Abstract
The goal of the present investigation is to identify the differentially expressed genes (DEGs) between SARS-CoV-2 infected and normal control samples to investigate the molecular mechanisms of infection with SARS-CoV-2. The microarray data of the dataset E-MTAB-8871 were retrieved from the ArrayExpress database. Pathway and Gene Ontology (GO) enrichment study, protein–protein interaction (PPI) network, modules, target gene–miRNA regulatory network, and target gene–TF regulatory network have been performed. Subsequently, the key genes were validated using an analysis of the receiver operating characteristic (ROC) curve. In SARS-CoV-2 infection, a total of 324 DEGs (76 up- and 248 down-regulated genes) were identified and enriched in a number of associated SARS-CoV-2 infection pathways and GO terms. Hub and target genes such as TP53, HRAS, MAPK11, RELA, IKZF3, IFNAR2, SKI, TNFRSF13C, JAK1, TRAF6, KLRF2, CD1A were identified from PPI network, target gene–miRNA regulatory network, and target gene–TF regulatory network. Study of the ROC showed that ten genes (CCL5, IFNAR2, JAK2, MX1, STAT1, BID, CD55, CD80, HAL-B, and HLA-DMA) were substantially involved in SARS-CoV-2 patients. The present investigation identified key genes and pathways that deepen our understanding of the molecular mechanisms of SARS-CoV-2 infection, and could be used for SARS-CoV-2 infection as diagnostic and therapeutic biomarkers.
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Introduction
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is known as novel coronavirus disease-2019 (COVID-19) and has spread widely throughout the globe in an epidemic proportion with the current pandemic risk (Li et al. 2020). This infection is related to respiratory diseases, and this virus mainly infects respiratory epithelial cells and transmits from human to human primarily through the respiratory tract, contributing to more deaths (Zou et al. 2020; Madurai Elavarasan and Pugazhendhi 2020). In the present situation, the survival rate of patients with SARS-CoV-2 infection has been slightly increased, and patients with this infection have no apparent benefit from the current antiviral drugs (Hoffmann et al. 2020). Knowing the molecular pathogenesis of the viral infections and their routes of transmission is completely necessary for the creation of new therapeutic targets.
Present situation for investigating the pathogenesis of SARS-CoV-2 infection is needed in molecular biology. Although the pathogenesis of SARS-CoV-2 infection remains to be clarified, abnormal gene expression in nasal epithelial cells can serve significant roles (Sungnak et al. 2020). Entry factors related genes such as angiotensin-converting enzyme 2 (ACE2) (Zhang et al. 2020); TMPRSS2 (Sungnak et al. 2020); and inflammatory related genes (IL-2, IL-7, IL-10, GCSF, IP-10, MCP-1, MIP-1A, and TNF-α) (Fu et al. 2020) were linked with pathogenesis of SARS-CoV-2 infections. Therefore, targeted regulation of these genes may reveal potential strategies for the treatment of SARS-CoV-2 infections. Therefore, targeted regulation of entry factors and inflammatory-related genes could become potential strategies for the treatment of SARS-CoV-2 infection.
Throughout this investigation, we used bioinformatics methods to examine differentially expressed genes (DEGs) between SARS-CoV-2-infected samples and standard control samples. We performed pathway enrichment and gene ontology (GO) analysis of DEGs, and established the protein–protein interactions (PPI) network, modules analysis, target gene–miRNA regulatory network, and target gene–TF regulatory network to reveal molecular mechanisms in SARS-CoV-2 infection. Finally, we performed validation hub genes by receiver operating characteristic (ROC) curve analysis. Finally, through receiver operating characteristic (ROC) curve analysis, we conducted validation hub genes. The aim of this study is thus to have a better understanding of the exact mechanisms of SARS-CoV-2 infection and to identify potential novel diagnostic or therapeutic targets through bioinformatics analysis.
Materials and methods
Microarray data selection
Microarray data of gene expression profile (E-MTAB-8871) was downloaded from ArrayExpress (https://www.ebi.ac.uk/arrayexpress), which is the largest resource of gene expression publicly available (Kolesnikov et al. 2015). Samples from this dataset were RNA extracted from the blood sample and processed for hybridization on NanoString nCounter Human Immunology V2 Panel Array. A total of 32 samples were investigated, including 22 SARS-CoV-2-infected samples, and 10 normal control samples. The study was designed according to the flowchart (Fig. 1).
The workflow representing the methodology and the major outcome of the study. SARS-CoV-2—Severe acute respiratory syndrome coronavirus 2 infection - breast cancer, GO—gene ontology, miRNA—MicroRNA, TF-transcription factor, DEGs—deferential expressed genes
Identification of DEGs
The DEGs between the SARS-CoV-2-infected samples and normal control samples were analyzed with various methods including data preparation (data normalization and summarization) and DEGs identification (up- and down-regulated genes). The limma package in R Software was used for background correction, quantile normalization and probe summarization, and limma package was also applied for DEGs identification (Ritchie et al. 2015). The development of DEGs choice included model design, linear model fitness, contrast matrix generation, bayesian model building and gene filtering, all of which were managed by the functions in the limma package. Genes with the p < 0.05, |log Fc| (fold change) > 1.5 were considered as DEGs (up- and down-regulated genes).
Pathway enrichment analysis for DEGs
BIOCYC (https://biocyc.org/) (Caspi et al. 2016), Kyoto Encyclopedia of Genes and Genomes (KEGG) (http://www.genome.jp/kegg/pathway.html) (Kanehisa et al. 2019), Pathway Interaction Database (PID) (https://wiki.nci.nih.gov/pages/viewpage.action?pageId=315491760) (Schaefer et al. 2009), REACTOME (https://reactome.org/) (Fabregat et al. 2018), GenMAPP (http://www.genmapp.org/) (Dahlquist et al. 2002), MSigDB C2 BIOCARTA (http://software.broadinstitute.org/gsea/msigdb/collections.jsp) (Subramanian et al. 2005), PantherDB (http://www.pantherdb.org/) (Mi et al. 2017), Pathway Ontology (http://www.obofoundry.org/ontology/pw.html) (Petri et al. 2014) and Small Molecule Pathway Database (SMPDB) (http://smpdb.ca/) (Jewison et al. 2014) are a data resource for genes and genomes with assigned corresponding functional importance. The ToppGene (ToppFun) (https://toppgene.cchmc.org/enrichment.jsp) (Chen et al. 2009) is an online resource for interpreting genes originating from genomic investigation with bioinformatics data. The p value < 0.05 was considered statistically significant.
Gene ontology (GO) enrichment analysis for DEGs
GO (http://www.geneontology.org/) (Lewis et al. 2017) was used to determine gene actions in three aspects: biological process (BP), cellular component (CC) and molecular function (MF). ToppGene (ToppFun) (https://toppgene.cchmc.org/enrichment.jsp) (Chen et al. 2009) is an online website that provides an extensive set of functional annotation tools to understand the biological meaning behind a massive list of genes. In the current investigation, the GO enrichment analyses for statistically important DEGs. The p value < 0.05 was considered statistically significant.
PPI network construction and module analysis
The common up and down-regulated genes of E-MTAB-8871 was analyzed using the online website STRING (https://string-db.org/, version 11) (Szklarczyk et al. 2019), with 0.700 (moderate confidence) as the minimum required interaction score. Then, the software Cytoscape (http://www.cytoscape.org/, version 3.8.0) (Shannon et al. 2003) was used to establish a PPI network. The Network Analyzer in Cytoscape was utilized to calculate node degree (Przulj et al. 2004), betweenness centrality (Nguyen et al. 2011), stress centrality (Shi and Zhang 2011), closeness centrality (Nguyen and Liu 2011) and clustering coefficient (Wang et al. 2012). PEWCC1 (http://apps.cytoscape.org/apps/PEWCC1) (Zaki et al. 2013) was used to perform module analysis.
Construction of target gene–miRNA regulatory network
miRNet database (https://www.mirnet.ca/) (Fan and Xia 2018) provides certain target gene–miRNA regulatory association pairs, which are verified by experiments and predicted by ten programs, including TarBase (http://diana.imis.athena-innovation.gr/DianaTools/index.php?r=tarbase/index) (Vlachos et al. 2015), miRTarBase (http://mirtarbase.mbc.nctu.edu.tw/php/download.php) (Chou et al. 2018), miRecords (http://miRecords.umn.edu/miRecords) (Xiao et al. 2009), miR2Disease (http://www.mir2disease.org/) (Jiang et al. 2009), HMDD (http://www.cuilab.cn/hmdd) (Huang et al. 2019), PhenomiR (http://mips.helmholtz-muenchen.de/phenomir/) (Ruepp et al. 2010), SM2miR (http://bioinfo.hrbmu.edu.cn/SM2miR/) (Liu et al. 2013), PharmacomiR (http://www.pharmaco-mir.org/) (Rukov et al. 2014), EpimiR (http://bioinfo.hrbmu.edu.cn/EpimiR/) (Dai et al. 2014) and starBase (http://starbase.sysu.edu.cn/) (Li et al. 2014). This investigation inputted the up- and down-regulated genes into the database to examine the regulatory association pairs between target gene and miRNA. Target gene–miRNA regulatory network was constructed and visualized by Cytoscape 3.8.0 software to show the target genes and miRNA. Therefore, these target genes and miRNA might play a potential role in the pathogenesis and treatment of SARS-CoV-2 infection.
Construction of target gene–TF regulatory network
NetworkAnalyst database (https://www.networkanalyst.ca/) (Zhou et al. 2019) provides certain target gene–TF regulatory association pairs, which are verified by experiments and predicted by JASPAR (http://jaspar.genereg.net/) (Khan et al. 2018) database. This investigation inputted the up- and down-regulated genes into the database to examine the regulatory association pairs between target gene and TF. Target gene–TF regulatory network was constructed and visualized by Cytoscape 3.8.0 software to show the target genes and TF. Therefore, these target genes and TF may play a potential role in the pathogenesis and treatment of SARS-CoV-2 infection.
Validation of hub genes
Receiver‐operating characteristic (ROC) analyses were operated to calculate the diagnostic value of the hub genes for SARS-CoV-2 infection. The ROC curve with area under curve (AUC) was determined using R “pROC” package (Robin et al. 2011).
Results
Identification of DEGs
Microarray dataset (E-MTAB-8871) was obtained from ArrayExpress database and normalized mRNA expression data through R language (Fig. 2). Volcano plot was generated to manifest up-regulated (green) and down-regulated (red) genes between SARS-CoV-2-infected samples and normal controls samples (Fig. 3) and were also visualized on a heatmap for up- and down-regulated genes (Figs. 4, 5). This approach indicated presence of a total of 324 statistically significant genes (P < 0.05, |log Fc| (fold change) > 1.5), of which 76 genes were up-regulated and 248 genes were down-regulated (Table 1).
Box plots of the normalized data. a 22 SARS-CoV-2 infected samples b 10 normal control samples. Horizontal axis represents the sample symbol and the vertical axis represents the gene expression values. The black line in the box plot represents the median value of gene expression
Volcano plot of differentially expressed genes. Genes with a significant change of more than twofold were selected. Green dot on right side (
) represented up regulated significant genes and red dot on left side (
) represented down regulated significant genes
Heat map of up regulated differentially expressed genes. Legend on the top left indicate log fold change of genes. White represents decreased expression of genes; light green represents not significant expression of genes; dark green represents increased expression of genes. (A1–A10 = Normal control samples; B1–B22 = SARS-CoV-2 infected samples)
Heat map of down regulated differentially expressed genes. Legend on the top left indicate log fold change of genes. White represents decreased expression of genes; light pink represents not significant expression of genes; dark pink represents increased expression of genes. (A1–A10 = Normal control samples; B1–B22 = SARS-CoV-2 infected samples)
Pathway enrichment analysis for DEGs
Pathway enrichment analysis of integrated DEGs showed the up-regulated genes were mainly involved in measles, herpes simplex infection, IL12-mediated signaling events, IL2-mediated signaling events, cytokine signaling in immune system, innate immune system, IL22 soluble receptor signaling pathway, bioactive peptide-induced signaling pathway, JAK/STAT signaling pathway, Inflammation mediated by chemokine and cytokine signaling pathway, G protein signaling, platelet-derived growth factor signaling, intracellular signalling through adenosine receptor A2a and adenosine, insulin signalling and other pathways (Table 2); the down-regulated genes were mainly involved in citrulline–nitric oxide cycle, phospholipases, cytokine–cytokine receptor interaction, hematopoietic cell lineage, IL4-mediated signaling events, IL12-mediated signaling events, cytokine signaling in immune system, signaling by interleukins, phenylalanine tyrosine and tryptophan biosynthesis, MAP kinase activity, genes encoding secreted soluble factors, cytokine network, interleukin signaling pathway, inflammation mediated by chemokine and cytokine signaling pathway, intrinsic apoptotic, interleukin-10 signaling, sulindac pathway, glycolysis and other pathways (Table 3).
Gene ontology (GO) enrichment analysis for DEGs
The GO enrichment analysis of up- and down-regulated genes can be split into three groups: BP, CC, and MF are listed in Tables 4, 5. In terms of BP, the up-regulated genes were mainly involved in regulation of immune system process, response to biotic stimulus and other functions; the down-regulated genes were mainly associated in regulation of immune system process, cytokine-mediated signaling pathway and other functions. As far as CC is concerned, the up-regulated genes were mainly involved in the side of membrane, receptor complex and other functions; the down-regulated genes were mainly located in the cell surface, leaflet of membrane layers and other functions. As for MF, the up-regulated genes mainly participated in kinase binding, signaling receptor binding and other functions; the down-regulated genes mainly participated in cytokine receptor binding, signaling receptor binding and other functions (Tables 4, 5).
PPI network construction and module analysis
To determine the expression relationships among up- and down-regulated genes, we inputted the up- and down-regulated genes to STRING PPI database. Then, PPI networks were visualized using the cytoscape software. As a result, a PPI network for up-regulated genes had 2912 nodes and 5967 edges (Fig. 6). Among these nodes, TP53, HRAS, CTNNB1, FYN, ABL1, STAT3, STAT1, JAK2, C1QBP, XBP1, BST2, CD99 and IFI35 were identified as hub genes with highest node degree distribution, betweenness centrality, stress centrality, closeness centrality and lowest clustering coefficient are listed in Table 6. The scatter plots for this network are shown in Fig. 7a–e. Enrichment analysis showed that the genes were mainly associated with measles, natural killer cell mediated cytotoxicity, HTLV-I infection, regulation of immune system process, viral myocarditis, Jak-STAT signaling pathway, herpes simplex infection, hemostasis, response to biotic stimulus, cytokine signaling in immune system, integral component of plasma membrane and response to biotic stimulus. A PPI network for down-regulated genes had 3083 nodes and 6491 edges (Fig. 8). Among these nodes, MAPK11, RELA, LCK, KIT, EGR1, IL20, ILF3, CASP3, IL19, ATG7, GPI and S1PR1 were identified as hub genes with highest node degree distribution, betweenness centrality, stress centrality, closeness centrality and lowest clustering coefficient are listed in Table 6. The scatter plots for this network are shown in Fig. 9a–e. Enrichment analysis showed that the genes were mainly associated with tuberculosis, inflammatory bowel disease (IBD), HTLV-I infection, cytokine–cytokine receptor interaction, cytokine-mediated signaling pathway, regulation of immune system process, response to biotic stimulus, response to cytokine, cytokine production, innate immune system, glycolysis, gluconeogenesis and the extracellular signal-regulated RAF/MEK/ERK signaling.
Protein–protein interaction network of up regulated genes. Green nodes (
) denotes up regulated genes; Blue lines (
) denotes edges (Interactions)
Scatter plot for up regulated genes. (A—Node degree; B—Betweenness centrality; C—Stress centrality; D—Closeness centrality; E—Clustering coefficient)
Protein–protein interaction network of down regulated genes. Red nodes (
) denotes down regulated genes; Pink lines (
) denotes edges (Interactions)
Scatter plot for down regulated genes. (A—Node degree; B—Betweenness centrality; C—Stress centrality; D—Closeness centrality; E—Clustering coefficient)
Based on STRING database, plug-ins PEWCC1 was used to carry out module analysis in Cytoscape software. We identified total 566 and 548 modules from the PPI network of up- and down-regulated genes according to the degree of importance and further analyzed with the plug-in PEWCC1. The top four significant modules of up-regulated were selected for further analysis (Fig. 10). Module 48 had 10 nodes and 34 edges, module 50 had 10 nodes and 33 edges, Module 64 had 10 nodes and 17 edges and module 65 had 10 nodes and 17 edges, respectively. Enrichment analysis showed that the genes in these modules were mainly involved in natural killer cell-mediated cytotoxicity, measles, Jak-STAT signaling pathway, viral myocarditis, herpes simplex infection, influenza A, osteoclast differentiation, HTLV-I infection, IL12-mediated signaling events, IL2-mediated signaling events, tuberculosis, malaria, paxillin-dependent events mediated by a4b1, TCR signaling in naive CD8 + T cells and cytokine signaling in Immune system. The top four significant modules of down-regulated were selected for further analysis (Fig. 11). Module 18 had 17 nodes and 44 edges, module 23 had 15 nodes and 20 edges, module 58 had 9 nodes and 22 edges and module 104 had 7 nodes and 12 edges, respectively. Enrichment analysis showed that the genes in these modules were mainly involved in apoptosis signaling pathway, tuberculosis, viral myocarditis, Jak-STAT signaling pathway, cytokine signaling in immune system, measles, innate immune system, cytokine–cytokine receptor interaction, influenza A, hematopoietic cell lineage, HTLV-I infection, signaling by interleukins, adaptive immune system, IL12-mediated signaling events, interleukin signaling pathway, inflammation mediated by chemokine and cytokine signaling pathway, inflammatory bowel disease (IBD), FAS (CD95) signaling pathway, cytokine-mediated signaling pathway, response to biotic stimulus, IL4-mediated signaling events, MAPK family signaling cascades and regulation of immune system process.
Modules in PPI network. The green nodes denote the up regulated genes. Green nodes (
) denotes up regulated genes; Blue lines (
) denotes edges (Interactions)
Modules in PPI network. The red nodes denote the down regulated genes. Red nodes (
) denotes down regulated genes; Pink lines (
) denotes edges (Interactions)
Construction of target gene–miRNA regulatory network
Using the miRNet database, target gene–miRNA regulatory network for up-regulated genes had 1008 nodes and 1613 interactions (Fig. 12). The network marked that each target genes have interactions with miRNAs. IKZF3 regulates 134 miRNAs (ex, hsa-mir-6860), TP53 regulates 130 miRNAs (ex, hsa-mir-5703), IFNAR2 regulates 109 miRNAs (ex, hsa-mir − 4510), SMAD5 regulates 83 miRNAs (ex, hsa-mir-6086) and STAT3 regulates by 80 miRNAs (ex, hsa-mir − 4270) are listed in Table 8. Enrichment analysis showed that the target genes in this network were mainly involved in IL2-mediated signaling events, measles, herpes simplex infection, ALK2 signaling events and cytokine signaling in immune system. Similarly, target gene–miRNA regulatory network for down-regulated genes had 1791 nodes and 3951 interactions (Fig. 13). SKI regulates 210 miRNAs (ex, hsa-mir-5100), TNFRSF13C regulates 136 miRNAs (ex, hsa-mir-3197), BCL2L11 regulates 122 miRNAs (ex, hsa-mir-8064), ICOSLG regulates 119 miRNAs (ex, hsa-mir-3672) and IL6R regulates 94 miRNAs (ex, hsa-mir-7641) are listed in Table 7. Enrichment analysis showed that the target genes in this network were mainly involved in molecular function regulator, cytokine–cytokine receptor interaction, apoptosis signaling pathway, adaptive immune system and cytokine–cytokine receptor interaction.
The network of up regulated genes and their related miRNAs. The green circles nodes (
) are the up regulated genes; yellow diamond nodes (
) are the miRNAs; Pink lines (
) denotes edges (Interactions)
The network of down regulated genes and their related miRNAs. The red circles nodes (
) are the down regulated genes; blue diamond nodes (
) are the miRNAs; Sku blue lines (
) denotes edges (Interactions)
Construction of target gene–TF regulatory network
Using the NetworkAnalyst database, target gene–TF regulatory network for up-regulated genes had 145 nodes and 634 interactions (Fig. 14). The network marked that each target genes have interactions with transcription factors (TFs). JAK1 regulates 46 TFs (ex, FOXC1), TRAF6 regulates 31 TFs (ex, GATA2), CLEC7A regulates 25 TFs (ex, YY1), STAT1 regulates 22 TFs (ex, CREB1) and IKZF1 regulates 22 TFs (ex, TFAP2A) are listed in Table 8. Enrichment analysis showed that the target genes in this network were mainly involved in measles, herpes simplex infection, tuberculosis, osteoclast differentiation and regulation of immune system process. Similarly, target gene–TF regulatory network of down-regulated genes had 1788 nodes and 235 interactions (Fig. 15). KLRF2 regulates 127 TFs (ex, FOXC1), CD1A regulates 102 TFs (ex, GATA2), TNFRSF4 regulates 75 TFs (ex, YY1), MME regulates 63 TFs (ex, FOXL1) and CXCL12 regulates 63 TFs (ex, FOXL1) are listed in Table 7. Enrichment analysis showed that the target genes in this network were mainly involved in cytokine-mediated signaling pathway, hematopoietic cell lineage, cytokine–cytokine receptor interaction, innate immune system and peptide ligand-binding receptors.
The network of up regulated genes and their related TFs. The green circles nodes (
) are the up regulated genes; Blue triangle nodes (
) are the TFs; Purple line (
) denotes edges (Interactions)
The network of down regulated genes and their related TFs. The Red circles nodes (
) are the down regulated genes; Blue triangle nodes (
) are the TFs; Yelow line (
) denotes edges (Interactions)
Validation of hub genes
The ROC curve analysis was accomplished to assess the diagnostic values of hub genes. Our finding revealed that CCL5 (AUC = 0.784), IFNAR2 (AUC = 0.750), JAK2 (AUC = 0.859), MX1 (AUC = 0.773), STAT1 (AUC = 0.873), BID (AUC = 0.848), CD55 (AUC = 0.973), CD80 (AUC = 0.870), HAL-B (AUC = 0.816) and HLA-DMA (AUC = 0.730) had significant diagnostic values for discriminating SARS-CoV-2 samples and normal controls (Fig. 16).
ROC curve validated the sensitivity, specificity of hub genes as a predictive biomarker for SARS-CoV-2 diagnosis. a CCL5 b IFNAR2 c JAK2 d MX1 e STAT1 f BID g CD55 h CD80 i HAL-B j HLA-DMA
Discussion
Currently, genetic and genomic-related exploration progress speedily and provide new prospect to illuminate the molecular pathogenesis of SARS-CoV-2 infections. And bioinformatics analysis also has developed phenomenally and is committed to search for candidate biomarkers to implement more correct screening, prompt diagnosis for SARS-CoV-2-infected patients based on enormous genetic and genomics data.
In the current investigation, a bioinformatics approach was used to identify candidate biomarker and therapeutic targets of SARS-CoV-2 infection. Following the analysis, 324 DEGs, including 76 up-regulated genes and 248 down-regulated genes were identified. Shi et al. (2007) found that expression of JAK1 was responsible for progression of adenovirus infection, but this gene may be linked with advancement of SARS-CoV-2 infection. Previously reported genes such as ZAP70 (Guntermann et al. 1997), CD22 (Ma et al. 2013) and MAPKAPK2 (Yang et al. 2012) are expressed and responsible for progression various viral infections, but our study found that these genes may important for development of SARS-CoV-2 infection. Previously reported genes such as CCR5 (Dawson et al. 2000) and TRAF6 (Tian et al. 2018) were highly expressed and involved in progression of influenza A viral infections, but these genes may be liable for advancement of SARS-CoV-2 infection. Zhivaki et al. (2017) noticed that expression of CX3CR1 is associated in progression of respiratory syncytial virus infection, but this gene may be linked with development of SARS-CoV-2 infection. Previous studies had reported that expression of CD45RB was key for progression of sendai virus infection (Hou and Doherty 1993), but this gene may liable for advancement of SARS-CoV-2 infection. Corominas et al. (2020) showed the possible involvement of IL6R in the development of SARS-CoV-2 infection. Evidence from Chi et al. (2013) study indicated that the HLA-DQB1 expression level is down-regulated in varicella-zoster virus infection, but low expression of this gene may be associated in progression of SARS-CoV-2 infection.
Pathway enrichment analysis results for up- and down-regulated gene might play important roles in the SARS-CoV-2 infection. Studies have found that over expression of enriched genes such as CCND3 (Fan et al. 2017), IRF7 (Rosenberger et al. 2017), MX1 (Pillai et al. 2016) and STAT4 (Bot et al. 2003) in influenza viral infection, but these genes may be important for progression of SARS-CoV-2 infection. JAK2 is a protein-coding gene which was first reported aberrantly expressed and plays important roles in SARS-CoV-2 infection (Wu and Yang 2020). After that, enriched up-regulated genes such as IFIH1 (Asgari et al. 2017), FYN (FYN proto-oncogene, Src family tyrosine kinase) (Kenney and Meng 2015), STAT1 (Patel et al. 2010), GZMB (granzyme B) (Loebbermann et al. 2012a, b), TRAF2 (Liu et al. 2019) and BST2 (Wang et al. 2019) were found to be involved in development of severe viral respiratory infections. Rice et al. (2016) suggested that TLR2 activity was involved in progression of pneumovirus infection, but this gene may be involved in development of SARS-CoV-2 infection. IL2RG has been shown to have an important role in adeno-associated viral infection (Hiramoto et al. 2018), but this gene may be involved in progression of SARS-CoV-2 infection. Reported enriched up-regulated genes such as STAT3 (Mizutani et al. 2004) and HLA-A (Ohno et al. 2009) contributes to the progression of SARS coronavirus infection, but this gene may be involved in SARS-CoV-2 infection. Several studies have reported that enriched genes such as STAT5B (Mukherjee et al. 2014), SOCS1 (Zheng et al. 2015), CCR1 (Miller et al. 2006) and CCL5 (Sali mi et al. 2017) were highly expressed in respiratory syncytial virus infection, but elevated expression these genes may be involved in development of SARS-CoV-2 infection. Increasing evidence shows that the enriched genes such as IFNAR2 (Romporn et al. 2013), TBX21 (Zhu et al. 2015), GBP1 (Anderson et al. 1999), IRF5 (Vandenbroeck et al. 2011) and IFI35 (Estrabaud et al. 2015) were over expressed in various viral infections, but high expression of these genes may be involved in infection of SARS-CoV-2 infection. Novel biomarkers such as IKBKE (inhibitor of nuclear factor kappa B kinase subunit epsilon), TP53, CD247, IL18RAP, IL18R1, HRAS (HRas proto-oncogene, GTPase), PSMB9, IKBKB (inhibitor of nuclear factor kappa B kinase subunit beta), ITGB2 and LTB4R were highly expressed and might be involved in progression of SARS-CoV-2 infection. Sanders et al. (2001) revealed that NOS2 was down-regulated in rhinovirus infection, but this gene may be involved in development of SARS-CoV-2 infection. The enriched down-regulated genes found in this study include IL10 (Loebbermann et al. 2012a, b), IL13 (Castilow et al. 2008), IL21 (Antunes et al. 2019), CCR6 (Shi et al. 2017), CXCL13 (Alturaiki et al. 2018), CCL20 (Shi et al. 2017), IL19 (Ermers et al. 2011), IL20 (Ermers et al. 2011), CD40 (Harcourt et al. 2003a, b), IL2 (Noma et al. 1996), IL3 (Bertrand et al. 2015), IL4 (Puthothu et al. 2006), IL9 (Dodd et al. 2009) and STAT6 (Srinivasa et al. 2016) were responsible for progression of respiratory syncytial virus infection, but these genes may be linked with progression of SARS-CoV-2 infection. Many previous studies have confirmed the roles of enriched down-regulated genes such as IL12B (Mueller et al. 2004), TNFRSF9 (Rodriguez et al. 2019), TNFRSF14 (Soroosh et al. 2014), IL17F (Wang et al. 2016), CCR8 (Calado et al. 2010), CCL18 (Malhotra et al. 2019), CCL22 (Yang et al. 2012), CXCL11 (Pineda-Tenor et al. 2014), CX3CL1 (Bertin et al. 2014), CXCL12 (Durrant et al. 2014), CCR10 (Nakayama et al. 2002), IFNA2 (Chen et al. 2017), IFNB1 (Gagné et al. 2017), IL7 (Golden‐Mason et al. 2006), IL26 (Miot et al. 2015), CXCR1 (Xu et al. 2016), CEBPB (CCAAT enhancer binding protein beta) (Liu et al. 2009), ETS1 (Posada et al. 2000), STAT5A (Warby et al. 2003), THY1 (Lu et al. 2011), IL16 (Caufour et al. 2001), HLA-B (Martin et al. 2007), HLA-C (Apps et al. 2013), HLA-DPA1 (Wasityastuti et al. 2016), HLA-DPB1 (Lambert et al. 2015), HLA-DQA1 (Tibbs et al. 1996), HLA-DRB1 (Chi et al. 2013), PSMB10 (Deng et al. 2019), BCL2 (Zuckerman et al. 2001), TOLLIP (toll interacting protein) (Li et al. 2016a, b), VCAM1 (Koraka et al. 2004), RAG1 (Winkler et al. 2017), IRF8 (Terry et al. 2015), EBI3 (Gehlert et al. 2004), EGR1 (Baer et al. 2016), IL27 (Swaminathan et al. 2013) and BID (BH3 interacting domain death agonist) (Hsu et al. 2003) were linked with development of various viral infections, but these genes may be associated with advancement of SARS-CoV-2 infection. Previous investigation demonstrated that enriched down-regulated genes such as IL17A (Wang et al. 2016), CCL11 (Suryadevara et al. 2013), CCL19 (Fleming-Canepa et al. 2011), XCR1 (Fossum et al. 2015), IFNAR1 (Lin et al. 2014), IL22 (Kumar et al. 2013), LTA (lymphotoxin alpha) (Morales-García et al. 2012), IL5 (Gorski et al. 2013), EGR2 (Du et al. 2014), RAG2 (Wu et al. 2010), CASP3 (Takahashi et al. 2013), S1PR1 (Zhao et al. 2019), CD80 (Lumsden et al. 2000), CD86 (Lumsden et al. 2000) and CD44 (Liu et al. 2014) were key for advancement of influenza virus infection, but these genes may be involved in progression of SARS-CoV-2 infection. Enriched down-regulated genes such as CCL7 (Girkin et al. 2015) and CXCR2 (Nagarkar et al. 2009) have been reported to be associated with rhinovirus 1B infection, but these genes may be responsible for infection of SARS-CoV-2. Accumulating evidence shows that enriched genes such as IFNG (interferon gamma) (Sainz et al. 2004) and TRAF3 (Siu et al. 2009) were low expressed in SARS-CoV, but decreased expression of these genes may be key for progression of SARS-CoV-2 infection. Conti et al. (2020) showed that IL6 was liable for progression of SARS-CoV-2 infection. Novel biomarkers such as IL10RA, IL12A, IL13RA1, PDGFB (platelet-derived growth factor subunit B), TNFSF12, IL17B, TNFRSF10C, CCL26, TNFRSF4, IL22RA2, CCL15, CCL16, CCL24, XCL1, KIT (KIT proto-oncogene, receptor tyrosine kinase), CCL13, PPBP (pro-platelet basic protein), IL23A, TGFBR1, LIF (LIF interleukin 6 family cytokine), CSF1R, CSF2, CSF2RB, CSF3R, TNFRSF13C, IL1RAP, IL4R, AICDA (activation-induced cytidinedeaminase), PTPN6, PIGR (polymeric immunoglobulin receptor), GATA3, PTAFR (platelet activating factor receptor), IL1RL2, PTK2, FN1, DUSP4, RELA (RELA proto-oncogene, NF-kA subunit), RELB (RELB proto-oncogene, NF-kB subunit), LCK (LCK proto-oncogene, Src family tyrosine kinase), IRAK4, RORC (RAR-related orphan receptor C), BCL2L11 and PLA2G2A were low expressed and might be involved in progression of SARS-CoV-2 infection.
GO enrichment analysis results for up- and down-regulated gene might play important roles in the SARS-CoV-2 infection. Enriched up-regulated genes such as ATG5 (Guévin et al. 2010), PDCD1 (Nasi et al. 2013), ABL1 (García et al. 2012), CD99 (Tochikura et al. 2003), LILRB2 (Alaoui et al. 2018), LAG3 (Tian et al. 2015), SERPING1 (Sanfilippo et al. 2017), XBP1 (Sharma et al. 2017), CTNNB1 (Tucci et al. 2013), RUNX1 (Zhao et al. 2016), SLAMF7 (O’Connell et al. 2019), ITGAL (integrin subunit alpha L) (Xu et al. 2018) and CEACAM1 (Hirai et al. 2010) appeared to be related in various types of viral infections, but these genes may be responsible for progression of SARS-CoV-2 infection. Hu et al. (2017) observed that high expression of C1QBP was liable for progression of respiratory syncytial viral infection, but elevated expression this gene may be associated with advancement of SARS-CoV-2 infection. Evidence demonstrated that high expression of enriched genes such as KLRD1 (Bongen et al. 2018) and NLRP3 (Pothlichet et al. 2013) were important for progression of influenza virus infection, but increased expression of these genes may be involved in advancement of SARS-CoV-2 infection. Novel biomarkers such as KLRK1, IKZF3, ZBTB16, CLEC7A, C2 (complement C2), IKZF1, LCP2, KLRC1, GFI1, CCRL2 and MAP4K2 were highly expressed and might be involved in progression of SARS-CoV-2 infection. Studies have reported that low expression of enriched genes such as IRGM (immunity-related GTPase M) (Hansen et al. 2017), MASP1 (El Saadany et al. 2011), CD244 (Raziorrouh et al. 2010), MBL2 (Spector et al. 2010), CD46 (Gaggar et al. 2003), C4A (Imakiire et al. 2012), C9 (Kim et al. 2013), ZEB1 (Lacher et al. 2011), ICAM2 (Wang et al. 2009), BTLA (B and T lymphocyte associated) (Cai et al. 2013), CD1A (Sacchi et al. 2007), CD19 (Zehender et al. 1997), ICAM5 (Wei et al. 2016), CD34 (Fahrbach et al. 2007), CD48 (Ezinne et al. 2014), CD59 (Amet et al. 2012), CD74 (Le Noury et al. 2015) and DEFB1 (Estrada-Aguirre et al. 2014) were linked with development of various viral infections, but low expression of these genes may be liable for progression of SARS-CoV-2 infection. Recent studies reported that enriched genes such as IDO1 (Fox et al. 2015), CD55 (Li et al. 2016), PTPN22 (Crabtree et al. 2016), FCGR2A (Maestri et al. 2016), CARD9 (Uematsu et al. 2015), MIF (macrophage migration inhibitory factor) (Arndt et al. 2002) and PLAU (plasminogen activator, urokinase) (Sidenius et al. 2000) were low expressed in influenza virus infection, but decrease expression of these genes may be key for progression of SARS-CoV-2 infection. Low expression of genes such as PECAM1 (Wang et al. 1998), TLR9 (Shafique et al. 2012) and CTLA4 (Ayukawa et al. 2004) were observed in respiratory syncytial virus infection, but decrease expression these genes may be associated with progression of SARS-CoV-2 infection. Chen et al. (2017) demonstrated CD83 was important for progression of respiratory syndrome virus, but decrease expression of this gene may be linked with advancement of SARS-CoV-2 infection. Many studies have reported the enriched down-regulated gene such as CD209 (Chan et al. 2010), DPP4 (Letko et al. 2018), ICAM3 (Chan et al. 2007), CD9 (Earnest et al. 2017) and MASP2 (Wang et al. 2009) were liable for advancement of SARS-CoV, but these genes may be linked with progression of SARS-CoV-2 infection. Treon et al. (2020) indicated that low expression of BTK (Bruton tyrosine kinase) was key for progression of SARS-CoV-2 infection. Novel biomarkers such as HLA-DMA, HLA-DMB, HLA-DOB, HLA-DRA, CTSC (cathepsin C), PTGER4, CFD (complement factor D), SLAMF6, FCER1A, FCGR2B, C1R, C1S, C4BPA, C6, C7, C8A, C8B, TAL1, KLRB1, SELE (selectin E), GPI (glucose-6-phosphate isomerase), ICOSLG (inducible T cell costimulator ligand), LILRA1, LILRA2, VTN (vitronectin), CLEC6A, ATG7, ICAM4, AIRE (autoimmune regulator), GPR183, CFI, CR2, LGALS3, TFRC (transferrin receptor), CD3E, CD8A, TIGIT (T cell immunoreceptor with Ig and ITIM domains), MS4A1, TIRAP (TIR domain containing adaptor protein), CD79A, CD79B, PAX5, HAMP (hepcidin antimicrobial peptide), MAPK11, CTSS (cathepsin S), MBP (myelin basic protein), ITGAE (integrin subunit alpha E), FCGRT (Fc fragment of IgG receptor and transporter), MME (membrane metalloendopeptidase), NT5E, CDH5, DEFB103B, DEFB4A and TRAF4 were low expressed and might be involved in progression of SARS-CoV-2 infection.
Construction of PPI network of up- and down-regulated genes might be helpful for understanding the relationship of developmental SARS-CoV-2 infection. Desai et al. (2018) showed that BATF3 was involved in progression of respiratory poxvirus infection, but this gene may be key for development of SARS-CoV-2 infection. Novel biomarker ILF3 was low expressed and might be involved in progression of SARS-CoV-2 infection.
A target gene–miRNA regulatory and target gene–TF regulatory network for up- and down-regulated genes were generated to determine the key target genes and provide valuable information for the analysis of cellular functions and biological processes in SARS-CoV-2 infection. SMAD5 was highly expressed in SARS-CoV-2 infection and might be consider as novel biomarker. Novel biomarkers such as SKI (SKI proto-oncogene) and KLRF2 were low expressed and might be involved in progression of SARS-CoV-2 infection.
Conclusion
It in earnestly hoped that this research will help in enhancing attempts to further understand the molecular characteristics of SARS-CoV-2 infection progression. CCL5, IFNAR2, JAK2, MX1, STAT1, BID, CD55, CD80, HAL-B and HLA-DMA may be used as biomarkers and therapeutic targets in patients with SARS-CoV-2 infection. This research, it is hoped promote ultimate molecularly targeted therapies for SARS-CoV-2 infection and provide acceptable local control and survival.
Availability of data and materials
The datasets supporting the conclusions of this article are available in the ArrayExpress (https://www.ebi.ac.uk/arrayexpress) repository. [(E-MTAB-8871) (https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-8871/)].
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We thank Eugenia Ong, Experimental Therapeutics Centre, Agency for Science, Technology and Research, Singapore, Singapore, very much, the author who deposited their microarray dataset, E-MTAB-8871, into the public ArrayExpress database.
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Basavaraj Vastrad participated in writing original draft and investigation, Chanabasayya Vastrad performed software, supervision, formal analysis and validation. Anandkumar Tengli performed editing and reviewing the manuscript.
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Vastrad, B., Vastrad, C. & Tengli, A. Identification of potential mRNA panels for severe acute respiratory syndrome coronavirus 2 (COVID-19) diagnosis and treatment using microarray dataset and bioinformatics methods. 3 Biotech 10, 422 (2020). https://doi.org/10.1007/s13205-020-02406-y
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DOI: https://doi.org/10.1007/s13205-020-02406-y