE164805 and GSE73953 for COVID-19 and IgAN from NCBI’s GEO data, respectively. Next, we analyzed the DEGs from both datasets to obtain common DEGs. Based on these common DEGs, the analysis of GO and signaling pathways enrichment and the construction of PPI network were determined. Then, gene-miRNA, TF-gene, and gene-disease interaction networks were constructed. Finally, we also predicted the candidate drugs.
GO was founded in 1998 and is a bioinformatics-based resource, which provides biological information on the gene product function [38]. The biological knowledge on GO is being constantly updated with time, both in terms of quality and quantity [39]. We subjected 312 common DEGs to GO enrichment analysis and facilitated further understanding of the potential biological association between IgAN and COVID-19 at the transcriptional level. GO enrichment analysis includes the following three aspects: biological process, cellular component, and molecular function. The most important GO term in the biological process involves the fatty-acyl-CoA biosynthetic process and regulation of inflammatory response. Fatty-acyl-CoA is a lipophilic thiol compound (RCO-CoA), which is formed by the binding of fatty acids and coenzyme A. In the eicosanoid signaling pathway of the heart, fatty-acyl-CoA is a promoter, which activates phospholipase A2 (PLA2) to mediate the production of arachidonic acid, triggering a series of inflammatory responses [40]. Therefore, fatty-acyl-CoA is of prime importance in the eicosanoid signaling pathway. In an animal experiment, the eicosanoid signaling pathway was reported to be engaged in the pathogenic process of SARS-COV-2 [41]. In our study, the fatty-acyl-CoA biosynthetic process was found to be enriched based on the common DEGs between both disorders. Therefore, we speculated that the fatty-acyl-CoA might be susceptible to playing a role in both diseases. The top GO terms in cellular components involve the nucleus and MHC class II protein complex. SARS-COV-2 enters the cells by binding its spike (S) protein to the corresponding receptor, namely angiotensin-converting enzyme 2 [42]. Subsequently, SARS-COV-2 enters nucleus and initiates viral replication. Similarly, the major GO pathways in molecular function involve RNA binding and single-stranded RNA binding genes. Due to SARS-COV-2 is an RNA virus, RNA binding is of great significance for it to infect hosts. In the active stage of infection, more than 300 proteins have been reported to bind the SARS-COV-2-RNA [43].
Signaling pathway analysis is the greatest method to reflect the organism’s response to internal environmental variations. Based on common DEGs, KEGG signaling pathway analysis was performed to reveal similar pathways between IgAN and COVID-19. The top ten KEGG signaling pathways include leishmaniasis, inflammatory bowel disease, proteoglycans in cancer, type I diabetes mellitus, influenza A, rheumatoid arthritis, phagosome, amebiasis, and graft-versus-host disease. Inflammatory bowel disease is an autoimmune disease caused by dysregulation of inflammatory factors and immune function [44]. Recently, a large number of inflammatory markers and pro-inflammatory cytokines were identified in the circulation of COVID-19 patients [45].
To further understand the biological functioning of proteins and predict potential drugs, we constructed a PPI network using the common DEGs between IgAN and COVID-19. Next, the Cytoscape plugin was used to identify hub genes that may act as biological markers or key drug targets for COVID-19. The top ten hub genes include IL1B, IFNG, CCL3, ACTB, MMP9, CD44, FCGR3B, IGF1, SPP1, and CD69. Interleukin-1 β (IL1B) is the subtype of IL1 produced by activated macrophages. In some in vivo and in vitro experiments, the lung damage caused by influenza virus infection was often accompanied by the cytokine storm and the upregulation of the cytokine-associated genes such as IL1B[46]. Moreover, some researchers showed that the IL1B variant might be correlated to the susceptibility, cytokine storm, and complications of COVID-19 [47]. Interferon gamma (IFNG), a typical Th1 cytokine, is mainly produced by NK and natural killer-T cells, which are activated by immune and inflammatory stimuli [48], indicating a close relationship of IFNG with inflammation and immune responses. Meanwhile, some studies have revealed that IFNG could limit the replication of SARS-CoV [49, 50].
Furthermore, studies have confirmed that inflammatory macrophages are extremely active and exert an important inflammatory function in severe COVID-19, which is combined with the secretion of various cytokines, including CCL3 and IL1B [51]. Meanwhile, few other studies have found that the C-C Motif Chemokine Ligand-3 (CCL3) was associated with the formation of Gd-IgA1, a key factor in IgAN [52]. Actin beta (ACTB) encodes β-actin, which is a cytoskeletal regulator protein that participates in the pathogenesis of various tumors, including lung cancer, hepatoma, and so on [53]. According to some reports, ACTB expression is aberrant in COVID-19 [54]. Matrix metallopeptidase 9 (MMP9) is a subtype of zinc-dependent endopeptidases, known as matrix metalloproteinases (MMPs). It plays a role in cleaving the extracellular matrix elements [55]. A clinical trial showed that MMP9 was closely related to the mortality of COVID-19, even during the hospital stay [56]. Besides, MMP9 assisted SARS-CoV-2 in penetrating the blood-brain barrier by disrupting the basement membrane [57]. It allowed the viral entry into the host brain, causing viral-associated encephalopathy [57]. Furthermore, MMP9 is a vital factor in renal tubular and interstitial fibrosis of the IgAN [58]. CD44 is an adhesion glycoprotein on the cell surface, which possesses a variety of biological functions [59]. In a mice experiment, CD44 was proven to contribute to the resolution of inflammatory response in lung inflammation by binding to its receptor, hyaluronic acid (HA) [60]. COVID-19 also results in lung inflammation which is regulated by SARS-COV-2. We speculated that CD44 could play a role in the pathophysiology of COVID-19. A study found the involvement of the Fc gamma receptor 3B (FCGR3B) gene in severe COVID-19 cases and also in its complications [61]. Moreover, FCGR3B had been founded that it may be a noninvasive biomarker of IgAN [62]. Additionally, studies have demonstrated that a higher concentration of insulin-like growth factor-1 (IGF1) in COVID-19 patients was accompanied by a lower risk of death [63]. Similarly, the secreted phosphoprotein 1 (SPP1) gene was confirmed to activate the CD14+ monocytes, promoting the development of PD-L1+ neutrophils [64]. The two cells can be considered as the biological markers of severe COVID-19 [64]. Moreover, a high expression of the CD69 gene on MAIT cells was demonstrated to be correlation with the COVID-19 poor prognosis [65].
Additionally, we explored the association between both diseases at the level of gene-miRNAs and the TFs-genes interactions. Gene transcription and expression are mainly controlled by the TFs, while post-transcriptional gene modification is regulated by miRNAs. Both are indispensable for further understanding of disease progression. In this study, we used the NetworkAnalyst platform to analyze the interactions among hub genes, miRNAs, and TFs. For example, YY1, POU2F2, RUNX2, TP53, USF2, SREBF1, and HOXA5 were related to various respiratory diseases [66‒72]. In gene-miRNAs association, three miRNAs (hsa-mir-16-5p, hsa-mir-15b-5p, and hsa-mir-1-3p) were found to be connected with COVID-19. Some studies found that hsa-mir-1-3p targeted the host genes related to the proteins of SARS-CoV-2 [73]. Hence, it was regarded as an antiviral miRNA for respiratory disorders [73]. The hsa-miR-15b-5p was directly associated with the SARS-CoV-2 genome, probably helping SARS-CoV-2 to escape the immune defense mechanism of hosts [74], while hsa-mir-16-5p was proven to bind to SARS-CoV-2, playing a role in the SARS-CoV-2 infection [74].
Also, to further understand the correlation between various diseases and the top ten genes, we carried out the gene-disease analysis. The outcomes showed a variety of diseases to be associated with COVID-19. For example, several genes were found related to lung diseases, including pneumonia, respiratory tract disease, and respiratory syncytial virus infections. Individuals with preexisting respiratory diseases were found to be more susceptible to the SARS-CoV-2 infection [36]. Second, our hub genes also interacted with some psychiatric disorders, such as paranoid schizophrenia, depressive disorder, and bipolar disorder. According to research statistics, a rapid increase has been observed in the number of patients with psychiatric disorders after the outbreak of COVID-19 [75]. Studies have shown that about 35% of COVID-19 patients showed psychiatric symptoms, including anxiety and depression, which persisted even after recovery [76]. Furthermore, many genes have been found engaged with autoimmune diseases, including lupus nephritis, rheumatoid arthritis, and granulomatosis with polyangiitis. Many reports have shown that COVID-19 was combined with autoimmune diseases, including autoimmune hemolytic anemia [8], Guillain-Barré syndrome (GBS) [6], and so on. This may be attributed to the cross-immune reaction of SARS-CoV-2 protein with a variety of extrapulmonary tissue antigens [77]. Besides, the SARS-CoV-2 vaccine may induce some autoimmune diseases [77]. Multicentral studies have shown that patients with autoimmune diseases were at greater risk of contracting SARS-CoV-2 than healthy individuals [78].
Some drugs and chemicals have previously served as the therapy for COVID-19. For example, remdesivir was the first antiviral drug to cure severe COVID-19 by limiting the SARS-CoV-2 replication [79]. Baricitinib is an anti-inflammatory agent which works by inhibiting Janus activated kinase 2 (JAK2) and Janus activated kinase 1 (JAK1) proteins selectively [80]. Also, a large study confirmed baricitinib to be beneficial for COVID-19 patients [81]. In a clinical trial, a combination of remdesivir and baricitinib was proven to reduce the time of recovery and speed up the improvement of clinical symptoms compared to that of a single drug [82]. Currently, only a few effective therapeutic agents are available to cure COVID-19 while the pandemic is still continuing. Hence, urgently exploring the possible therapeutic agents or compounds for managing COVID-19 is necessary. We used common DEGs to identify some of the drugs. For instance, PD 98059, a p42/44 mitogen-activated protein kinase (MAPK) inhibitor, can reduce platelet aggregation by inhibiting the conversion of arachidonic acid to pro-aggregatory metabolite thromboxane (Tx) A2[83]. According to a recent study, COVID-19 was regarded as an endothelial disease, which was accompanied by the development of coagulopathy, oxidative stress, inflammation, and cytokine storm [84]. This increased the viscosity of blood, accelerating the aggregation of platelet and the formation of thrombosis. Also, the major reasons for morbidity and mortality of COVID-19 patients involve the complications of thrombosis [85]. Therefore, since PD 98059 reduces platelet aggregation, it may be considered a possible treatment for COVID-19.
Another identified drug for COVID-19 is selenium, which is an essential microelement. It regulates immune functions and preserves immune cells from oxidative stress by the redox-regulating activity of selenoproteins [86]. During the course of COVID-19, oxidative stress exerts an important effect in enhancing the affinity of the S protein of SARS-CoV-2 to angiotensin-converting enzyme, which indicates an increase in the severity of COVID-19 infection [84]. Therefore, we speculated that selenium could be a potential drug to treat COVID-19. Tamibarotene is also reported as an effective and safe candidate drug against SARS-CoV-2 [87]. Gemcitabine is a cytidine analog and was used for the management of all kinds of tumors in the past. Recently, it was found to serve as a broad-spectrum antiviral drug [88], providing resistance to several RNA and DNA viruses, including Zika virus, herpes simplex virus type 1, Middle East Respiratory Syndrome Coronavirus, enteroviruses, and influenza virus [89]. A cell study demonstrated that gemcitabine prevented the SARS-CoV-2 from replicating, thereby hindering the release of viral progeny [90]. Furthermore, one animal experiment confirmed that simvastatin acted against the production of inflammatory cytokine and avoided the entry of SARS-CoV-2 into the animal body, thus, serving as a candidate drug to cure COVID-19 [91]. Therefore, the abovementioned drugs may have the potential to treat COVID-19. However, since the literature has only a few reports on these investigated drugs, more studies are needed in the future.
Conclusion
In this study, we used common DEGs between IgAN and COVID-19 to explore the association between both diseases. IgAN was a probable high-risk factor for COVID-19 infection. Additionally, we detected 312 common DEGs based on the differentially expressed genes of IgAN and COVID-19. Next, we constructed a PPI network, identified hub genes, and predicted candidate drugs. These drugs could be considered a possible treatment for COVID-19. With the ongoing COVID-19 pandemic and constantly mutating SARS-CoV-2, continuous studies are needed to explore potential drugs and vaccine. The hub genes may serve as the biomarkers of COVID-19 and be regarded as the targets for development of COVID-19 drugs and vaccine.
Acknowledgments
We are thankful to all of patients who donated their blood samples and the GEO database which provided the platform for us to obtain databases.
Statement of Ethics
An ethics statement is not applicable because all of the data in this study are available in public databases. Meanwhile, there is no participation statement.
Conflict of Interest Statement
The authors declare no conflict of interest.
Funding Sources
This work was supported by Xiamen Medical and Health Guidance Project [Grant No. 3502Z2019173].
Author Contributions
Conception and design: Xiaodan Guo; administrative support: Xiaodan Guo and Tianjun Guan; collection and assembly of data, data analysis, and interpretation: Xiaoqi Deng and Yu Luo; and manuscript writing: all authors.
Additional Information
Xiaoqi Deng and Yu Luo contributed equally to this work.
Data Availability Statement
The data that support the findings of this study are available in the Gene Expression Omnibus at https://www.ncbi.nlm.nih.gov/geo/, reference number [14]. Further inquiries can be directed to the corresponding author.