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Potential Molecular Mechanisms and Remdesivir Treatment for Acute Respiratory Syndrome Corona Virus 2 Infection/COVID 19 Through RNA Sequencing and Bioinformatics Analysis
Associated Data
Abstract
Introduction:
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 remdesivir treatment for SARS-CoV-2 infection remain unclear. Here, we used bioinformatics to investigate the candidate genes associated in the molecular pathogenesis of remdesivir-treated SARS-CoV-2-infected patients.
Methods:
Expression profiling by high-throughput sequencing dataset (GSE149273) was downloaded from the Gene Expression Omnibus, and the differentially expressed genes (DEGs) in remdesivir-treated SARS-CoV-2 infection samples and nontreated SARS-CoV-2 infection samples with an adjusted P value of <.05 and a |log fold change| > 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 NetworkAnalyzer plugin and the other bioinformatics approaches including protein-protein interaction network analysis, module analysis, target gene—miRNA regulatory network, and target gene—TF regulatory network. Finally, a receiver-operating characteristic analysis was performed for diagnostic values associated with hub genes.
Results:
A total of 909 DEGs were identified, including 453 upregulated genes and 457 downregulated genes. As for the pathway and GO enrichment analysis, the upregulated genes were mainly linked with influenza A and defense response, whereas downregulated genes were mainly linked with drug metabolism—cytochrome P450 and reproductive process. In addition, 10 hub genes (VCAM1, IKBKE, STAT1, IL7R, ISG15, E2F1, ZBTB16, TFAP4, ATP6V1B1, and APBB1) were identified. Receiver-operating characteristic analysis showed that hub genes (CIITA, HSPA6, MYD88, SOCS3, TNFRSF10A, ADH1A, CACNA2D2, DUSP9, FMO5, and PDE1A) had good diagnostic values.
Conclusion:
This study provided insights into the molecular mechanism of remdesivir-treated SARS-CoV-2 infection that might be useful in further investigations.
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 vary in severity from a simple cold to severe illness, and can lead to death. Despite the fact that great progress has been made in antivirals and vaccination for this SARS-CoV-2 infection, the survival rate is less. Remdesivir is the only antiviral drug for treatment of SARS-CoV-2 infection. 2 Because the precise molecular changes after remdesivir treatment for SARS-CoV-2 infection remain unknown, it is extremely essential to examine molecular changes during remdesivir treatment in SARS-CoV-2 infection. 3
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. 4 With the rapid advancement of next-generation sequencing (NGS) technology to find out differentially expressed genes (DEGs) during diagonosis of viral infections. 5 We rationally presume that DEGs can affect the promotion of various viral infections. Now, through expression profiling by high-throughput sequencing investigation using NGS technology, more and more DEGs were linked with SARS-CoV-2 infection during remdesivir treatment and understanding its biological characteristics is essential in improving clinical treatment outcomes.
In the current investigation, we downloaded the RNA-seq dataset GSE149273 from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/) 6 and conducted a bioinformatics analysis to study the DEGs between remdesivir-treated SARS-CoV-2 infection samples and nontreated SARS-CoV-2 infection samples. We performed gene ontology (GO) and pathway enrichment analyses, protein-protein interaction (PPI) network construction and analysis, modules analysis, target gene—miRNA regulatory network, and target gene—TF regulatory network construction and analysis. Finally, we performed receiver-operating characteristic (ROC) analyses for diagnostic values of hub genes. The findings in our study may contribute to novel molecular changes during remdesivir treatment for SARS-CoV-2 infection.
Materials and Methods
Data resource
The study was designed according to the flowchart (Figure 1). Expression profiling by high-throughput sequencing dataset GSE149273 based on GPL21290 Illumina HiSeq 3000 (Homo sapiens) platform was downloaded from the GEO database, a public depository database of gene expression data. GSE149273 contains 60 samples, including 30 remdesivir-treated SARS-CoV-2 infection samples and 30 nontreated SARS-CoV-2 infection samples.
Screening of the DEGs
For the expression profiling by high-throughput sequencing dataset, the R package limma 7 was applied for performing the differential analysis between 30 remdesivir-treated SARS-CoV-2 infection samples and nontreated SARS-CoV-2 infection samples. The P values were adjusted by Benjamini and Hochberg method. 8 Based on the |log fold change (FC)| values and the P values, the DEGs (thresholds: |logFC| > 1.3 for upregulated genes and |logFC| < −1.3 for downregulated genes, adjusted P < .05).
Pathway enrichment analysis for DEGs
To analyze the functions of DEGs, BIOCYC (https://biocyc.org/), 9 Kyoto Encyclopedia of Genes and Genomes (http://www.genome.jp/kegg/pathway.html), 10 Pathway Interaction Database (https://wiki.nci.nih.gov/pages/viewpage.action?pageId=315491760), 11 REACTOME (https://reactome.org/), 12 GenMAPP (http://www.genmapp.org/), 13 MSigDB C2 BIOCARTA (http://software.broadinstitute.org/gsea/msigdb/collections.jsp), 14 PantherDB (http://www.pantherdb.org/), 15 Pathway Ontology (http://www.obofoundry.org/ontology/pw.html), 16 and Small Molecule Pathway Database (http://smpdb.ca/) 17 pathway analysis were performed by using the ToppGene (ToppFun) (https://toppgene.cchmc.org/enrichment.jsp) 18 online tool. P < .05 was set as the cut-off point.
Gene ontology enrichment analysis for DEGs
The ToppGene (ToppFun) (https://toppgene.cchmc.org/enrichment.jsp) 18 was used to study GO enrichment analyses of DEGs. The ToppGene online tool for GO analysis (http://www.geneontology.org) 19 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 < .05 was considered statistically significant for all analyses.
Protein-protein interaction network construction and module analysis
The IMEX: The International Molecular Exchange Consortium (https://www.imexconsortium.org/) 20 is a biological database designed for predicting PPI networks and integrated with PPI databases such as Database of Interacting Proteins (http://dip.doe-mbi.ucla.edu/dip/Main.cgi), 21 IntAct Molecular Interaction Database (https://www.ebi.ac.uk/intact/), 22 the Molecular INTeraction database (https://mint.bio.uniroma2.it/), 23 InnateDB (https://www.innatedb.com/), 24 Human Protein Reference Database (http://www.hprd.org/), 25 BioGRID (https://thebiogrid.org/), 26 Integrated Interactions Database from a well-known online server (http://iid.ophid.utoronto.ca), 27 and MatrixDB (http://matrixdb.univ-lyon1.fr/). 28 Cytoscape (http://www.cytoscape.org/, version 3.8.0), 29 open software, was used to visualize the PPI networks. The top genes with the highest node degree, 30 betweenness centrality, 31 stress centrality, 32 closeness centrality, 31 and lowest clustering coefficient 33 were considered as hub genes based on the analysis using NetworkAnalyzer from Cytoscape. PEWCC1 (http://apps.cytoscape.org/apps/PEWCC1), 34 a plugin 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 upregulated and downregulated genes was constructed by Cytoscape (http://cytoscape.org/). 29
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 upregulated and downregulated genes was constructed by Cytoscape (http://cytoscape.org/). 29
Validation of hub genes
To identify the diagnostic value of upregulated and downregulated hub genes in SARS-CoV-2 infection, pROC package 48 in R language to illustrate ROC curves was used in this investigation and area under the curve (AUC) of ROC curves was determined to check the act of each upregulated and downregulated hub genes. When the AUC value was greater than 0.6, the upregulated and downregulated hub genes were able of distinguishing remdesivir-treated SARS-CoV-2 infection samples and nontreated SARS-CoV-2 infection samples. The diagnostic value of upregulated and downregulated hub genes in GSE149273 dataset was estimated in our research work.
Results
Screening of the DEGs
A total of 909 DEGs (453 upregulated genes and 457 downregulated genes) were identified between remdesivir-treated SARS-CoV-2 infection and nontreated SARS-CoV-2 infection (|logFC| > 1.3 for upregulated genes and |logFC| < −1.3 for downregulated genes, adjusted P < .05) and volcano plots showing the results of differential analysis are given in Figure 2. The upregulated genes and downregulated genes are listed in Supplemental Table 1. Heatmaps are shown in Figures 3 and and4,4, respectively.
Volcano plot of differentially expressed genes. Genes with a significant change of more than 2-fold were selected. Green dot represented upregulated significant genes and red dot represented downregulated significant genes.
Heat map of upregulated differentially expressed genes. Legend on the top left indicates log fold change of genes (A1-A30 = nontreated SARS-CoV-2 infection samples [blue color box]; B1-B30 = remdesivir-treated SARS-CoV-2 infection samples [green color box]). SARS-CoV-2 indicates severe acute respiratory syndrome corona virus 2.
Heat map of downregulated differentially expressed genes. Legend on the top left indicates log fold change of genes (A1-A30 = nontreated SARS-CoV-2 infection samples [blue color box]; B1-B30 = remdesivir-treated SARS-CoV-2 infection samples [green color box]). SARS-CoV-2 indicates severe acute respiratory syndrome corona virus 2.
Pathway enrichment analysis for DEGs
To further understand the function and mechanism of the identified upregulated and downregulated genes, pathway enrichment analysis was performed using the ToppGene web tool. Upregulated genes were particularly enriched in pyrimidine deoxyribonucleoside 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 extracellular matrix (ECM)-associated proteins including ECM-affiliated 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 are listed in Supplemental Table 2. Similarly, downregulated 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-affiliated proteins, ECM regulators and secreted factors, notch signaling pathway, transforming growth factor-beta signaling pathway, notch signaling, wnt signaling, sulfate/sulfite metabolism, and leukotriene C4 synthesis deficiency are listed in Supplemental Table 3.
Gene ontology enrichment analysis for DEGs
Gene ontology term enrichment analysis was performed using web tool ToppGene. Supplemental Tables 4 and 5 show the functions of the identified upregulated and downregulated genes. Upregulated genes of BP were associated with defense response and response to external biotic stimulus. Downregulated genes of BP were associated with reproductive process and positive regulation of transcription by RNA polymerase II. Upregulated genes of CC were associated with cell surface and external side of plasma membrane. Downregulated genes of CC were associated with intrinsic component of plasma membrane and nuclear chromatin. Upregulated genes of MF were associated with cytokine activity and receptor ligand activity. Downregulated genes of MF were associated with transporter activity and cation transmembrane transporter activity.
Protein-protein interaction network construction and module analysis
The PPI network of upregulated genes consisting of 206 nodes and 412 edges was constructed in the IMEX database (Figure 5). Top hub genes were selected by the NetworkAnalyzer (Supplemental 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 clustering coefficient are displayed in Figure 6A to toE. EnrichmentE. Enrichment analysis revealed that hub genes in PPI network were mainly associated with malaria, influenza A, defense response, cytokine-cytokine receptor interaction, cytokine signaling in immune system, direct p53 effectors, activating transcription factor-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-affiliated proteins, ECM regulators, and secreted factors. Similarly, PPI network of downregulated genes consisting of 206 nodes and 412 edges was constructed in the IMEX database (Figure 7). Top hub genes were selected by the NetworkAnalyzer (Supplemental 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 clustering coefficient are displayed in Figure 8A to toE. EnrichmentE. Enrichment analysis revealed that hub genes in 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 ribonucleotide biosynthesis, neuronal system, transcription regulatory region sequence-specific DNA binding, signaling receptor binding, and MF regulator.
Scatter plot for upregulated genes. (A) Node degree. (B) Betweenness centrality. (C) Stress centrality. (D) Closeness centrality. (E) Clustering coefficient.
Scatter plot for downregulated genes. (A) Node degree. (B) Betweenness centrality. (C) Stress centrality. (D) Closeness centrality. (E) Clustering coefficient.
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 upregulated genes. Four significant modules were identified: 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 Figure 9. Enrichment analysis revealed that hub genes in 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 downregulated genes. Four significant modules were identified: 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 Figure 10. Enrichment analysis revealed that hub genes in modules were mainly associated with multiorganism reproductive process, iron uptake and transport, neuroactive ligand-receptor interaction, and cell-cell signaling.
Modules in PPI network. The green nodes denote the upregulated genes. PPI indicates protein-protein interaction.
Construction of target genes—miRNA regulatory network
The upregulated and downregulated genes were analyzed using the miRNet database. Target genes—miRNA regulatory network for upregulated genes consisting of 2182 nodes (1862 miRNAs and 320 upregulated genes) and 5899 edges (Figure 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) are listed in Supplemental Table 7. Enrichment analyses revealed that target genes in network were mainly associated with cytokine-mediated signaling pathway, viral carcinogenesis, adaptive immune system, and purine metabolism. Target genes—miRNA regulatory network for downregulated genes consisting of 2345 nodes (1783 miRNAs and 262 downregulated genes) and 4885 edges (Figure 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) are listed in Supplemental Table 7. Enrichment analysis revealed that target genes in network were mainly associated with regulation of actin cytoskeleton, positive regulation of developmental process, and transcription regulatory region sequence-specific DNA binding.
The network of upregulated genes and their related miRNAs. The green circle nodes are the upregulated genes, and yellow diamond nodes are the miRNAs.
Construction of target genes—TF regulatory network
The upregulated and downregulated genes were analyzed using the NetworkAnalyst database. Target genes—TF regulatory network for upregulated genes consisting of 516 nodes (92 TFs and 424 upregulated genes) and 3459 edges (Figure 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) are listed in Supplemental Table 8. Enrichment analysis revealed that target genes in network were mainly associated with fas signaling pathway, ensemble of genes encoding ECM and ECM-associated proteins, ensemble of genes encoding ECM and ECM-associated proteins, and influenza A. Target genes—TF regulatory network for downregulated genes consisting of 516 nodes (80 TFs and 458 downregulated genes) and 2424 edges (Figure 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) are listed in Supplemental Table 8. Enrichment analysis revealed that target genes in network were mainly associated with calcium signaling pathway, signaling receptor binding, transmembrane transport, and cell-cell signaling.
The network of upregulated genes and their related TFs. The green circle nodes are the upregulated genes, and purple triangle nodes are the TFs.
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 .00022, .00714, .00152, .00038, .00054, .00275, .00093, .00092, .00294, and .00252, respectively (Figure 15).
ROC curve validated the sensitivity and 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. ROC indicates receiver-operating characteristic; SARS-CoV-2, severe acute respiratory syndrome corona virus 2.
Discussion
Outbreaks of appearing and reappearing of SARS-CoV-2 infection are frequent threats to human health across globe. When a novel virus was detected and linked with human disease, it is necessary to understand molecular changes during antiviral treatment in SARS-CoV-2 infection. 49 In this investigation, we performed a series of bioinformatics analysis to screen hub genes and pathways were associated with remdesivir-treated SARS-CoV-2 infection. The expression profiling by high-throughput RNA sequencing found that 49 upregulated genes and 72 downregulated genes were identified in remdesivir-treated SARS-CoV-2 infection compared with nontreated SARS-CoV-2 infection. IRF7, 50 MX2, 51 TRIM25, 52 TRIM14, 53 IFIT5, 54 and IFIT1 55 have been shown to be a meaningful advance factor for progression of influenza virus infection, but these novel genes expressed in remdesivir-treated SARS-CoV-2 infection. Genes including OAS3, 56 OASL (2′-5′-oligoadenylate synthetase like), 57 and USP18 58 were a preferred anticancer target, but these novel genes expressed in remdesivir-treated SARS-CoV-2 infection. Kurokawa et al 59 demonstrated that altered expression of RSAD2 during measles virus infection, but this novel gene might be expressed in remdesivir-treated SARS-CoV-2 infection.
The ToppGene online tool was used to perform a pathway enrichment analysis. Xia et al 60 showed that DDX58 promoted aggressiveness of measles virus infection, but this novel gene was expressed in remdesivir-treated SARS-CoV-2 infection. CIITA (class II major histocompatibility complex transactivator), 61 CCL2, 62 PML (promyelocytic leukemia), 63 ICAM1, 64 IL1A, 65 MX1, 66 CXCL8, 67 MYD88, 67 CXCL10, 68 STAT1, 69 STAT2, 70 SOCS3, 71 CASP1, 72 TLR3, 73 TNF (tumor necrosis factor), 74 IL32, 75 TRIM22, 76 IFITM3, 77 FGF2, 78 IFITM1, 79 IFITM2, 80 IFI27, 81 ISG15, 82 SOCS1, 83 IRF1, 84 ISG20, 85 IL22RA1, 86 SOCS2, 87 GBP5, 88 BST2, 89 HERC5, 90 IL27, 91 CXCL13, 92 CXCL3, 93 TLR2, 94 and TNFAIP3 95 proved to be positively correlated with the progress of influenza virus infection, but these novel genes were expressed in remdesivir-treated SARS-CoV-2 infection. CCL5, 96 IL19, 97 CCL3, 96 CCL4, 98 CCL20, 99 IFIT3, 100 CSF3, 101 and IL7R 102 proved to be an independent diagnostic factors in respiratory syncytial virus infection, but these novel genes were expressed in remdesivir-treated SARS-CoV-2 infection. Conti et al 103 and Wu and Yang 104 found expression of IL6 and JAK2 was correlated with SARS-CoV-2 infection progression. TICAM1, 105 OAS1, 106 OAS2, 107 CXCL9, 108 EREG (epiregulin), 109 CCL22, 110 VCAM1, 111 IFI35, 112 IFIT2, 113 TRIM5, 114 XAF1, 115 IFI6, 116 IL7, 117 SP100, 118 GBP1, 119 GBP2, 120 IRF4, 121 MIR5193, 122 IFNL3, 123 CYP21A2, 124 CXCL5, 125 CX3CL1, 126 CCL4L1, 127 WNT16, 128 GNB3, 129 FLG (filaggrin), 130 and HEY1 131 have been found to be differentially expressed in various viral infections, but these novel genes were expressed in remdesivir-treated SARS-CoV-2 infection. Sanders et al 132 believed that NOS2 plays an important role in the pathophysiology of rhinovirus infection, but this novel gene was expressed in remdesivir-treated SARS-CoV-2 infection. Bonville et al 133 reported that the expression of the gene CCR1 is correlated with pneumovirus infection, but this novel gene was expressed in remdesivir-treated SARS-CoV-2 infection. IRAK2 is a promising biomarker in bronchitis virus infection 134 detection and diagnosis, but this novel gene was expressed in remdesivir-treated SARS-CoV-2 infection.
The functions of the upregulated and downregulated genes were identified by GO enrichment analysis. The involvement of TREX1, 135 IFNL4, 136 MICB (MHC class I polypeptide-related sequence B), 137 RAB43, 138 APOL1, 139 IFI16, 140 APOBEC3B, 141 SLAMF7, 142 HDAC9, 143 APOBEC3A, 144 SERPING1, 145 TAP2, 146 LAG3, 147 OPTN (optineurin), 148 CD68, 149 SP140, 150 PDCD1, 151 PLVAP (plasmalemma vesicle-associated protein), 152 CD34, 153 CD38, 154 CD69, 155 SLC30A8, 156 and ATP6V1G2 157 with various viral infections was demonstrated previously, but these novel genes were expressed in remdesivir-treated SARS-CoV-2 infection. The altered expression of APOBEC3G, 158 ADAM8, 159 ZBP1, 160 NLRC5, 161 AIM2, 162 DUOX2, 163 NOX1, 164 IDO1, 165 CEACAM1, 166 PTX3, 167 TAP1, 168 FFAR2, 169 and E2F1 170 was observed to be associated with the progression of influenza virus infection, but these novel genes were expressed in remdesivir-treated SARS-CoV-2 infection. Currently, CD83 has been reported to be very important in progression of respiratory syndrome virus infection, 171 but this novel gene was expressed in remdesivir-treated SARS-CoV-2 infection. ACE2 is recognized as an important molecular marker of SARS-CoV-2 infection. 172 Cheng et al 173 found the expression of NMI (N-myc and STAT interactor) in patients with severe acute respiratory syndrome corona virus infection, but this novel gene was expressed in remdesivir-treated SARS-CoV-2 infection. Previous studies had shown that the altered expression of CD274 was closely related to the occurrence of rhino virus infection, 174 but this novel gene was expressed in remdesivir-treated SARS-CoV-2 infection.
The construction of protein-protein interaction network and module analysis for upregulated and downregulated genes have been proven to be useful in the analysis of hub genes involved in remdesivir-treated SARS-CoV-2 infection. Fusco et al 175 revealed that HELZ2 may be the potential targets for dengue virus infection diagnosis and treatment, but this novel gene was expressed in remdesivir-treated SARS-CoV-2 infection. BATF3 levels are correlated with disease severity in patients with respiratory poxvirus infection, 176 but this novel gene was expressed in remdesivir-treated 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) might 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 upregulated and downregulated genes—has been proven to be useful in the analysis of target genes involved in remdesivir-treated SARS-CoV-2 infection. Uckun et al 177 and Purdy et al 178 revealed that CD7 and ELOVL7 are associated with HIV infection, but these novel genes were expressed in remdesivir-treated 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 might play key roles in the action mechanism of remdesivir treated SARS-CoV-2 infection.
However, in addition to the objection of sample collection, huge obstacles in the analysis need to be overcome. In addition, due to the smallness of available datasets in the GEO database, the sample size in this study was finite. We will raise the sample size in a future investigation if Supplemental datasets can be replaced from the database.
In conclusion, we conducted a comprehensive bioinformatics analysis on NGS data of remdesivir-treated SARS-CoV-2 infection. Pivotal DEGs (upregulated and downregulated genes) and pathways were diagnosed and screened to provide a theoretical basis for molecular changes during antiviral treatment in SARS-CoV-2 infection. Ten hub genes, especially CIITA, HSPA6, MYD88, SOCS3, TNFRSF10A, ADH1A, CACNA2D2, DUSP9, FMO5, and PDE1A, were found to differentiate remdesivir-treated SARS-CoV-2 infection from nontreated SARS-CoV-2 infection. Nevertheless, additional relevant investigations are needed to further confirm the identified upregulated and downregulated genes, and pathways in remdesivir-treated SARS-CoV-2 infection.
Supplemental Material
Supplemental material, sj-docx-1-bbi-10.1177_11779322211067365 for Potential Molecular Mechanisms and Remdesivir Treatment for Acute Respiratory Syndrome Corona Virus 2 Infection/COVID 19 Through RNA Sequencing and Bioinformatics Analysis by G Prashanth, Basavaraj Vastrad, Chanabasayya Vastrad and Shivakumar Kotrashetti in Bioinformatics and Biology Insights
Supplemental material, sj-docx-2-bbi-10.1177_11779322211067365 for Potential Molecular Mechanisms and Remdesivir Treatment for Acute Respiratory Syndrome Corona Virus 2 Infection/COVID 19 Through RNA Sequencing and Bioinformatics Analysis by G Prashanth, Basavaraj Vastrad, Chanabasayya Vastrad and Shivakumar Kotrashetti in Bioinformatics and Biology Insights
Supplemental material, sj-docx-3-bbi-10.1177_11779322211067365 for Potential Molecular Mechanisms and Remdesivir Treatment for Acute Respiratory Syndrome Corona Virus 2 Infection/COVID 19 Through RNA Sequencing and Bioinformatics Analysis by G Prashanth, Basavaraj Vastrad, Chanabasayya Vastrad and Shivakumar Kotrashetti in Bioinformatics and Biology Insights
Supplemental material, sj-docx-4-bbi-10.1177_11779322211067365 for Potential Molecular Mechanisms and Remdesivir Treatment for Acute Respiratory Syndrome Corona Virus 2 Infection/COVID 19 Through RNA Sequencing and Bioinformatics Analysis by G Prashanth, Basavaraj Vastrad, Chanabasayya Vastrad and Shivakumar Kotrashetti in Bioinformatics and Biology Insights
Supplemental material, sj-docx-5-bbi-10.1177_11779322211067365 for Potential Molecular Mechanisms and Remdesivir Treatment for Acute Respiratory Syndrome Corona Virus 2 Infection/COVID 19 Through RNA Sequencing and Bioinformatics Analysis by G Prashanth, Basavaraj Vastrad, Chanabasayya Vastrad and Shivakumar Kotrashetti in Bioinformatics and Biology Insights
Supplemental material, sj-docx-6-bbi-10.1177_11779322211067365 for Potential Molecular Mechanisms and Remdesivir Treatment for Acute Respiratory Syndrome Corona Virus 2 Infection/COVID 19 Through RNA Sequencing and Bioinformatics Analysis by G Prashanth, Basavaraj Vastrad, Chanabasayya Vastrad and Shivakumar Kotrashetti in Bioinformatics and Biology Insights
Supplemental material, sj-docx-7-bbi-10.1177_11779322211067365 for Potential Molecular Mechanisms and Remdesivir Treatment for Acute Respiratory Syndrome Corona Virus 2 Infection/COVID 19 Through RNA Sequencing and Bioinformatics Analysis by G Prashanth, Basavaraj Vastrad, Chanabasayya Vastrad and Shivakumar Kotrashetti in Bioinformatics and Biology Insights
Supplemental material, sj-docx-8-bbi-10.1177_11779322211067365 for Potential Molecular Mechanisms and Remdesivir Treatment for Acute Respiratory Syndrome Corona Virus 2 Infection/COVID 19 Through RNA Sequencing and Bioinformatics Analysis by G Prashanth, Basavaraj Vastrad, Chanabasayya Vastrad and Shivakumar Kotrashetti in Bioinformatics and Biology Insights
Acknowledgments
We thank Eugene H Chang, The University of Arizona, Department of Otolaryngology, Eugene Lab, Tucson, Arizona, USA, very much, the author who deposited their next-generation sequencing dataset, GSE149273, into the public Gene Expression Omnibus database.
Footnotes
Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.
Author Contributions: GP helped in methodology and validation; BV helped in writing original draft, and review and editing; CV helped in software and investigation; and SK helped in supervision and validation.
Availability of Data and Materials: The datasets supporting the conclusions of this article are available in the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/) repository. ([GSE149273] https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE149273)
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 was obtained because this study does not contain human or animals participants.
ORCID iDs: Basavaraj Vastrad 
https://orcid.org/0000-0003-2202-7637
Chanabasayya Vastrad
https://orcid.org/0000-0003-3615-4450
Supplemental Material: Supplemental material for this article is available online.