Integrated bioinformatics analysis for the screening of hub genes and therapeutic drugs in severe acute respiratory syndrome corona virus 2 infection/COVID 19

Integrated bioinformatics analysis for the screening of hub genes and therapeutic drugs in severe acute respiratory syndrome corona virus 2 infection/COVID 19 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrated bioinformatics analysis for the screening of hub genes and therapeutic drugs in severe acute respiratory syndrome corona virus 2 infection/COVID 19 Basavaraj Vastrad, Chanabasayya Vastrad , Iranna Kotturshetti This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-133291/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infections (COVID 19) is a progressive viral infection that has been investigated extensively. However, genetic features and molecular pathogenesis underlying SARS-CoV-2 infection remain unclear. Here we used bioinformatics to investigate the candidate genes associated in the molecular pathogenesis of SARS-CoV-2 infection. Expression profiling by high throughput sequencing (GSE149273) was downloaded from the Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) in remdesivir traded SARS-CoV-2 infection samples and non treated SARS-CoV-2 infection samples with an adjusted P-value 1.3 were first identified by limma in R software package. Next, Pathway and Gene Ontology (GO) enrichment analysis of these DEGs was performed. Then, the hub genes were identified by the Network Analyzer plugin and the other bioinformatics approaches including protein-protein interaction (PPI) network analysis, module analysis, target gene - miRNA regulatory network, and target gene - TF regulatory network construction was also performed. Finally, receiver‐operating characteristic (ROC) analyses were for diagnostic values associated with hub genes. A total of 909 DEGs were identified, including 453 up regulated genes and 457 down regulated genes. As for the pathway and GO enrichment analysis, the up regulated genes were mainly linked with influenza A and defense response, whereas down regulated genes were mainly linked with Drug metabolism - cytochrome P450 and reproductive process. Additionally, 10 hub genes (VCAM1, IKBKE, STAT1, IL7R, ISG15, E2F1, ZBTB16, TFAP4, ATP6V1B1 and APBB1) were identified. ROC analysis showed that hub genes (CIITA, HSPA6, MYD88, SOCS3, TNFRSF10A, ADH1A, CACNA2D2, DUSP9, FMO5 and PDE1A) had good diagnostic values. In summary, the data may produce new insights regarding pathogenesis of SARS-CoV-2 infection and treatment. Hub genes and candidate drugs may improve individualized diagnosis and therapy for SARS-CoV-2 infection in future. Bioinformatics SARS-CoV-2 infection Differentially expressed genes Pathway enrichment analysis Protein-protein interaction ROC analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Introduction At the December of 2019, a novel corona virus, called severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) or novel corona virus 2019 (2019-nCoV) is a single-stranded RNA, nonsegmented, enveloped viruses, resulted fast spreading from its origin in China to the rest of the globe [1]. Symptoms of this viral infection ranging in severity from the common cold to severe illness, and finally lead to death. Despite the fact that great progress has been made in antivirals and vaccination for this SARS-CoV-2 infection, survival rates is less. Since the precise molecular pathogenesis of SARS-CoV-2 infection (virus replication and dissemination) remains unknown, it is extremely essential to examine molecular pathogenesis and to develop effective therapeutic strategies in SARS-CoV-2 infection and to control the disease [2]. Expression profiling by high throughput sequencing is very essential to understand the molecular pathogenesis of viral infection and also to the advancement of novel antivirals drugs and vaccines for the novel viral infections [3]. With the rapid advancement of bioinformatics such as microarray technology, some high throughput platforms for analysis of gene expression are commonly used to find the differentially expressed genes (DEGs) during viral infections [4]. We rationally presume that differentially expressed genes (DEGs) can affect the promotion of a various viral infections. Now, through Expression profiling by high throughput sequencing investigation using microarray technology, more and more DEGs were linked with SARS-CoV-2 infection and understanding its biological characteristics is essential in improving clinical treatment outcomes. In the current investigation, we downloaded the RNA-seq data GSE149273 from the Gene Expression Omnibus database and conducted a bioinformatics analysis to study the differentially expressed genes (DEGs) between remdesivir traded SARS-CoV-2 infection samples and non treated SARS-CoV-2 infection samples. We performed function and pathways analyses, as well as protein–protein interaction (PPI) network analysis, modules analysis, target gene - miRNA regulatory network, and target gene - TF regulatory network and diagnostic values associated with hub genes was also assessed with the receiver‐operating characteristic (ROC) analyses. [] Material And Methods Microarray data and preprocessing Expression profiling by high throughput sequencing GSE149273 based on GPL21290 Illumina HiSeq 3000 (Homo sapiens) Array was downloaded from the NCBI GEO [National Center for Biotechnology Information, Gene Expression Omnibus database] (http://www.ncbi.nlm.nih.gov/geo/) a public depository database of gene expression data [5]. GSE149273 contains 60 samples, including 30 remdesivir traded SARS-CoV-2 infection samples and 30 non treated SARS-CoV-2 infection samples. The downloaded raw data were preprocessed, including background adjustment and normalization using limma package of R software (version 3.10.3, https://bioconduct or.org/packa ges/relea se/bioc/html/limma.html) [6]. [ ] Screening of the DEGs For the expression profiling by high throughput sequencing dataset, the R package limma was applied for performing the differential analysis between 30 remdesivir traded SARS-CoV-2 infection samples and non treated SARS-CoV-2 infection samples. The p-values were adjusted by Benjamini & Hochberg method [7] . Based on the |log fold change (FC)| values and the p-values, the differentially expressed genes (DEGs; thresholds: |logFC| > 1.3 for up regulated genes and |logFC| < - 1.3 for down regulated genes , adjusted p < 0.05). Pathway enrichment analysis for DEGs To analyze the functions of DEGs, BIOCYC ( https://biocyc.org/) [8], Kyoto Encyclopedia of Genes and Genomes (KEGG) (http://www.genome.jp/kegg/pathway.html) [9], Pathway Interaction Database (PID) ( https://wiki.nci.nih.gov/pages/viewpage.action?pageId=315491760) [10], REACTOME ( https://reactome.org/ ) [11] , GenMAPP ( http://www.genmapp.org/ ) [12] , MSigDB C2 BIOCARTA ( http://software.broadinstitute.org/gsea/msigdb/collections.jsp) [13], PantherDB (http://www.pantherdb.org/) [14], Pathway Ontology (http://www.obofoundry.org/ontology/pw.html) [15] and Small Molecule Pathway Database (SMPDB) (http://smpdb.ca/) [16] pathway analysis were carried out by using the ToppGene (ToppFun) (https://toppgene.cchmc.org/enrichment.jsp) [17] online tool. P<.05 was set as the cut-off point. Gene ontology (GO) enrichment analysis for DEGs The ToppGene (ToppFun) (https://toppgene.cchmc.org/enrichment.jsp) [17] was used to study GO enrichment analyses of DEGs. The ToppGene online tool for GO analysis (http://www.geneontology.org) [18] was used to complete the function of DEGs. Data from biological processes (BP), cellular components (CC) and molecular functions (MF) were documented from each set of genes. A p < 0.05 was considered statistically significant for all analyses. PPI network construction and module analysis The IMEX: The International Molecular Exchange Consortium (https://www.imexconsortium.org/) [19] is a biological database designed for predicting PPI networks and integrated with PPI data bases such as Database of Interacting Proteins (DIP) (http://dip.doe-mbi.ucla.edu/dip/Main.cgi) [20], IntAct Molecular Interaction Database ( https://www.ebi.ac.uk/intact/ ) [21], the Molecular INTeraction database (MINT) (https://mint.bio.uniroma2.it/) [22], InnateDB ( https://www.innatedb.com/ ) [23], Human Protein Reference Database (HPRD) ( http://www.hprd.org/ ) 24 , (BioGRID) ( https://thebiogrid.org/ ) [25], IID (Integrated Interactions Database) from a well‑known online server (http://iid.ophid.utoronto.ca) [26] and MatrixDB ( http://matrixdb.univ-lyon1.fr/ ) [27]. Cytoscape ( http://www.cytoscape.org/ , version 3.8.0) [28], an open software, was used to visualize the PPI networks. The top genes with the highest node degree [29], betweenness centrality [30], stress centrality [31], closeness centrality [32] and lowest clustering coefficient [33] were considered as hub genes based on the analysis using Network Analyzer from Cytoscape. PEWCC1 (http://apps.cytoscape.org/apps/PEWCC1) [34], a plug-in of Cytoscape, can screen a significant module from the PPI network. Construction of target genes - miRNA regulatory network The miRNet database ( https://www.mirnet.ca/ ) [35] is the biggest collection of predicted and experimentally verified target gene - miRNA interactions using 10 algorithms such as TarBase (http://diana.imis.athena-innovation.gr/DianaTools/index.php?r=tarbase/index) [36], miRTarBase (http://mirtarbase.mbc.nctu.edu.tw/php/download.php) [37], miRecords ( http://miRecords.umn.edu/miRecords ) [38], miR2Disease (http://www.mir2disease.org/) [39], HMDD ( http://www.cuilab.cn/hmdd ) [40], PhenomiR ( http://mips.helmholtz-muenchen.de/phenomir/ ) [41], SM2miR (http://bioinfo.hrbmu.edu.cn/SM2miR/) [42], PharmacomiR ( http://www.pharmaco-mir.org/ ) [43], EpimiR ( http://bioinfo.hrbmu.edu.cn/EpimiR/ ) [44] and starBase ( http://starbase.sysu.edu.cn/ ) [45]. Target genes - miRNA regulatory network among up and down regulated genes was constructed by Cytoscape (http://cytoscape.org/) [28]. Construction of target genes - TF regulatory network The NetworkAnalyst database (https://www.networkanalyst.ca/) [46] is the biggest collection of predicted and experimentally verified target gene - TF interactions using JASPAR (http://jaspar.genereg.net/) 47 database. Target genes - TF regulatory network among up and down regulated genes was constructed by Cytoscape (http://cytoscape.org/) [28]. Validation of hub genes In order to identify the diagnostic value of up and down regulated hub genes in SARS-CoV-2 infection, pROC package [48] in R language for illustrate receiver operating characteristic (ROC) curves was used in this investigation and area under the curve (AUC) of ROC curves was determined to check the act of each up and down regulated hub genes. When AUC value was greater than 0.6, the up and down regulated hub genes was treated able of distinguishing remdesivir traded SARS-CoV-2 infection samples and non treated SARS-CoV-2 infection samples. The diagnostic value of up and down regulated hub genes in GSE149273 dataset was estimated in our research work. Results Preprocessing and Screening of the DEGs After preprocessing (Fig. 1A and Fig. 1B), a total of 909 DEGs (453 up regulated genes and 457 down regulated genes) were identified between remdesivir traded SARS-CoV-2 infection and non treated SARS-CoV-2 infection (|logFC| > 1.3 for up regulated genes and |logFC| < - 1.3 for down regulated genes , adjusted p < 0.05) and volcano plots showing the results of differential analysis is given in Fig 2. The up regulated genes and down regulated genes are listed in Table 1. Heatmaps as shown in Fig. 3 and Fig. 4, respectively, indicated that these up and down regulated genes were good in distinguishing remdesivir traded SARS-CoV-2 infection samples and non treated SARS-CoV-2 infection samples. Pathway enrichment analysis for DEGs To further understand the function and mechanism of the identified up and down regulated genes, pathway enrichment analyses were performed using the ToppGene web tool. The main pathways that were particularly enriched by up regulated genes were pyrimidine deoxyribonucleosides degradation, tryptophan degradation to 2-amino-3-carboxymuconate semialdehyde, influenza A, cytokine-cytokine receptor interaction, IL23-mediated signaling events, direct p53 effectors, cytokine signaling in immune system, interferon signaling, C21 steroid hormone metabolism, purine metabolism, genes encoding secreted soluble factors, ensemble of genes encoding ECM-associated proteins including ECM-affilaited proteins, ECM regulators and secreted factors, toll receptor signaling pathway, inflammation mediated by chemokine and cytokine signaling pathway, JAK-STAT signaling, purine metabolic, steroidogenesis and pyrimidine metabolism and are listed in Table 2. Similarly, down regulated genes were notably enriched in pyridoxal 5'-phosphate salvage, glutamine degradation/glutamate biosynthesis, Drug metabolism - cytochrome P450, chemical carcinogenesis, signaling events mediated by the hedgehog family, glypican 2 network, GPCR ligand binding, phase 2 - plateau phase, glycolysis, gluconeogenesis, type III secretion system, genes encoding secreted soluble factors, ensemble of genes encoding ECM-associated proteins including ECM-affilaited proteins, ECM regulators and secreted factors, notch signaling pathway, TGF-beta signaling pathway, notch signaling, wnt signaling, sulfate/sulfite metabolism and leukotriene C4 synthesis deficiency and are listed in Table 3. Gene ontology (GO) enrichment analysis for DEGs GO term enrichment analyses were performed using web tool ToppGene. Table 4 and Table 5 show the functions of the identified up and down regulated genes. Up regulated genes of BP were associated in defense response and response to external biotic stimulus. Down regulated genes of BP were associated in reproductive process and positive regulation of transcription by RNA polymerase II. Up regulated genes of CC were associated in cell surface and external side of plasma membrane. Down regulated genes of CC were associated in intrinsic component of plasma membrane and nuclear chromatin. Up regulated genes of MF were associated in cytokine activity and receptor ligand activity. Down regulated genes of MF were associated in transporter activity and cation transmembrane transporter activity. PPI network construction and module analysis The PPI network of up regulated genes consisting of 206 nodes and 412 edges was constructed in the IMEX database (Fig. 5). A top hub genes were selected by the Network Analyzer (Table 6), including VCAM1, IKBKE, STAT1, IL7R, ISG15, PML, NOS2, FBXO6, IRF1, IRF7, ADAM8, SBK1, ARL14 and TGM2, and statistical results in scatter plot for node degree distribution, betweenness centrality, stress centrality, closeness centrality and clustring coefficient are displayed in Fig. 6A - 6E. Enrichment analyses revealed that hub genes in this PPI network were mainly associated with malaria, influenza A, defense response, cytokine-cytokine receptor interaction, cytokine signaling in immune system, direct p53 effectors, ATF-2 transcription factor network, adaptive immune system, IL6-mediated signaling events, measles, innate immune system and ensemble of genes encoding ECM-associated proteins including ECM-affilaited proteins, ECM regulators and secreted factors. Similarly, PPI network of down regulated genes consisting of 206 nodes and 412 edges was constructed in the IMEX database (Fig. 7). A top hub genes were selected by the Network Analyzer (Table 6), including E2F1, ZBTB16, TFAP4, ATP6V1B1, APBB1, ELF5, CBX2, USP2, ERP27, DSCAML1, KCNF1, DLX3, EGFL6 and AMIGO1, and statistical results in scatter plot for node degree distribution, betweenness centrality, stress centrality, closeness centrality and clustring coefficient are displayed in Fig. 8A - 8E. Enrichment analyses revealed that hub genes in this PPI network were mainly associated with notch-mediated HES/HEY network, map kinase inactivation of SMRT corepressor, positive regulation of transcription by RNA polymerase II, iron uptake and transport, positive regulation of RNA metabolic process, nuclear chromatin, reproductive process, positive regulation of developmental process, de novo pyrimidine ribonucleotidesbiosythesis, neuronal system, transcription regulatory region sequence-specific DNA binding, signaling receptor binding and molecular function regulator. Analysis using the PEWCC1 Cytoscape software plugin was used to create modules for the PPI networks. A total of 423 modules were created from PPI network of up regulated genes. Four significant modules were identified, including module 1 (nodes 44 and edges 173), module 6 (nodes 24 and edges 69), module 12 (nodes 20 and edges 38) and module 16 (nodes 18 and edges 33) are shown in Fig. 9. Enrichment analyses revealed that hub genes in these modules were mainly associated with influenza A, measles, chemokine signaling pathway, cytokine signaling in immune system, defense response, response to external biotic stimulus and innate immune response. A total of 219 modules were created from PPI network of down regulated genes. Four significant modules were identified, including module 4 (nodes 87 and edges 86), module 5 (nodes 77 and edges 76), module 13 (nodes 41 and edges 41) and module 16 (nodes 29 and edges 28) are shown in Fig. 10. Enrichment analyses revealed that hub genes in these modules were mainly associated with multi-organism reproductive process, iron uptake and transport, neuroactive ligand-receptor interaction and cell-cell signaling. Construction of target genes - miRNA regulatory network The up and down regulated genes were analyzed using the miRNet database. Target genes - miRNA regulatory network for up regulated genes consisting of 2182 nodes (1862 miRNAs and 320 up regulated genes) and 5899 edges (Fig. 11). The results of the topological property analysis demonstrated that SOD2 (degree = 257; ex, hsa-mir-4298), PMAIP1 (degree = 147; ex, hsa-mir-5697), APOL6 (degree = 127; ex, hsa-mir-4478), ICOSLG (degree = 119; ex, hsa-mir-4739) and NPR1 (degree = 118; ex, hsa-mir-6131) and are listed in Table 7. Enrichment analyses revealed that target genes in this network were mainly associated with cytokine-mediated signaling pathway, viral carcinogenesis, adaptive immune system and purine metabolism. Target genes - miRNA regulatory network for down regulated genes consisting of 2345 nodes (1783 miRNAs and 262 down regulated genes) and 4885 edges (Fig. 12). The results of the topological property analysis demonstrated that VAV3 (degree = 165; ex, hsa-mir-4315), ZNF703 (degree = 115; ex, hsa-mir-5787), FAXC (degree = 112; ex, hsa-mir-4279), GPR137C (degree = 97; ex, hsa-mir-3914) and ZNF704 (degree = 86; ex, hsa-mir-1538) and are listed in Table 7. Enrichment analyses revealed that target genes in this network were mainly associated with regulation of actin cytoskeleton, positive regulation of developmental process and transcription regulatory region sequence-specific DNA binding. Construction of target genes - TF regulatory network The up and down regulated genes were analyzed using the NetworkAnalyst database. Target genes - TF regulatory network for up regulated genes consisting of 516 nodes (92 TFs and 424 up regulated genes) and 3459 edges (Fig. 13). The results of the topological property analysis demonstrated that CD7 (degree = 265; ex, FOXC1), ELOVL7 (degree = 195; ex, GATA2), NTNG2 (degree = 136; ex, YY1), CXCL2 (degree = 125; ex, FOXL1) and (degree = 102; ex, NFKB1) and are listed in Table 8. Enrichment analyses revealed that target genes in this network were mainly associated with fas signaling pathway, ensemble of genes encoding extracellular matrix and extracellular matrix-associated proteins , ensemble of genes encoding extracellular matrix and extracellular matrix-associated proteins and influenza A. Target genes - TF regulatory network for down regulated genes consisting of 516 nodes (80 TFs and 458 down regulated genes) and 2424 edges (Fig. 14). The results of the topological property analysis demonstrated that ABCA17P (degree = 217; ex, FOXC1), TACR1 (degree = 182; ex, GATA2), REEP1 (degree = 97; ex, YY1), TRAM1L1 (degree = 97; ex, FOXL1) and FGF9 (degree = 74; ex, TFAP2A) and are listed in Table 8. Enrichment analyses revealed that target genes in this network were mainly associated with calcium signaling pathway, signaling receptor binding, transmembrane transport and cell-cell signaling. Validation of hub gene The prediction achievement by ROC analysis showed that as single classifiers, CIITA, HSPA6, MYD88, SOCS3, TNFRSF10A, ADH1A, CACNA2D2, DUSP9, FMO5 and PDE1A had significant predictive values with AUCs of 0.956, 0.752, 0.992, 0.914, 0.837, 0.759, 0.781, 0.788, 0.833 and 0.788, and p-values of 0.00022, 0.00714, 0.00152, 0.00038, 0.00054, 0.00275, 0.00093, 0.00092, 0.00294 and 0.00252, respectively (Fig. 15). Discussion Outbreaks of appearing and reappearing of SARS-CoV-2 infection are frequent threats to human health across globe. When a novel virus was detection and linked with human disease, it is necessary to understand molecular pathogenesis of SARS-CoV-2 infection as soon as possible to progress treatment to the disease such as vaccines and antiviral drugs [49]. In this investigation, we performed a series of bioinformatics analysis to screen hub genes and pathways associated with SARS-CoV-2 infection. The expression profiling by high throughput RNA sequencing found that 49 up regulated genes and 72 down regulated genes were identified in remdesivir traded SARS-CoV-2 infection compared to non treated SARS-CoV-2 infection. Genes such as IRF7 [50], MX2 [51], TRIM25 [52], TRIM14 [53], IFIT5 [54] and IFIT1 [55] were liable for progression of influenza virus infection, but these genes may be responsible for advancement of SARS-CoV-2 infection. Genes such as OAS3 [56], OASL (2'-5'-oligoadenylate synthetase like) [57] and USP18 [58] were linked with progression of various viral infections, but these genes may be key for progression of SARS-CoV-2 infection. RSAD2 was involved in pathogenesis of measles virus infection [59], but this gene may be associated with progression of SARS-CoV-2 infection. The ToppGene online tool was used to perform a pathway enrichment analysis. DDX58 was involved in the progression of measles virus infection [60], but this gene may be linked with advancement of SARS-CoV-2 infection. Enriched genes such as CIITA (class II major histocompatibility complex transactivator) [61], CCL2 [62], PML (promyelocyticleukemia) [63], ICAM1 [64], IL1A [65], MX1 [66], CXCL8 [67], MYD88 [68], CXCL10 [69], STAT1 [70], STAT2 [71], SOCS3 [72], CASP1 [73], TLR3 [74], TNF (tumor necrosis factor) [75], IL32 [76], TRIM22 [77], IFITM3 [78], FGF2 [79], IFITM1 [80], IFITM2 [81], IFI27 [82], ISG15 [83], SOCS1 [84], IRF1 [85], ISG20 [86], IL22RA1 [87], SOCS2 [88], GBP5 [89], BST2 [90], HERC5 [91], IL27 [92], CXCL13 [93], CXCL3 [94], TLR2 [95] and TNFAIP3 [96] were liable for development of influenza virus infection, but these genes may be essential for progression of SARS-CoV-2 infection. Enriched genes such as CCL5 [97], IL19 [98], CCL3 [97], CCL4 [99], CCL20 [100], IFIT3 [101], CSF3 [102] and IL7R [103] were important for development of respiratory syncytial virus infection, but these genes may be liable for advancement of SARS-CoV-2 infection. Enriched genes such as IL6 [104] and JAK2 [105] were responsible for progression of SARS-CoV-2 infection. Enriched genes such as TICAM1 [106], OAS1 [107], OAS2 [108], CXCL9 [109], EREG (epiregulin) [110], CCL22 [111], VCAM1 [112], IFI35 [113], IFIT2 [114], TRIM5 [115], XAF1 [116], IFI6 [117], IL7 [118], SP100 [119], GBP1 [120], GBP2 [121], IRF4 [122], MIR5193 [123], IFNL3 [124], CYP21A2 [125], CXCL5 [126], CX3CL1 [127], CCL4L1 [128], WNT16 [129], GNB3 [130], FLG (filaggrin) [131] and HEY1 [132] were responsible for progression of various viral infections, but these genes may be involved in the development of SARS-CoV-2 infection. Expression of NOS2 was associated with development of rhinovirus infection [133], but this gene may be involved in progression of SARS-CoV-2 infection. Expression of CCR1 was liable for progression of pneumovirus infection [134], but this gene may be linked with advancement of SARS-CoV-2 infection. Expression of IRAK2 was important for progression of bronchitis virus infection [135], but this gene may be involved in development of SARS-CoV-2 infection. In general, the our findings suggested that novel biomarkers such as SCO2, TYMP (thymidine phosphorylase), HSPA6, IFNB1, IKBKE (inhibitor of nuclear factor kappa B kinase subunit epsilon), EIF2AK2, TNFSF10, TNFRSF10A, IFIH1, IL23A, UBE2L6, HLA-F, RASGRP3, TRIM38, BATF (basic leucine zipper ATF-like transcription factor), NRG2, BIRC3, MT2A, CSF1, TNFSF13B, IL15RA, GBP7, IL36A, IL17C, PSMB9, TNFRSF6B, GBP3, TRIM21, PTGS2, GBP4, BTC (betacellulin), TNFSF18, HBEGF (heparin binding EGF like growth factor), DUSP5, TRIM31, RET (ret proto-oncogene), CXCL2, TRIM10, LGALS9, LIF (LIF interleukin 6 family cytokine), LIFR (LIF receptor subunit alpha), EBI3, IL36G, HCK (HCK proto-oncogene, Src family tyrosine kinase), IFNL2, IFNL1, HSD11B1, CXCL11, S100A7A, ANGPTL1, KLHL23, PDXP (pyridoxal phosphatase), ADH1A, ADH1C, ADH6, GSTA5, ALDH3B1, FMO5, GSTT2, PTCH2, IHH (Indian hedgehog signaling molecule), CDON (cell adhesion associated, oncogene regulated), F2R, TAS2R50, WNT8B, WNT9A, OXGR1, PTGFR (prostaglandin F receptor), TACR1, GPER1, GRM4, CCKBR (cholecystokinin B receptor), OPRL1, RLN2, TAS1R1, ALDH3A2, GDF5, FGF9, EGFL6, FGF22, INHA (inhibin subunit alpha), INHBC (inhibin subunit beta C), GDF7, FGFBP3, BMP15, HES5, LFNG (LFNG O-fucosylpeptide 3-beta-N-acetylglucosaminyltransferase), HEYL (hes related family bHLH transcription factor with YRPW motif like), SULT1E1 and SULT2B1 may play key roles in the action mechanism of SARS-CoV-2 infection. The functions of the up and down regulated genes were identified by GO enrichment analysis. Enriched genes such as TREX1 [136], IFNL4 [137], MICB (MHC class I polypeptide-related sequence B) [138], RAB43 [139], APOL1 [140], IFI16 [141], APOBEC3B [142], SLAMF7 [143], HDAC9 [144], APOBEC3A [145], SERPING1 [146], TAP2 [147], LAG3 [148], OPTN (optineurin) [149], CD68 [150], SP140 [151], PDCD1 [152], PLVAP (plasmalemma vesicle associated protein) [153], CD34 [154], CD38 [155], CD69 [156], SLC30A8 [157] and ATP6V1G2 [158] were liable for progression of various viral infections, but these genes may be involved in the progression of SARS-CoV-2 infection. Enriched genes such as APOBEC3G [159], ADAM8 [160], ZBP1 [161], NLRC5 [162], AIM2 [163], DUOX2 [164], NOX1 [165], IDO1 [166], CEACAM1 [167], PTX3 [168], TAP1 [169], FFAR2 [170] and E2F1 [171] were linked with progression of influenza virus infection, but these genes may be associated with progression of SARS-CoV-2 infection. CD83 was responsible for advancement of respiratory syndrome virus [172], but this gene may be essential for development of SARS-CoV-2 infection. ACE2 was linked with progression of SARS-CoV-2 infection [173]. NMI (N-myc and STAT interactor) was liable for progression of severe acute respiratory syndrome corona virus [174], but this gene may be important for development of SARS-CoV-2 infection. CD274 was associated with progression of rhino virus infection [175], but this gene may be essential for progression of SARS-CoV-2 infection. In general, the our findings suggested that novel biomarkers such as PIK3AP1, NT5C3A, NCF1, TNIP3, CLEC1A, CLEC7A, DTX3L, MEF2C, MEP1B, ADM (adrenomedullin), SAA2, SAA4, SERPINB9, MUC17, ABCD1, APOL2, PLSCR1, PMAIP1, FOXF1, DUOXA2, THEMIS2, ZC3H12A, DHX58, DDX60, C2CD4A, MUC13, PARP14, BATF2, PLA2G4C, NUB1, STX11, ZC3HAV1, TAGAP (T cell activation RhoGTPase activating protein), RNF19B, GCH1, PTGIR (prostaglandin I2 receptor), RTP4, ARID5A, TMEM106A, PRDM1, DEFB4A, IFIT1B, C1R, C1S, C3AR1, PARP9, IFI44L, HERC6, ICOSLG (inducible T cell costimulator ligand), TRIM15, NUPR1, TGM2, APOL3, TNFAIP6, MAPK8IP2, ACHE (acetylcholinesterase (Cartwright blood group)), CHRNA1, FZD9, PLAUR (plasminogen activator, urokinase receptor), SELL (selectin L), ACKR4, PDCD1LG2, KCNA1, SECTM1, WIPF3, APELA (apelin receptor early endogenous ligand), THEG (theg spermatid protein), PKDREJ (polycystin family receptor for egg jelly), TFCP2L1, TDRD6, TXNDC8, ZFP37, RIMBP3, NLRP14, FMN2, RIMBP3B, MYBL1, ZBTB16, INHBB (inhibin subunit beta B), NDP (norrincystine knot growth factor NDP), GGT3P, STOX2, GJB1, SOHLH2, BUB1B, DLX3, RAB3A, CBX2, GPR3, SPATA31A3, DPY19L2, BCL2L10, SOX30, E2F8, RLN1, VASH2, SLC29A3, CACNA2D2, PKD1L2, TMC3, AMIGO1, TRPM3, HPX (hemopexin), AKAP6, KCTD7, NLGN1, SLC47A2, SLC32A1, SLC9A2, TMEM37, CACNG4, ATP6V1B1, SLC46A1, SLC30A2, GPD1L, PPARGC1A, KCNF1, TMEM150C, CACNA1D, AQP10, CACNG1, SLC52A1, SLC16A9, HCN4, NGB (neuroglobin), TMEM63C, ABCC5, P2RX6, SLC16A14, SLC25A19 and SLC29A1 may play key roles in the action mechanism of SARS-CoV-2 infection. The construction of protein-protein interaction network and module analysis for up and down regulated genes, has been proven to be useful in the analysis of hub genes involved in SARS-CoV-2 infection. HELZ2 was associated with development of dengue virus infection [176], but this gene may be liable for progression of SARS-CoV-2 infection. BATF3 was involved in advancement of respiratory poxvirus infection [177], but this gene may essential for development of SARS-CoV-2 infection. In general, our findings suggested that novel biomarkers such as FBXO6, SBK1, ARL14, LMO2, LAP3, TFAP4, APBB1, ELF5, USP2, ERP27, DSCAML1, NGEF (neuronal guanine nucleotide exchange factor), MARC1, GPRASP1, RAB26, DEPTOR (DEP domain containing MTOR interacting protein), HMGCS2, EEPD1, CAMKK1, PDE1A, PPP1R3C, WDR88, SERF1A, KLHL32, SMTNL2, RASL11B, ABLIM1, TOX2, LMCD1, TMCC2 and CERK (ceramide kinase) may play key roles in the action mechanism of SARS-CoV-2 infection. The construction of target genes - miRNA regulatory network and target genes - TF regulatory network analysis for up and down regulated genes, has been proven to be useful in the analysis of target genes involved in SARS-CoV-2 infection. Target genes such as CD7 [178] and ELOVL7 [179] were liable for advancement of HIV infection, but these genes may be associated with progression of SARS-CoV-2 infection. In general, our findings suggested that novel biomarkers such as SOD2, APOL6, NPR1, NTNG2, VAV3, ZNF703, FAXC (failed axon connections homolog, metaxin like GST domain), GPR137C, ZNF704, ABCA17P, REEP1 and TRAM1L1 may play key roles in the action mechanism of SARS-CoV-2 infection. In conclusion, we conducted a comprehensive bioinformatics analysis on microarray data of SARS-CoV-2 infection. Pivotal DEGs (up and down regulated genes) and pathways were diagnosed and screened to provide a theoretical basis for potential drug target discovery and the molecular pathogensis of SARS-CoV-2 infection. 10 hub genes, especially CIITA, HSPA6, MYD88, SOCS3, TNFRSF10A, ADH1A, CACNA2D2, DUSP9, FMO5 and PDE1A, were found to differentiate remdesivir traded SARS-CoV-2 infection from non treated SARS-CoV-2 infection. Nevertheless, additional relevant investigation are needed to further confirm the identified up and down regulated genes, and pathways in SARS-CoV-2 infection. Declarations Acknowledgement I thank Eugene H Chang, The University of Arizona, Department of Otolaryngology, Eugene Lab, Tucson, Arizona, USA, very much, the author who deposited their microarray dataset, GSE149273, into the public GEO database. Conflict of interest The authors declare that they have no conflict of interest. Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors. Informed consent No informed consent because this study does not contain human or animals participants. Availability of data and materials The datasets supporting the conclusions of this article are available in the GEO (Gene Expression Omnibus) (https://www.ncbi.nlm.nih.gov/geo/) repository. [(GSE149273) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE149273] Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Author Contributions B. V. - Writing original draft, and review and editing C. V. - Software and investigation I. K. - Supervision and resources Authors Basavaraj Vastrad ORCID ID: 0000-0003-2202-7637 Chanabasayya Vastrad ORCID ID: 0000-0003-3615-4450 Iranna Kotturshetti ORCID ID: 0000-0003-1988-7345 References Alhazzani W, Møller MH, Arabi YM, Loeb M, Gong MN, Fan E, Oczkowski S, Levy MM, Derde L, Dzierba A, et al. Surviving Sepsis Campaign: guidelines on the management of critically ill adults with Coronavirus Disease 2019 (COVID-19). Intensive Care Med. 2020;46(5):854‐887. doi:1007/s00134-020-06022-5 Li X, Geng M, Peng Y, Meng L, Lu S. Molecular immune pathogenesis and diagnosis of COVID-19. J Pharm Anal. 2020;10(2):102‐108. doi:1016/j.jpha.2020.03.001 Steuerman Y, Cohen M, Peshes-Yaloz N, Valadarsky L, Cohn O, David E, Frishberg A, Mayo L, Bacharach E, Amit I et al. Dissection of Influenza Infection In Vivo by Single-Cell RNA Sequencing. Cell Syst. 2018;6(6):679‐691.e4. doi:1016/j.cels.2018.05.008 Souza GAP, Salvador EA, de Oliveira FR, Cotta Malaquias LC, Abrahão JS, Leomil Coelho LF. An in silico integrative protocol for identifying key genes and pathways useful to understand emerging virus disease pathogenesis. Virus Res. 2020;284:197986. doi:1016/j.virusres.2020.197986 Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, et al. NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res. 2013;41(Database issue):D991‐D995. doi:1093/nar/gks1193 Ritchie ME, Phipson B, Wu DI, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. doi:1093/nar/gkv007 Abbas A, Kong XB, Liu Z, Jing BY, Gao X. Automatic peak selection by a Benjamini-Hochberg-based algorithm. PLoS One. 2013;8(1):e53112. doi:1371/journal.pone.0053112 Caspi R, Billington R, Ferrer L, Foerster H, Fulcher CA, Keseler IM, Kothari A, Krummenacker M, Latendresse M, Mueller LA et al The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 2016;44(D1):D471–D480. doi:1093/nar/gkv1164 Kanehisa M, Sato Y, Furumichi M, Morishima K, Tanabe M. New approach for understanding genome variations in KEGG. Nucleic Acids Res. 2019;47(D1):D590–D595. doi:1093/nar/gky962 Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, Buetow KH. PID: the Pathway Interaction Database. Nucleic Acids Res. 2009;37(Database issue):D674–D679. doi:1093/nar/gkn653 Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, Haw R, Jassal B, Korninger F, May B et al The Reactome Pathway Knowledgebase. Nucleic Acids Res. 2018;46(D1):D649–D655. doi:1093/nar/gkx1132 Dahlquist KD, Salomonis N, Vranizan K, Lawlor SC, Conklin BR. GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways. Nat Genet. 2002;31(1):19–20. doi:1038/ng0502-19 Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES et al Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545–15550. doi:1073/pnas.0506580102 Mi H, Huang X, Muruganujan A, Tang H, Mills C, Kang D, Thomas PD. PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements. Nucleic Acids Res. 2017;45(D1):D183–D189. doi:1093/nar/gkw1138 Petri V, Jayaraman P, Tutaj M, Hayman GT, Smith JR, De Pons J, Laulederkind SJ, Lowry TF, Nigam R, Wang SJ et al The pathway ontology - updates and applications. J Biomed Semantics. 2014;5(1):7. doi:1186/2041-1480-5-7 Jewison T, Su Y, Disfany FM, Liang Y, Knox C, Maciejewski A, Poelzer J, Huynh J, Zhou Y, Arndt D et al SMPDB 2.0: big improvements to the Small Molecule Pathway Database. Nucleic Acids Res. 2014;42(Database issue):D478–D484. doi:1093/nar/gkt1067 Chen J, Bardes EE, Aronow BJ, Jegga AG. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 2009;37(Web Server issue):W305-W311. doi:1093/nar/gkp427 Lewis SE. The Vision and Challenges of the Gene Ontology. Methods Mol Biol. 2017;1446:291–302. doi:1007/978-1-4939-3743-1_21 Orchard S, Kerrien S, Abbani S, Aranda B, Bhate J, Bidwell S, Bridge A, Briganti L, Brinkman FS, Cesareni G, et al. Protein interaction data curation: the International Molecular Exchange (IMEx) consortium. Nat Methods. 2012;9(4):345‐350. doi:1038/nmeth.1931 Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU, Eisenberg D. The Database of Interacting Proteins: 2004 update. Nucleic Acids Res. 2004;32(Database issue):D449–D451. doi:1093/nar/gkh086 Orchard S, Ammari M, Aranda B, Breuza L, Briganti L, Broackes-Carter F, Campbell NH, Chavali G, Chen C, del-Toro N, Duesbury M et al The MIntAct project--IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res. 2014;42(Database issue):D358–D363. doi:1093/nar/gkt1115 Licata L, Briganti L, Peluso D, Perfetto L, Iannuccelli M, Galeota E, Sacco F, Palma A, Nardozza AP, Santonico E et al. MINT, the molecular interaction database: 2012 update. Nucleic Acids Res. 2012;40(Database issue):D857–D861. doi:1093/nar/gkr930 Breuer K, Foroushani AK, Laird MR, Chen C, Sribnaia A, Lo R, Winsor GL, Hancock RE, Brinkman FS, Lynn DJ. InnateDB: systems biology of innate immunity and beyond--recent updates and continuing curation. Nucleic Acids Res. 2013;41(Database issue):D1228–D1233. doi:1093/nar/gks1147 Keshava Prasad TS, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D, Raju R, Shafreen B, Venugopal A et al Human Protein Reference Database--2009 update. Nucleic Acids Res. 2009;37(Database issue):D767–D772. doi:1093/nar/gkn892 Oughtred R, Stark C, Breitkreutz BJ, Rust J, Boucher L, Chang C, Kolas N, O’Donnell L, Leung G, McAdam R et al. The BioGRID interaction database: 2019 update. Nucleic Acids Res. 2019;47(D1):D529–D541. doi:1093/nar/gky1079 Kotlyar M, Pastrello C, Malik Z, Jurisica I. IID 2018 update: context-specific physical protein-protein interactions in human, model organisms and domesticated species. Nucleic Acids Res. 2019;47(D1):D581–D589. doi:1093/nar/gky1037 Clerc O, Deniaud M, Vallet SD, Naba A, Rivet A, Perez S, Thierry-Mieg N, Ricard-Blum S. MatrixDB: integration of new data with a focus on glycosaminoglycan interactions. Nucleic Acids Res. 2019;47(D1):D376‐D381. doi:1093/nar/gky1035 Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–2504. doi:1101/gr.1239303 Przulj N, Wigle DA, Jurisica I. Functional topology in a network of protein interactions. Bioinformatics. 2004;20(3):340–348. doi:1093/bioinformatics/btg415 Nguyen TP, Liu WC, Jordán F. Inferring pleiotropy by network analysis: linked diseases in the human PPI network. BMC Syst Biol. 2011;5:179. Published 2011 Oct 31. doi:1186/1752-0509-5-179 Shi Z, Zhang B. Fast network centrality analysis using GPUs. BMC Bioinformatics. 2011;12:149. doi:1186/1471-2105-12-149 Nguyen TP, Liu WC, Jordán F. Inferring pleiotropy by network analysis: linked diseases in the human PPI network. BMC Syst Biol. 2011;5:179. doi:1186/1752-0509-5-179 Wang J, Li M, Wang H, Pan Y. Identification of essential proteins based on edge clustering coefficient. IEEE/ACM Trans Comput Biol Bioinform. 2012;9(4):1070–1080. doi:1109/TCBB.2011.147 Zaki N, Efimov D, Berengueres J. Protein complex detection using interaction reliability assessment and weighted clustering coefficient. BMC Bioinformatics. 2013;14:163. doi:1186/1471-2105-14-163 Fan Y, Xia J. miRNet-Functional Analysis and Visual Exploration of miRNA-Target Interactions in a Network Context. Methods Mol Biol. 2018;1819:215-233. doi:1007/978-1-4939-8618-7_10 Vlachos IS, Paraskevopoulou MD, Karagkouni D, Georgakilas G, Vergoulis T, Kanellos I, Anastasopoulos IL, Maniou S, Karathanou K, Kalfakakou D et al DIANA-TarBase v7.0: indexing more than half a million experimentally supported miRNA:mRNA interactions. Nucleic Acids Res. 2015;43(Database issue):D153-D159. doi:1093/nar/gku1215 Chou CH, Shrestha S, Yang CD, Chang NW, Lin YL, Liao KW, Huang WC, Sun TH, Tu SJ, Lee WH et al miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Res. 2018;46(D1):D296-D302. doi:1093/nar/gkx1067 Xiao F, Zuo Z, Cai G, Kang S, Gao X, Li T. miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res. 2009;37(Database issue):D105-D110. doi:1093/nar/gkn851 Jiang Q, Wang Y, Hao Y, Juan L, Teng M, Zhang X, Li M, Wang G, Liu Y. miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res. 2009;37(Database issue):D98-104. doi:1093/nar/gkn714 Huang Z, Shi J, Gao Y, Cui C, Zhang S, Li J, Zhou Y, Cui Q. HMDD v3.0: a database for experimentally supported human microRNA-disease associations. Nucleic Acids Res. 2019;47(D1):D1013-D1017. doi:1093/nar/gky1010Z Ruepp A, Kowarsch A, Schmidl D, Buggenthin F, Brauner B, Dunger I, Fobo G, Frishman G, Montrone C, Theis FJ. PhenomiR: a knowledgebase for microRNA expression in diseases and biological processes. Genome Biol. 2010;11(1):R6. doi:1186/gb-2010-11-1-r6 Liu X, Wang S, Meng F, Wang J, Zhang Y, Dai E, Yu X, Li X, Jiang W. SM2miR: a database of the experimentally validated small molecules' effects on microRNA expression. Bioinformatics. 2013;29(3):409-411. doi:1093/bioinformatics/bts698 Rukov JL, Wilentzik R, Jaffe I, Vinther J, Shomron N. Pharmaco-miR: linking microRNAs and drug effects. Brief Bioinform. 2014;15(4):648-659. doi:1093/bib/bbs082 Dai E, Yu X, Zhang Y, Meng F, Wang S, Liu X, Liu D, Wang J, Li X, Jiang W. EpimiR: a database of curated mutual regulation between miRNAs and epigenetic modifications. Database (Oxford). 2014;2014:bau023. doi:1093/database/bau023 Li JH, Liu S, Zhou H, Qu LH, Yang JH. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res. 2014;42(Database issue):D92-D97. doi:1093/nar/gkt1248 Zhou G, Soufan O, Ewald J, Hancock REW, Basu N, Xia J. NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res. 2019. doi:1093/nar/gkz240 Khan A, Fornes O, Stigliani A, Gheorghe M, Castro-Mondragon JA, van der Lee R, Bessy A, Chèneby J, Kulkarni SR, Tan G et al. JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework. Nucleic Acids Res. 2018;46(D1):D260-D266. doi:1093/nar/gkx1126 Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Müller M. (2011) pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 12:77. doi:1186/1471-2105-12-77 Liu C, Zhou Q, Li Y, Garner LV, Watkins SP, Carter LJ, Smoot J, Gregg AC, Daniels AD, Jervey S, et al. Research and Development on Therapeutic Agents and Vaccines for COVID-19 and Related Human Coronavirus Diseases. ACS Cent Sci. 2020;6(3):315‐331. doi:1021/acscentsci.0c00272 Ciancanelli MJ, Huang SX, Luthra P, Garner H, Itan Y, Volpi S, Lafaille FG, Trouillet C, Schmolke M, Albrecht RA, et al. Infectious disease. Life-threatening influenza and impaired interferon amplification in human IRF7 deficiency. Science. 2015;348(6233):448‐453. doi:1126/science.aaa1578 Jin HK, Yoshimatsu K, Takada A, Ogino M, Asano A, Arikawa J, Watanabe T. Mouse Mx2 protein inhibits hantavirus but not influenza virus replication. Arch Virol. 2001;146(1):41‐49. doi:1007/s007050170189 Koliopoulos MG, Lethier M, van der Veen AG, Haubrich K, Hennig J, Kowalinski E, Stevens RV, Martin SR, e Sousa CR, Cusack S, et al. Molecular mechanism of influenza A NS1-mediated TRIM25 recognition and inhibition. Nat Commun. 2018;9(1):1820. doi:1038/s41467-018-04214-8 QIN FX, Wu X, Wang J, Wang S, Wu F, Chen Z, Li C, Cheng G. Inhibition of Influenza A Virus Replication by TRIM14 via Its Multifaceted Protein-Protein Interaction With NP. Front Microbiol. 2019;10:344. doi:3389/fmicb.2019.00344 Rohaim MA, Santhakumar D, Naggar RFE, Iqbal M, Hussein HA, Munir M. Chickens Expressing IFIT5 Ameliorate Clinical Outcome and Pathology of Highly Pathogenic Avian Influenza and Velogenic Newcastle Disease Viruses. Front Immunol. 2018;9:2025. doi:3389/fimmu.2018.02025 Feng B, Zhang Q, Wang J, Dong H, Mu X, Hu G, Zhang T. IFIT1 Expression Patterns Induced by H9N2 Virus and Inactivated Viral Particle in Human Umbilical Vein Endothelial Cells and Bronchus Epithelial Cells. Mol Cells. 2018;41(4):271‐281. doi:14348/molcells.2018.2091 Gad HH, Paulous S, Belarbi E, Diancourt L, Drosten C, Kümmerer BM, Plate AE, Caro V, Desprès P. The E2-E166K substitution restores Chikungunya virus growth in OAS3 expressing cells by acting on viral entry. Virology. 2012;434(1):27‐37. doi:1016/j.virol.2012.07.019 Ishibashi M, Wakita T, Esumi M. 2',5'-Oligoadenylate synthetase-like gene highly induced by hepatitis C virus infection in human liver is inhibitory to viral replication in vitro. Biochem Biophys Res Commun. 2010;392(3):397‐402. doi:1016/j.bbrc.2010.01.034 Chen L, Li S, McGilvray I. The ISG15/USP18 ubiquitin-like pathway (ISGylation system) in hepatitis C virus infection and resistance to interferon therapy. Int J Biochem Cell Biol. 2011;43(10):1427‐1431. doi:1016/j.biocel.2011.06.006 Kurokawa C, Iankov ID, Galanis E. A key anti-viral protein, RSAD2/VIPERIN, restricts the release of measles virus from infected cells. Virus Res. 2019;263:145‐150. doi:1016/j.virusres.2019.01.014 Xia M, Gonzalez P, Li C, Meng G, Jiang A, Wang H, Gao Q, Debatin KM, Beltinger C, Wei J.Mitophagy enhances oncolytic measles virus replication by mitigating DDX58/RIG-I-like receptor signaling. J Virol. 2014;88(9):5152‐5164. doi:1128/JVI.03851-13 Hang do TT, Song JY, Kim MY, Park JW, Shin YK. Involvement of NF-κB in changes of IFN-γ-induced CIITA/MHC-II and iNOS expression by influenza virus in macrophages. Mol Immunol. 2011;48(9-10):1253‐1262. doi:1016/j.molimm.2011.03.010 Lai C, Wang K, Zhao Z, Zhang L, Gu H, Yang P, Wang X. C-C Motif Chemokine Ligand 2 (CCL2) Mediates Acute Lung Injury Induced by Lethal Influenza H7N9 Virus. Front Microbiol. 2017;8:587. doi:3389/fmicb.2017.00587 Li W, Wang G, Zhang H, Zhang D, Zeng J, Chen X, Xu Y, Li K. Differential suppressive effect of promyelocytic leukemia protein on the replication of different subtypes/strains of influenza A virus. Biochem Biophys Res Commun. 2009;389(1):84‐89. doi:1016/j.bbrc.2009.08.091 Jiang H, Shen SM, Yin J, Zhang PP, Shi Y. Sphingosine 1-phosphate receptor 1 (S1PR1) agonist CYM5442 inhibits expression of intracellular adhesion molecule 1 (ICAM1) in endothelial cells infected with influenza A viruses. PLoS One. 2017;12(4):e0175188. doi:1371/journal.pone.0175188 Liu Y, Li S, Zhang G, Nie G, Meng Z, Mao D, Chen C, Chen X, Zhou B, Zeng G.. Genetic variants in IL1A and IL1B contribute to the susceptibility to 2009 pandemic H1N1 influenza A virus. BMC Immunol. 2013;14:37. doi:1186/1471-2172-14-37 Verhelst J, Parthoens E, Schepens B, Fiers W, Saelens X. Interferon-inducible protein Mx1 inhibits influenza virus by interfering with functional viral ribonucleoprotein complex assembly. J Virol. 2012;86(24):13445‐13455. doi:1128/JVI.01682-12 Huipao N, Borwornpinyo S, Wiboon-Ut S, Campbell CR, Lee IH, Hiranyachattada S, Sukasem C, Thitithanyanont A, Pholpramool C, Cook DI, et al. P2Y6 receptors are involved in mediating the effect of inactivated avian influenza virus H5N1 on IL-6 & CXCL8 mRNA expression in respiratory epithelium. PLoS One. 2017;12(5):e0176974. doi:1371/journal.pone.0176974 Zhang R, Ai X, Duan Y, Xue M, He W, Wang C, Xu T, Xu M, Liu B, Li C, et al. P2Y6 receptors are involved in mediating the effect of inactivated avian influenza virus H5N1 on IL-6 & CXCL8 mRNA expression in respiratory epithelium. PLoS One. 2017;12(5):e0176974. doi:1371/journal.pone.0176974 Law AH, Lee DC, Yuen KY, Peiris M, Lau AS. Cellular response to influenza virus infection: a potential role for autophagy in CXCL10 and interferon-alpha induction. Cell Mol Immunol. 2010;7(4):263‐270. doi:1038/cmi.2010.25 Lee B, Gopal R, Manni ML, McHugh KJ, Mandalapu S, Robinson KM, Alcorn JF. et al. STAT1 Is Required for Suppression of Type 17 Immunity during Influenza and Bacterial Superinfection. Immunohorizons. 2017;1(6):81‐91. doi:4049/immunohorizons.1700030 Warnking K, Klemm C, Löffler B, Niemann S, van Krüchten A, Peters G, Ludwig S, Ehrhardt C. Super-infection with Staphylococcus aureus inhibits influenza virus-induced type I IFN signalling through impaired STAT1-STAT2 dimerization. Cell Microbiol. 2015;17(3):303‐317. doi:1111/cmi.12375 Lin X, Yu S, Ren P, Sun X, Jin M. Human microRNA-30 inhibits influenza virus infection by suppressing the expression of SOCS1, SOCS3, and NEDD4. Cell Microbiol. 2020;22(5):e13150. doi:1111/cmi.13150 Ren R, Wu S, Cai J, Yang Y, Ren X, Feng Y, Chen L, Qin B, Xu C, Yang H, et al. The H7N9 influenza A virus infection results in lethal inflammation in the mammalian host via the NLRP3-caspase-1 inflammasome. Sci Rep. 2017;7(1):7625. doi:1038/s41598-017-07384-5 Wu W, Zhang W, Duggan ES, Booth JL, Zou MH, Metcalf JP. RIG-I and TLR3 are both required for maximum interferon induction by influenza virus in human lung alveolar epithelial cells. Virology. 2015;482:181‐188. doi:1016/j.virol.2015.03.048 Ishikawa E, Nakazawa M, Yoshinari M, Minami M. Role of tumor necrosis factor-related apoptosis-inducing ligand in immune response to influenza virus infection in mice. J Virol. 2005;79(12):7658‐7663. doi:1128/JVI.79.12.7658-7663.2005 Wang J, Wang Q, Han T, Li YK, Zhu SL, Ao F, Feng J, Jing MZ, Wang L, Ye LB, et al. Soluble interleukin-6 receptor is elevated during influenza A virus infection and mediates the IL-6 and IL-32 inflammatory cytokine burst. Cell Mol Immunol. 2015;12(5):633‐644. doi:1038/cmi.2014.80 Di Pietro A, Kajaste-Rudnitski A, Oteiza A, Nicora L, Towers GJ, Mechti N, Vicenzi E. TRIM22 inhibits influenza A virus infection by targeting the viral nucleoprotein for degradation. J Virol. 2013;87(8):4523‐4533. doi:1128/JVI.02548-12 Sun X, Zeng H, Kumar A, Belser JA, Maines TR, Tumpey TM. Constitutively Expressed IFITM3 Protein in Human Endothelial Cells Poses an Early Infection Block to Human Influenza Viruses. J Virol. 2016;90(24):11157‐11167. Published 2016 Nov 28. doi:1128/JVI.01254-16 Wang K, Lai C, Gu H, Zhao L, Xia M, Yang P, Wang X. miR-194 Inhibits Innate Antiviral Immunity by Targeting FGF2 in Influenza H1N1 Virus Infection. Front Microbiol. 2017;8:2187. doi:3389/fmicb.2017.02187 Yu M, Qi W, Huang Z, Zhang K, Ye J, Liu R, Wang H, Ma Y, Liao M, Ning Z. Expression profile and histological distribution of IFITM1 and IFITM3 during H9N2 avian influenza virus infection in BALB/c mice. Med Microbiol Immunol. 2015;204(4):505‐514. doi:1007/s00430-014-0361-2 Wang HF, Chen L, Luo J, He HX. KLF5 is involved in regulation of IFITM1, 2, and 3 genes during H5N1 virus infection in A549 cells. Cell Mol Biol (Noisy-le-grand). 2016;62(13):65‐70. doi:14715/cmb/2016.62.13.12 Tang BM, Shojaei M, Parnell GP, Huang S, Nalos M, Teoh S, O'Connor K, Schibeci S, Phu AL, Kumar A, et al. A novel immune biomarker IFI27 discriminates between influenza and bacteria in patients with suspected respiratory infection. Eur Respir J. 2017;49(6):1602098. doi:1183/13993003.02098-2016 Sanyal S, Ashour J, Maruyama T, Altenburg AF, Cragnolini JJ, Bilate A, Avalos AM, Kundrat L, García-Sastre A, Ploegh HL. Type I interferon imposes a TSG101/ISG15 checkpoint at the Golgi for glycoprotein trafficking during influenza virus infection. Cell Host Microbe. 2013;14(5):510‐521. doi:1016/j.chom.2013.10.011 Ye S, Lowther S, Stambas J. Inhibition of reactive oxygen species production ameliorates inflammation induced by influenza A viruses via upregulation of SOCS1 and SOCS3. J Virol. 2015;89(5):2672‐2683. doi:1128/JVI.03529-14 Kuriakose T, Zheng M, Neale G, Kanneganti TD. IRF1 Is a Transcriptional Regulator of ZBP1 Promoting NLRP3 Inflammasome Activation and Cell Death during Influenza Virus Infection. J Immunol. 2018;200(4):1489‐1495. doi:4049/jimmunol.1701538 Chai W, Li J, Shangguan Q, Liu Q, Li X, Qi D, Tong X, Liu W, Ye X. Lnc-ISG20 Inhibits Influenza A Virus Replication by Enhancing ISG20 Expression. J Virol. 2018;92(16):e00539-18. doi:1128/JVI.00539-18 Hebert KD, Mclaughlin N, Zhang Z, Cipriani A, Alcorn JF, Pociask DA. IL-22Ra1 is induced during influenza infection by direct and indirect TLR3 induction of STAT1. Respir Res. 2019;20(1):184. doi:1186/s12931-019-1153-4 Kedzierski L, Tate MD, Hsu AC, Kolesnik TB, Linossi EM, Dagley L, Dong Z, Freeman S, Infusini G, Starkey MR, et al. Suppressor of cytokine signaling (SOCS)5 ameliorates influenza infection via inhibition of EGFR signaling. Elife. 2017;6:e20444. doi:7554/eLife.20444 Feng J, Cao Z, Wang L, Wan Y, Peng N, Wang Q, Chen X, Zhou Y, Zhu Y. Inducible GBP5 Mediates the Antiviral Response via Interferon-Related Pathways during Influenza A Virus Infection. J Innate Immun. 2017;9(4):419‐435. doi:1159/000460294 Londrigan SL, Tate MD, Job ER, Moffat JM, Wakim LM, Gonelli CA, Purcell DF, Brooks AG, Villadangos JA, Reading PC, et al. Endogenous Murine BST-2/Tetherin Is Not a Major Restriction Factor of Influenza A Virus Infection. PLoS One. 2015;10(11):e0142925. doi:1371/journal.pone.0142925 Tang Y, Zhong G, Zhu L, Liu X, Shan Y, Feng H, Bu Z, Chen H, Wang C. Herc5 attenuates influenza A virus by catalyzing ISGylation of viral NS1 protein. J Immunol. 2010;184(10):5777‐5790. doi:4049/jimmunol.0903588 Kumar P, Rajasekaran K, Nanbakhsh A, Gorski J, Thakar MS, Malarkannan S. IL-27 promotes NK cell effector functions via Maf-Nrf2 pathway during influenza infection. Sci Rep. 2019;9(1):4984. doi:1038/s41598-019-41478-6 Rangel-Moreno J, Moyron-Quiroz JE, Hartson L, Kusser K, Randall TD. Pulmonary expression of CXC chemokine ligand 13, CC chemokine ligand 19, and CC chemokine ligand 21 is essential for local immunity to influenza. Proc Natl Acad Sci U S A. 2007;104(25):10577‐10582. doi:1073/pnas.0700591104 Carlin LE, Hemann EA, Zacharias ZR, Heusel JW, Legge KL. Natural Killer Cell Recruitment to the Lung During Influenza A Virus Infection Is Dependent on CXCR3, CCR5, and Virus Exposure Dose. Front Immunol. 2018;9:781. doi:3389/fimmu.2018.00781 Dai J, Gu L, Su Y, Wang Q, Zhao Y, Chen X, Deng H, Li W, Wang G, Li K. Inhibition of curcumin on influenza A virus infection and influenzal pneumonia via oxidative stress, TLR2/4, p38/JNK MAPK and NF-κB pathways. Int Immunopharmacol. 2018;54:177‐187. doi:1016/j.intimp.2017.11.009 Maelfait J, Roose K, Bogaert P, Sze M, Saelens X, Pasparakis M, Carpentier I, van Loo G, Beyaert R. A20 (Tnfaip3) deficiency in myeloid cells protects against influenza A virus infection. PLoS Pathog. 2012;8(3):e1002570. doi:1371/journal.ppat.1002570 Salimi V, Ramezani A, Mirzaei H, Tahamtan A, Faghihloo E, Rezaei F, Naseri M, Bont L, Mokhtari-Azad T, Tavakoli-Yaraki M. Evaluation of the expression level of 12/15 lipoxygenase and the related inflammatory factors (CCL5, CCL3) in respiratory syncytial virus infection in mice model. Microb Pathog. 2017;109:209‐213. doi:1016/j.micpath.2017.05.045 Ermers MJ, Janssen R, Onland-Moret NC, Hodemaekers HM, Rovers MM, Houben ML, Kimpen JL, Bont LJ. IL10 family member genes IL19 and IL20 are associated with recurrent wheeze after respiratory syncytial virus bronchiolitis. Pediatr Res. 2011;70(5):518‐523. doi:1203/PDR.0b013e31822f5863 Al-Afif A, Alyazidi R, Oldford SA, Huang YY, King CA, Marr N, Haidl ID, Anderson R, Marshall JS. Respiratory syncytial virus infection of primary human mast cells induces the selective production of type I interferons, CXCL10, and CCL4. J Allergy Clin Immunol. 2015;136(5):1346‐54.e1. doi:1016/j.jaci.2015.01.042 Shi T, He Y, Sun W, Wu Y, Li L, Jie Z, Su X. Respiratory Syncytial virus infection compromises asthma tolerance by recruiting interleukin-17A-producing cells via CCR6-CCL20 signaling [published correction appears in Mol Immunol. 2018 Jan;93:285]. Mol Immunol. 2017;88:45‐57. doi:1016/j.molimm.2017.05.017 Ternette N, Wright C, Kramer HB, Altun M, Kessler BM. Label-free quantitative proteomics reveals regulation of interferon-induced protein with tetratricopeptide repeats 3 (IFIT3) and 5'-3'-exoribonuclease 2 (XRN2) during respiratory syncytial virus infection. Virol J. 2011;8(1):442. doi:1186/1743-422X-8-442 Touzelet O, Broadbent L, Armstrong SD, Aljabr W, Cloutman-Green E, Power UF, Hiscox JA. The Secretome Profiling of a Pediatric Airway Epithelium Infected with hRSV Identified Aberrant Apical/Basolateral Trafficking and Novel Immune Modulating (CXCL6, CXCL16, CSF3) and Antiviral (CEACAM1) Proteins. Mol Cell Proteomics. 2020;19(5):793‐807. doi:1074/mcp.RA119.001546 Inchley CS, Osterholt HC, Sonerud T, Fjærli HO, Nakstad B. Downregulation of IL7R, CCR7, and TLR4 in the cord blood of children with respiratory syncytial virus disease. J Infect Dis. 2013;208(9):1431‐1435. doi:1093/infdis/jit336 Conti P, Ronconi G, Caraffa AL, Gallenga CE, Ross R, Frydas I, Kritas SK. 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. J Biol Regul Homeost Agents. 2020;34(2):1. doi:23812/CONTI-E Wu D, Yang XO. TH17 responses in cytokine storm of COVID-19: An emerging target of JAK2 inhibitor Fedratinib. J Microbiol Immunol Infect. 2020;S1684-1182(20)30065-7. doi:1016/j.jmii.2020.03.005 Oshiumi H, Okamoto M, Fujii K, Kawanishi T, Matsumoto M, Koike S, Seya T. The TLR3/TICAM-1 pathway is mandatory for innate immune responses to poliovirus infection. J Immunol. 2011;187(10):5320‐5327. doi:4049/jimmunol.1101503 Lim JK, Lisco A, McDermott DH, Huynh L, Ward JM, Johnson B, Johnson H, Pape J, Foster GA, Krysztof D, et al. Genetic variation in OAS1 is a risk factor for initial infection with West Nile virus in man. PLoS Pathog. 2009;5(2):e1000321. doi:1371/journal.ppat.1000321 García-Álvarez M, Berenguer J, Jiménez-Sousa MA, Pineda-Tenor D, Aldámiz-Echevarria T, Tejerina F, Diez C, Vázquez-Morón S, Resino S. Mx1, OAS1 and OAS2 polymorphisms are associated with the severity of liver disease in HIV/HCV-coinfected patients: A cross-sectional study. Sci Rep. 2017;7:41516. doi:1038/srep41516 Huang W, Hu K, Luo S, Zhang M, Li C, Jin W, Liu Y, Griffin GE, Shattock RJ, Hu Q. Herpes simplex virus type 2 infection of human epithelial cells induces CXCL9 expression and CD4+ T cell migration via activation of p38-CCAAT/enhancer-binding protein-β pathway. J Immunol. 2012;188(12):6247‐6257. doi:4049/jimmunol.1103706 Ding X, Wang F, Duan M, Yang J, Wang S. Epiregulin as a key molecule to suppress hepatitis B virus propagation in vitro. Arch Virol. 2009;154(1):9‐17. doi:1007/s00705-008-0259-7 Riezu-Boj JI, Larrea E, Aldabe R, Guembe L, Casares N, Galeano E, Echeverria I, Sarobe P, Herrero I, Sangro B, et al. Hepatitis C virus induces the expression of CCL17 and CCL22 chemokines that attract regulatory T cells to the site of infection. J Hepatol. 2011;54(3):422‐431. doi:1016/j.jhep.2010.07.014 Koraka P, Murgue B, Deparis X, van Gorp EC, Setiati TE, Osterhaus AD, Groen J. Elevation of soluble VCAM-1 plasma levels in children with acute dengue virus infection of varying severity. J Med Virol. 2004;72(3):445‐450. doi:1002/jmv.20007 Das A, Dinh PX, Panda D, Pattnaik AK. Interferon-inducible protein IFI35 negatively regulates RIG-I antiviral signaling and supports vesicular stomatitis virus replication. J Virol. 2014;88(6):3103‐3113. doi:1128/JVI.03202-13 Fensterl V, Wetzel JL, Sen GC. Interferon-induced protein Ifit2 protects mice from infection of the peripheral nervous system by vesicular stomatitis virus. J Virol. 2014;88(18):10303‐10311. doi:1128/JVI.01341-14 Berthoux L, Sebastian S, Sokolskaja E, Luban J. Cyclophilin A is required for TRIM5{alpha}-mediated resistance to HIV-1 in Old World monkey cells. Proc Natl Acad Sci U S A. 2005;102(41):14849‐14853. doi:1073/pnas.0505659102 Long X, Li Y, Qi Y, Xu J, Wang Z, Zhang X, Zhang D, Zhang L, Huang J. XAF1 contributes to dengue virus-induced apoptosis in vascular endothelial cells. FASEB J. 2013;27(3):1062‐1073. doi:1096/fj.12-213967 Chen S, Li S, Chen L. Interferon-inducible Protein 6-16 (IFI-6-16, ISG16) promotes Hepatitis C virus replication in vitro. J Med Virol. 2016;88(1):109‐114. doi:1002/jmv.24302 Levy Y, Lacabaratz C, Weiss L, Viard JP, Goujard C, Lelièvre JD, Boué F, Molina JM, Rouzioux C, Avettand-Fénoêl V, et al. Enhanced T cell recovery in HIV-1-infected adults through IL-7 treatment. J Clin Invest. 2009;119(4):997‐1007. doi:1172/JCI38052 Kim YE, Lee JH, Kim ET, Shin HJ, Gu SY, Seol HS, Ling PD, Lee CH, Ahn JH. Human cytomegalovirus infection causes degradation of Sp100 proteins that suppress viral gene expression. J Virol. 2011;85(22):11928‐11937. doi:1128/JVI.00758-11 Pan W, Zuo X, Feng T, Shi X, Dai J. Guanylate-binding protein 1 participates in cellular antiviral response to dengue virus. Virol J. 2012;9:292. doi:1186/1743-422X-9-292 Fukutani ER, Ramos PI, Gonçalves K, Irahe J, Azevedo LG, Rodrigues MM, Lima JV, Junior HF, Fukutani KF, Queiroz AT. Meta-Analysis of HTLV-1-Infected Patients Identifies CD40LG and GBP2 as Markers of ATLL and HAM/TSP Clinical Status: Two Genes Beat as One. Front Genet. 2019;10:1056. doi:3389/fgene.2019.01056 Grusdat M, McIlwain DR, Xu HC, Pozdeev VI, Knievel J, Crome SQ, Robert-Tissot C, Dress RJ, Pandyra AA, Speiser DE, et al. IRF4 and BATF are critical for CD8⁺ T-cell function following infection with LCMV. Cell Death Differ. 2014;21(7):1050‐1060. doi:1038/cdd.2014.19 Khlaiphuengsin A, Panjaworayan T, Thienprasert N, Tangkijvanich P, Posuwan N, Makkoch J, Poovorawan Y, Payungporn S. Human miR-5193 Triggers Gene Silencing in Multiple Genotypes of Hepatitis B Virus. Microrna. 2015;4(2):123‐130. doi:2174/2211536604666150819195743 Manuel O, Wójtowicz A, Bibert S, Mueller NJ, Van Delden C, Hirsch HH, Steiger J, Stern M, Egli A, Garzoni C et al. Influence of IFNL3/4 polymorphisms on the incidence of cytomegalovirus infection after solid-organ transplantation. J Infect Dis. 2015;211(6):906‐914. doi:1093/infdis/jiu557 Malikova J, Zingg T, Fingerhut R, Sluka S, Grössl M, Brixius-Anderko S, Bernhardt R, McDougall J, Pandey AV, Flück CE. HIV Drug Efavirenz Inhibits CYP21A2 Activity with Possible Clinical Implications. Horm Res Paediatr. 2019;91(4):262‐270. doi:1159/000500522 Guha D, Klamar CR, Reinhart T, Ayyavoo V. Transcriptional Regulation of CXCL5 in HIV-1-Infected Macrophages and Its Functional Consequences on CNS Pathology. J Interferon Cytokine Res. 2015;35(5):373‐384. doi:1089/jir.2014.0135 Bertin J, Jalaguier P, Barat C, Roy MA, Tremblay MJ. 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. Virology. 2014;454-455:128‐138. doi:1016/j.virol.2014.02.007 Shao W, Tang J, Song W, Wang C, Li Y, Wilson CM, Kaslow RA. CCL3L1 and CCL4L1: variable gene copy number in adolescents with and without human immunodeficiency virus type 1 (HIV-1) infection. Genes Immun. 2007;8(3):224‐231. doi:1038/sj.gene.6364378 Xie L, Huang Y, Zhong J, Wei H, Chen S, Jiang K, Li S, Qin X. Short Communication: The Association of WNT16 Polymorphisms with the CD4+ T Cell Count in the HIV-Infected Population. AIDS Res Hum Retroviruses. 2020;36(2):119‐121. doi:1089/AID.2019.0038 Juno J, Tuff J, Choi R, Card C, Kimani J, Wachihi C, Koesters-Kiazyk S, Ball TB, Farquhar C, Plummer FA, et al. The role of G protein gene GNB3 C825T polymorphism in HIV-1 acquisition, progression and immune activation. Retrovirology. 2012;9:1. doi:1186/1742-4690-9-1 Oyoshi MK, Beaupré J, Venturelli N, Lewis CN, Iwakura Y, Geha RS. Filaggrin deficiency promotes the dissemination of cutaneously inoculated vaccinia virus. J Allergy Clin Immunol. 2015;135(6):1511‐8.e6. doi:1016/j.jaci.2014.12.1923 Wang X, He Z, Xia T, Li X, Liang D, Lin X, Wen H, Lan K. Latency-associated nuclear antigen of Kaposi sarcoma-associated herpesvirus promotes angiogenesis through targeting notch signaling effector Hey1. Cancer Res. 2014;74(7):2026‐2037. doi:1158/0008-5472.CAN-13-1467 Sanders SP, Siekierski ES, Richards SM, Porter JD, Imani F, Proud D. Rhinovirus infection induces expression of type 2 nitric oxide synthase in human respiratory epithelial cells in vitro and in vivo. J Allergy Clin Immunol. 2001;107(2):235‐243. doi:1067/mai.2001.112028 Bonville CA, Lau VK, DeLeon JM, Gao JL, Easton AJ, Rosenberg HF, Domachowske JB. Functional antagonism of chemokine receptor CCR1 reduces mortality in acute pneumovirus infection in vivo. J Virol. 2004;78(15):7984‐7989. doi:1128/JVI.78.15.7984-7989.2004 Liu H, Yang X, Zhang ZK, Zou WC, Wang HN. miR-146a-5p promotes replication of infectious bronchitis virus by targeting IRAK2 and TNFRSF18. Microb Pathog. 2018;120:32‐36. doi:1016/j.micpath.2018.04.046 Wheeler LA, Trifonova RT, Vrbanac V, Barteneva NS, Liu X, Bollman B, Onofrey L, Mulik S, Ranjbar S, Luster AD, et al. TREX1 Knockdown Induces an Interferon Response to HIV that Delays Viral Infection in Humanized Mice. Cell Rep. 2016;15(8):1715‐1727. doi:1016/j.celrep.2016.04.048 O’Brien TR, Pfeiffer RM, Paquin A, Kuhs KA, Chen S, Bonkovsky HL, Edlin BR, Howell CD, Kirk GD, Kuniholm MH, et al. Comparison of functional variants in IFNL4 and IFNL3 for association with HCV clearance. J Hepatol. 2015;63(5):1103‐1110. doi:1016/j.jhep.2015.06.035 Libraty DH, Zhang L, Obcena A, Brion JD, Capeding RZ. Circulating levels of soluble MICB in infants with symptomatic primary dengue virus infections. PLoS One. 2014;9(5):e98509. doi:1371/journal.pone.0098509 Zenner HL, Yoshimura S, Barr FA, Crump CM. Analysis of Rab GTPase-activating proteins indicates that Rab1a/b and Rab43 are important for herpes simplex virus 1 secondary envelopment. J Virol. 2011;85(16):8012‐8021. doi:1128/JVI.00500-11 Estrella MM, Li M, Tin A, Abraham AG, Shlipak MG, Penugonda S, Hussain SK, Palella Jr FJ, Wolinsky SM, Martinson JJ, et al. The association between APOL1 risk alleles and longitudinal kidney function differs by HIV viral suppression status. Clin Infect Dis. 2015;60(4):646‐652. doi:1093/cid/ciu765 Orzalli MH, Broekema NM, Diner BA, Hancks DC, Elde NC, Cristea IM, Knipe DM. cGAS-mediated stabilization of IFI16 promotes innate signaling during herpes simplex virus infection. Proc Natl Acad Sci U S A. 2015;112(14):E1773‐E1781. doi:1073/pnas.1424637112 Kim EY, Lorenzo-Redondo R, Little SJ, Chung YS, Phalora PK, Berry IM, Archer J, Penugonda S, Fischer W, Richman DD, et al. Human APOBEC3 induced mutation of human immunodeficiency virus type-1 contributes to adaptation and evolution in natural infection. PLoS Pathog. 2014;10(7):e1004281. doi:1371/journal.ppat.1004281 O’Connell P, Pepelyayeva Y, Blake MK, Hyslop S, Crawford RB, Rizzo MD, Pereira-Hicks C, Godbehere S, Dale L, Gulick P, et al. SLAMF7 Is a Critical Negative Regulator of IFN-α-Mediated CXCL10 Production in Chronic HIV Infection. J Immunol. 2019;202(1):228‐238. doi:4049/jimmunol.1800847 Chen J, Wang N, Dong M, Guo M, Zhao Y, Zhuo Z, Zhang C, Chi X, Pan Y, Jiang J, et al. The Metabolic Regulator Histone Deacetylase 9 Contributes to Glucose Homeostasis Abnormality Induced by Hepatitis C Virus Infection. Diabetes. 2015;64(12):4088‐4098. doi:2337/db15-0197 Berger G, Durand S, Fargier G, Nguyen XN, Cordeil S, Bouaziz S, Muriaux D, Darlix JL, Cimarelli A. APOBEC3A is a specific inhibitor of the early phases of HIV-1 infection in myeloid cells. PLoS Pathog. 2011;7(9):e1002221. doi:1371/journal.ppat.1002221 Sanfilippo C, Cambria D, Longo A, Palumbo M, Avola R, Pinzone M, Nunnari G, Condorelli F, Musumeci G, Imbesi R, et al. SERPING1 mRNA overexpression in monocytes from HIV+ patients. Inflamm Res. 2017;66(12):1107‐1116. doi:1007/s00011-017-1091-x Soundravally R, Hoti SL. Significance of transporter associated with antigen processing 2 (TAP2) gene polymorphisms in susceptibility to dengue viral infection. J Clin Immunol. 2008;28(3):256‐262. doi:1007/s10875-007-9154-3 Tian X, Zhang A, Qiu C, Wang W, Yang Y, Qiu C, Liu A, Zhu L, Yuan S, Hu H, et al. The upregulation of LAG-3 on T cells defines a subpopulation with functional exhaustion and correlates with disease progression in HIV-infected subjects. J Immunol. 2015;194(8):3873‐3882. doi:4049/jimmunol.1402176 Waisner H, Kalamvoki M. The ICP0 Protein of Herpes Simplex Virus 1 (HSV-1) Downregulates Major Autophagy Adaptor Proteins Sequestosome 1 and Optineurin during the Early Stages of HSV-1 Infection. J Virol. 2019;93(21):e01258-19. doi:1128/JVI.01258-19 McGuinness PH, Painter D, Davies S, McCaughan GW. Increases in intrahepatic CD68 positive cells, MAC387 positive cells, and proinflammatory cytokines (particularly interleukin 18) in chronic hepatitis C infection. Gut. 2000;46(2):260‐269. doi:1136/gut.46.2.260 Madani N, Millette R, Platt EJ, Marin M, Kozak SL, Bloch DB, Kabat D. Implication of the lymphocyte-specific nuclear body protein Sp140 in an innate response to human immunodeficiency virus type 1. J Virol. 2002;76(21):11133‐11138. doi:1128/jvi.76.21.11133-11138.2002 Nasi M, Riva A, Borghi V, D’Amico R, Del Giovane C, Casoli C, Galli M, Vicenzi E, Gibellini L, De Biasi S, et al. Novel genetic association of TNF-α-238 and PDCD1-7209 polymorphisms with long-term non-progressive HIV-1 infection. Int J Infect Dis. 2013;17(10):e845‐e850. doi:1016/j.ijid.2013.01.003 Tse D, Armstrong DA, Oppenheim A, Kuksin D, Norkin L, Stan RV. Plasmalemmal vesicle associated protein (PV1) modulates SV40 virus infectivity in CV-1 cells. Biochem Biophys Res Commun. 2011;412(2):220‐225. doi:1016/j.bbrc.2011.07.063 Fahrbach KM, Barry SM, Ayehunie S, Lamore S, Klausner M, Hope TJ. Activated CD34-derived Langerhans cells mediate transinfection with human immunodeficiency virus. J Virol. 2007;81(13):6858‐6868. doi:1128/JVI.02472-06 Benito JM, López M, Lozano S, Martinez P, González-Lahoz J, Soriano V. CD38 expression on CD8 T lymphocytes as a marker of residual virus replication in chronically HIV-infected patients receiving antiretroviral therapy. AIDS Res Hum Retroviruses. 2004;20(2):227‐233. doi:1089/08892220477300495 Yong YK, Tan HY, Saeidi A, Rosmawati M, Atiya N, Ansari AW, Rajarajeswaran J, Vadivelu J, Velu V, Larsson M, et al. Decrease of CD69 levels on TCR Vα7.2+CD4+ innate-like lymphocytes is associated with impaired cytotoxic functions in chronic hepatitis B virus-infected patients. Innate Immun. 2017;23(5):459‐467. doi:1177/1753425917714854 Pineda-Tenor D, Micheloud D, Berenguer J, Jiménez-Sousa MA, Fernández-Rodríguez A, García-Broncano P, Guzmán-Fulgencio M, Diez C, Bellón JM, Carrero A, Aldámiz-Echevarria T. SLC30A8 rs13266634 polymorphism is related to a favorable cardiometabolic lipid profile in HIV/hepatitis C virus-coinfected patients. AIDS. 2014;28(9):1325‐1332. doi:1097/QAD.0000000000000215 Shichi D, Kikkawa EF, Ota M, Katsuyama Y, Kimura A, Matsumori A, Kulski JK, Naruse TK, Inoko H. The haplotype block, NFKBIL1-ATP6V1G2-BAT1-MICB-MICA, within the class III-class I boundary region of the human major histocompatibility complex may control susceptibility to hepatitis C virus-associated dilated cardiomyopathy. Tissue Antigens. 2005;66(3):200‐208. doi:1111/j.1399-0039.2005.00457.x Pauli EK, Schmolke M, Hofmann H, Ehrhardt C, Flory E, Münk C, Ludwig S. High level expression of the anti-retroviral protein APOBEC3G is induced by influenza A virus but does not confer antiviral activity. Retrovirology. 2009;6:38. doi:1186/1742-4690-6-38 Ma GF, Miettinen S, Porola P, Hedman K, Salo J, Konttinen YT. Human parainfluenza virus type 2 (HPIV2) induced host ADAM8 expression in human salivary adenocarcinoma cell line (HSY) during cell fusion. BMC Microbiol. 2009;9:55. doi:1186/1471-2180-9-55 Zhang T, Yin C, Boyd DF, Quarato G, Ingram JP, Shubina M, Ragan KB, Ishizuka T, Crawford JC, Tummers B et al. Influenza Virus Z-RNAs Induce ZBP1-Mediated Necroptosis. Cell. 2020;180(6):1115‐1129.e13. doi:1016/j.cell.2020.02.050 Ranjan P, Singh N, Kumar A, Neerincx A, Kremmer E, Cao W, Davis WG, Katz JM, Gangappa S, Lin R, et al. NLRC5 interacts with RIG-I to induce a robust antiviral response against influenza virus infection. Eur J Immunol. 2015;45(3):758‐772. doi:1002/eji.201344412 Zhang H, Luo J, Alcorn JF, Chen K, Fan S, Pilewski J, Liu A, Chen W, Kolls JK, Wang J. AIM2 Inflammasome Is Critical for Influenza-Induced Lung Injury and Mortality. J Immunol. 2017;198(11):4383‐4393. doi:4049/jimmunol.1600714 Kim BJ, Cho SW, Jeon YJ, An S, Jo A, Lim JH, Kim DY, Won TB, Han DH, Rhee CS, et al. Intranasal delivery of Duox2 DNA using cationic polymer can prevent acute influenza A viral infection in vivo lung. Appl Microbiol Biotechnol. 2018;102(1):105‐115. doi:1007/s00253-017-8512-1 Selemidis S, Seow HJ, Broughton BR, Vinh A, Bozinovski S, Sobey CG, Drummond GR, Vlahos R. Nox1 oxidase suppresses influenza a virus-induced lung inflammation and oxidative stress. PLoS One. 2013;8(4):e60792. doi:1371/journal.pone.0060792 Fox JM, Crabtree JM, Sage LK, Tompkins SM, Tripp RA. Interferon Lambda Upregulates IDO1 Expression in Respiratory Epithelial Cells After Influenza Virus Infection. J Interferon Cytokine Res. 2015;35(7):554‐562. doi:1089/jir.2014.0052 Ye S, Cowled CJ, Yap CH, Stambas J. Deep sequencing of primary human lung epithelial cells challenged with H5N1 influenza virus reveals a proviral role for CEACAM1. Sci Rep. 2018;8(1):15468. doi:1038/s41598-018-33605-6 Job ER, Bottazzi B, Short KR, Deng YM, Mantovani A, Brooks AG, Reading PC. A single amino acid substitution in the hemagglutinin of H3N2 subtype influenza A viruses is associated with resistance to the long pentraxin PTX3 and enhanced virulence in mice. J Immunol. 2014;192(1):271‐281. doi:4049/jimmunol.1301814 Asp L, Holtze M, Powell SB, Karlsson H, Erhardt S. Neonatal infection with neurotropic influenza A virus induces the kynurenine pathway in early life and disrupts sensorimotor gating in adult Tap1-/- mice. Int J Neuropsychopharmacol. 2010;13(4):475‐485. doi:1017/S1461145709990253 Wang G, Jiang L, Wang J, Zhang J, Kong F, Li Q, Yan Y, Huang S, Zhao Y, Liang L, et al. The G Protein-Coupled Receptor FFAR2 Promotes Internalization during Influenza A Virus Entry. J Virol. 2020;94(2):e01707-19. doi:1128/JVI.01707-19 Mayank AK, Sharma S, Nailwal H, Lal SK. Nucleoprotein of influenza A virus negatively impacts antiapoptotic protein API5 to enhance E2F1-dependent apoptosis and virus replication. Cell Death Dis. 2015;6(12):e2018. doi:1038/cddis.2015.360 Chen X, Zhang Q, Bai J, Zhao Y, Wang X, Wang H, Jiang P. 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. J Virol. 2017;91(18):e00986-17. doi:1128/JVI.00986-17 Hoffmann M, Kleine-Weber H, Schroeder S, Krüger N, Herrler T, Erichsen S, Schiergens TS, Herrler G, Wu NH, Nitsche A, et al. SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor. Cell. 2020;181(2):271‐280.e8. doi:1016/j.cell.2020.02.052 Cheng W, Chen S, Li R, Chen Y, Wang M, Guo D. Severe acute respiratory syndrome coronavirus protein 6 mediates ubiquitin-dependent proteosomal degradation of N-Myc (and STAT) interactor. Virol Sin. 2015;30(2):153‐161. doi:1007/s12250-015-3581-8 Seyerl M, Kirchberger S, Majdic O, Seipelt J, Jindra C, Schrauf C, Stöckl J. Human rhinoviruses induce IL-35-producing Treg via induction of B7-H1 (CD274) and sialoadhesin (CD169) on DC. Eur J Immunol. 2010;40(2):321‐329. doi:1002/eji.200939527 Fusco DN, Pratt H, Kandilas S, Cheon SS, Lin W, Cronkite DA, Basavappa M, Jeffrey KL, Anselmo A, Sadreyev R, et al. Fusco DN, Pratt H, Kandilas S, et al. HELZ2 Is an IFN Effector Mediating Suppression of Dengue Virus. Front Microbiol. 2017;8:240. doi:3389/fmicb.2017.00240 Desai P, Tahiliani V, Abboud G, Stanfield J, Salek-Ardakani S. Batf3-Dependent Dendritic Cells Promote Optimal CD8 T Cell Responses against Respiratory Poxvirus Infection. J Virol. 2018;92(16):e00495-18. Published 2018 Jul 31. doi:1128/JVI.00495-18 Uckun FM, Chelstrom LM, Tuel-Ahlgren L, Dibirdik I, Irvin JD, Langlie MC, Myers DE. TXU (anti-CD7)-pokeweed antiviral protein as a potent inhibitor of human immunodeficiency virus. Antimicrob Agents Chemother. 1998;42(2):383‐388. Purdy JG, Shenk T, Rabinowitz JD. Fatty acid elongase 7 catalyzes lipidome remodeling essential for human cytomegalovirus replication. Cell Rep. 2015;10(8):1375‐1385. doi:1016/j.celrep.2015.02.003 Tables Due to technical limitations, Tables 1-8 are only available as a download in the supplementary files section. Supplementary Files Tables.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-133291","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":6857240,"identity":"43832bae-8265-4757-8e87-9ac87d731906","order_by":0,"name":"Basavaraj Vastrad","email":"","orcid":"https://orcid.org/0000-0003-2202-7637","institution":"Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India.","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Basavaraj","middleName":"","lastName":"Vastrad","suffix":""},{"id":6857241,"identity":"33b14dff-ac86-4816-9857-6806d29f24bf","order_by":1,"name":"Chanabasayya Vastrad ","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYDACCRQemw2QYGw8QKQWZpCWNJCWBpK0HAYz8Wrhn9387HFhG0Nif//5Yx9+lJ23W9t+GGhLjU00TkvuHDM3ngnUMuNGMvPMnnO3k7edSQRqOZaW24BDi4FEgpk0bxuDMcMNZmYG3rbbyWYHgFoYGw7j0ZL+DaxF/vxhZsa/beeSzc4/JKQlB2yLnMGBZGZm3rYDdmY3CNgicSOnTJrnnISc4Y1kY2aZc8kJZjeAtiTg8Qv/jPRt0jxlNjxy5w8+ZnxTZmdvdj794YMPNTY4tcAsg7MSwSoT8CtHBfakKB4Fo2AUjIKRAQDSZlsK1y3XFwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-3615-4450","institution":"Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. ","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Chanabasayya","middleName":"","lastName":"Vastrad","suffix":""},{"id":6857242,"identity":"328e2fba-ef50-4539-ad58-e4f3940bb67c","order_by":2,"name":"Iranna Kotturshetti ","email":"","orcid":"https://orcid.org/0000-0003-1988-7345","institution":"Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron, Karnataka 562209, India.","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Iranna","middleName":"","lastName":"Kotturshetti","suffix":""}],"badges":[],"createdAt":"2020-12-21 14:40:37","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-133291/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-133291/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":4431180,"identity":"7c8535a7-6202-42f2-b326-ef6918d1eca4","added_by":"auto","created_at":"2020-12-22 00:16:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":75678,"visible":true,"origin":"","legend":"Box plots of the normalized data. (A) 30 non traded SARS-CoV-2 infection samples (B) 30 remdesivir traded SARS-CoV-2 infection 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. (A1 – A30 = non traded SARS-CoV-2 infection samples (blue color box); B1 – B30 = remdesivir traded SARS-CoV-2 infection samples (green color box))","description":"","filename":"F1.png","url":"https://assets-eu.researchsquare.com/files/rs-133291/v1/c8a8f8ea47f6bb1e352983d3.png"},{"id":4431024,"identity":"667874dc-85a3-4dec-b95e-5be8a6bcf3c3","added_by":"auto","created_at":"2020-12-22 00:10:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":234230,"visible":true,"origin":"","legend":"Volcano plot of differentially expressed genes. Genes with a significant change of more than two-fold were selected. Green dot represented up regulated significant genes and red dot represented down regulated significant genes","description":"","filename":"F2.png","url":"https://assets-eu.researchsquare.com/files/rs-133291/v1/eb3e9cfd18828901ad8be9c0.png"},{"id":4431026,"identity":"4b301046-5ea4-4a95-905e-3370495f722f","added_by":"auto","created_at":"2020-12-22 00:10:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":167652,"visible":true,"origin":"","legend":"Heat map of up regulated differentially expressed genes. Legend on the top left indicate log fold change of genes. (A1 – A30 = non traded SARS-CoV-2 infection samples (blue color box); B1 – B30 = remdesivir traded SARS-CoV-2 infection samples (green color box))","description":"","filename":"F3.png","url":"https://assets-eu.researchsquare.com/files/rs-133291/v1/5c5c6d56dabf1fcf862ddbc8.png"},{"id":4431025,"identity":"6c67c956-52e7-4d55-8113-246b81b9754b","added_by":"auto","created_at":"2020-12-22 00:10:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":207638,"visible":true,"origin":"","legend":"Heat map of down regulated differentially expressed genes. Legend on the top left indicate log fold change of genes. (A1 – A30 = non traded SARS-CoV-2 infection samples (blue color box); B1 – B30 = remdesivir traded SARS-CoV-2 infection samples (green color box))","description":"","filename":"F4.png","url":"https://assets-eu.researchsquare.com/files/rs-133291/v1/1450cd56ac68901249029f5c.png"},{"id":4431148,"identity":"b8358e99-7834-484f-9d12-028e2f12e3d6","added_by":"auto","created_at":"2020-12-22 00:13:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":802993,"visible":true,"origin":"","legend":"Protein–protein interaction network of up regulated genes. Green nodes denotes up regulated genes.","description":"","filename":"F5.png","url":"https://assets-eu.researchsquare.com/files/rs-133291/v1/377a5389576932766f8f059c.png"},{"id":4431029,"identity":"bc74775d-9394-481b-b81d-e63c2fbc8845","added_by":"auto","created_at":"2020-12-22 00:10:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":218186,"visible":true,"origin":"","legend":"Scatter plot for up regulated genes. (A- Node degree; B- Betweenness centrality; C- Stress centrality ; D-Closeness centrality; E- Clustering coefficient)","description":"","filename":"F6.png","url":"https://assets-eu.researchsquare.com/files/rs-133291/v1/7202b62ab144887d601f618d.png"},{"id":4431152,"identity":"6468dc69-f734-42a7-81d6-9cbccd32f88b","added_by":"auto","created_at":"2020-12-22 00:13:53","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":622890,"visible":true,"origin":"","legend":"Protein–protein interaction network of down regulated genes. Red nodes denotes down regulated genes","description":"","filename":"F7.png","url":"https://assets-eu.researchsquare.com/files/rs-133291/v1/3bb39bbc04b2549fcff499cf.png"},{"id":4431146,"identity":"ba6fc36d-4dde-4fa9-aac6-9f91106915b8","added_by":"auto","created_at":"2020-12-22 00:13:53","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":224469,"visible":true,"origin":"","legend":"Scatter plot for down regulated genes. (A- Node degree; B- Betweenness centrality; C- Stress centrality ; D-Closeness centrality; E- Clustering coefficient)","description":"","filename":"F8.png","url":"https://assets-eu.researchsquare.com/files/rs-133291/v1/76c931d900d89c5395baf9a9.png"},{"id":4431033,"identity":"83f7eb35-1e50-4050-9fa7-23d62ea1b16c","added_by":"auto","created_at":"2020-12-22 00:10:53","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":381459,"visible":true,"origin":"","legend":"Modules in PPI network. The green nodes denote the up regulated genes","description":"","filename":"F9.png","url":"https://assets-eu.researchsquare.com/files/rs-133291/v1/74f734c4d4574f8238f392a3.png"},{"id":4431037,"identity":"4f50cb01-bae5-4617-9c21-ebbef9712eb6","added_by":"auto","created_at":"2020-12-22 00:10:54","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":435652,"visible":true,"origin":"","legend":"Modules in PPI network. The red nodes denote the down regulated genes","description":"","filename":"F10.png","url":"https://assets-eu.researchsquare.com/files/rs-133291/v1/80cc1f498182af920c8e2c7f.png"},{"id":4431035,"identity":"f5b72249-adce-4b07-a122-2c14811e8ae6","added_by":"auto","created_at":"2020-12-22 00:10:53","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":757713,"visible":true,"origin":"","legend":"The network of up regulated genes and their related miRNAs. The green circles nodes are the up regulated genes, and yellow diamond nodes are the miRNAs","description":"","filename":"F11.png","url":"https://assets-eu.researchsquare.com/files/rs-133291/v1/b18b95f2c02bc05b71c20096.png"},{"id":4431038,"identity":"16944999-155f-4e69-ba2f-1ac247ca38bb","added_by":"auto","created_at":"2020-12-22 00:10:54","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":696101,"visible":true,"origin":"","legend":"The network of down regulated genes and their related miRNAs. The red circles nodes are the down regulated genes, and blue diamond nodes are the miRNAs","description":"","filename":"F12.png","url":"https://assets-eu.researchsquare.com/files/rs-133291/v1/7bde3000f124421dc8ca699f.png"},{"id":4431039,"identity":"49ba4da4-152e-449c-be02-af0dbb642a3c","added_by":"auto","created_at":"2020-12-22 00:10:54","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":918295,"visible":true,"origin":"","legend":"The network of up regulated genes and their related TFs. The green circles nodes are the up regulated genes, and purple triangle nodes are the TFs","description":"","filename":"F13.png","url":"https://assets-eu.researchsquare.com/files/rs-133291/v1/93756cb09840569bf1eceba0.png"},{"id":4431151,"identity":"64bc2128-7c6b-4a1c-aa12-d67da7a2273d","added_by":"auto","created_at":"2020-12-22 00:13:53","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":951424,"visible":true,"origin":"","legend":"The network of down regulated genes and their related TFs. The green circles nodes are the down regulated genes, and blue triangle nodes are the TFs.","description":"","filename":"F14.png","url":"https://assets-eu.researchsquare.com/files/rs-133291/v1/608cc777b22a6025511d419e.png"},{"id":4431150,"identity":"3b6042b1-17a0-4d27-8601-0d98c9852f35","added_by":"auto","created_at":"2020-12-22 00:13:53","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":105280,"visible":true,"origin":"","legend":"ROC curve validated the sensitivity, specificity of hub genes as a predictive biomarker for SARS-CoV-2 infection. A) CIITA B) HSPA6 C) MYD88 D) SOCS3 E) TNFRSF10A F) ADH1A G) CACNA2D2 H) DUSP9 I) FMO5 J) PDE1A","description":"","filename":"F15.png","url":"https://assets-eu.researchsquare.com/files/rs-133291/v1/1d3f2bac870207336649f47c.png"},{"id":13638940,"identity":"71c5bbb7-ac10-43a2-8300-cdbfee346121","added_by":"auto","created_at":"2021-09-17 08:53:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5621365,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-133291/v1/25657371-403b-42be-a94d-176e4b25ab64.pdf"},{"id":4431181,"identity":"4a7b425c-dbda-4807-9255-8d91318219e4","added_by":"auto","created_at":"2020-12-22 00:16:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":353092,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-133291/v1/9adb1320b57df8ae7cfbeae2.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eIntegrated bioinformatics analysis for the screening of hub genes and therapeutic drugs in severe acute respiratory syndrome corona virus 2 infection/COVID 19\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAt the December of 2019, a novel corona virus, called severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) or novel corona virus 2019 (2019-nCoV) is a single-stranded RNA, nonsegmented, enveloped viruses, resulted fast spreading from its origin in China to the rest of the globe [1]. Symptoms of this viral infection ranging in severity from the common cold to severe illness, and finally lead to death.\u0026nbsp; Despite the fact that great progress has been made in antivirals and vaccination for this SARS-CoV-2 infection, survival rates is less. Since the precise molecular pathogenesis of SARS-CoV-2 infection (virus replication and dissemination) remains unknown, it is extremely essential to examine molecular pathogenesis and to develop effective therapeutic strategies in SARS-CoV-2 infection and to control the disease [2].\u003c/p\u003e\n\u003cp\u003eExpression profiling by high throughput sequencing is very essential to understand the molecular pathogenesis of viral infection and also to the advancement of novel antivirals drugs and vaccines for the novel viral infections [3]. With the rapid advancement of bioinformatics such as microarray technology, some high throughput platforms for analysis of gene expression are commonly used to find the differentially expressed genes (DEGs) during viral infections [4]. \u0026nbsp;We rationally presume that differentially expressed genes (DEGs) can affect the promotion of a various viral infections. Now, through Expression profiling by high throughput sequencing investigation using microarray technology, more and more DEGs were linked with SARS-CoV-2 infection and understanding its biological characteristics is essential in improving clinical treatment outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the current investigation, we downloaded the RNA-seq data GSE149273 from the Gene Expression Omnibus database and conducted a bioinformatics analysis to study the differentially expressed genes (DEGs) between remdesivir \u0026nbsp;traded SARS-CoV-2 infection samples and non treated SARS-CoV-2 infection samples. We performed function and pathways analyses, as well as protein\u0026ndash;protein interaction (PPI) network analysis, modules analysis, target gene - miRNA regulatory network, and target gene - TF regulatory network and diagnostic values associated with hub genes was also assessed with the receiver‐operating characteristic (ROC) analyses. []\u003c/p\u003e"},{"header":"Material And Methods","content":"\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eMicroarray data and preprocessing\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eExpression profiling by high throughput sequencing GSE149273 based on GPL21290 Illumina HiSeq 3000 (Homo sapiens) Array was downloaded from the NCBI GEO [National Center for Biotechnology Information, Gene Expression Omnibus database] (http://www.ncbi.nlm.nih.gov/geo/) a public depository database of gene expression data [5]. GSE149273 contains 60 samples, including 30 remdesivir traded SARS-CoV-2 infection samples and 30 non treated SARS-CoV-2 infection samples. The downloaded raw data were preprocessed, including background adjustment and normalization using limma package of R software (version 3.10.3, https://bioconduct or.org/packa ges/relea se/bioc/html/limma.html) [6]. [ ]\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eScreening of the DEGs \u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eFor the expression profiling by high throughput sequencing dataset,\u0026nbsp; the R package limma was applied for performing the differential analysis between 30 remdesivir traded SARS-CoV-2 infection samples and \u0026nbsp;non treated SARS-CoV-2 infection samples. The p-values were adjusted by Benjamini \u0026amp; Hochberg method [7] . Based on the |log fold change (FC)| values and the p-values, the differentially expressed genes (DEGs; thresholds: |logFC| \u0026gt; 1.3 for up regulated genes and |logFC| \u0026lt; - 1.3 for down regulated genes , adjusted p \u0026lt; 0.05).\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003ePathway enrichment analysis for DEGs\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eTo analyze the functions of DEGs, BIOCYC (\u003ca style=\"color: #000000;\" href=\"https://biocyc.org/)8\"\u003ehttps://biocyc.org/)\u003c/a\u003e [8], Kyoto Encyclopedia of Genes and Genomes (KEGG) (http://www.genome.jp/kegg/pathway.html) [9], Pathway Interaction Database (PID) \u0026nbsp;\u0026nbsp;\u0026nbsp;(\u003ca style=\"color: #000000;\" href=\"https://wiki.nci.nih.gov/pages/viewpage.action?pageId=315491760)10\"\u003ehttps://wiki.nci.nih.gov/pages/viewpage.action?pageId=315491760)\u003c/a\u003e\u003csup\u003e\u0026nbsp; \u003c/sup\u003e[10], REACTOME (\u003ca style=\"color: #000000;\" href=\"https://reactome.org/\"\u003ehttps://reactome.org/\u003c/a\u003e) [11] , GenMAPP (\u003ca style=\"color: #000000;\" href=\"http://www.genmapp.org/\"\u003ehttp://www.genmapp.org/\u003c/a\u003e) [12] ,\u0026nbsp;\u0026nbsp; MSigDB C2 BIOCARTA (\u003ca style=\"color: #000000;\" href=\"http://software.broadinstitute.org/gsea/msigdb/collections.jsp)13\"\u003ehttp://software.broadinstitute.org/gsea/msigdb/collections.jsp)\u003c/a\u003e\u003csup\u003e\u0026nbsp; \u003c/sup\u003e[13], PantherDB (http://www.pantherdb.org/) [14], Pathway Ontology (http://www.obofoundry.org/ontology/pw.html) [15] and Small Molecule Pathway Database (SMPDB) (http://smpdb.ca/) [16] pathway analysis were carried out by using the ToppGene (ToppFun)\u0026nbsp; (https://toppgene.cchmc.org/enrichment.jsp) [17] online tool. P\u0026lt;.05 was set as the cut-off point.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eGene ontology (GO) enrichment analysis for DEGs\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eThe ToppGene (ToppFun) (https://toppgene.cchmc.org/enrichment.jsp) [17] \u0026nbsp;was used to study GO enrichment analyses of DEGs. The ToppGene online tool for GO analysis (http://www.geneontology.org) [18] was used to complete the function of DEGs. Data from biological processes (BP), cellular components (CC) and molecular functions (MF) were documented from each set of genes. A p \u0026lt; 0.05 was considered statistically significant for all analyses.\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003ePPI network construction and module analysis\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eThe IMEX: The International Molecular Exchange Consortium \u0026nbsp;(https://www.imexconsortium.org/) [19] is a biological database designed for predicting PPI networks and integrated with PPI data bases such as Database of Interacting Proteins (DIP) (http://dip.doe-mbi.ucla.edu/dip/Main.cgi) [20], IntAct Molecular Interaction Database (\u003ca style=\"color: #000000;\" href=\"https://www.ebi.ac.uk/intact/\"\u003ehttps://www.ebi.ac.uk/intact/\u003c/a\u003e) [21], the Molecular INTeraction database (MINT) (https://mint.bio.uniroma2.it/) [22], InnateDB (\u003ca style=\"color: #000000;\" href=\"https://www.innatedb.com/\"\u003ehttps://www.innatedb.com/\u003c/a\u003e) [23], Human Protein Reference Database (HPRD) (\u003ca style=\"color: #000000;\" href=\"http://www.hprd.org/\"\u003ehttp://www.hprd.org/\u003c/a\u003e)\u003csup\u003e24\u003c/sup\u003e, (BioGRID) (\u003ca style=\"color: #000000;\" href=\"https://thebiogrid.org/\"\u003ehttps://thebiogrid.org/\u003c/a\u003e) [25], IID (Integrated Interactions Database) from a well‑known online server (http://iid.ophid.utoronto.ca) [26] and MatrixDB (\u003ca style=\"color: #000000;\" href=\"http://matrixdb.univ-lyon1.fr/\"\u003ehttp://matrixdb.univ-lyon1.fr/\u003c/a\u003e) [27]. Cytoscape (\u003ca style=\"color: #000000;\" href=\"http://www.cytoscape.org/\"\u003ehttp://www.cytoscape.org/\u003c/a\u003e, version 3.8.0) [28], an open software, was used to visualize the PPI networks. The top genes with the highest node degree [29], betweenness centrality [30], stress centrality [31], closeness centrality [32] and lowest clustering coefficient [33] were considered as hub genes based on the analysis using Network Analyzer from Cytoscape. PEWCC1 (http://apps.cytoscape.org/apps/PEWCC1) [34], a plug-in of Cytoscape, can screen a significant module from the PPI network.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eConstruction of target genes - miRNA regulatory network\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eThe miRNet database (\u003ca style=\"color: #000000;\" href=\"https://www.mirnet.ca/\"\u003ehttps://www.mirnet.ca/\u003c/a\u003e) [35] is the biggest collection of predicted and experimentally verified target gene - miRNA interactions using 10 algorithms such as TarBase (http://diana.imis.athena-innovation.gr/DianaTools/index.php?r=tarbase/index) [36], miRTarBase (http://mirtarbase.mbc.nctu.edu.tw/php/download.php) [37], miRecords (\u003ca style=\"color: #000000;\" href=\"http://miRecords.umn.edu/miRecords\"\u003ehttp://miRecords.umn.edu/miRecords\u003c/a\u003e) [38], miR2Disease (http://www.mir2disease.org/) [39], HMDD (\u003ca style=\"color: #000000;\" href=\"http://www.cuilab.cn/hmdd\"\u003ehttp://www.cuilab.cn/hmdd\u003c/a\u003e) [40], PhenomiR (\u003ca style=\"color: #000000;\" href=\"http://mips.helmholtz-muenchen.de/phenomir/\"\u003ehttp://mips.helmholtz-muenchen.de/phenomir/\u003c/a\u003e) [41], SM2miR (http://bioinfo.hrbmu.edu.cn/SM2miR/) [42], PharmacomiR (\u003ca style=\"color: #000000;\" href=\"http://www.pharmaco-mir.org/\"\u003ehttp://www.pharmaco-mir.org/\u003c/a\u003e) [43], EpimiR (\u003ca style=\"color: #000000;\" href=\"http://bioinfo.hrbmu.edu.cn/EpimiR/\"\u003ehttp://bioinfo.hrbmu.edu.cn/EpimiR/\u003c/a\u003e) [44] and starBase (\u003ca style=\"color: #000000;\" href=\"http://starbase.sysu.edu.cn/\"\u003ehttp://starbase.sysu.edu.cn/\u003c/a\u003e) [45]. Target genes - miRNA regulatory network among up and down regulated genes was constructed by Cytoscape (http://cytoscape.org/) [28].\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eConstruction of target genes - TF regulatory network\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eThe NetworkAnalyst database (https://www.networkanalyst.ca/) [46] is the biggest collection of predicted and experimentally verified target gene - TF interactions using JASPAR (http://jaspar.genereg.net/)\u003csup\u003e47\u003c/sup\u003e database. Target genes - TF regulatory network among up and down regulated genes was constructed by Cytoscape (http://cytoscape.org/) [28].\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eValidation of hub genes\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eIn order to identify the diagnostic value of up and down regulated hub genes in SARS-CoV-2 infection, pROC package [48] in R language for illustrate receiver operating characteristic (ROC) curves was used in this investigation and area under the curve (AUC) of ROC curves was determined to check the act of each up and down regulated hub genes. When AUC value was greater than 0.6, the up and down regulated hub genes was treated able of distinguishing remdesivir traded SARS-CoV-2 infection samples and \u0026nbsp;non treated SARS-CoV-2 infection samples. The diagnostic value of up and down regulated hub genes in GSE149273 dataset was estimated in our research work.\u003c/span\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003ePreprocessing and Screening of the DEGs\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eAfter preprocessing (Fig. 1A and \u0026nbsp;Fig. 1B), a total of 909 DEGs (453 up regulated genes and 457 down regulated genes) were identified between remdesivir traded SARS-CoV-2 infection and \u0026nbsp;non treated SARS-CoV-2 infection (|logFC| \u0026gt; 1.3 for up regulated genes and |logFC| \u0026lt; - 1.3 for down regulated genes , adjusted p \u0026lt; 0.05) and volcano plots showing the results of differential analysis is given in Fig 2. The up regulated genes and down regulated genes are listed in Table 1. Heatmaps as shown in Fig. 3 and Fig. 4, respectively, indicated that these up and down regulated genes were good in distinguishing remdesivir traded SARS-CoV-2 infection samples and non treated SARS-CoV-2 infection samples.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003ePathway enrichment analysis for DEGs\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eTo further understand the function and mechanism of the identified up and down regulated genes, pathway enrichment analyses were performed using the ToppGene web tool.\u0026nbsp; The main pathways that were particularly enriched by up regulated genes were pyrimidine deoxyribonucleosides degradation, tryptophan degradation to 2-amino-3-carboxymuconate semialdehyde, influenza A, cytokine-cytokine receptor interaction, IL23-mediated signaling events, direct p53 effectors, cytokine signaling in immune system, interferon signaling, C21 steroid hormone metabolism, purine metabolism, genes encoding secreted soluble factors, ensemble of genes encoding ECM-associated proteins including ECM-affilaited proteins, ECM regulators and secreted factors, toll receptor signaling pathway, inflammation mediated by chemokine and cytokine signaling pathway, JAK-STAT signaling, purine metabolic, steroidogenesis and pyrimidine metabolism and are listed in Table 2. Similarly, down regulated genes were notably enriched in pyridoxal 5'-phosphate salvage, glutamine degradation/glutamate biosynthesis, Drug metabolism - cytochrome P450, chemical carcinogenesis, signaling events mediated by the hedgehog family, glypican 2 network, GPCR ligand binding, phase 2 - plateau phase, glycolysis, gluconeogenesis, type III secretion system, genes encoding secreted soluble factors, ensemble of genes encoding ECM-associated proteins including ECM-affilaited proteins, ECM regulators and secreted factors, notch signaling pathway, TGF-beta signaling pathway, notch signaling, wnt signaling, sulfate/sulfite metabolism and leukotriene C4 synthesis deficiency and are listed in Table 3.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eGene ontology (GO) enrichment analysis for DEGs\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eGO term enrichment analyses were performed using web tool ToppGene. Table 4 and Table 5 show the functions of the identified up and down regulated genes. Up regulated genes of BP were associated in defense response and response to external biotic stimulus. Down regulated genes of BP were associated in reproductive process and positive regulation of transcription by RNA polymerase II. Up regulated genes of CC were associated in cell surface and external side of plasma membrane. Down regulated genes of CC were associated in intrinsic component of plasma membrane and nuclear chromatin. Up regulated genes of MF were associated in cytokine activity and receptor ligand activity. Down regulated genes of MF were associated in transporter activity and cation transmembrane transporter activity.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003ePPI network construction and module analysis \u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eThe PPI network of up regulated genes consisting of 206 nodes and 412 edges was constructed in the IMEX database (Fig. 5). A top hub genes \u0026nbsp;were selected by the Network Analyzer (Table 6), including VCAM1, IKBKE, STAT1, IL7R, ISG15, PML, NOS2, FBXO6, IRF1, IRF7, ADAM8, SBK1, ARL14 and TGM2, and statistical results in scatter plot for node degree distribution, betweenness centrality, stress centrality, closeness centrality and clustring coefficient are displayed in Fig. 6A - 6E.\u0026nbsp; Enrichment analyses revealed that \u0026nbsp;hub genes in this PPI network were mainly associated with malaria, influenza A, defense response, cytokine-cytokine receptor interaction, cytokine signaling in immune system, direct p53 effectors, ATF-2 transcription factor network, adaptive immune system, IL6-mediated signaling events, measles, innate immune system and ensemble of genes encoding ECM-associated proteins including ECM-affilaited proteins, ECM regulators and secreted factors. Similarly, PPI network of down regulated genes consisting of 206 nodes and 412 edges was constructed in the IMEX database (Fig. 7). A top hub genes were selected by the Network Analyzer (Table 6), including E2F1, ZBTB16, TFAP4, ATP6V1B1, APBB1, ELF5, CBX2, USP2, ERP27, DSCAML1, KCNF1, DLX3, EGFL6 and AMIGO1, and statistical results in scatter plot for node degree distribution, betweenness centrality, stress centrality, closeness centrality and clustring coefficient are displayed in Fig. 8A - 8E.\u0026nbsp; Enrichment analyses revealed that \u0026nbsp;hub genes in this PPI network were mainly associated with\u0026nbsp; notch-mediated HES/HEY network, map kinase inactivation of SMRT corepressor, positive regulation of transcription by RNA polymerase II, iron uptake and transport, positive regulation of RNA metabolic process, nuclear chromatin, reproductive process, positive regulation of developmental process, de novo pyrimidine ribonucleotidesbiosythesis, neuronal system, transcription regulatory region sequence-specific DNA binding, signaling receptor binding and molecular function regulator.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eAnalysis using the PEWCC1 Cytoscape software plugin was used to create modules for the PPI networks. A total of 423 modules were created from PPI network of up regulated genes. Four significant modules were identified, including module 1 (nodes 44 \u0026nbsp;and edges 173), module 6 (nodes 24 and edges 69), module 12 (nodes 20 and edges 38) and module 16 (nodes 18 and edges 33) are shown in Fig. 9.\u0026nbsp; Enrichment analyses revealed that hub genes in these modules were mainly associated with influenza A, measles, chemokine signaling pathway, cytokine signaling in immune system, defense response, response to external biotic stimulus and innate immune response. A total of 219 modules were created from PPI network of down regulated genes. Four significant modules were identified, including module 4 (nodes 87 \u0026nbsp;and edges 86), module 5 (nodes 77 and edges 76), module 13 (nodes 41 and edges 41) and module 16 (nodes 29 and edges 28) are shown in Fig. 10. Enrichment analyses revealed that hub genes in these modules were mainly associated with multi-organism reproductive process, iron uptake and transport, neuroactive ligand-receptor interaction and cell-cell signaling.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eConstruction of target genes - miRNA regulatory network \u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eThe up and down regulated genes were analyzed using the miRNet database. Target genes - miRNA regulatory network for up regulated genes consisting of 2182 \u0026nbsp;nodes (1862 miRNAs and 320 up regulated genes) and 5899 edges (Fig. 11). The results of the topological property analysis demonstrated that SOD2 (degree = 257; ex, hsa-mir-4298), PMAIP1 (degree = 147; ex, hsa-mir-5697), APOL6 (degree =\u0026nbsp; 127; ex, hsa-mir-4478), \u0026nbsp;ICOSLG (degree = 119;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; ex, hsa-mir-4739) and NPR1 (degree = 118; ex, hsa-mir-6131) and are listed in Table 7. Enrichment analyses revealed that target genes in this network were mainly associated with cytokine-mediated signaling pathway, viral carcinogenesis, adaptive immune system and purine metabolism. Target genes - miRNA regulatory network for down regulated genes consisting of 2345 \u0026nbsp;nodes (1783 miRNAs and 262 down regulated genes) and 4885 edges (Fig. 12). The results of the topological property analysis demonstrated that VAV3 (degree = 165; ex, hsa-mir-4315), ZNF703 (degree = 115; ex, hsa-mir-5787), FAXC (degree = 112; ex, hsa-mir-4279), GPR137C (degree = 97; ex, hsa-mir-3914) and ZNF704 (degree = 86; ex, hsa-mir-1538) and are listed in Table 7. Enrichment analyses revealed that target genes in this network were mainly associated with regulation of actin cytoskeleton, positive regulation of developmental process and transcription regulatory region sequence-specific DNA binding.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eConstruction of target genes - TF regulatory network \u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eThe up and down regulated genes were analyzed using the NetworkAnalyst database. Target genes - TF regulatory network for up regulated genes consisting of \u0026nbsp;516 \u0026nbsp;nodes (92 TFs and 424 up regulated genes) and 3459 edges (Fig. 13). The results of the topological property analysis demonstrated that CD7 (degree = 265; ex, FOXC1), ELOVL7 (degree = 195; ex, GATA2), NTNG2 (degree =\u0026nbsp; 136; ex, YY1), \u0026nbsp;CXCL2 (degree = 125;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; ex, FOXL1) and (degree = 102; ex, NFKB1) and are listed in Table 8. Enrichment analyses revealed that target genes in this network were mainly associated with fas signaling pathway, ensemble of genes encoding extracellular matrix and extracellular matrix-associated proteins , ensemble of genes encoding extracellular matrix and extracellular matrix-associated proteins and influenza A. Target genes - TF regulatory network for down regulated genes consisting of \u0026nbsp;516 \u0026nbsp;nodes (80 TFs and 458 down regulated genes) and 2424 edges (Fig. 14). The results of the topological property analysis demonstrated that ABCA17P (degree = 217; ex, FOXC1), TACR1 (degree = 182; ex, GATA2), REEP1 (degree = 97; ex, YY1), \u0026nbsp;TRAM1L1 (degree = 97; ex, FOXL1) and FGF9 (degree = 74; ex, TFAP2A) and are listed in Table 8.\u0026nbsp; Enrichment analyses revealed that target genes in this network were mainly associated with calcium signaling pathway, signaling receptor binding, transmembrane transport and cell-cell signaling. \u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eValidation of hub gene\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eThe prediction achievement by ROC analysis showed that as single classifiers, CIITA, HSPA6, MYD88, SOCS3, TNFRSF10A, ADH1A, CACNA2D2, DUSP9, FMO5 and PDE1A had significant predictive values with AUCs of 0.956, 0.752, 0.992, 0.914, 0.837, 0.759, 0.781, 0.788, 0.833 and 0.788, and p-values of 0.00022, 0.00714, 0.00152, 0.00038, \u0026nbsp;0.00054, 0.00275, 0.00093, 0.00092, 0.00294 and 0.00252, respectively (Fig. 15).\u003c/span\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOutbreaks of appearing and reappearing of SARS-CoV-2 infection are frequent threats to human health across globe. When a novel virus was detection and linked with human disease, it is necessary to understand molecular pathogenesis of\u0026nbsp; SARS-CoV-2 infection as soon as possible to progress treatment to the disease such as vaccines and antiviral drugs [49]. In this investigation, we performed a series of bioinformatics analysis to screen hub genes and pathways associated with SARS-CoV-2 infection. The expression profiling by high throughput RNA sequencing found that 49 up regulated genes and 72 down regulated genes were identified in remdesivir traded SARS-CoV-2 infection compared to non treated SARS-CoV-2 infection.\u0026nbsp; Genes such as IRF7 [50], MX2 [51], TRIM25 [52], TRIM14 [53], IFIT5 [54] and\u0026nbsp; IFIT1 [55] were liable for progression of\u0026nbsp; influenza virus infection, but these genes may be responsible for advancement of SARS-CoV-2 infection. Genes such as OAS3 [56], OASL (2'-5'-oligoadenylate synthetase like) [57] and USP18 [58] were linked with progression of various viral infections, but these genes may be key for progression of SARS-CoV-2 infection. RSAD2 was involved in pathogenesis of measles virus infection [59], but this gene may be associated with progression of SARS-CoV-2 infection.\u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe ToppGene online tool was used to perform a pathway enrichment analysis. DDX58 was involved in the progression of measles virus infection [60], but this gene may be linked with advancement of SARS-CoV-2 infection.\u0026nbsp; \u0026nbsp;Enriched genes such as CIITA (class II major histocompatibility complex transactivator) [61], CCL2 [62], PML (promyelocyticleukemia) [63], ICAM1 [64], IL1A [65], MX1 [66], CXCL8 [67], MYD88 [68],\u0026nbsp; CXCL10 [69], STAT1 [70], STAT2 [71], SOCS3 [72], CASP1 [73], TLR3 [74], TNF (tumor necrosis factor) [75], IL32 [76], TRIM22 [77], IFITM3 [78], FGF2 [79], IFITM1 [80], IFITM2 [81], IFI27 [82], ISG15 [83], SOCS1 [84], IRF1 [85], ISG20 [86], IL22RA1 [87], SOCS2 [88], GBP5 [89], BST2 [90], HERC5 [91], IL27 [92], CXCL13 [93], CXCL3 [94], TLR2 [95] and TNFAIP3 [96] were liable for development of influenza virus infection, but these genes may be essential for progression of SARS-CoV-2 infection.\u0026nbsp; Enriched genes such as CCL5 [97], IL19 [98], CCL3 [97], CCL4 [99], CCL20 [100], IFIT3 [101], CSF3 [102] and IL7R [103] were important for development of respiratory syncytial virus infection, but these genes may be liable for advancement of SARS-CoV-2 infection. \u0026nbsp;Enriched genes such as IL6 [104] and JAK2 [105] were responsible for progression of SARS-CoV-2 infection. Enriched genes such as TICAM1 [106], OAS1 [107], OAS2 [108], CXCL9 [109], EREG (epiregulin) [110], CCL22 [111], VCAM1 [112], IFI35 [113], IFIT2 [114], TRIM5 [115], XAF1 [116], IFI6 [117], IL7 [118], SP100 [119], GBP1 [120], GBP2 [121], IRF4 [122], MIR5193 [123], IFNL3 [124], CYP21A2 [125], CXCL5 [126], CX3CL1 [127], CCL4L1 [128], WNT16 [129], GNB3 [130], FLG (filaggrin) [131] and HEY1 [132] were responsible for progression of various viral infections, but these genes may be involved in the development of SARS-CoV-2 infection. Expression of NOS2 was associated with development of rhinovirus infection [133], but this gene may be involved in progression of SARS-CoV-2 infection. Expression of CCR1 was liable for progression of pneumovirus infection [134], but this gene may be linked with advancement of SARS-CoV-2 infection. Expression of IRAK2 was important for progression of bronchitis virus infection [135], but this gene may be involved in development of SARS-CoV-2 infection. In general, the our findings suggested that novel biomarkers such as SCO2, TYMP (thymidine phosphorylase), HSPA6, IFNB1, IKBKE (inhibitor of nuclear factor kappa B kinase subunit epsilon), EIF2AK2, TNFSF10, TNFRSF10A, IFIH1, IL23A, UBE2L6, HLA-F, RASGRP3, TRIM38, BATF (basic leucine zipper ATF-like transcription factor), NRG2, BIRC3, MT2A, CSF1, TNFSF13B, IL15RA, GBP7, IL36A, IL17C, PSMB9, TNFRSF6B, GBP3, TRIM21, PTGS2, GBP4, BTC (betacellulin), TNFSF18, HBEGF (heparin binding EGF like growth factor), DUSP5, TRIM31, RET (ret proto-oncogene), CXCL2, TRIM10, LGALS9, LIF (LIF interleukin 6 family cytokine), LIFR (LIF receptor subunit alpha), EBI3, IL36G, HCK (HCK proto-oncogene, Src family tyrosine kinase), IFNL2, IFNL1, HSD11B1, CXCL11, S100A7A, ANGPTL1, KLHL23, PDXP (pyridoxal phosphatase), ADH1A, ADH1C, ADH6, GSTA5, ALDH3B1, FMO5, GSTT2, PTCH2, IHH (Indian hedgehog signaling molecule), CDON (cell adhesion associated, oncogene regulated), F2R, TAS2R50, WNT8B, WNT9A, OXGR1, PTGFR (prostaglandin F receptor), TACR1, GPER1, GRM4, CCKBR (cholecystokinin B receptor), OPRL1, RLN2, TAS1R1, ALDH3A2, GDF5, FGF9, EGFL6, FGF22, INHA (inhibin subunit alpha), INHBC (inhibin subunit beta C), GDF7, FGFBP3, BMP15, HES5, LFNG (LFNG O-fucosylpeptide 3-beta-N-acetylglucosaminyltransferase), HEYL (hes related family bHLH transcription factor with YRPW motif like), SULT1E1 and SULT2B1 may play key roles in the action mechanism of SARS-CoV-2 infection.\u003c/p\u003e\n\u003cp\u003eThe functions of the up and down regulated genes were identified by GO enrichment analysis. Enriched genes such as TREX1 [136], IFNL4 [137], MICB (MHC class I polypeptide-related sequence B) [138], RAB43 [139], APOL1 [140], IFI16 [141], APOBEC3B [142], SLAMF7 [143], HDAC9 [144], APOBEC3A [145], SERPING1 [146], TAP2 [147], LAG3 [148], OPTN (optineurin) [149], CD68 [150], SP140 [151], PDCD1 [152], PLVAP (plasmalemma vesicle associated protein) [153], CD34 [154], CD38 [155], CD69 [156], SLC30A8 [157] and ATP6V1G2 [158] were liable for progression of\u0026nbsp; various viral infections, but these genes may be involved in the progression of SARS-CoV-2 infection. Enriched genes such as APOBEC3G [159], ADAM8 [160], ZBP1 [161], NLRC5 [162], AIM2 [163], DUOX2 [164], NOX1 [165], IDO1 [166], CEACAM1 [167], PTX3 [168], TAP1 [169], FFAR2 [170] and E2F1 [171] were linked with progression of influenza virus infection, but these genes may be associated with progression of SARS-CoV-2 infection. CD83 was responsible for advancement of respiratory syndrome virus [172], \u0026nbsp;but this gene may be \u0026nbsp;essential for development of SARS-CoV-2 infection. ACE2 was linked with progression of SARS-CoV-2 infection [173]. NMI (N-myc and STAT interactor) was liable for progression of\u0026nbsp; severe acute respiratory syndrome corona virus [174], but this gene may be important for development of SARS-CoV-2 infection. \u0026nbsp;CD274 was associated with progression of rhino virus infection [175], but this gene may be essential for progression of SARS-CoV-2 infection. \u0026nbsp;In general, the our findings suggested that novel biomarkers such as PIK3AP1, NT5C3A, NCF1, TNIP3, CLEC1A, CLEC7A, DTX3L, MEF2C, MEP1B, ADM (adrenomedullin), SAA2, SAA4, SERPINB9, MUC17, ABCD1, APOL2, PLSCR1, PMAIP1, FOXF1, DUOXA2, THEMIS2, ZC3H12A, DHX58, DDX60, C2CD4A, MUC13, PARP14, BATF2, PLA2G4C, NUB1, STX11, ZC3HAV1, TAGAP (T cell activation RhoGTPase activating protein), RNF19B, GCH1, PTGIR (prostaglandin I2 receptor), RTP4, ARID5A, TMEM106A, PRDM1, DEFB4A, IFIT1B, C1R, C1S, C3AR1, PARP9, IFI44L, HERC6, ICOSLG (inducible T cell costimulator ligand), TRIM15, NUPR1, TGM2, APOL3, TNFAIP6, MAPK8IP2, ACHE (acetylcholinesterase (Cartwright blood group)), CHRNA1, FZD9, PLAUR (plasminogen activator, urokinase receptor), SELL (selectin L), ACKR4, PDCD1LG2, KCNA1, SECTM1, WIPF3, APELA\u0026nbsp; (apelin receptor early endogenous ligand), THEG (theg spermatid protein), PKDREJ (polycystin family receptor for egg jelly), TFCP2L1, TDRD6, TXNDC8, ZFP37, RIMBP3, NLRP14, FMN2, RIMBP3B, MYBL1, ZBTB16, INHBB (inhibin subunit beta B), NDP (norrincystine knot growth factor NDP), GGT3P, STOX2, GJB1, SOHLH2, BUB1B, DLX3, RAB3A, CBX2, GPR3, SPATA31A3, DPY19L2, BCL2L10, SOX30, E2F8, RLN1, VASH2, SLC29A3, CACNA2D2, PKD1L2, TMC3, AMIGO1, TRPM3, HPX (hemopexin), AKAP6, KCTD7, NLGN1, SLC47A2, SLC32A1, SLC9A2, TMEM37, CACNG4, ATP6V1B1, SLC46A1, SLC30A2, GPD1L, PPARGC1A, KCNF1, TMEM150C, CACNA1D, AQP10, CACNG1, SLC52A1, SLC16A9, HCN4, NGB (neuroglobin), TMEM63C, ABCC5, P2RX6, SLC16A14, SLC25A19 and SLC29A1 may play key roles in the action mechanism of SARS-CoV-2 infection.\u003c/p\u003e\n\u003cp\u003eThe construction of protein-protein interaction network and module analysis for up and down regulated genes, has been proven to be useful in the analysis of hub genes involved in SARS-CoV-2 infection. HELZ2 was associated with development of dengue virus infection [176], but this gene may be liable for progression of SARS-CoV-2 infection. BATF3 was involved in advancement of respiratory poxvirus infection [177], but this gene may essential for development of SARS-CoV-2 infection. In general, our findings suggested that novel biomarkers such as FBXO6, SBK1, ARL14, LMO2, LAP3, TFAP4, APBB1, ELF5, USP2, ERP27, DSCAML1, NGEF (neuronal guanine nucleotide exchange factor), MARC1, GPRASP1, RAB26, DEPTOR (DEP domain containing MTOR interacting protein), HMGCS2, EEPD1, CAMKK1, PDE1A, PPP1R3C, WDR88, SERF1A, KLHL32, SMTNL2, RASL11B, ABLIM1, TOX2, LMCD1, TMCC2 and CERK (ceramide kinase) may play key roles in the action mechanism of SARS-CoV-2 infection.\u003c/p\u003e\n\u003cp\u003eThe construction of target genes - miRNA regulatory network and target genes - TF regulatory network analysis for up and down regulated genes, has been proven to be useful in the analysis of target genes involved in SARS-CoV-2 infection.\u0026nbsp; Target genes such as\u0026nbsp; CD7 [178] and ELOVL7 [179] were liable for advancement of\u0026nbsp; HIV infection, but these genes may be associated with progression of SARS-CoV-2 infection. In general, our findings suggested that novel biomarkers such as \u0026nbsp;SOD2, APOL6, NPR1, NTNG2, VAV3, ZNF703, FAXC (failed axon connections homolog, metaxin like GST domain),\u0026nbsp; GPR137C, ZNF704, ABCA17P,\u0026nbsp; REEP1 and TRAM1L1 may play key roles in the action mechanism of SARS-CoV-2 infection.\u003c/p\u003e\n\u003cp\u003eIn conclusion, we conducted a comprehensive bioinformatics analysis on microarray data of SARS-CoV-2 infection. Pivotal DEGs (up and down regulated genes) and pathways were diagnosed and screened to provide a theoretical basis for potential drug target discovery and the molecular pathogensis of SARS-CoV-2 infection. 10 hub genes, especially CIITA, HSPA6, MYD88, SOCS3, TNFRSF10A, ADH1A, CACNA2D2, DUSP9, FMO5 and PDE1A, were found to differentiate remdesivir traded SARS-CoV-2 infection from non treated SARS-CoV-2 infection. Nevertheless, additional relevant investigation are needed to further confirm the identified up and down regulated genes, and pathways in SARS-CoV-2 infection.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eI thank Eugene H Chang, The University of Arizona, Department of Otolaryngology, Eugene Lab, Tucson, Arizona, USA, very much, the author who deposited their microarray dataset, GSE149273, into the public GEO database.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eThe authors declare that they have no conflict of interest.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eNo informed consent because this study does not contain human or animals participants.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eThe datasets supporting the conclusions of this article are available in the GEO (Gene Expression Omnibus) (https://www.ncbi.nlm.nih.gov/geo/) repository. [(GSE149273) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE149273]\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eNot applicable.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eThe authors declare that they have no competing interests.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eB. V. - Writing original draft, and review and editing\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eC. V. - Software and investigation\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eI. K. - Supervision and resources\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eBasavaraj Vastrad\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; ORCID ID: \u003ca style=\"color: #000000;\" href=\"http://orcid.org/0000-0003-2202-7637?lang=en\"\u003e0000-0003-2202-7637\u003c/a\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eChanabasayya\u0026nbsp; Vastrad\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; ORCID ID: \u003ca style=\"color: #000000;\" href=\"http://orcid.org/0000-0003-3615-4450\"\u003e0000-0003-3615-4450\u003c/a\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style=\"color: #000000;\"\u003eIranna\u0026nbsp; Kotturshetti\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;ORCID ID:\u0026nbsp; 0000-0003-1988-7345\u003c/span\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlhazzani W, M\u0026oslash;ller MH, Arabi YM, Loeb M, Gong MN, Fan E, Oczkowski S, Levy MM, Derde L, Dzierba A, et al. Surviving Sepsis Campaign: guidelines on the management of critically ill adults with Coronavirus Disease 2019 (COVID-19). Intensive Care Med. 2020;46(5):854‐887. doi:1007/s00134-020-06022-5\u003c/li\u003e\n\u003cli\u003eLi X, Geng M, Peng Y, Meng L, Lu S. Molecular immune pathogenesis and diagnosis of COVID-19. J Pharm Anal. 2020;10(2):102‐108. doi:1016/j.jpha.2020.03.001\u003c/li\u003e\n\u003cli\u003eSteuerman Y, Cohen M, Peshes-Yaloz N, Valadarsky L, Cohn O, David E, Frishberg A, Mayo L, Bacharach E, Amit I et al. Dissection of Influenza Infection In Vivo by Single-Cell RNA Sequencing. Cell Syst. 2018;6(6):679‐691.e4. doi:1016/j.cels.2018.05.008\u003c/li\u003e\n\u003cli\u003eSouza GAP, Salvador EA, de Oliveira FR, Cotta Malaquias LC, Abrah\u0026atilde;o JS, Leomil Coelho LF. An in silico integrative protocol for identifying key genes and pathways useful to understand emerging virus disease pathogenesis. Virus Res. 2020;284:197986. doi:1016/j.virusres.2020.197986\u003c/li\u003e\n\u003cli\u003eBarrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, et al. NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res. 2013;41(Database issue):D991‐D995. doi:1093/nar/gks1193\u003c/li\u003e\n\u003cli\u003eRitchie ME, Phipson B, Wu DI, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. doi:1093/nar/gkv007\u003c/li\u003e\n\u003cli\u003eAbbas A, Kong XB, Liu Z, Jing BY, Gao X. Automatic peak selection by a Benjamini-Hochberg-based algorithm. PLoS One. 2013;8(1):e53112. doi:1371/journal.pone.0053112\u003c/li\u003e\n\u003cli\u003eCaspi R, Billington R, Ferrer L, Foerster H, Fulcher CA, Keseler IM, Kothari A, Krummenacker M, Latendresse M, Mueller LA et al The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 2016;44(D1):D471\u0026ndash;D480. doi:1093/nar/gkv1164\u003c/li\u003e\n\u003cli\u003eKanehisa M, Sato Y, Furumichi M, Morishima K, Tanabe M. New approach for understanding genome variations in KEGG. Nucleic Acids Res. 2019;47(D1):D590\u0026ndash;D595. doi:1093/nar/gky962\u003c/li\u003e\n\u003cli\u003eSchaefer CF, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, Buetow KH. PID: the Pathway Interaction Database. Nucleic Acids Res. 2009;37(Database issue):D674\u0026ndash;D679. doi:1093/nar/gkn653\u003c/li\u003e\n\u003cli\u003eFabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, Haw R, Jassal B, Korninger F, May B et al The Reactome Pathway Knowledgebase. Nucleic Acids Res. 2018;46(D1):D649\u0026ndash;D655. doi:1093/nar/gkx1132\u003c/li\u003e\n\u003cli\u003eDahlquist KD, Salomonis N, Vranizan K, Lawlor SC, Conklin BR. GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways. Nat Genet. 2002;31(1):19\u0026ndash;20. doi:1038/ng0502-19\u003c/li\u003e\n\u003cli\u003eSubramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES et al Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545\u0026ndash;15550. doi:1073/pnas.0506580102\u003c/li\u003e\n\u003cli\u003eMi H, Huang X, Muruganujan A, Tang H, Mills C, Kang D, Thomas PD. PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements. Nucleic Acids Res. 2017;45(D1):D183\u0026ndash;D189. doi:1093/nar/gkw1138\u003c/li\u003e\n\u003cli\u003ePetri V, Jayaraman P, Tutaj M, Hayman GT, Smith JR, De Pons J, Laulederkind SJ, Lowry TF, Nigam R, Wang SJ et al The pathway ontology - updates and applications. J Biomed Semantics. 2014;5(1):7. doi:1186/2041-1480-5-7\u003c/li\u003e\n\u003cli\u003eJewison T, Su Y, Disfany FM, Liang Y, Knox C, Maciejewski A, Poelzer J, Huynh J, Zhou Y, Arndt D et al SMPDB 2.0: big improvements to the Small Molecule Pathway Database. Nucleic Acids Res. 2014;42(Database issue):D478\u0026ndash;D484. doi:1093/nar/gkt1067\u003c/li\u003e\n\u003cli\u003eChen J, Bardes EE, Aronow BJ, Jegga AG. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 2009;37(Web Server issue):W305-W311. doi:1093/nar/gkp427\u003c/li\u003e\n\u003cli\u003eLewis SE. The Vision and Challenges of the Gene Ontology. Methods Mol Biol. 2017;1446:291\u0026ndash;302. doi:1007/978-1-4939-3743-1_21\u003c/li\u003e\n\u003cli\u003eOrchard S, Kerrien S, Abbani S, Aranda B, Bhate J, Bidwell S, Bridge A, Briganti L, Brinkman FS, Cesareni G, et al. Protein interaction data curation: the International Molecular Exchange (IMEx) consortium. Nat Methods. 2012;9(4):345‐350. doi:1038/nmeth.1931\u003c/li\u003e\n\u003cli\u003eSalwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU, Eisenberg D. The Database of Interacting Proteins: 2004 update. Nucleic Acids Res. 2004;32(Database issue):D449\u0026ndash;D451. doi:1093/nar/gkh086\u003c/li\u003e\n\u003cli\u003eOrchard S, Ammari M, Aranda B, Breuza L, Briganti L, Broackes-Carter F, Campbell NH, Chavali G, Chen C, del-Toro N, Duesbury M et al The MIntAct project--IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res. 2014;42(Database issue):D358\u0026ndash;D363. doi:1093/nar/gkt1115\u003c/li\u003e\n\u003cli\u003eLicata L, Briganti L, Peluso D, Perfetto L, Iannuccelli M, Galeota E, Sacco F, Palma A, Nardozza AP, Santonico E et al. MINT, the molecular interaction database: 2012 update. Nucleic Acids Res. 2012;40(Database issue):D857\u0026ndash;D861. doi:1093/nar/gkr930\u003c/li\u003e\n\u003cli\u003eBreuer K, Foroushani AK, Laird MR, Chen C, Sribnaia A, Lo R, Winsor GL, Hancock RE, Brinkman FS, Lynn DJ. InnateDB: systems biology of innate immunity and beyond--recent updates and continuing curation. Nucleic Acids Res. 2013;41(Database issue):D1228\u0026ndash;D1233. doi:1093/nar/gks1147\u003c/li\u003e\n\u003cli\u003eKeshava Prasad TS, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D, Raju R, Shafreen B, Venugopal A et al Human Protein Reference Database--2009 update. Nucleic Acids Res. 2009;37(Database issue):D767\u0026ndash;D772. doi:1093/nar/gkn892\u003c/li\u003e\n\u003cli\u003eOughtred R, Stark C, Breitkreutz BJ, Rust J, Boucher L, Chang C, Kolas N, O\u0026rsquo;Donnell L, Leung G, McAdam R et al. The BioGRID interaction database: 2019 update. Nucleic Acids Res. 2019;47(D1):D529\u0026ndash;D541. doi:1093/nar/gky1079\u003c/li\u003e\n\u003cli\u003eKotlyar M, Pastrello C, Malik Z, Jurisica I. IID 2018 update: context-specific physical protein-protein interactions in human, model organisms and domesticated species. Nucleic Acids Res. 2019;47(D1):D581\u0026ndash;D589. doi:1093/nar/gky1037\u003c/li\u003e\n\u003cli\u003eClerc O, Deniaud M, Vallet SD, Naba A, Rivet A, Perez S, Thierry-Mieg N, Ricard-Blum S. MatrixDB: integration of new data with a focus on glycosaminoglycan interactions. Nucleic Acids Res. 2019;47(D1):D376‐D381. doi:1093/nar/gky1035\u003c/li\u003e\n\u003cli\u003eShannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498\u0026ndash;2504. doi:1101/gr.1239303\u003c/li\u003e\n\u003cli\u003ePrzulj N, Wigle DA, Jurisica I. Functional topology in a network of protein interactions. Bioinformatics. 2004;20(3):340\u0026ndash;348. doi:1093/bioinformatics/btg415\u003c/li\u003e\n\u003cli\u003eNguyen TP, Liu WC, Jord\u0026aacute;n F. Inferring pleiotropy by network analysis: linked diseases in the human PPI network. BMC Syst Biol. 2011;5:179. Published 2011 Oct 31. doi:1186/1752-0509-5-179\u003c/li\u003e\n\u003cli\u003eShi Z, Zhang B. Fast network centrality analysis using GPUs. BMC Bioinformatics. 2011;12:149. doi:1186/1471-2105-12-149\u003c/li\u003e\n\u003cli\u003eNguyen TP, Liu WC, Jord\u0026aacute;n F. Inferring pleiotropy by network analysis: linked diseases in the human PPI network. BMC Syst Biol. 2011;5:179. doi:1186/1752-0509-5-179\u003c/li\u003e\n\u003cli\u003eWang J, Li M, Wang H, Pan Y. Identification of essential proteins based on edge clustering coefficient. IEEE/ACM Trans Comput Biol Bioinform. 2012;9(4):1070\u0026ndash;1080. doi:1109/TCBB.2011.147\u003c/li\u003e\n\u003cli\u003eZaki N, Efimov D, Berengueres J. Protein complex detection using interaction reliability assessment and weighted clustering coefficient. BMC Bioinformatics. 2013;14:163. doi:1186/1471-2105-14-163\u003c/li\u003e\n\u003cli\u003eFan Y, Xia J. miRNet-Functional Analysis and Visual Exploration of miRNA-Target Interactions in a Network Context. Methods Mol Biol. 2018;1819:215-233. doi:1007/978-1-4939-8618-7_10\u003c/li\u003e\n\u003cli\u003eVlachos IS, Paraskevopoulou MD, Karagkouni D, Georgakilas G, Vergoulis T, Kanellos I, Anastasopoulos IL, Maniou S, Karathanou K, Kalfakakou D et al DIANA-TarBase v7.0: indexing more than half a million experimentally supported miRNA:mRNA interactions. Nucleic Acids Res. 2015;43(Database issue):D153-D159. doi:1093/nar/gku1215\u003c/li\u003e\n\u003cli\u003eChou CH, Shrestha S, Yang CD, Chang NW, Lin YL, Liao KW, Huang WC, Sun TH, Tu SJ, Lee WH et al miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Res. 2018;46(D1):D296-D302. doi:1093/nar/gkx1067\u003c/li\u003e\n\u003cli\u003eXiao F, Zuo Z, Cai G, Kang S, Gao X, Li T. miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res. 2009;37(Database issue):D105-D110. doi:1093/nar/gkn851\u003c/li\u003e\n\u003cli\u003eJiang Q, Wang Y, Hao Y, Juan L, Teng M, Zhang X, Li M, Wang G, Liu Y. miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res. 2009;37(Database issue):D98-104. doi:1093/nar/gkn714\u003c/li\u003e\n\u003cli\u003eHuang Z, Shi J, Gao Y, Cui C, Zhang S, Li J, Zhou Y, Cui Q. HMDD v3.0: a database for experimentally supported human microRNA-disease associations. Nucleic Acids Res. 2019;47(D1):D1013-D1017. doi:1093/nar/gky1010Z\u003c/li\u003e\n\u003cli\u003eRuepp A, Kowarsch A, Schmidl D, Buggenthin F, Brauner B, Dunger I, Fobo G, Frishman G, Montrone C, Theis FJ. PhenomiR: a knowledgebase for microRNA expression in diseases and biological processes. Genome Biol. 2010;11(1):R6. doi:1186/gb-2010-11-1-r6\u003c/li\u003e\n\u003cli\u003eLiu X, Wang S, Meng F, Wang J, Zhang Y, Dai E, Yu X, Li X, Jiang W. SM2miR: a database of the experimentally validated small molecules' effects on microRNA expression. Bioinformatics. 2013;29(3):409-411. doi:1093/bioinformatics/bts698\u003c/li\u003e\n\u003cli\u003eRukov JL, Wilentzik R, Jaffe I, Vinther J, Shomron N. Pharmaco-miR: linking microRNAs and drug effects. Brief Bioinform. 2014;15(4):648-659. doi:1093/bib/bbs082\u003c/li\u003e\n\u003cli\u003eDai E, Yu X, Zhang Y, Meng F, Wang S, Liu X, Liu D, Wang J, Li X, Jiang W. EpimiR: a database of curated mutual regulation between miRNAs and epigenetic modifications. Database (Oxford). 2014;2014:bau023. doi:1093/database/bau023\u003c/li\u003e\n\u003cli\u003eLi JH, Liu S, Zhou H, Qu LH, Yang JH. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res. 2014;42(Database issue):D92-D97. doi:1093/nar/gkt1248\u003c/li\u003e\n\u003cli\u003eZhou G, Soufan O, Ewald J, Hancock REW, Basu N, Xia J. NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res. 2019. doi:1093/nar/gkz240\u003c/li\u003e\n\u003cli\u003eKhan A, Fornes O, Stigliani A, Gheorghe M, Castro-Mondragon JA, van der Lee R, Bessy A, Ch\u0026egrave;neby J, Kulkarni SR, Tan G et al. JASPAR 2018: update of the open-access database of transcription factor binding profiles and its web framework. Nucleic Acids Res. 2018;46(D1):D260-D266. doi:1093/nar/gkx1126\u003c/li\u003e\n\u003cli\u003eRobin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, M\u0026uuml;ller M. (2011) pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 12:77. doi:1186/1471-2105-12-77\u003c/li\u003e\n\u003cli\u003eLiu C, Zhou Q, Li Y, Garner LV, Watkins SP, Carter LJ, Smoot J, Gregg AC, Daniels AD, Jervey S, et al. Research and Development on Therapeutic Agents and Vaccines for COVID-19 and Related Human Coronavirus Diseases. ACS Cent Sci. 2020;6(3):315‐331. doi:1021/acscentsci.0c00272\u003c/li\u003e\n\u003cli\u003eCiancanelli MJ, Huang SX, Luthra P, Garner H, Itan Y, Volpi S, Lafaille FG, Trouillet C, Schmolke M, Albrecht RA, et al. Infectious disease. Life-threatening influenza and impaired interferon amplification in human IRF7 deficiency. Science. 2015;348(6233):448‐453. doi:1126/science.aaa1578\u003c/li\u003e\n\u003cli\u003eJin HK, Yoshimatsu K, Takada A, Ogino M, Asano A, Arikawa J, Watanabe T. Mouse Mx2 protein inhibits hantavirus but not influenza virus replication. Arch Virol. 2001;146(1):41‐49. doi:1007/s007050170189\u003c/li\u003e\n\u003cli\u003eKoliopoulos MG, Lethier M, van der Veen AG, Haubrich K, Hennig J, Kowalinski E, Stevens RV, Martin SR, e Sousa CR, Cusack S, et al. Molecular mechanism of influenza A NS1-mediated TRIM25 recognition and inhibition. Nat Commun. 2018;9(1):1820. doi:1038/s41467-018-04214-8\u003c/li\u003e\n\u003cli\u003eQIN FX, Wu X, Wang J, Wang S, Wu F, Chen Z, Li C, Cheng G. Inhibition of Influenza A Virus Replication by TRIM14 via Its Multifaceted Protein-Protein Interaction With NP. Front Microbiol. 2019;10:344. doi:3389/fmicb.2019.00344\u003c/li\u003e\n\u003cli\u003eRohaim MA, Santhakumar D, Naggar RFE, Iqbal M, Hussein HA, Munir M. Chickens Expressing IFIT5 Ameliorate Clinical Outcome and Pathology of Highly Pathogenic Avian Influenza and Velogenic Newcastle Disease Viruses. Front Immunol. 2018;9:2025. doi:3389/fimmu.2018.02025\u003c/li\u003e\n\u003cli\u003eFeng B, Zhang Q, Wang J, Dong H, Mu X, Hu G, Zhang T. IFIT1 Expression Patterns Induced by H9N2 Virus and Inactivated Viral Particle in Human Umbilical Vein Endothelial Cells and Bronchus Epithelial Cells. Mol Cells. 2018;41(4):271‐281. doi:14348/molcells.2018.2091\u003c/li\u003e\n\u003cli\u003eGad HH, Paulous S, Belarbi E, Diancourt L, Drosten C, K\u0026uuml;mmerer BM, Plate AE, Caro V, Despr\u0026egrave;s P. The E2-E166K substitution restores Chikungunya virus growth in OAS3 expressing cells by acting on viral entry. Virology. 2012;434(1):27‐37. doi:1016/j.virol.2012.07.019\u003c/li\u003e\n\u003cli\u003eIshibashi M, Wakita T, Esumi M. 2',5'-Oligoadenylate synthetase-like gene highly induced by hepatitis C virus infection in human liver is inhibitory to viral replication in vitro. Biochem Biophys Res Commun. 2010;392(3):397‐402. doi:1016/j.bbrc.2010.01.034\u003c/li\u003e\n\u003cli\u003eChen L, Li S, McGilvray I. The ISG15/USP18 ubiquitin-like pathway (ISGylation system) in hepatitis C virus infection and resistance to interferon therapy. Int J Biochem Cell Biol. 2011;43(10):1427‐1431. doi:1016/j.biocel.2011.06.006\u003c/li\u003e\n\u003cli\u003eKurokawa C, Iankov ID, Galanis E. A key anti-viral protein, RSAD2/VIPERIN, restricts the release of measles virus from infected cells. Virus Res. 2019;263:145‐150. doi:1016/j.virusres.2019.01.014\u003c/li\u003e\n\u003cli\u003eXia M, Gonzalez P, Li C, Meng G, Jiang A, Wang H, Gao Q, Debatin KM, Beltinger C, Wei J.Mitophagy enhances oncolytic measles virus replication by mitigating DDX58/RIG-I-like receptor signaling. J Virol. 2014;88(9):5152‐5164. doi:1128/JVI.03851-13\u003c/li\u003e\n\u003cli\u003eHang do TT, Song JY, Kim MY, Park JW, Shin YK. Involvement of NF-\u0026kappa;B in changes of IFN-\u0026gamma;-induced CIITA/MHC-II and iNOS expression by influenza virus in macrophages. Mol Immunol. 2011;48(9-10):1253‐1262. doi:1016/j.molimm.2011.03.010\u003c/li\u003e\n\u003cli\u003eLai C, Wang K, Zhao Z, Zhang L, Gu H, Yang P, Wang X. C-C Motif Chemokine Ligand 2 (CCL2) Mediates Acute Lung Injury Induced by Lethal Influenza H7N9 Virus. Front Microbiol. 2017;8:587. doi:3389/fmicb.2017.00587\u003c/li\u003e\n\u003cli\u003eLi W, Wang G, Zhang H, Zhang D, Zeng J, Chen X, Xu Y, Li K. Differential suppressive effect of promyelocytic leukemia protein on the replication of different subtypes/strains of influenza A virus. Biochem Biophys Res Commun. 2009;389(1):84‐89. doi:1016/j.bbrc.2009.08.091\u003c/li\u003e\n\u003cli\u003eJiang H, Shen SM, Yin J, Zhang PP, Shi Y. Sphingosine 1-phosphate receptor 1 (S1PR1) agonist CYM5442 inhibits expression of intracellular adhesion molecule 1 (ICAM1) in endothelial cells infected with influenza A viruses. PLoS One. 2017;12(4):e0175188. doi:1371/journal.pone.0175188\u003c/li\u003e\n\u003cli\u003eLiu Y, Li S, Zhang G, Nie G, Meng Z, Mao D, Chen C, Chen X, Zhou B, Zeng G.. Genetic variants in IL1A and IL1B contribute to the susceptibility to 2009 pandemic H1N1 influenza A virus. BMC Immunol. 2013;14:37. doi:1186/1471-2172-14-37\u003c/li\u003e\n\u003cli\u003eVerhelst J, Parthoens E, Schepens B, Fiers W, Saelens X. Interferon-inducible protein Mx1 inhibits influenza virus by interfering with functional viral ribonucleoprotein complex assembly. J Virol. 2012;86(24):13445‐13455. doi:1128/JVI.01682-12\u003c/li\u003e\n\u003cli\u003eHuipao N, Borwornpinyo S, Wiboon-Ut S, Campbell CR, Lee IH, Hiranyachattada S, Sukasem C, Thitithanyanont A, Pholpramool C, Cook DI, et al. P2Y6 receptors are involved in mediating the effect of inactivated avian influenza virus H5N1 on IL-6 \u0026amp; CXCL8 mRNA expression in respiratory epithelium. PLoS One. 2017;12(5):e0176974. doi:1371/journal.pone.0176974\u003c/li\u003e\n\u003cli\u003eZhang R, Ai X, Duan Y, Xue M, He W, Wang C, Xu T, Xu M, Liu B, Li C, et al. P2Y6 receptors are involved in mediating the effect of inactivated avian influenza virus H5N1 on IL-6 \u0026amp; CXCL8 mRNA expression in respiratory epithelium. PLoS One. 2017;12(5):e0176974. doi:1371/journal.pone.0176974\u003c/li\u003e\n\u003cli\u003eLaw AH, Lee DC, Yuen KY, Peiris M, Lau AS. Cellular response to influenza virus infection: a potential role for autophagy in CXCL10 and interferon-alpha induction. Cell Mol Immunol. 2010;7(4):263‐270. doi:1038/cmi.2010.25\u003c/li\u003e\n\u003cli\u003eLee B, Gopal R, Manni ML, McHugh KJ, Mandalapu S, Robinson KM, Alcorn JF. et al. STAT1 Is Required for Suppression of Type 17 Immunity during Influenza and Bacterial Superinfection. Immunohorizons. 2017;1(6):81‐91. doi:4049/immunohorizons.1700030\u003c/li\u003e\n\u003cli\u003eWarnking K, Klemm C, L\u0026ouml;ffler B, Niemann S, van Kr\u0026uuml;chten A, Peters G, Ludwig S, Ehrhardt C. Super-infection with Staphylococcus aureus inhibits influenza virus-induced type I IFN signalling through impaired STAT1-STAT2 dimerization. Cell Microbiol. 2015;17(3):303‐317. doi:1111/cmi.12375\u003c/li\u003e\n\u003cli\u003eLin X, Yu S, Ren P, Sun X, Jin M. Human microRNA-30 inhibits influenza virus infection by suppressing the expression of SOCS1, SOCS3, and NEDD4. Cell Microbiol. 2020;22(5):e13150. doi:1111/cmi.13150\u003c/li\u003e\n\u003cli\u003eRen R, Wu S, Cai J, Yang Y, Ren X, Feng Y, Chen L, Qin B, Xu C, Yang H, et al. The H7N9 influenza A virus infection results in lethal inflammation in the mammalian host via the NLRP3-caspase-1 inflammasome. Sci Rep. 2017;7(1):7625. doi:1038/s41598-017-07384-5\u003c/li\u003e\n\u003cli\u003eWu W, Zhang W, Duggan ES, Booth JL, Zou MH, Metcalf JP. RIG-I and TLR3 are both required for maximum interferon induction by influenza virus in human lung alveolar epithelial cells. Virology. 2015;482:181‐188. doi:1016/j.virol.2015.03.048\u003c/li\u003e\n\u003cli\u003eIshikawa E, Nakazawa M, Yoshinari M, Minami M. Role of tumor necrosis factor-related apoptosis-inducing ligand in immune response to influenza virus infection in mice. J Virol. 2005;79(12):7658‐7663. doi:1128/JVI.79.12.7658-7663.2005\u003c/li\u003e\n\u003cli\u003eWang J, Wang Q, Han T, Li YK, Zhu SL, Ao F, Feng J, Jing MZ, Wang L, Ye LB, et al. Soluble interleukin-6 receptor is elevated during influenza A virus infection and mediates the IL-6 and IL-32 inflammatory cytokine burst. Cell Mol Immunol. 2015;12(5):633‐644. doi:1038/cmi.2014.80\u003c/li\u003e\n\u003cli\u003eDi Pietro A, Kajaste-Rudnitski A, Oteiza A, Nicora L, Towers GJ, Mechti N, Vicenzi E. TRIM22 inhibits influenza A virus infection by targeting the viral nucleoprotein for degradation. J Virol. 2013;87(8):4523‐4533. doi:1128/JVI.02548-12\u003c/li\u003e\n\u003cli\u003eSun X, Zeng H, Kumar A, Belser JA, Maines TR, Tumpey TM. Constitutively Expressed IFITM3 Protein in Human Endothelial Cells Poses an Early Infection Block to Human Influenza Viruses. J Virol. 2016;90(24):11157‐11167. Published 2016 Nov 28. doi:1128/JVI.01254-16\u003c/li\u003e\n\u003cli\u003eWang K, Lai C, Gu H, Zhao L, Xia M, Yang P, Wang X. miR-194 Inhibits Innate Antiviral Immunity by Targeting FGF2 in Influenza H1N1 Virus Infection. Front Microbiol. 2017;8:2187. doi:3389/fmicb.2017.02187\u003c/li\u003e\n\u003cli\u003eYu M, Qi W, Huang Z, Zhang K, Ye J, Liu R, Wang H, Ma Y, Liao M, Ning Z. Expression profile and histological distribution of IFITM1 and IFITM3 during H9N2 avian influenza virus infection in BALB/c mice. Med Microbiol Immunol. 2015;204(4):505‐514. doi:1007/s00430-014-0361-2\u003c/li\u003e\n\u003cli\u003eWang HF, Chen L, Luo J, He HX. KLF5 is involved in regulation of IFITM1, 2, and 3 genes during H5N1 virus infection in A549 cells. Cell Mol Biol (Noisy-le-grand). 2016;62(13):65‐70. doi:14715/cmb/2016.62.13.12\u003c/li\u003e\n\u003cli\u003eTang BM, Shojaei M, Parnell GP, Huang S, Nalos M, Teoh S, O'Connor K, Schibeci S, Phu AL, Kumar A, et al. A novel immune biomarker IFI27 discriminates between influenza and bacteria in patients with suspected respiratory infection. Eur Respir J. 2017;49(6):1602098. doi:1183/13993003.02098-2016\u003c/li\u003e\n\u003cli\u003eSanyal S, Ashour J, Maruyama T, Altenburg AF, Cragnolini JJ, Bilate A, Avalos AM, Kundrat L, Garc\u0026iacute;a-Sastre A, Ploegh HL. Type I interferon imposes a TSG101/ISG15 checkpoint at the Golgi for glycoprotein trafficking during influenza virus infection. Cell Host Microbe. 2013;14(5):510‐521. doi:1016/j.chom.2013.10.011\u003c/li\u003e\n\u003cli\u003eYe S, Lowther S, Stambas J. Inhibition of reactive oxygen species production ameliorates inflammation induced by influenza A viruses via upregulation of SOCS1 and SOCS3. J Virol. 2015;89(5):2672‐2683. doi:1128/JVI.03529-14\u003c/li\u003e\n\u003cli\u003eKuriakose T, Zheng M, Neale G, Kanneganti TD. IRF1 Is a Transcriptional Regulator of ZBP1 Promoting NLRP3 Inflammasome Activation and Cell Death during Influenza Virus Infection. J Immunol. 2018;200(4):1489‐1495. doi:4049/jimmunol.1701538\u003c/li\u003e\n\u003cli\u003eChai W, Li J, Shangguan Q, Liu Q, Li X, Qi D, Tong X, Liu W, Ye X. Lnc-ISG20 Inhibits Influenza A Virus Replication by Enhancing ISG20 Expression. J Virol. 2018;92(16):e00539-18. doi:1128/JVI.00539-18\u003c/li\u003e\n\u003cli\u003eHebert KD, Mclaughlin N, Zhang Z, Cipriani A, Alcorn JF, Pociask DA. IL-22Ra1 is induced during influenza infection by direct and indirect TLR3 induction of STAT1. Respir Res. 2019;20(1):184. doi:1186/s12931-019-1153-4\u003c/li\u003e\n\u003cli\u003eKedzierski L, Tate MD, Hsu AC, Kolesnik TB, Linossi EM, Dagley L, Dong Z, Freeman S, Infusini G, Starkey MR, et al. Suppressor of cytokine signaling (SOCS)5 ameliorates influenza infection via inhibition of EGFR signaling. Elife. 2017;6:e20444. doi:7554/eLife.20444\u003c/li\u003e\n\u003cli\u003eFeng J, Cao Z, Wang L, Wan Y, Peng N, Wang Q, Chen X, Zhou Y, Zhu Y. Inducible GBP5 Mediates the Antiviral Response via Interferon-Related Pathways during Influenza A Virus Infection. J Innate Immun. 2017;9(4):419‐435. doi:1159/000460294\u003c/li\u003e\n\u003cli\u003eLondrigan SL, Tate MD, Job ER, Moffat JM, Wakim LM, Gonelli CA, Purcell DF, Brooks AG, Villadangos JA, Reading PC, et al. Endogenous Murine BST-2/Tetherin Is Not a Major Restriction Factor of Influenza A Virus Infection. PLoS One. 2015;10(11):e0142925. doi:1371/journal.pone.0142925\u003c/li\u003e\n\u003cli\u003eTang Y, Zhong G, Zhu L, Liu X, Shan Y, Feng H, Bu Z, Chen H, Wang C. Herc5 attenuates influenza A virus by catalyzing ISGylation of viral NS1 protein. J Immunol. 2010;184(10):5777‐5790. doi:4049/jimmunol.0903588\u003c/li\u003e\n\u003cli\u003eKumar P, Rajasekaran K, Nanbakhsh A, Gorski J, Thakar MS, Malarkannan S. IL-27 promotes NK cell effector functions via Maf-Nrf2 pathway during influenza infection. Sci Rep. 2019;9(1):4984. doi:1038/s41598-019-41478-6\u003c/li\u003e\n\u003cli\u003eRangel-Moreno J, Moyron-Quiroz JE, Hartson L, Kusser K, Randall TD. Pulmonary expression of CXC chemokine ligand 13, CC chemokine ligand 19, and CC chemokine ligand 21 is essential for local immunity to influenza. Proc Natl Acad Sci U S A. 2007;104(25):10577‐10582. doi:1073/pnas.0700591104\u003c/li\u003e\n\u003cli\u003eCarlin LE, Hemann EA, Zacharias ZR, Heusel JW, Legge KL. Natural Killer Cell Recruitment to the Lung During Influenza A Virus Infection Is Dependent on CXCR3, CCR5, and Virus Exposure Dose. Front Immunol. 2018;9:781. doi:3389/fimmu.2018.00781\u003c/li\u003e\n\u003cli\u003eDai J, Gu L, Su Y, Wang Q, Zhao Y, Chen X, Deng H, Li W, Wang G, Li K. Inhibition of curcumin on influenza A virus infection and influenzal pneumonia via oxidative stress, TLR2/4, p38/JNK MAPK and NF-\u0026kappa;B pathways. Int Immunopharmacol. 2018;54:177‐187. doi:1016/j.intimp.2017.11.009\u003c/li\u003e\n\u003cli\u003eMaelfait J, Roose K, Bogaert P, Sze M, Saelens X, Pasparakis M, Carpentier I, van Loo G, Beyaert R. A20 (Tnfaip3) deficiency in myeloid cells protects against influenza A virus infection. PLoS Pathog. 2012;8(3):e1002570. doi:1371/journal.ppat.1002570\u003c/li\u003e\n\u003cli\u003eSalimi V, Ramezani A, Mirzaei H, Tahamtan A, Faghihloo E, Rezaei F, Naseri M, Bont L, Mokhtari-Azad T, Tavakoli-Yaraki M. Evaluation of the expression level of 12/15 lipoxygenase and the related inflammatory factors (CCL5, CCL3) in respiratory syncytial virus infection in mice model. Microb Pathog. 2017;109:209‐213. doi:1016/j.micpath.2017.05.045\u003c/li\u003e\n\u003cli\u003eErmers MJ, Janssen R, Onland-Moret NC, Hodemaekers HM, Rovers MM, Houben ML, Kimpen JL, Bont LJ. IL10 family member genes IL19 and IL20 are associated with recurrent wheeze after respiratory syncytial virus bronchiolitis. Pediatr Res. 2011;70(5):518‐523. doi:1203/PDR.0b013e31822f5863\u003c/li\u003e\n\u003cli\u003eAl-Afif A, Alyazidi R, Oldford SA, Huang YY, King CA, Marr N, Haidl ID, Anderson R, Marshall JS. Respiratory syncytial virus infection of primary human mast cells induces the selective production of type I interferons, CXCL10, and CCL4. J Allergy Clin Immunol. 2015;136(5):1346‐54.e1. doi:1016/j.jaci.2015.01.042\u003c/li\u003e\n\u003cli\u003eShi T, He Y, Sun W, Wu Y, Li L, Jie Z, Su X. Respiratory Syncytial virus infection compromises asthma tolerance by recruiting interleukin-17A-producing cells via CCR6-CCL20 signaling [published correction appears in Mol Immunol. 2018 Jan;93:285]. Mol Immunol. 2017;88:45‐57. doi:1016/j.molimm.2017.05.017\u003c/li\u003e\n\u003cli\u003eTernette N, Wright C, Kramer HB, Altun M, Kessler BM. Label-free quantitative proteomics reveals regulation of interferon-induced protein with tetratricopeptide repeats 3 (IFIT3) and 5'-3'-exoribonuclease 2 (XRN2) during respiratory syncytial virus infection. Virol J. 2011;8(1):442. doi:1186/1743-422X-8-442\u003c/li\u003e\n\u003cli\u003eTouzelet O, Broadbent L, Armstrong SD, Aljabr W, Cloutman-Green E, Power UF, Hiscox JA. The Secretome Profiling of a Pediatric Airway Epithelium Infected with hRSV Identified Aberrant Apical/Basolateral Trafficking and Novel Immune Modulating (CXCL6, CXCL16, CSF3) and Antiviral (CEACAM1) Proteins. Mol Cell Proteomics. 2020;19(5):793‐807. doi:1074/mcp.RA119.001546\u003c/li\u003e\n\u003cli\u003eInchley CS, Osterholt HC, Sonerud T, Fj\u0026aelig;rli HO, Nakstad B. Downregulation of IL7R, CCR7, and TLR4 in the cord blood of children with respiratory syncytial virus disease. J Infect Dis. 2013;208(9):1431‐1435. doi:1093/infdis/jit336\u003c/li\u003e\n\u003cli\u003eConti P, Ronconi G, Caraffa AL, Gallenga CE, Ross R, Frydas I, Kritas SK. 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. J Biol Regul Homeost Agents. 2020;34(2):1. doi:23812/CONTI-E\u003c/li\u003e\n\u003cli\u003eWu D, Yang XO. TH17 responses in cytokine storm of COVID-19: An emerging target of JAK2 inhibitor Fedratinib. J Microbiol Immunol Infect. 2020;S1684-1182(20)30065-7. doi:1016/j.jmii.2020.03.005\u003c/li\u003e\n\u003cli\u003eOshiumi H, Okamoto M, Fujii K, Kawanishi T, Matsumoto M, Koike S, Seya T. The TLR3/TICAM-1 pathway is mandatory for innate immune responses to poliovirus infection. J Immunol. 2011;187(10):5320‐5327. doi:4049/jimmunol.1101503\u003c/li\u003e\n\u003cli\u003eLim JK, Lisco A, McDermott DH, Huynh L, Ward JM, Johnson B, Johnson H, Pape J, Foster GA, Krysztof D, et al. Genetic variation in OAS1 is a risk factor for initial infection with West Nile virus in man. PLoS Pathog. 2009;5(2):e1000321. doi:1371/journal.ppat.1000321\u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a-\u0026Aacute;lvarez M, Berenguer J, Jim\u0026eacute;nez-Sousa MA, Pineda-Tenor D, Ald\u0026aacute;miz-Echevarria T, Tejerina F, Diez C, V\u0026aacute;zquez-Mor\u0026oacute;n S, Resino S. Mx1, OAS1 and OAS2 polymorphisms are associated with the severity of liver disease in HIV/HCV-coinfected patients: A cross-sectional study. Sci Rep. 2017;7:41516. doi:1038/srep41516\u003c/li\u003e\n\u003cli\u003eHuang W, Hu K, Luo S, Zhang M, Li C, Jin W, Liu Y, Griffin GE, Shattock RJ, Hu Q. Herpes simplex virus type 2 infection of human epithelial cells induces CXCL9 expression and CD4+ T cell migration via activation of p38-CCAAT/enhancer-binding protein-\u0026beta; pathway. J Immunol. 2012;188(12):6247‐6257. doi:4049/jimmunol.1103706\u003c/li\u003e\n\u003cli\u003eDing X, Wang F, Duan M, Yang J, Wang S. Epiregulin as a key molecule to suppress hepatitis B virus propagation in vitro. Arch Virol. 2009;154(1):9‐17. doi:1007/s00705-008-0259-7\u003c/li\u003e\n\u003cli\u003eRiezu-Boj JI, Larrea E, Aldabe R, Guembe L, Casares N, Galeano E, Echeverria I, Sarobe P, Herrero I, Sangro B, et al. Hepatitis C virus induces the expression of CCL17 and CCL22 chemokines that attract regulatory T cells to the site of infection. J Hepatol. 2011;54(3):422‐431. doi:1016/j.jhep.2010.07.014\u003c/li\u003e\n\u003cli\u003eKoraka P, Murgue B, Deparis X, van Gorp EC, Setiati TE, Osterhaus AD, Groen J. Elevation of soluble VCAM-1 plasma levels in children with acute dengue virus infection of varying severity. J Med Virol. 2004;72(3):445‐450. doi:1002/jmv.20007\u003c/li\u003e\n\u003cli\u003eDas A, Dinh PX, Panda D, Pattnaik AK. Interferon-inducible protein IFI35 negatively regulates RIG-I antiviral signaling and supports vesicular stomatitis virus replication. J Virol. 2014;88(6):3103‐3113. doi:1128/JVI.03202-13\u003c/li\u003e\n\u003cli\u003eFensterl V, Wetzel JL, Sen GC. Interferon-induced protein Ifit2 protects mice from infection of the peripheral nervous system by vesicular stomatitis virus. J Virol. 2014;88(18):10303‐10311. doi:1128/JVI.01341-14\u003c/li\u003e\n\u003cli\u003eBerthoux L, Sebastian S, Sokolskaja E, Luban J. Cyclophilin A is required for TRIM5{alpha}-mediated resistance to HIV-1 in Old World monkey cells. Proc Natl Acad Sci U S A. 2005;102(41):14849‐14853. doi:1073/pnas.0505659102\u003c/li\u003e\n\u003cli\u003eLong X, Li Y, Qi Y, Xu J, Wang Z, Zhang X, Zhang D, Zhang L, Huang J. XAF1 contributes to dengue virus-induced apoptosis in vascular endothelial cells. FASEB J. 2013;27(3):1062‐1073. doi:1096/fj.12-213967\u003c/li\u003e\n\u003cli\u003eChen S, Li S, Chen L. Interferon-inducible Protein 6-16 (IFI-6-16, ISG16) promotes Hepatitis C virus replication in vitro. J Med Virol. 2016;88(1):109‐114. doi:1002/jmv.24302\u003c/li\u003e\n\u003cli\u003eLevy Y, Lacabaratz C, Weiss L, Viard JP, Goujard C, Leli\u0026egrave;vre JD, Bou\u0026eacute; F, Molina JM, Rouzioux C, Avettand-F\u0026eacute;no\u0026ecirc;l V, et al. Enhanced T cell recovery in HIV-1-infected adults through IL-7 treatment. J Clin Invest. 2009;119(4):997‐1007. doi:1172/JCI38052\u003c/li\u003e\n\u003cli\u003eKim YE, Lee JH, Kim ET, Shin HJ, Gu SY, Seol HS, Ling PD, Lee CH, Ahn JH. Human cytomegalovirus infection causes degradation of Sp100 proteins that suppress viral gene expression. J Virol. 2011;85(22):11928‐11937. doi:1128/JVI.00758-11\u003c/li\u003e\n\u003cli\u003ePan W, Zuo X, Feng T, Shi X, Dai J. Guanylate-binding protein 1 participates in cellular antiviral response to dengue virus. Virol J. 2012;9:292. doi:1186/1743-422X-9-292\u003c/li\u003e\n\u003cli\u003eFukutani ER, Ramos PI, Gon\u0026ccedil;alves K, Irahe J, Azevedo LG, Rodrigues MM, Lima JV, Junior HF, Fukutani KF, Queiroz AT. Meta-Analysis of HTLV-1-Infected Patients Identifies CD40LG and GBP2 as Markers of ATLL and HAM/TSP Clinical Status: Two Genes Beat as One. Front Genet. 2019;10:1056. doi:3389/fgene.2019.01056\u003c/li\u003e\n\u003cli\u003eGrusdat M, McIlwain DR, Xu HC, Pozdeev VI, Knievel J, Crome SQ, Robert-Tissot C, Dress RJ, Pandyra AA, Speiser DE, et al. IRF4 and BATF are critical for CD8⁺ T-cell function following infection with LCMV. Cell Death Differ. 2014;21(7):1050‐1060. doi:1038/cdd.2014.19\u003c/li\u003e\n\u003cli\u003eKhlaiphuengsin A, Panjaworayan T, Thienprasert N, Tangkijvanich P, Posuwan N, Makkoch J, Poovorawan Y, Payungporn S. Human miR-5193 Triggers Gene Silencing in Multiple Genotypes of Hepatitis B Virus. Microrna. 2015;4(2):123‐130. doi:2174/2211536604666150819195743\u003c/li\u003e\n\u003cli\u003eManuel O, W\u0026oacute;jtowicz A, Bibert S, Mueller NJ, Van Delden C, Hirsch HH, Steiger J, Stern M, Egli A, Garzoni C et al. Influence of IFNL3/4 polymorphisms on the incidence of cytomegalovirus infection after solid-organ transplantation. J Infect Dis. 2015;211(6):906‐914. doi:1093/infdis/jiu557\u003c/li\u003e\n\u003cli\u003eMalikova J, Zingg T, Fingerhut R, Sluka S, Gr\u0026ouml;ssl M, Brixius-Anderko S, Bernhardt R, McDougall J, Pandey AV, Fl\u0026uuml;ck CE. HIV Drug Efavirenz Inhibits CYP21A2 Activity with Possible Clinical Implications. Horm Res Paediatr. 2019;91(4):262‐270. doi:1159/000500522\u003c/li\u003e\n\u003cli\u003eGuha D, Klamar CR, Reinhart T, Ayyavoo V. Transcriptional Regulation of CXCL5 in HIV-1-Infected Macrophages and Its Functional Consequences on CNS Pathology. J Interferon Cytokine Res. 2015;35(5):373‐384. doi:1089/jir.2014.0135\u003c/li\u003e\n\u003cli\u003eBertin J, Jalaguier P, Barat C, Roy MA, Tremblay MJ. 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. Virology. 2014;454-455:128‐138. doi:1016/j.virol.2014.02.007\u003c/li\u003e\n\u003cli\u003eShao W, Tang J, Song W, Wang C, Li Y, Wilson CM, Kaslow RA. CCL3L1 and CCL4L1: variable gene copy number in adolescents with and without human immunodeficiency virus type 1 (HIV-1) infection. Genes Immun. 2007;8(3):224‐231. doi:1038/sj.gene.6364378\u003c/li\u003e\n\u003cli\u003eXie L, Huang Y, Zhong J, Wei H, Chen S, Jiang K, Li S, Qin X. Short Communication: The Association of WNT16 Polymorphisms with the CD4+ T Cell Count in the HIV-Infected Population. AIDS Res Hum Retroviruses. 2020;36(2):119‐121. doi:1089/AID.2019.0038\u003c/li\u003e\n\u003cli\u003eJuno J, Tuff J, Choi R, Card C, Kimani J, Wachihi C, Koesters-Kiazyk S, Ball TB, Farquhar C, Plummer FA, et al. The role of G protein gene GNB3 C825T polymorphism in HIV-1 acquisition, progression and immune activation. Retrovirology. 2012;9:1. doi:1186/1742-4690-9-1\u003c/li\u003e\n\u003cli\u003eOyoshi MK, Beaupr\u0026eacute; J, Venturelli N, Lewis CN, Iwakura Y, Geha RS. Filaggrin deficiency promotes the dissemination of cutaneously inoculated vaccinia virus. J Allergy Clin Immunol. 2015;135(6):1511‐8.e6. doi:1016/j.jaci.2014.12.1923\u003c/li\u003e\n\u003cli\u003eWang X, He Z, Xia T, Li X, Liang D, Lin X, Wen H, Lan K. Latency-associated nuclear antigen of Kaposi sarcoma-associated herpesvirus promotes angiogenesis through targeting notch signaling effector Hey1. Cancer Res. 2014;74(7):2026‐2037. doi:1158/0008-5472.CAN-13-1467\u003c/li\u003e\n\u003cli\u003eSanders SP, Siekierski ES, Richards SM, Porter JD, Imani F, Proud D. Rhinovirus infection induces expression of type 2 nitric oxide synthase in human respiratory epithelial cells in vitro and in vivo. J Allergy Clin Immunol. 2001;107(2):235‐243. doi:1067/mai.2001.112028\u003c/li\u003e\n\u003cli\u003eBonville CA, Lau VK, DeLeon JM, Gao JL, Easton AJ, Rosenberg HF, Domachowske JB. Functional antagonism of chemokine receptor CCR1 reduces mortality in acute pneumovirus infection in vivo. J Virol. 2004;78(15):7984‐7989. doi:1128/JVI.78.15.7984-7989.2004\u003c/li\u003e\n\u003cli\u003eLiu H, Yang X, Zhang ZK, Zou WC, Wang HN. miR-146a-5p promotes replication of infectious bronchitis virus by targeting IRAK2 and TNFRSF18. Microb Pathog. 2018;120:32‐36. doi:1016/j.micpath.2018.04.046\u003c/li\u003e\n\u003cli\u003eWheeler LA, Trifonova RT, Vrbanac V, Barteneva NS, Liu X, Bollman B, Onofrey L, Mulik S, Ranjbar S, Luster AD, et al. TREX1 Knockdown Induces an Interferon Response to HIV that Delays Viral Infection in Humanized Mice. Cell Rep. 2016;15(8):1715‐1727. doi:1016/j.celrep.2016.04.048\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Brien TR, Pfeiffer RM, Paquin A, Kuhs KA, Chen S, Bonkovsky HL, Edlin BR, Howell CD, Kirk GD, Kuniholm MH, et al. Comparison of functional variants in IFNL4 and IFNL3 for association with HCV clearance. J Hepatol. 2015;63(5):1103‐1110. doi:1016/j.jhep.2015.06.035\u003c/li\u003e\n\u003cli\u003eLibraty DH, Zhang L, Obcena A, Brion JD, Capeding RZ. Circulating levels of soluble MICB in infants with symptomatic primary dengue virus infections. PLoS One. 2014;9(5):e98509. doi:1371/journal.pone.0098509\u003c/li\u003e\n\u003cli\u003eZenner HL, Yoshimura S, Barr FA, Crump CM. Analysis of Rab GTPase-activating proteins indicates that Rab1a/b and Rab43 are important for herpes simplex virus 1 secondary envelopment. J Virol. 2011;85(16):8012‐8021. doi:1128/JVI.00500-11\u003c/li\u003e\n\u003cli\u003eEstrella MM, Li M, Tin A, Abraham AG, Shlipak MG, Penugonda S, Hussain SK, Palella Jr FJ, Wolinsky SM, Martinson JJ, et al. The association between APOL1 risk alleles and longitudinal kidney function differs by HIV viral suppression status. Clin Infect Dis. 2015;60(4):646‐652. doi:1093/cid/ciu765\u003c/li\u003e\n\u003cli\u003eOrzalli MH, Broekema NM, Diner BA, Hancks DC, Elde NC, Cristea IM, Knipe DM. cGAS-mediated stabilization of IFI16 promotes innate signaling during herpes simplex virus infection. Proc Natl Acad Sci U S A. 2015;112(14):E1773‐E1781. doi:1073/pnas.1424637112\u003c/li\u003e\n\u003cli\u003eKim EY, Lorenzo-Redondo R, Little SJ, Chung YS, Phalora PK, Berry IM, Archer J, Penugonda S, Fischer W, Richman DD, et al. Human APOBEC3 induced mutation of human immunodeficiency virus type-1 contributes to adaptation and evolution in natural infection. PLoS Pathog. 2014;10(7):e1004281. doi:1371/journal.ppat.1004281\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Connell P, Pepelyayeva Y, Blake MK, Hyslop S, Crawford RB, Rizzo MD, Pereira-Hicks C, Godbehere S, Dale L, Gulick P, et al. SLAMF7 Is a Critical Negative Regulator of IFN-\u0026alpha;-Mediated CXCL10 Production in Chronic HIV Infection. J Immunol. 2019;202(1):228‐238. doi:4049/jimmunol.1800847\u003c/li\u003e\n\u003cli\u003eChen J, Wang N, Dong M, Guo M, Zhao Y, Zhuo Z, Zhang C, Chi X, Pan Y, Jiang J, et al. The Metabolic Regulator Histone Deacetylase 9 Contributes to Glucose Homeostasis Abnormality Induced by Hepatitis C Virus Infection. Diabetes. 2015;64(12):4088‐4098. doi:2337/db15-0197\u003c/li\u003e\n\u003cli\u003eBerger G, Durand S, Fargier G, Nguyen XN, Cordeil S, Bouaziz S, Muriaux D, Darlix JL, Cimarelli A. APOBEC3A is a specific inhibitor of the early phases of HIV-1 infection in myeloid cells. PLoS Pathog. 2011;7(9):e1002221. doi:1371/journal.ppat.1002221\u003c/li\u003e\n\u003cli\u003eSanfilippo C, Cambria D, Longo A, Palumbo M, Avola R, Pinzone M, Nunnari G, Condorelli F, Musumeci G, Imbesi R, et al. SERPING1 mRNA overexpression in monocytes from HIV+ patients. Inflamm Res. 2017;66(12):1107‐1116. doi:1007/s00011-017-1091-x\u003c/li\u003e\n\u003cli\u003eSoundravally R, Hoti SL. Significance of transporter associated with antigen processing 2 (TAP2) gene polymorphisms in susceptibility to dengue viral infection. J Clin Immunol. 2008;28(3):256‐262. doi:1007/s10875-007-9154-3\u003c/li\u003e\n\u003cli\u003eTian X, Zhang A, Qiu C, Wang W, Yang Y, Qiu C, Liu A, Zhu L, Yuan S, Hu H, et al. The upregulation of LAG-3 on T cells defines a subpopulation with functional exhaustion and correlates with disease progression in HIV-infected subjects. J Immunol. 2015;194(8):3873‐3882. doi:4049/jimmunol.1402176\u003c/li\u003e\n\u003cli\u003eWaisner H, Kalamvoki M. The ICP0 Protein of Herpes Simplex Virus 1 (HSV-1) Downregulates Major Autophagy Adaptor Proteins Sequestosome 1 and Optineurin during the Early Stages of HSV-1 Infection. J Virol. 2019;93(21):e01258-19. doi:1128/JVI.01258-19\u003c/li\u003e\n\u003cli\u003eMcGuinness PH, Painter D, Davies S, McCaughan GW. Increases in intrahepatic CD68 positive cells, MAC387 positive cells, and proinflammatory cytokines (particularly interleukin 18) in chronic hepatitis C infection. Gut. 2000;46(2):260‐269. doi:1136/gut.46.2.260\u003c/li\u003e\n\u003cli\u003eMadani N, Millette R, Platt EJ, Marin M, Kozak SL, Bloch DB, Kabat D. Implication of the lymphocyte-specific nuclear body protein Sp140 in an innate response to human immunodeficiency virus type 1. J Virol. 2002;76(21):11133‐11138. doi:1128/jvi.76.21.11133-11138.2002\u003c/li\u003e\n\u003cli\u003eNasi M, Riva A, Borghi V, D\u0026rsquo;Amico R, Del Giovane C, Casoli C, Galli M, Vicenzi E, Gibellini L, De Biasi S, et al. Novel genetic association of TNF-\u0026alpha;-238 and PDCD1-7209 polymorphisms with long-term non-progressive HIV-1 infection. Int J Infect Dis. 2013;17(10):e845‐e850. doi:1016/j.ijid.2013.01.003\u003c/li\u003e\n\u003cli\u003eTse D, Armstrong DA, Oppenheim A, Kuksin D, Norkin L, Stan RV. Plasmalemmal vesicle associated protein (PV1) modulates SV40 virus infectivity in CV-1 cells. Biochem Biophys Res Commun. 2011;412(2):220‐225. doi:1016/j.bbrc.2011.07.063\u003c/li\u003e\n\u003cli\u003eFahrbach KM, Barry SM, Ayehunie S, Lamore S, Klausner M, Hope TJ. Activated CD34-derived Langerhans cells mediate transinfection with human immunodeficiency virus. J Virol. 2007;81(13):6858‐6868. doi:1128/JVI.02472-06\u003c/li\u003e\n\u003cli\u003eBenito JM, L\u0026oacute;pez M, Lozano S, Martinez P, Gonz\u0026aacute;lez-Lahoz J, Soriano V. CD38 expression on CD8 T lymphocytes as a marker of residual virus replication in chronically HIV-infected patients receiving antiretroviral therapy. AIDS Res Hum Retroviruses. 2004;20(2):227‐233. doi:1089/08892220477300495\u003c/li\u003e\n\u003cli\u003eYong YK, Tan HY, Saeidi A, Rosmawati M, Atiya N, Ansari AW, Rajarajeswaran J, Vadivelu J, Velu V, Larsson M, et al. Decrease of CD69 levels on TCR V\u0026alpha;7.2+CD4+ innate-like lymphocytes is associated with impaired cytotoxic functions in chronic hepatitis B virus-infected patients. Innate Immun. 2017;23(5):459‐467. doi:1177/1753425917714854\u003c/li\u003e\n\u003cli\u003ePineda-Tenor D, Micheloud D, Berenguer J, Jim\u0026eacute;nez-Sousa MA, Fern\u0026aacute;ndez-Rodr\u0026iacute;guez A, Garc\u0026iacute;a-Broncano P, Guzm\u0026aacute;n-Fulgencio M, Diez C, Bell\u0026oacute;n JM, Carrero A, Ald\u0026aacute;miz-Echevarria T. SLC30A8 rs13266634 polymorphism is related to a favorable cardiometabolic lipid profile in HIV/hepatitis C virus-coinfected patients. AIDS. 2014;28(9):1325‐1332. doi:1097/QAD.0000000000000215\u003c/li\u003e\n\u003cli\u003eShichi D, Kikkawa EF, Ota M, Katsuyama Y, Kimura A, Matsumori A, Kulski JK, Naruse TK, Inoko H. The haplotype block, NFKBIL1-ATP6V1G2-BAT1-MICB-MICA, within the class III-class I boundary region of the human major histocompatibility complex may control susceptibility to hepatitis C virus-associated dilated cardiomyopathy. Tissue Antigens. 2005;66(3):200‐208. doi:1111/j.1399-0039.2005.00457.x\u003c/li\u003e\n\u003cli\u003ePauli EK, Schmolke M, Hofmann H, Ehrhardt C, Flory E, M\u0026uuml;nk C, Ludwig S. High level expression of the anti-retroviral protein APOBEC3G is induced by influenza A virus but does not confer antiviral activity. Retrovirology. 2009;6:38.\u0026nbsp; doi:1186/1742-4690-6-38\u003c/li\u003e\n\u003cli\u003eMa GF, Miettinen S, Porola P, Hedman K, Salo J, Konttinen YT. Human parainfluenza virus type 2 (HPIV2) induced host ADAM8 expression in human salivary adenocarcinoma cell line (HSY) during cell fusion. BMC Microbiol. 2009;9:55. doi:1186/1471-2180-9-55\u003c/li\u003e\n\u003cli\u003eZhang T, Yin C, Boyd DF, Quarato G, Ingram JP, Shubina M, Ragan KB, Ishizuka T, Crawford JC, Tummers B et al. Influenza Virus Z-RNAs Induce ZBP1-Mediated Necroptosis. Cell. 2020;180(6):1115‐1129.e13. doi:1016/j.cell.2020.02.050\u003c/li\u003e\n\u003cli\u003eRanjan P, Singh N, Kumar A, Neerincx A, Kremmer E, Cao W, Davis WG, Katz JM, Gangappa S, Lin R, et al. NLRC5 interacts with RIG-I to induce a robust antiviral response against influenza virus infection. Eur J Immunol. 2015;45(3):758‐772. doi:1002/eji.201344412\u003c/li\u003e\n\u003cli\u003eZhang H, Luo J, Alcorn JF, Chen K, Fan S, Pilewski J, Liu A, Chen W, Kolls JK, Wang J. AIM2 Inflammasome Is Critical for Influenza-Induced Lung Injury and Mortality. J Immunol. 2017;198(11):4383‐4393. doi:4049/jimmunol.1600714\u003c/li\u003e\n\u003cli\u003eKim BJ, Cho SW, Jeon YJ, An S, Jo A, Lim JH, Kim DY, Won TB, Han DH, Rhee CS, et al. Intranasal delivery of Duox2 DNA using cationic polymer can prevent acute influenza A viral infection in vivo lung. Appl Microbiol Biotechnol. 2018;102(1):105‐115. doi:1007/s00253-017-8512-1\u003c/li\u003e\n\u003cli\u003eSelemidis S, Seow HJ, Broughton BR, Vinh A, Bozinovski S, Sobey CG, Drummond GR, Vlahos R. Nox1 oxidase suppresses influenza a virus-induced lung inflammation and oxidative stress. PLoS One. 2013;8(4):e60792. doi:1371/journal.pone.0060792\u003c/li\u003e\n\u003cli\u003eFox JM, Crabtree JM, Sage LK, Tompkins SM, Tripp RA. Interferon Lambda Upregulates IDO1 Expression in Respiratory Epithelial Cells After Influenza Virus Infection. J Interferon Cytokine Res. 2015;35(7):554‐562. doi:1089/jir.2014.0052\u003c/li\u003e\n\u003cli\u003eYe S, Cowled CJ, Yap CH, Stambas J. Deep sequencing of primary human lung epithelial cells challenged with H5N1 influenza virus reveals a proviral role for CEACAM1. Sci Rep. 2018;8(1):15468. doi:1038/s41598-018-33605-6\u003c/li\u003e\n\u003cli\u003eJob ER, Bottazzi B, Short KR, Deng YM, Mantovani A, Brooks AG, Reading PC. A single amino acid substitution in the hemagglutinin of H3N2 subtype influenza A viruses is associated with resistance to the long pentraxin PTX3 and enhanced virulence in mice. J Immunol. 2014;192(1):271‐281. doi:4049/jimmunol.1301814\u003c/li\u003e\n\u003cli\u003eAsp L, Holtze M, Powell SB, Karlsson H, Erhardt S. Neonatal infection with neurotropic influenza A virus induces the kynurenine pathway in early life and disrupts sensorimotor gating in adult Tap1-/- mice. Int J Neuropsychopharmacol. 2010;13(4):475‐485. doi:1017/S1461145709990253\u003c/li\u003e\n\u003cli\u003eWang G, Jiang L, Wang J, Zhang J, Kong F, Li Q, Yan Y, Huang S, Zhao Y, Liang L, et al. The G Protein-Coupled Receptor FFAR2 Promotes Internalization during Influenza A Virus Entry. J Virol. 2020;94(2):e01707-19. doi:1128/JVI.01707-19\u003c/li\u003e\n\u003cli\u003eMayank AK, Sharma S, Nailwal H, Lal SK. Nucleoprotein of influenza A virus negatively impacts antiapoptotic protein API5 to enhance E2F1-dependent apoptosis and virus replication. Cell Death Dis. 2015;6(12):e2018. doi:1038/cddis.2015.360\u003c/li\u003e\n\u003cli\u003eChen X, Zhang Q, Bai J, Zhao Y, Wang X, Wang H, Jiang P. The Nucleocapsid Protein and Nonstructural Protein 10 of Highly Pathogenic Porcine Reproductive and Respiratory Syndrome Virus Enhance CD83 Production via NF-\u0026kappa;B and Sp1 Signaling Pathways. J Virol. 2017;91(18):e00986-17. doi:1128/JVI.00986-17\u003c/li\u003e\n\u003cli\u003eHoffmann M, Kleine-Weber H, Schroeder S, Kr\u0026uuml;ger N, Herrler T, Erichsen S, Schiergens TS, Herrler G, Wu NH, Nitsche A, et al. SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor. Cell. 2020;181(2):271‐280.e8. doi:1016/j.cell.2020.02.052\u003c/li\u003e\n\u003cli\u003eCheng W, Chen S, Li R, Chen Y, Wang M, Guo D. Severe acute respiratory syndrome coronavirus protein 6 mediates ubiquitin-dependent proteosomal degradation of N-Myc (and STAT) interactor. Virol Sin. 2015;30(2):153‐161. doi:1007/s12250-015-3581-8\u003c/li\u003e\n\u003cli\u003eSeyerl M, Kirchberger S, Majdic O, Seipelt J, Jindra C, Schrauf C, St\u0026ouml;ckl J. Human rhinoviruses induce IL-35-producing Treg via induction of B7-H1 (CD274) and sialoadhesin (CD169) on DC. Eur J Immunol. 2010;40(2):321‐329. doi:1002/eji.200939527\u003c/li\u003e\n\u003cli\u003eFusco DN, Pratt H, Kandilas S, Cheon SS, Lin W, Cronkite DA, Basavappa M, Jeffrey KL, Anselmo A, Sadreyev R, et al. Fusco DN, Pratt H, Kandilas S, et al. HELZ2 Is an IFN Effector Mediating Suppression of Dengue Virus. Front Microbiol. 2017;8:240. doi:3389/fmicb.2017.00240\u003c/li\u003e\n\u003cli\u003eDesai P, Tahiliani V, Abboud G, Stanfield J, Salek-Ardakani S. Batf3-Dependent Dendritic Cells Promote Optimal CD8 T Cell Responses against Respiratory Poxvirus Infection. J Virol. 2018;92(16):e00495-18. Published 2018 Jul 31. doi:1128/JVI.00495-18\u003c/li\u003e\n\u003cli\u003eUckun FM, Chelstrom LM, Tuel-Ahlgren L, Dibirdik I, Irvin JD, Langlie MC, Myers DE. TXU (anti-CD7)-pokeweed antiviral protein as a potent inhibitor of human immunodeficiency virus. Antimicrob Agents Chemother. 1998;42(2):383‐388.\u003c/li\u003e\n\u003cli\u003ePurdy JG, Shenk T, Rabinowitz JD. Fatty acid elongase 7 catalyzes lipidome remodeling essential for human cytomegalovirus replication. Cell Rep. 2015;10(8):1375‐1385. doi:1016/j.celrep.2015.02.003\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eDue to technical limitations, Tables 1-8 are only available as a download in the supplementary files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"SARS-CoV-2 infection, Differentially expressed genes, Pathway enrichment analysis, Protein-protein interaction, ROC analysis","lastPublishedDoi":"10.21203/rs.3.rs-133291/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-133291/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSevere acute respiratory syndrome corona virus 2 (SARS-CoV-2) infections (COVID 19) is a progressive viral infection that has been investigated extensively. However, genetic features and molecular pathogenesis underlying SARS-CoV-2 infection remain unclear. Here we used bioinformatics to investigate the candidate genes associated in the molecular pathogenesis of SARS-CoV-2 infection. Expression profiling by high throughput sequencing (GSE149273) was downloaded from the Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) in remdesivir\u0026nbsp;traded SARS-CoV-2 infection samples and non treated SARS-CoV-2 infection samples with an adjusted P-value \u0026lt; 0.05 and a |log fold change (FC)| \u0026gt; 1.3 were first identified by limma in R software package. Next, Pathway and Gene Ontology (GO) enrichment analysis of these DEGs was performed.\u0026nbsp;Then, the hub genes were identified by the Network Analyzer plugin and the other bioinformatics approaches including protein-protein interaction (PPI) network analysis, module analysis, target gene - miRNA regulatory network, and target gene - TF regulatory network construction was also performed. Finally, receiver‐operating characteristic (ROC) analyses were for diagnostic values associated with hub genes. A total of 909 DEGs were identified, including 453 up regulated genes and 457 down regulated genes. As for the pathway and GO enrichment analysis, the up regulated genes were mainly linked with influenza A and defense response, whereas down regulated genes were mainly linked with Drug metabolism - cytochrome P450 and reproductive process. Additionally, 10 hub genes (VCAM1, IKBKE, STAT1, IL7R, ISG15, E2F1, ZBTB16, TFAP4, ATP6V1B1 and APBB1) were identified. \u0026nbsp;ROC analysis showed that hub genes (CIITA, HSPA6, MYD88, SOCS3, TNFRSF10A, ADH1A, CACNA2D2, DUSP9, FMO5 and PDE1A) had good diagnostic values. In summary, the data may produce new insights regarding pathogenesis of SARS-CoV-2 infection and treatment. Hub genes and candidate drugs may improve individualized diagnosis and therapy for SARS-CoV-2 infection in future.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","manuscriptTitle":"Integrated bioinformatics analysis for the screening of hub genes and therapeutic drugs in severe acute respiratory syndrome corona virus 2 infection/COVID 19","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-12-22 00:10:51","doi":"10.21203/rs.3.rs-133291/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a01d12c8-5612-4567-869c-61a82031e855","owner":[],"postedDate":"December 22nd, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":1568217,"name":"Bioinformatics"}],"tags":[],"updatedAt":"2020-12-22T00:10:51+00:00","versionOfRecord":[],"versionCreatedAt":"2020-12-22 00:10:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-133291","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-133291","identity":"rs-133291","version":["v1"]},"buildId":"GqpaHPwrfC8PjnIFayRh5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}