key: cord-0779792-ng5xxg44
authors: Vastrad, Basavaraj; Vastrad, Chanabasayya; Tengli, Anandkumar
title: Identification of potential mRNA panels for severe acute respiratory syndrome coronavirus 2 (COVID-19) diagnosis and treatment using microarray dataset and bioinformatics methods
date: 2020-09-11
journal: 3 Biotech
DOI: 10.1007/s13205-020-02406-y
sha: ebb5439ac46acc1d82f0d882e12236d74b5635df
doc_id: 779792
cord_uid: ng5xxg44

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.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is known as novel coronavirus disease-2019 and has spread widely throughout the globe in an epidemic proportion with the current pandemic risk ). 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) ); 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 1 3 422 Page 2 of 65 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.

Microarray data of gene expression profile (E-MTAB-8871) was downloaded from ArrayExpress (https ://www.ebi. ac.uk/array expre ss), 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 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 downregulated 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).

pages /viewp age.actio n?pageI d=31549 1760) (Schaefer et al. 2009 ), REACTOME (https ://react ome.org/) (Fabregat et al. 2018) , GenMAPP (http://www.genma pp.org/) (Dahlquist et al. 2002) , MSigDB C2 BIOCARTA (http://softw are.broad insti tute.org/gsea/msigd b/colle ction s.jsp) (Subramanian et al. 2005) , PantherDB (http://www.panth erdb.org/) (Mi et al. 2017) , Pathway Ontology (http://www.obofo undry .org/ ontol ogy/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 Top-pGene (ToppFun) (https ://toppg ene.cchmc .org/enric hment .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.

GO (http://www.geneo ntolo gy.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 ://toppg ene.cchmc .org/enric hment .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.

The common up and down-regulated genes of E-MTAB-8871 was analyzed using the online website STRING (https ://strin g-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.cytos cape.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.cytos cape.org/apps/PEWCC 1) (Zaki et al. 2013 ) was used to perform module analysis.

miRNet database (https ://www.mirne t.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.athen a-innov ation .gr/Diana Tools /index .php?r=tarba se/index ) (Vlachos et al. 2015) , miRTarBase (http://mirta rbase .mbc.nctu.edu.tw/php/downl oad.php) (Chou et al. 2018) , miRecords (http://miRec ords.umn.edu/miRec ords) (Xiao et al. 2009 ), miR2Disease (http://www.mir2d iseas e.org/) (Jiang et al. 2009 ), HMDD (http://www.cuila b.cn/ hmdd) , PhenomiR (http://mips.helmh oltz-muenc hen.de/pheno mir/) (Ruepp et al. 2010) , SM2miR (http://bioin fo.hrbmu .edu.cn/SM2mi R/) , PharmacomiR (http://www.pharm aco-mir.org/) (Rukov et al. 2014) , EpimiR (http://bioin fo.hrbmu .edu.cn/Epimi R/) (Dai et al. 2014 ) and starBase (http://starb ase.sysu.edu.cn/) . This investigation inputted the up-and downregulated 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.

NetworkAnalyst database (https ://www.netwo rkana lyst.ca/) ) provides certain target gene-TF regulatory association pairs, which are verified by experiments and predicted by JASPAR (http://jaspa r.gener eg.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.

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) .

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) .

Pathway enrichment analysis of integrated DEGs showed the up-regulated genes were mainly involved in measles, herpes simplex infection, IL12-mediated signaling events, IL2mediated 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, (Table 2 ); the downregulated 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) .

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 ).

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. 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 IL10, IL10RA, IL12A, IL12B, IL13,  IL13RA1, TNFRSF9, IL17A, PDGFB,  TNFSF12, IL17B, TNFRSF14,  TNFRSF10C, CCL26,IL17F, TNFRSF4,   IL22RA2, IL21, CCL7, CCR6, CCL11,  CCL13, CCR8, CCL15, CCL16, CCL18,  CCL19, CCL20, CCL22, CCL24, CXCL11,  XCL1, KIT, CX3CL1, CXCL12, IL19,  CCR10, XCR1, CXCL13, PPBP, IL23A,  IFNA2, IFNAR1, IFNB1, IFNG, TGFBR1,  LIF, CSF1R, CSF2, CSF2RB, CSF3R, IL20,  IL22,, CD40, TNFRSF13C, LTA, IL1RAP,  IL2, IL3, IL4, IL4R, IL5, IL6, IL6R, IL7,  IL26, IL10, IL10RA, IL12A, IL12B, IL13,  IL13RA1, BCL2, STAT5A, STAT6,  PTPN6, IL22RA2, IL21, IL19, IL23A,  IFNA2, IFNAR1, IFNB1, IFNG, LIF, CSF2,  CSF2RB, CSF3R, IL20, IL22, IL2, IL3, IL4,   IL4R, IL5, IL6, IL6R, IL7, IL12A, IL12B, IL13, CD46, TNFRSF10C,  CD209, STAT5A, FCGR2B, RELA, TLR9,  IFNA2, IFNAR1, IFNB1, IFNG, CD3E,  IRAK4, IL2, IL4, IL6 83074 Antigen processing and presentation 7. IL10, IL10RA, IL12A, IL12B, IL13,  IL13RA1, IL16, TRAF3, TNFRSF9, IL17A,  HLA-B, PDGFB, HLA-C, TNFSF12,  HLA-DPA1, HLA-DPB1, HLA-DQA1,  HLA-DQB1, HLA-DRA, HLA-DRB1,  TNFRSF14, GATA3, PSMB10, BCL2,   PTAFR, IL1RL2, IL17F, PTK2, STAT5A,  STAT6, TNFRSF4, PTPN6, IL22RA2,  TOLLIP, CCL11, CCL19, CCL20, CCL22,   KIT, NOS2, VCAM1, IL19, RAG1, RAG2,  ZEB1, FN1, DUSP4, IRF8, CASP3, RELA,  RELB, LCK, IL23A, S1PR1, IFNA2,  IFNAR1, IFNB1, IFNG, LIF, CSF1R, CSF2,  CSF2RB, CSF3R, EBI3, EGR1, IL20,  CD80, CD86, IL22, CD40, IRAK4, CD44,  TNFRSF13C, LTA, RORC, IL1RAP, IL2,   IL3, IL4, IL4R, IL5, IL6, IL6R, IL7, IL27,  IL9 1269318 IL10, IL10RA, IL12A, IL12B, IL13,  IL13RA1, IL16, IL17A, PDGFB, GATA3,  PSMB10, BCL2, PTAFR, IL1RL2, IL17F,  PTK2, STAT5A, STAT6, PTPN6, IL22RA2,  TOLLIP, CCL11, CCL19, CCL20, CCL22,  KIT,NOS2, VCAM1, IL19, RAG1, RAG2,   ZEB1, FN1, DUSP4, CASP3, RELA,  LCK, IL23A, S1PR1, LIF, CSF1R, CSF2,  CSF2RB, CSF3R, EBI3, IL20, CD80,  CD86, IL22, IRAK4, RORC, IL1RAP, IL2,  IL3, IL4, IL4R, IL5, IL6, IL6R,IL7, IL27 , IL9 TRAF3, HLA-B, PDGFB, HLA-C,  MAPKAPK2, CTSC, PECAM1, MBL2,   PSMB10, CD55, CD46, BCL2, DEFB103B,  PTAFR, CD209, PTK2, STAT6, DEFB1,  DEFB4A, CFD, PTPN6, FCAR, FCER1A,  PIGR, FCGR2A, CARD9, BTK, MIF,  PLA2G2A, TOLLIP, C1R, C1S, C4A,   PLAU, C4BPA, CCR6, MME, C6, C7,  C8A, C8B, C9, KIT, NOS2, GPI, LILRA3,  VTN, CLEC6A, FN1, ATG7, ITLN1,  DUSP4, ICAM2, ICAM3, RELA, RELB,  TLR9, LCK, PPBP, CFI, IFNA2, LGALS3,  IFNB1, CSF2, CSF2RB, CD19, SIGIRR,  CD80, CD86, TIRAP, IRAK4, CD44, CD59,  MAPK11, IL2, IL3, IL5, CTSS, -DRB1, TNFRSF14,  PSMB10, PTPN22, CD209, PTPN6,  SLAMF6, FCGR2B, BTK, KIT, KLRB1,  LILRB5, VCAM1, ICOSLG, LILRA1,  LILRA3, LILRA2, ATG7, ICAM2, ICAM3,  ICAM4, RELA, BTLA, LCK, CD1A,  CD3E, CD8A, CD19, CD22, CD80, CD86,  ICAM5, TIRAP, CD34, CD40, CD74,  CD79A, CD79B, CTLA4, CTSS   1269201 Immunoregulatory interactions between a

Lymphoid and a non-Lymphoid cell 5.01E − 14 2.66E − 12 1.82E − 11 2.66E − 11 21 IL10, IL12A, IL12B, IL13, IL16, IL17A,  PDGFB, TNFSF12, IL17B, CCL26, IL17F,  CCL7, CCL11, CCL13, CCL15, CCL16,  CCL18, CCL19, CCL20, CCL22, CCL24,  CXCL11, XCL1, CX3CL1, CXCL12, IL19,  CXCL13, PPBP, IL23A, IFNA2, IFNB1,  IFNG, LIF, CSF2, EBI3, IL20, IL22, LTA,  IL2, IL3, IL4, IL5, IL6, IL7, IL26, IL9 M17406 IL10, IL12A, IL12B, IL13, MASP1, IL16,  IL17A, PDGFB, TNFSF12, CTSC, IL17B,  MBL2, CCL26, IL17F, CD209, PLAU,  CCL7, CCL11, CCL13, CCL15, CCL16,  CCL18, CCL19, CCL20, CCL22, CCL24,  CXCL11, XCL1, CX3CL1, CXCL12, IL19,  CLEC6A, ITLN1, CXCL13, ITLN2, PPBP,  IL23A, IFNA2, LGALS3, IFNB1, IFNG,  LIF, CSF2, EBI3, IL20, IL22, LTA, IL2,  IL3, IL4, IL5, CTSS, IL6, IL7, IL26, IL10, IL12A, IL12B, IL13, MASP1,  IL16, IL17A, PDGFB, TNFSF12,  SPP1, CTSC,IL17B, MBL2, CCL26,  IL17F, CD209, PLAU, CCL7,CCL11,  CCL13, CCL15,CCL16, CCL18, CCL19,  CCL20,CCL22, CCL24, CXCL11,XCL1,   CX3CL1, CXCL12, IL19, VTN,CLEC6A,   FN1, ITLN1, CXCL13,ITLN2, PPBP,  IL23A,IFNA2, LGALS3, IFNB1, IFNG,   TGFBI, LIF, CSF2, EBI3, IL20, IL22, LTA,  IL2, IL3, IL4, IL5, CTSS, IL6, IL7, IL26 IL10,IL10RA, IL12A,IL13, IL13RA1, IL17A,  MAPKAPK2, IL17F, STAT5A, STAT6,   IL21, IL23A, IL2, IL4, IL4R, IL5, IL6,  IL6R, IL7, CXCR1, IL9, ICOSLG, LILRA1,  LILRA2, ZEB1, VTN, CLEC6A, ATG7, ICAM2,  ICAM3, ICAM4, CXCL13, CASP3, AIRE, RELA,  BTLA, RELB, GPR183, TLR9, LCK, CFI, CR2,  IL23A, IFNA2, LGALS3, TFRC,IFNB1, IFNG,  LIF, CD1A, CD3E, CSF1R, CSF2, CD8A, THY1,  CD9, CSF3R, CD19, TIGIT, MS4A1, EBI3, CD22,  IL20, CD80, CD86, ICAM5, TIRAP, CD34, CD40,  IRAK4, CD44, CD48, TNFRSF13C, CD59, CD74,  CD79A, CD79B, LTA, RORC, CTLA4, PAX5,  HAMP, MAPK11, IL2, IL3, IL4, IL4R, IL5, CTSS,  IL6, IL6R, IL7 , IL27, MASP2, CXCR2 IL10, IL10RA, IL12A, IL12B, IL13, IL13RA1,  IRGM, IL16, TRAF3, TNFRSF9, IL17A, CEBPB,  HLA-B, PDGFB, HLA-C, TNFSF12, SPP1,  HLA-DPA1, HLA-DPB1, HLA-DQA1, MAP-KAPK2, HLA-DQB1, HLA-DRA, HLA-DRB1,  TNFRSF14, GATA3, ETS1, PSMB10, BCL2,   PTAFR, IL1RL2, CCL26, IL17F,STAT5A, STAT6,  TNFRSF4, PTPN6, IL22RA2, NFIL3, BTK, MIF,   IL21, TOLLIP, CCL7, CCR6, CCL11, CCL13,  CCR8, CCL15, MME, CCL16, CCL18, CCL19,  CCL20, CCL22,CCL24, CXCL11, XCL1, KIT,  CX3CL1, NOS2, CXCL12, VCAM1, IL19, SELE,  SELPLG, CCR10, XCR1, TCF7, ZEB1, FN1, IRF8,  CXCL13, CASP3, RELA, RELB, PPBP, IL23A,  S1PR1, IFNA2, TFRC, IFNAR1, IFNB1, IFNG,  LIF, CSF1R, CSF2, CSF2RB, CSF3R, EBI3, EGR1,  SIGIRR, IL20, CD80, CD86, TIRAP, IL22, CD40,  IRAK4, CD44, TNFRSF13C, CD74, LTA, RORC,  HAMP, MAPK11, IL1RAP, IL2, IL3, IL4, IL4R,  IL5, IL6, IL6R, IL7, IL26, IL27, CXCR1, IL9 , CXCR2 IL10, IL10RA, IL12A, IL12B, IL13, IL13RA1,  IRGM, IL16, TRAF3, TNFRSF9, IL17A, CEBPB,  HLA-B, PDGFB, HLA-C, TNFSF12, SPP1, HLA-DPA1, HLA-DPB1, HLA-DQA1, MAPKAPK2,  HLA-DQB1, HLA-DRA, HLA-DRB1, TNFRSF14,   GATA3, ETS1, PSMB10, BCL2, PTAFR, IL1RL2,  CCL26, IL17F, STAT5A, STAT6, TNFRSF4,  PTPN6, IL22RA2, NFIL3, BTK, MIF, IL21, TOL-LIP, CCL7, CCR6, CCL11, CCL13, CCR8, CCL15,  MME, CCL16, CCL18, CCL19, CCL20, CCL22,  CCL24, CXCL11, XCL1, KIT, CX3CL1, NOS2,  CXCL12, VCAM1, IL19, SELE, SELPLG, CCR10,  XCR1, TCF7, ZEB1, FN1, IRF8, CXCL13, CASP3,  RELA, RELB, PPBP, IL23A, S1PR1, IFNA2,  TFRC, IFNAR1, IFNB1, IFNG, LIF, CSF1R,  CSF2, CSF2RB, CSF3R, EBI3, EGR1, SIGIRR,  IL20, CD80, CD86, TIRAP, IL22, CD40, IRAK4,  CD44, TNFRSF13C, CD74, LTA, RORC, HAMP,  MAPK11, IL1RAP, IL2, IL3, IL4, IL4R, IL5, IL6, IL6R, IL7, IL26, IL27, CXCR1, IL9, CXCR2 IL12A, IL12B, IL13, TRAF3, TNFRSF9,  IL17A, CEBPB, CD244, IDO1, HLA-DPA1, HLA-DPB1, MAPKAPK2, IL17B, MBP, TNFRSF14,  GATA3, CD55, PTPN22, CD46, PTAFR, CD83,  PTGER4, IL1RL2, IL17F, STAT5A, STAT6,  TNFRSF4, EOMES, PTPN6, SLAMF6, FCER1A,  FCGR2B, CARD9, BTK, KLRF2, MIF, IL21,  CCL19, CCL20, XCL1, KIT, CX3CL1, NOS2, IL19,  ICOSLG, LILRA2, CLEC6A, FN1, IRF8, AIRE,  RELA, RELB, TLR9, IL23A, IFNA2, IFNAR1,  IFNB1, IFNG, CD3E, CSF1R, CSF2, TIGIT, EBI3,  EGR1,SIGIRR, CD80, CD86, TIRAP, CD34,   CD40, IRAK4, TNFRSF13C, CD74,LTA, RORC,  MAPK11, IL1RAP, IL2, IL4, IL4R, IL6, IL6R, IL7 TNFRSF14, GATA3,  CD55, PTPN22, CD46, BCL2, PTAFR, CD83,  IL1RL2, CD209, STAT5A, STAT6, TNFRSF4,  PTPN6, FCER1A, FCGR2B, BTK, MIF, IL21,  CCR6, CCL19, CCL20, XCL1, CX3CL1, VCAM1,  RAG1, DPP4, ICOSLG, ZEB1, CASP3, BTLA,  GPR183, TLR9, LCK, IL23A, IFNA2, LGALS3,  TFRC, IFNB1, IFNG, CD3E, THY1, CD19, TIGIT,  EBI3, CD22, CD80, CD86, TIRAP, CD40, CD44,  TNFRSF13C, CD74, RORC, CTLA4, HAMP, IL2, IL4, IL4R, IL5, IL6, IL6R, IL7, IL27 IL12B, IL13, IRGM, IL17A, CEBPB, IDO1,  SPP1, MAPKAPK2, CTSC, IL17B, MBL2, GATA3,  ETS1, PTAFR, PLA2G2E, PTGER4, IL1RL2,  CCL26, IL17F, STAT5A, TNFRSF4, IL22RA2,  FCER1A, FCGR2B, BTK,MIF, IL21, PLA2G2A,  TOLLIP, C4A, CCL7, CCR6,CCL11, CCL13,  CCL15, CCL16, CCL18, CCL19, CCL20, CCL22,  CCL24, CXCL11, XCL1, KIT, CX3CL1, NOS2,  VCAM1, SELE, XCR1, FN1, NT5E, CXCL13,  RELA, RELB, TLR9, PPBP, IL23A, IFNA2,  EDNRB, IFNG, CSF1R, SIGIRR, IL20, TIRAP,  IL22, CD40, CD44, LTA, HAMP, IL1RAP, IL2,  CDH5, IL4, IL4R, IL5, CTSS, IL6, IL6R, IL27, ITGAE, PTGER4, IL1RL2, CD209, PTK2,  STAT5A, PTPN6, FCGR2B, IL21, PLAU, CCL11,  CCR8, CCL19, XCL1, KIT, MAP4K4, CX3CL1,  CXCL12, VCAM1, SELE, SELPLG, RAG1, DPP4,  ICOSLG, VTN, FN1, BCL2L11, NT5E, ICAM2,  ICAM3, ICAM4, CXCL13, CASP3, RELA, BTLA,  LCK, IL23A, S1PR1, IFNA2, LGALS3, TFRC,  IFNB1, IFNG, TGFBI, ARHGDIB, CD3E, THY1,  CD9, CSF3R, TIGIT, EBI3, CD22, CD80, CD86,  ICAM5, CD34, CD44, TNFRSF13C, CD74,  CTLA4,IL1RAP, , IL2, CDH5, IL4, IL4R, IL6, IL6R, IL7 IL12A, IL12B, IL13, TRAF3, TNFSF12,  GATA3, DEFB103B, CCL26, IL17F, BID, DEFB1,  DEFB4A, MIF, IL21, TOLLIP, CCL7, CCL11,  CCL13, CCL15, CCL16, CCL18, CCL19, CCL20,  CCL22, CCL24, CXCL11, XCL1, CX3CL1,  CXCL12, CXCL13, CASP3, TLR9, PPBP, IL23A,  IFNA2, IFNB1, IFNG, TGFBR1, LIF, TRAF4,  CSF2, EBI3, IL20, IL22,IRAK4, CD44, LTA,  IL1RAP, IL2,IL3, CDH5,IL4, IL5, IL6, IL6R, IL7 , HLA-DRA, IL17B, MBL2,  GATA3, DEFB103B, CCL26, IL17F, PTK2, BID,  DEFB1, DEFB4A, PTPN6, PIGR, MIF, IL21,  TOLLIP, CCL7, CCL11, CCL13, CCL15, CCL16,  CCL18, CCL19, CCL20, CCL22, CCL24, CXCL11,  XCL1, CX3CL1, CXCL12, VCAM1, IL19,  SELPLG, GPI, DPP4, ICOSLG, VTN, FN1, ATG7,  ICAM2, ICAM3, ICAM4, CXCL13, CASP3, TLR9,  LCK, PPBP, IL23A, S1PR1, IFNA2, EDNRB,  LGALS3, IFNB1, IFNG, TGFBI, TGFBR1, LIF,  TRAF4, CD3E,BCAP31, CSF2, CD8A, THY1,  CD9, TIGIT, MS4A1, EBI3, CD22, IL20, CD86,  ICAM5, TIRAP, IL22, IRAK4, CD44, CD74, LTA,  HAMP, IL1RAP, IL2, IL3, CDH5, IL4, IL5, IL6 IL12A, IL12B, IL13, IRGM, IL16, IL17A,  PDGFB, TNFSF12, SPP1, TAGAP, CTSC, IL17B,  CD46, BCL2, DEFB103B, CCL26, IL17F, EOMES,  DEFB4A, MIF, IL21, C4A, CCL7, CCL11,  CCL13, CCL15, CCL16, CCL18, CCL19, CCL20,  CCL22, CCL24, CXCL11, XCL1, CX3CL1, NOS2,  CXCL12, IL19, GPI, FN1, CXCL13, CASP3, SKI,  PPBP, IL23A, IFNA2, LGALS3, IFNB1, IFNG,  LIF, ARHGDIB, TRAF4, CSF2, THY1, EBI3, IL20,  TIRAP, IL22, LTA, HAMP, IL2, IL3, IL4, IL5, IL6,  IL6R , IL7, IL26,IL27, IL9 IL12A, IL12B, MASP1, CEBPB, PDGFB,  HLA-DQA1, PECAM1, GATA3, BCL2, IL17F,   BID, CARD9, KLRF2, CCL11, MME, TAL1,  XCL1, KIT, NOS2, RAG1, DPP4, TCF4, IKZF2,  BCL2L11, ATG7, CXCL13, RELA, TLR9, CR2,  LGALS3, TFRC, TGFBR1, TRAF4, CD3E, CSF1R,  CD8A, TIRAP, CD79A, CD79B, CDH5, IL6R 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.

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 miR-NAs (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 miR-NAs (ex, hsa-mir-8064), ICOSLG regulates 119 miRNAs IL12B, IRGM, PDGFB, MAPKAPK2, PTPN22,  PTK2, TNFRSF4, PTPN6, FCER1A, BTK, MIF,  CCL19, TAL1, KIT, MAP4K4, NOS2, GPI, DUSP4,  CASP3, TLR9, LCK, IL23A, IFNG, TGFBR1,  TRAF4, CSF1R, THY1, CD19, EGR1, TIRAP,  CD40, IRAK4, CD44, CD74, MAPK11, IL2, IL3,  IL4, IL6, IL6R BP biological process, CC cellular component, MF molecular functions (ex, hsa-mir-3672) and IL6R regulates 94 miRNAs (ex, hsamir-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.

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 ligandbinding receptors.

The ROC curve analysis was accomplished to assess the diagnostic values of hub genes. Our finding revealed that CCL5 ( 

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 son 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 downregulated 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 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 (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 , 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 (Du et al. 2014 ), RAG2 , CASP3 (Takahashi et al. 2013) , S1PR1 , CD80 (Lumsden et al. 2000) , CD86 (Lumsden et al. 2000) and CD44 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, (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 ), 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 ) were liable for advancement of SARS-CoV, but these genes may be linked with progression of SARS-CoV-2 infection. Treon et al. (2020) 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. 

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.

Early SIV and HIV infection promotes the LILRB2/ MHC-I inhibitory axis in cDCs

Expression of the B cell differentiation factor BAFF and chemokine CXCL13 in a murine model of respiratory syncytial virus infection

CD59 incorporation protects hepatitis C virus against complement-mediated destruction

Interferon-induced guanylate binding protein-1 (GBP-1) mediates an antiviral effect against vesicular stomatitis virus and encephalomyocarditis virus

Respiratory syncytial virus reduces STAT3 phosphorylation in human memory CD8 T cells stimulated with IL-21

Influence of HLA-C expression level on HIV control

Release of macrophage migration inhibitory factor and CXCL8/interleukin-8 from lung epithelial cells rendered necrotic by influenza A virus infection

Severe viral respiratory infections in children with IFIH1 loss-of-function mutations

Expression of CTLA − 4 (CD152) in peripheral blood T cells of children with influenza virus infection including encephalopathy in comparison with respiratory syncytial virus infection

Venezuelan equine encephalitis virus induces apoptosis through the unfolded protein response activation of EGR1

Exposure of human astrocytes to leukotriene C4 promotes a CX3CL1/ fractalkine-mediated transmigration of HIV-1-infected CD4+ T cells across an in vitro blood-brain barrier model

Elevated IL-3 and IL-12p40 levels in the lower airway of infants with RSV-induced bronchiolitis correlate with recurrent wheezing

KLRD1-expressing natural killer cells predict influenza susceptibility

Infection-triggered regulatory mechanisms override the role of STAT 4 in control of the immune response to influenza virus antigens

B and T lymphocyte attenuator is highly expressed on intrahepatic T cells during chronic HBV infection and regulates their function

Coreceptor usage by HIV-1 and HIV-2 primary isolates: the relevance of CCR8 chemokine receptor as an alternative coreceptor

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases

IL-13 is required for eosinophil entry into the lung during respiratory syncytial virus vaccine-enhanced disease

Longitudinal analysis of CD8(+) T-cell phenotype and IL-7, IL-15 and IL-16 mRNA expression in different tissues during primary simian immunodeficiency virus infection

Association of ICAM3 genetic variant with severe acute respiratory syndrome

> G promoter polymorphism and severe acute respiratory syndrome in Hong Kong Chinese

ToppGene Suite for gene list enrichment analysis and candidate gene prioritization

IFNA2 p.Ala120Thr impairs the inhibitory activity of Interferon-α2 against the hepatitis B virus through altering its binding to the receptor

The nucleocapsid protein and nonstructural protein 10 of highly pathogenic porcine reproductive and respiratory syndrome virus enhance CD83 production via NF-κB and Sp1 signaling pathways

Anti-IFN-γ autoantibodies in adults with disseminated nontuberculous mycobacterial infections are associated with HLA-DRB1*16:02 and HLA-DQB1*05:02 and the reactivation of latent varicella-zoster virus infection

miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions

Induction of pro-inflammatory cytokines (IL-1 and IL-6) and lung inflammation by Coronavirus-19 (COVI-19 or SARS-CoV-2): anti-inflammatory strategies

Facing the SARS-CoV-2 (COVID-19) outbreak with IL-6R antagonists

Autoimmune Variant PTPN22 C1858T Is Associated With Impaired Responses to Influenza Vaccination

GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways

EpimiR: a database of curated mutual regulation between miRNAs and epigenetic modifications

Contrasting effects of CCR5 and CCR2 deficiency in the pulmonary inflammatory response to influenza A virus

Host cell protein PSMB10 interacts with viral NS3 protein and inhibits the growth of classical swine fever virus

Batf3-dependent dendritic cells promote optimal CD8 T cell responses against respiratory poxvirus infection

IL-9 regulates pathology during primary and memory responses to respiratory syncytial virus infection

EGR2 is critical for peripheral naïve T-cell differentiation and the T-cell response to influenza

IL-1R1 signaling regulates CXCL12-mediated T cell localization and fate within the central nervous system during West Nile Virus encephalitis

The tetraspanin CD9 facilitates MERS-coronavirus entry by scaffolding host cell receptors and proteases

Fibrosis severity and mannan-binding lectin (MBL)/MBL-associated serine protease 1 (MASP-1) complex in HCV-infected patients

IL10 family member genes IL19 and IL20 are associated with recurrent wheeze after respiratory syncytial virus bronchiolitis

IFI35, mir-99a and HCV genotype to predict sustained virological response to pegylated-interferon plus ribavirin in chronic hepatitis C

DEFB1 5′UTR polymorphisms modulate the risk of HIV-1 infection in Mexican women

HTLV-1 specific CD8 + T cell function augmented by blockade of 2B4/CD48 interaction in HTLV-1 infection

The reactome pathway knowledgebase

Activated CD34-derived Langerhans cells mediate transinfection with human immunodeficiency virus

miRNet-functional analysis and visual exploration of mirna-target interactions in a network context

Cell cycle-independent role of cyclin D3 in host restriction of influenza virus infection

Expression of duck CCL19 and CCL21 and CCR7 receptor in lymphoid and influenza-infected tissues

Vaccine molecules targeting Xcr1 on cross-presenting DCs induce protective CD8 + T-cell responses against influenza virus

Interferon lambda upregulates IDO1 expression in respiratory epithelial cells after influenza virus infection

Understanding SARS-CoV-2-mediated inflammatory responses: from mechanisms to potential therapeutic tools

CD46 is a cellular receptor for group B adenoviruses

Importin β1 targeting by hepatitis C virus NS3/4A protein restricts IRF3 and NF-κB signaling of IFNB1 antiviral response

Productive replication of Ebola virus is regulated by the c-Abl1 tyrosine kinase

Epstein-Barr virus (EBV) infection and expression of the interleukin-12 family member EBV-induced gene 3 (EBI3) in chronic inflammatory bowel disease

CCL7 and IRF-7 Mediate Hallmark Inflammatory and IFN Responses following Rhinovirus 1B Infection

Loss of IL-7 receptor alphachain (CD127) expression in acute HCV infection associated with viral persistence

Group 2 innate lymphoid cell production of IL-5 is regulated by NKT cells during influenza virus infection

Autophagy protein ATG5 interacts transiently with the hepatitis C virus RNA polymerase (NS5B) early during infection

Human immunodeficiency virus infection abolishes CD4-dependent activation of ZAP-70 by inhibition of p56lck

Hepatitis C virus triggers Golgi fragmentation and autophagy through the immunity-related GTPase M

CD40 ligand (CD154) improves the durability of respiratory syncytial virus DNA vaccination in BALB/c mice

Role of mouse hepatitis virus (MHV) receptor murine CEACAM1 in the resistance of mice to MHV infection: studies of mice with chimeric mCEACAM1a and mCEACAM1b

Nuclease-free adeno-associated virus-mediated Il2rg gene editing in X-SCID mice

SARS-CoV-2 cell entry depends on ACE2 and TMPRSS2 and is blocked by a clinically proven protease inhibitor

Partitioning of responder CD8 + T cells in lymph node and lung of mice with Sendai virus pneumonia by LECAM-1 and CD45RB phenotype

Modified apoptotic molecule (BID) reduces hepatitis C virus infection in mice with chimeric human livers

Mitochondrial protein p32/HAPB1/gC1qR/C1qbp is required for efficient respiratory syncytial virus production

HMDD v.30: a database for experimentally supported human microRNA-disease associations

Difference in serum complement component C4a levels between hepatitis C virus carriers with persistently normal alanine aminotransferase levels or chronic hepatitis C

SMPDB 2.0: big improvements to the Small Molecule Pathway Database

miR2Disease: a manually curated database for micro-RNA deregulation in human disease

New approach for understanding genome variations in KEGG

An SH3 binding motif within the nucleocapsid protein of porcine reproductive and respiratory syndrome virus interacts with the host cellular signaling proteins STAMI, TXK, Fyn, Hck, and cortactin

2018: update of the open-access database of transcription factor binding profiles and its web framework

Hepatitis C virus suppresses C9 complement synthesis and impairs membrane attack complex function

Arrayexpress update-simplifying data submissions

Elevation of soluble VCAM-1 plasma levels in children with acute dengue virus infection of varying severity

IL-22 from conventional NK cells is epithelial regenerative and inflammation protective during influenza infection

ZEB1 limits adenoviral infectability by transcriptionally repressing the coxsackie virus and adenovirus receptor

Polymorphisms in HLA-DPB1 are associated with differences in rubella virus-specific humoral immunity after vaccination

Functional roles of HIV-1 Vpu and CD74: details and implications of the Vpu-CD74 interaction

Adaptive evolution of MERS-CoV to Species Variation in DPP4

The vision and challenges of the gene ontology

starBase v20: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data

Toll-interacting protein inhibits HIV-1 infection and regulates viral latency

Parainfluenza virus 5 upregulates CD55 expression to produce virions with enhanced resistance to complement-mediated neutralization

Clinical and transmission characteristics of Covid-19-a retrospective study of 25 cases from a single thoracic surgery department

The pathological effects of CCR2 + inflammatory monocytes are amplified by an IFNAR1-triggered chemokine feedback loop in highly pathogenic influenza infection

CCAAT/enhancer-binding proteins and the pathogenesis of retrovirus infection

SM2miR: a database of the experimentally validated small molecules' effects on microRNA expression

Severe influenza A(H1N1)pdm09 infection induces thymic atrophy through activating innate CD8(+)CD44(hi) T cells by upregulating IFN-γ

Overexpression of RACK1 enhanced the replication of porcine reproductive and respiratory syndrome virus in Marc-145 cells and promoted the NF-κB activation via upregulating the expression and phosphorylation of TRAF2

IL-10 regulates viral lung immunopathology during acute respiratory syncytial virus infection in mice

Regulatory T cells expressing granzyme B play a critical role in controlling lung inflammation during acute viral infection

Overexpression of Thy1/CD90 in human hepatocellular carcinoma is associated with HBV infection and poor prognosis

Differential requirement for CD80 and CD80/CD86-dependent costimulation in the lung immune response to an influenza virus infection

CD22 is required for protection against West Nile virus Infection

Restructured society and environment: a review on potential technological strategies to control the COVID-19 pandemic

The His131Arg substitution in the FCGR2A gene (rs1801274) is not associated with the severity of influenza A(H1N1)pdm09 infection

Increased plasma levels of the TH2 chemokine CCL18 associated with low CD4 + T cell counts in HIV-1-infected patients with a suppressed viral load

Innate partnership of HLA-B and KIR3DL1 subtypes against HIV-1

PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements

Deletion of CCR1 attenuates pathophysiologic responses during respiratory syncytial virus infection

IL-26 is overexpressed in chronically HCV-infected patients and enhances TRAIL-mediated cytotoxicity and interferon production by human NK cells

Tyrosine dephosphorylation of STAT3 in SARS coronavirus-infected Vero E6 cells

Pandemic influenza A/H1N1 virus infection and TNF, LTA, IL1B, IL6, IL8, and CCL polymorphisms in Mexican population: a case-control study

Influence of interleukin 12B (IL12B) polymorphisms on spontaneous and treatment-induced recovery from hepatitis C virus infection

STAT5-induced lunatic fringe during Th2 development alters delta-like 4-mediated Th2 cytokine production in respiratory syncytial virus-exacerbated airway allergic disease

CXCR2 is required for neutrophilic airway inflammation and hyperresponsiveness in a mouse model of human rhinovirus infection

Human B cells immortalized with Epstein-Barr virus upregulate CCR6 and CCR10 and downregulate CXCR4 and CXCR5

Novel genetic association of TNF-α-238 and PDCD1-7209 polymorphisms with long-term non-progressive HIV-1 infection

Inferring pleiotropy by network analysis: linked diseases in the human PPI network

Induction of allergen-specific IL-2 responsiveness of lymphocytes after respiratory syncytial virus infection and prediction of onset of recurrent wheezing and bronchial asthma

SLAMF7 is a critical negative regulator of IFNα-mediated CXCL10 production in chronic HIV infection

Synthetic peptides coupled to the surface of liposomes effectively induce SARS coronavirus-specific cytotoxic T lymphocytes and viral clearance in HLA-A*0201 transgenic mice

Porcine reproductive and respiratory syndrome virus inhibits type I interferon signaling by blocking STAT1/STAT2 nuclear translocation

The pathway ontology -updates and applications

Mx1 reveals innate pathways to antiviral resistance and lethal influenza disease

CXCL9, CXCL10 and CXCL11 polymorphisms are associated with sustained virologic response in HIV/HCV-coinfected patients

Suppression of HIV type 1 replication by a dominant-negative Ets-1 mutant

Type I IFN triggers RIG-I/TLR3/NLRP3-dependent inflammasome activation in influenza A virus infected cells

Functional topology in a network of protein interactions

Association between severe respiratory syncytial virus infection and IL13/IL4 haplotypes

The immunoregulatory role of CD244 in chronic hepatitis B infection and its inhibitory potential on virus-specific CD8 + T-cell function

Signaling via pattern recognition receptors NOD2 and TLR2 contributes to immunomodulatory control of lethal pneumovirus infection

limma powers differential expression analyses for RNAsequencing and microarray studies

pROC: an open-source package for R and S + to analyze and compare ROC curves

Concomitant PIK3CD and TNFRSF9 deficiencies cause chronic active Epstein-Barr virus infection of T cells

Association of IFNAR2 and IL10RB genes in chronic hepatitis B virus infection

miR-144 attenuates the host response to influenza virus by targeting the TRAF6-IRF7 signaling axis

PhenomiR: a knowledgebase for microRNA expression in diseases and biological processes

Pharmaco-miR: linking microRNAs and drug effects

Differentiation of monocytes into CD1a-dendritic cells correlates with disease progression in HIV-infected patients

Interferon-beta and interferon-gamma synergistically inhibit the replication of severe acute respiratory syndrome-associated coronavirus (SARS-CoV)

Evaluation of the expression level of 12/15 lipoxygenase and the related inflammatory factors (CCL5, CCL3) in respiratory syncytial virus infection in mice model

Rhinovirus infection induces expression of type 2 nitric oxide synthase in human respiratory epithelial cells in vitro and in vivo

SERP-ING1 mRNA overexpression in monocytes from HIV + patients

PID: the pathway interaction database

Induction of mucosal and systemic immunity against respiratory syncytial virus by inactivated virus supplemented with TLR9 and NOD2 ligands

Cytoscape: a software environment for integrated models of biomolecular interaction networks

Japanese encephalitis virus activates autophagy through XBP1 and ATF6 ER stress sensors in neuronal cells

Fast network centrality analysis using GPUs

Inhibition of Jak1-dependent signal transduction in airway epithelial cells infected with adenovirus

Respiratory Syncytial virus infection compromises asthma tolerance by recruiting interleukin-17A-producing cells via CCR6-CCL20 signaling

Serum level of soluble urokinase-type plasminogen activator receptor is a strong and independent predictor of survival in human immunodeficiency virus infection

Severe acute respiratory syndrome coronavirus M protein inhibits type I interferon production by impeding the formation of TRAF3.TANK.TBK1/IKKepsilon complex

Interactions between herpesvirus entry mediator (TNFRSF14) and latency-associated transcript during herpes simplex virus 1 latency

APOE epsilon4 and MBL-2 O/O genotypes are associated with neurocognitive impairment in HIV-infected plasma donors

STAT6 inhibitory peptide given during RSV infection of neonatal mice reduces exacerbated airway responses upon adult reinfection

Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles

SARS-CoV-2 entry factors are highly expressed in nasal epithelial cells together with innate immune genes

Local production of CCL3, CCL11, and IFN-γ correlates with disease severity in murine parainfluenza virus infection

Evaluating the potential of IL-27 as a novel therapeutic agent in HIV-1 infection

STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

Sulfatide regulates caspase-3-independent apoptosis of influenza A virus through viral PB1-F2 protein

Defective inflammatory monocyte development in IRF8-deficient mice abrogates migration to the West Nile virus-infected brain

The upregulation of LAG-3 on T cells defines a subpopulation with functional exhaustion and correlates with disease progression in HIV-infected subjects

Novel effect of methionine enkephalin against influenza A virus infection through inhibiting TLR7-MyD88-TRAF6-NF-κB p65 signaling pathway

Evidence that the HLA DQA1*03 allele confers protection from chronic HCV-infection in Northern European Caucasoids

Further characterization of a CD99-related 21-kDa transmembrane protein (VAP21) expressed in Syrian hamster cells and its possible involvement in vesicular stomatitis virus production

The BTK-inhibitor ibrutinib may protect against pulmonary injury in COVID-19 infected patients

B cells in chronically hepatitis C virus-infected individuals lack a virus-induced mutation signature in the TP53, CTNNB1, and BCL6 genes

Loss of CARD9-mediated innate activation attenuates severe influenza pneumonia without compromising host viral immunity

Validation of IRF5 as multiple sclerosis risk gene: putative role in interferon beta therapy and human herpes virus-6 infection

DIANA-TarBase v7.0: indexing more than half a million experimentally supported miRNA:mRNA interactions

Shedding of L-selectin and PECAM-1 and upregulation of Mac-1 and ICAM-1 on neutrophils in RSV bronchiolitis

Intercellular adhesion molecule 1 (ICAM-1), but not ICAM-2 and -3, is important for dendritic cell-mediated human immunodeficiency virus type 1 transmission

Lack of association between polymorphisms of MASP2 and susceptibility to SARS coronavirus infection

Identification of essential proteins based on edge clustering coefficient

Association of IL-17A and IL-17F gene polymorphisms with chronic hepatitis B and hepatitis B virus-related liver cirrhosis in a Chinese population: a casecontrol study

IL-17A promotes pulmonary B-1a cell differentiation via induction of blimp-1 expression during influenza virus infection

Severe acute respiratory syndrome coronavirus spike protein counteracts BST2-mediated restriction of virus-like particle release

Human immunodeficiency virus type 1 infection inhibits granulocyte-macrophage colonystimulating factor-induced activation of STAT5A in human monocyte-derived macrophages

Protective effects of HLA-DPA1/DPB1 variants against Hepatitis B virus infection in an Indonesian population

ICAM-5/telencephalin is a functional entry receptor for enterovirus D68

Sexual and Vertical Transmission of Zika Virus in anti-interferon receptor-treated Rag1-deficient mice

TH17 responses in cytokine storm of COVID-19: an emerging target of JAK2 inhibitor Fedratinib

Sustained viral load and late death in Rag2-/-mice after influenza A virus infection

miRecords: an integrated resource for microRNA-target interactions

Low expression of CXCR1/2 on neutrophils predicts poor survival in patients with hepatitis B virusrelated acute-on-chronic liver failure

Low responsiveness to a hepatitis B virus vaccine in a Chinese population lacks association with ITGAL, CD58, TNFSF15, CCL15, TGFB3, and BCL6 gene variants

TGF-β-miR-34a-CCL22 signaling-induced Treg cell recruitment promotes venous metastases of HBV-positive hepatocellular carcinoma

Protein complex detection using interaction reliability assessment and weighted clustering coefficient

Detection of hepatitis C virus RNA in CD19 peripheral blood mononuclear cells of chronically infected patients

Angiotensin-converting enzyme 2 (ACE2) as a SARS-CoV-2 receptor: molecular mechanisms and potential therapeutic target

) miR-27b attenuates apoptosis induced by transmissible gastroenteritis virus (TGEV) infection via targeting runt-related transcription factor 1 (RUNX1)

Combination of sphingosinE − 1-phosphate receptor 1 (S1PR1) agonist and antiviral drug: a potential therapy against pathogenic influenza virus

A respiratory syncytial virus persistent-infected cell line system reveals the involvement of SOCS1 in the innate antiviral response

Respiratory syncytial virus infects regulatory B cells in human neonates via chemokine receptor CX3CR1 and promotes lung disease severity

Net-workAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis

TBX21 polymorphisms are associated with virus persistence in hepatitis C virus infection patients from a high-risk Chinese population

SARS-CoV-2 Viral Load in Upper Respiratory Specimens of Infected Patients

bcl-2 and immunoglobulin gene rearrangement in patients with hepatitis C virus infection

Acknowledgements 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.