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. 2021 Mar 26;54(5):845–857. doi: 10.1016/j.jmii.2021.03.007

Gene signatures and potential therapeutic targets of Middle East respiratory syndrome coronavirus (MERS-CoV)-infected human lung adenocarcinoma epithelial cells

Yen-Hung Wu a,b, I-Jeng Yeh a,b, Nam Nhut Phan c, Meng-Chi Yen a,b, Jui-Hsiang Hung d, Chung-Chieh Chiao e, Chien-Fu Chen e, Zhengda Sun f, Hui-Ping Hsu g,h,∗∗, Chih-Yang Wang i,j,∗, Ming-Derg Lai k,l,∗∗∗
PMCID: PMC7997684  PMID: 34176764

Abstract

Background

Pathogenic coronaviruses include Middle East respiratory syndrome coronavirus (MERS-CoV), severe acute respiratory syndrome coronavirus (SARS-CoV), and SARS-CoV-2. These viruses have induced outbreaks worldwide, and there are currently no effective medications against them. Therefore, there is an urgent need to develop potential drugs against coronaviruses.

Methods

High-throughput technology is widely used to explore differences in messenger (m)RNA and micro (mi)RNA expression profiles, especially to investigate protein–protein interactions and search for new therapeutic compounds. We integrated miRNA and mRNA expression profiles in MERS-CoV-infected cells and compared them to mock-infected controls from public databases.

Results

Through the bioinformatics analysis, there were 251 upregulated genes and eight highly differentiated miRNAs that overlapped in the two datasets. External validation verified that these genes had high expression in MERS-CoV-infected cells, including RC3H1, NF-κB, CD69, TNFAIP3, LEAP-2, DUSP10, CREB5, CXCL2, etc. We revealed that immune, olfactory or sensory system-related, and signal-transduction networks were discovered from upregulated mRNAs in MERS-CoV-infected cells. In total, 115 genes were predicted to be related to miRNAs, with the intersection of upregulated mRNAs and miRNA-targeting prediction genes such as TCF4, NR3C1, and POU2F2. Through the Connectivity Map (CMap) platform, we suggested potential compounds to use against MERS-CoV infection, including diethylcarbamazine, harpagoside, bumetanide, enalapril, and valproic acid.

Conclusions

The present study illustrates the crucial roles of miRNA-mRNA interacting networks in MERS-CoV-infected cells. The genes we identified are potential targets for treating MERS-CoV infection; however, these could possibly be extended to other coronavirus infections.

Keywords: Coronavirus, Middle East respiratory syndrome coronavirus (MERS-CoV), miRNA, Bioinformatics, Connectivity map, Lung adenocarcinoma

Introduction

Coronaviruses (CoVs) are a category of enveloped positive-sense single-stranded RNA viruses composed of club-like spikes that project from their surfaces; these CoVs considerably infect the human respiratory system and central nervous system (CNS). To the present, at least six strains of coronavirus have been identified. Four human coronaviruses (HCoVs), including HCoVHKU1, HCoV-OC43, HCoV-NL63, and HCoV-229E, are not highly morbific and have only slight effects on the respiratory system.1 However, two other strains of coronaviruses, Middle East respiratory syndrome coronavirus (MERS-CoV) and severe acute respiratory syndrome coronavirus (SARS-CoV) are highly pathogenic. MERS-CoV induced 858 deaths and about 2494 cases at the end of November 2019.2 SARS-CoV caused two large-scale pandemics and resulted in more than 8422 cases and 916 deaths in 2003.3 Coronavirus outbreaks indicate that some coronaviruses can become highly pathogenic when they are transmitted from animals to humans. Therefore, it is important to explore antiviral treatments and vaccines targeting highly pathogenic coronaviruses.4 In December 2019, a third pathogenic HCoV, called the 2019 novel coronavirus (abbreviated as SARS-CoV-2) was detected. According to statistical reports from the World Health Organization (WHO) (https://www.who.int/emergencies/diseases/novel-coronavirus-2019) to March 22, 2021, there had been over 122,822,505 confirmed cases worldwide with over 2,709,041 deaths. Human-to-human droplet and contact transmission were reported. However, there are currently no effective medications against coronaviruses; nor are effective vaccines available. Therefore, there is an urgent need to develop therapeutic drugs for coronavirus diseases.5 , 6

High-throughput technology extensively applies a holistic approach from expressions of thousands of genes to functional genomics and biological systems. The interactions of microRNAs (miRNAs) with their corresponding mRNA targets play a major role in the wide field of epigenetic gene regulation, and the mRNA-miRNA network also provides numerous possible results.7 , 8 In addition, a combination and analysis of multiple databases may reveal the universal regulation network. miRNAs inhibit translation of proteins or enhance degradation of transcripts by binding to the 3’ untranslated region (UTR) of mRNAs. miRNAs bind to the viral genome or through virus-mediated changes in the host transcriptome, affect replication and pathogenesis of RNA viruses. Meanwhile, since miRNAs are stable during long-term storage in blood,9 they are potential candidates as biomarkers for the screening, diagnosis, and prediction of viral infections. There are several high-throughput datasets that contain mRNAs and miRNAs after a coronavirus infection, and recent studies have demonstrated that some of these downstream genes are potential targets for therapy or prediction.10 , 11 In addition, the Calu-3 cell is an ideal model for investigating and studying coronavirus infection.12 Therefore, we selected Calu-3 cells as a MERS-CoV infection model for the present study.7 , 8 , 13 A previous study used MERS-CoV to infect Calu-3 epithelial cells and identified 3.4% of differences in the expression of circRNAs, 5.3% of differences in the expression of miRNAs, and 29.9% of differences in the expression of mRNAs.13 However, integrated analyses of mRNA and miRNA sequencing from coronavirus-infected human cell models are limited. In the current study, we hypothesize that investigating mRNA/miRNA interactions may contribute evidence to decrease the translational gap between laboratory research and clinical applications, which will be worthwhile in the development of efficient treatment strategies for coronavirus outbreaks. Therefore, we chose both RNA and miRNA high throughput database for further investigation.

Effective treatments for coronavirus infections urgently need to be developed; however, discovering new drugs costs considerable time and effort. Drug repurposing is an effective strategy to shorten the time and lessen the costs of development. The Gene Expression Omnibus (GEO) database combined with the Connectivity Map (CMap) platform provides comprehensive testing of US Food and Drug Administration (FDA)-approved compounds. Recent research also used these databases to obtain drug-gene signatures for the novel coronavirus SARS-CoV-2. In the present study, we used a bioinformatics approach to study MERS-CoV-infected human lung adenocarcinoma epithelial cells. Databases of miRNA and mRNA expression were integrated to explore the miRNA-mRNA interactive network. MERS-CoV-associated miRNAs and their target genes are potential candidates for therapy, and FDA-approved compounds were searched using the CMap platform. From results of the present study, we provide sufficient evidence of miRNA-mRNA interactive networks associated with MERS-CoV infection with their possible extension to other strains of coronavirus infections.

Materials and methods

Bioinformatics and high-throughput database analysis

We searched GEO databases for available datasets comparing MERS-CoV-infected and mock-infected epithelial cells of human lung adenocarcinomas. Two high-throughput datasets (GSE65574 and GSE139516) with differential expression data for mRNAs and miRNAs were acquired from National Center for Biotechnology Information (NCBI) GEO databases,7 , 13 and the GSE56677 dataset was used for external validation.8 The raw data were downloaded, and we used CLC Genomics Workbench v10.1 for analysis.14 , 15 The Database for Annotation, Visualization and Integrated Discovery (DAVID, vers. 6.8) includes gene functional classifications, various processes and pathways in gene ontology (GO), and the Kyoto Encyclopedia of Genes and Genomes (KEGG). We used an agglomeration algorithm in DAVID to cluster genes into different groups according to their biological functions, signaling pathways, as well as the associated diseases. The top 5% of highly expressed mRNAs in MERS-CoV-infected cells were compared to mock-infected controls, and the P value was set at 0.05 according to previous methods.16, 17, 18, 19, 20, 21 Significantly differentially expressed genes were input to the GO database and gene set enrichment analysis (GSEA) software to construct biological networks or regulatory pathways, and the cutoff point of significant enrichment was set to P < 0.05.

Analyses using miRNA-targeting prediction databases

We used miRWalk 2.0 to predict potential miRNA-targeted genes; this program contains eight different software packages: mirWalk, MicroT4, miRanda, miRDB, miRmap, RNA22, RNAhybrid, and TargetScan.22 The miRmap score is the repression strength of miRNA binding to its target mRNA. We set the cutoff of the miRmap score to >99, and the top 5% of significantly highly expressed miRNAs from MERS-CoV-infected cells compared to mock-infected controls were analyzed. The results were imported into miRtest, which evaluates both individual miRNAs and their target mRNAs in the same analysis for consistency.

Investigation of protein–protein interactions (PPIs)

The Search Tool for the Retrieval of Interacting Genes (STRING) software contains protein–protein interaction (PPI) networks from 5090 organisms, 24.6 million proteins, and more than 2000 million interactions.23 We used STRING (vers. 11.0) to analyze PPIs based on the top 5% of genes with highly differential expression. The k-means clustering algorithm was selected to classify the proteins into different groups.

Connections between potential therapeutic compounds and miRNA-targeted genes via Connectivity Map

The miRNA-targeted genes from MERS-CoV-infected cells were compared to those from mock-infected controls through the CMap platform.24 , 25 CMap applies a systematic approach to reveal interactions among diseases and to reveal FDA-approved compounds, based on alterations in the gene expressions of MERS-CoV-infected cells.

Results

The miRNA and mRNA profiles of MERS-CoV-infected human lung adenocarcinoma epithelial cells

We identified differential expressions of mRNAs and miRNAs from MERS-CoV-infected and mock-infected epithelial cells of human lung adenocarcinomas and analyzed them using miRtest. Upregulated mRNAs and miRNAs in MERS-CoV-infected cells were examined with an ingenuity pathway analysis (IPA) to find downstream networks, with STRING software to detect PPIs, and with DAVID to investigate associated pathways. We also used miRmap and Venn diagrams to screen miRNA-targeted genes and potential miRNA-mRNA interactions (Fig. 1 ).

Figure 1.

Figure 1

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Schematic illustration of the study design. The high-throughput datasets of MERS-CoV-infected human lung adenocarcinoma epithelial cells and mock-infected controls were acquired from GEO. Differentially expressed miRNAs (>2 multiples of change) were analyzed with miRWalk and miRtest for targeting predictions. The top 5% of differentially expressed mRNAs were identified and analyzed with IPA for the pathway analysis and with DAVID for functional interpretations. Downstream signaling pathways were analyzed by GO software. miRNA-mRNA interactions were determined by a Venn diagram analysis. FDA-approved compounds were simulated by CMap. Abbreviations: CMap, Connectivity Map; CoV, coronavirus; DAVID, Database for Annotation, Visualization and Integrated Discovery; FDA, Food and Drug Administration; GEO, gene expression omnibus; GO, gene ontology; IPA, ingenuity pathway analysis; MERS, Middle East respiratory syndrome; miRNA, micro-ribonucleic acid; mRNA, messenger ribonucleic acid.

The top 5% of highly expressed mRNAs in MERS-CoV-infected cells were compared to the mock-infected controls. There were 1636 mRNAs selected from the GSE65574 dataset and 2831 mRNAs from the GSE139516 dataset (Fig. 2 A). We compared these data in a Venn diagram and found that there were 251 upregulated genes in MERS-CoV-infected cells that overlapped in these two datasets. These genes were uploaded into KEGG; the associated pathways are shown in Fig. 2B. We also used GO software to analyze pathways to connected biological processes (Fig. 2C), related molecular functions (Fig. 2D), and associated cellular components (Fig. 2E). The gene lists in each pathway are shown in Supplementary Tables 1–3. From the above analysis, we found that “viral carcinogenesis” may play a critical role in MERS-CoV-infected cells (Supplementary Figure 1). In order to confirm the significance of the 251 upregulated genes in Fig. 2A, we investigated their expression levels in MERS-infected Calu-3 cells in the GSE56677 dataset. Upregulated genes in MERS-infected cells included RC3H1, GPNMB, NFKBIA, GEM, CD69, TNFAIP3, LONRF3, LEAP2, C16orf72, ENO3, etc (Fig. 3 , Supplementary Table 4).

Figure 2.

Figure 2

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Upregulated genes in MERS-CoV-infected cells and associated pathways. (A) Upregulated genes in the GSE65574 and GSE139516 datasets were merged in a Venn diagram. MERS-CoV-infected cells were compared to mock-infected controls. In total, 251 overlapping genes were obtained. (B) Potential regulatory networks from analyzing these 251 upregulated genes in KEGG databases. (C) Biological processes in the GO analysis of 251 upregulated genes. (D) Molecular function in the GO analysis of 251 upregulated genes. (E) Cellular components in the GO analysis of 251 upregulated genes. The pathways with a P value of <0.05 were considered significant. The area and number of each pathway reflect -log10 values of the significance level. Abbreviations: CoV, coronavirus; DAVID, Database for Annotation, Visualization and Integrated Discovery; DNA, deoxyribonucleic acid; GO, gene ontology; IPA, ingenuity pathway analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes; MERS, Middle East respiratory syndrome; mRNA, messenger ribonucleic acid; NF-κB, nuclear factor kappa B; NOD, nucleotide-binding oligomerization domain; PI3K/Akt, phosphoinositide 3-kinase/protein kinase B; TNF, tumor necrosis factor.

Figure 3.

Figure 3

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External validation of MERS-CoV-regulated genes by MERS-CoV-infected Calu-3 cells. The 251 MERS-CoV-regulated genes obtained from the GSE65574 and GSE139516 datasets were further validated with the GSE56677 dataset. Gene expressions in individual MERS-CoV-infected Calu-3 cells are shown. Violin plots show expression levels of each gene from the 36-gene differentiation signature in individual cells. The P values for each gene are shown in Supplementary Table 4. Abbreviations: CoV, coronavirus; MERS, Middle East respiratory syndrome.

MERS-CoV-infection-related pathways in the GSEA

We utilized a public database to verify the importance of targeted pathways, as in Fig. 2. Differentially expressed genes (DEGs) in MERS-CoV-infected cells compared to mock-infected controls were analyzed using the KEGG, GO, BioCarta, and Hallmark databases. Upregulated genes were analyzed using a GSEA to identify potential regulated networks. Copious amounts of serum interferons (IFNs) are one of the clinical features of patients with MERS-CoV infection, and we detected positive correlations of IFN-α/γ responses in MERS-CoV-infected cells. There were several immune-related pathways enriched after MERS-CoV infection (Fig. 4A–I). Enriched pathways included olfactory and sensory systems, and olfactory neuropathy is one of the symptoms associated with SARS-CoV and SARS-CoV-2.26, 27, 28 Enriched pathways also included cellular defense responses to viral infections (Fig. 4 J–M). Some signaling pathways were also upregulated, including the Janus kinase (JAK)/signal transduction and activator of transcription (STAT), the neuroactive ligand receptor, and the electron transfer system (ETS) pathways (Fig. 4N–P).

Figure 4.

Figure 4

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Enriched pathways in MERS-CoV-infected cells were associated with immune, olfactory, and sensory systems and cellular response-related pathways. (A) KEGG analysis of cytokine–cytokine receptor interactions. (B) Hallmark analysis of IFN-α responses. (C) Hallmark analysis of IFN-γ responses. (D) Hallmark analysis of TNF-α signaling via NF-κB. (E) Hallmark analysis of inflammatory responses. (F) Biocarta analysis of TNF receptor 2 pathways. (G) Biocarta analysis of B cell survival pathways. (H) Biocarta analysis of NO2 IL-12 pathways. (I) GO analysis of dense responses to the virus. (J) KEGG analysis of olfactory transduction. (K) GO analysis of olfactory receptor activity. (L) GO analysis of odorant binding. (M) GO analysis of sensory perception of chemical stimuli. (N) KEGG analysis of the JAK/STAT signaling pathway. (O) KEGG analysis of neuroactive ligand receptor interaction. (P) Biocarta analysis of the ETS pathway. The 251 upregulated genes from the GSE65574 and GSE139516 datasets in Fig. 2 were imported for analysis. MERS-CoV-infected cells were compared to mock-infected controls. An enrichment score (upper third of each graph) of >0 was defined as upregulation. Each bar over the middle third indicates one gene located in the ranking. Meanwhile, he red represents a positive correlation, whereas the blue indicates a negative correlation. The distribution of the ranked list along with the gene list is shown as the gray part in the lower third of each graph. Abbreviation: CoV, coronavirus; ETS, electron transfer system; IFN, interferon; JAK, Janus kinase; KEGG, Kyoto Encyclopedia of Genes and Genomes; IL-12, interleukin-12; MERS, Middle East respiratory syndrome; NF-κB, nuclear factor kappa B; NO2, nitrogen dioxide; STAT, signal transducer and activator of transcription; TNF, tumor necrosis factor.

Discovering miRNA-mRNA interactions in MERS-CoV-infected human lung adenocarcinoma epithelial cells

The top 5% of highly expressed miRNAs were identified in MERS-CoV-infected cells compared to the mock-infected controls. There were 139 miRNAs from the GSE65574 dataset and 228 miRNAs from the GSE139516 dataset. The intersection of these two groups of miRNAs contained hsa-miR-1247-3p, hsa-miR-127-5p, hsa-miR-376a-3p, hsa-miR-3180-3p, hsa-miR-3663-3p, hsa-miR-622, hsa-miR-3180, hsa-miR-4660, and hsa-miR-3173-3p. Hsa-miR-376a-3p was excluded, because this miRNA targeted fewer than 1000 genes, and it failed to establish intersections with other miRNAs. Therefore, eight miRNAs were uploaded to the miRWalk software to predict miRNA-targeted genes. In total, 115 genes were identified (Fig. 5 A). Through a Venn diagram, we compared these miRNA-targeted genes with upregulated mRNAs from MERS-CoV-infected cells in the GSE65574 and GSE139516 datasets. The analytical results of miRNA-mRNA interactions detected three genes: transcription factor 4 (TCF4), nuclear receptor subfamily 3 group C member 1 (NR3C1), and POU class 2 homeobox 2 (POU2F2) (Fig. 5B). Potential PPI networks among the TCF4, NR3C1, and POU2F2 genes were drawn using STRING software (Fig. 5C).

Figure 5.

Figure 5

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Interacting network of miRNAs and mRNAs in MERS-CoV-infected cells. (A) Highly differential expressions of miRNAs in the GSE65574 and GSE139516 datasets were merged in a Venn diagram. MERS-CoV-infected cells were compared to mock-infected controls. In total, eight overlapping miRNAs were obtained and uploaded to miRWalk, and 115 targeted genes were obtained. (B) miRNA-targeted genes and upregulated mRNAs were merged in a Venn diagram. Three overlapping genes were obtained. (C) The TCF4, NR3C1, and POU2F2 genes were input into STRING software to predict protein–protein interactions. Using k-means clustering, the network was further clustered into different subclusters. Abbreviation: CoV, coronavirus; MERS, Middle East respiratory syndrome; miRNA, micro-ribonucleic acid; mRNA, messenger ribonucleic acid; NR3C1, nuclear receptor subfamily 3 group C member 1; POU2F2, POU class 2 homeobox 2; TCF4, transcription factor 4.

Identification of potential inhibitory compounds from the Connectivity Map

We used CMap to investigate potential compounds that might inhibit upregulated genes in MERS-CoV-infected cells. In total, 1309 compounds were collected, and more than 7000 treatments with different dosages were found in different cell lines to obtain gene expression profiles. The compounds with the most negative correlations, which may have the highest potential of suppressive effects against MERS-CoV-infected cells (Fig. 6 A). The top 30 negatively correlated compounds in MERS-CoV-infected cells from the GSE65574 dataset were compared to those from the GSE139516 dataset. Only compounds in the intersection of the two datasets are listed in Fig. 6B, and 50 potential therapeutic drugs are listed in Supplementary Table 5. Further investigation is necessary to confirm the therapeutic potential of these compounds against MERS-CoV infection and other coronavirus infections.

Figure 6.

Figure 6

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Connectivity Map (CMap) analysis of potential compounds for MERS-CoV-infected cells. (A) We uploaded up- and downregulated genes identified in MERS-CoV-infected cells into CMap software to predict potential compounds compared to mock-infected controls. (B) The 30 bottom compounds from the GSE65574 dataset were compared to those in the GSE139516 dataset. The 17 common compounds in both datasets are listed. Compounds with negative correlations were those with the most powerful ability against MERS-CoV-infected cells. Abbreviations: CoV, coronavirus; MERS, Middle East respiratory syndrome.

Discussion

Although significant improvements in biomedical and pharmaceutical research have occurred in recent decades, the annual number of new drugs approved by the US FDA is limited. Drug repurposing shortens the time and reduces the costs of drug development.29, 30, 31 During the ongoing SARS-CoV-2 pandemic, the search for therapeutic drugs is extremely urgent. Studying previous coronavirus infections due to SARS-CoV and MERS-CoV is a readily available scheme.32 , 33 The present study used comprehensive bioinformatics to investigate miRNA-mRNA interactions in MERS-CoV-infected models. We also explored MERS infection-related pathways to explain clinical manifestations of patients with a coronavirus infection. Conceivable drugs for treating MERS-CoV infection were predicted by CMap. The current study provides therapeutic advice for patients with MERS infection, and the results can potentially be extended to SARS-CoV-2 infections.

High-throughput methods with RNA sequencing have improved accuracy and sensitivity in studying miRNAs, which have become an important point in virus studies, because they regulate viral replication and gene expressions, which also control expressions of essential genes for viral proliferation. However, there are only a few studies about miRNAs in coronavirus-infected human cells, and integrated research into miRNA and mRNA databases in MERS-CoV-infected cells is currently lacking. Previous studies have demonstrated that coronavirus infection is correlated with cytokine storms and inflammation.34 , 35 The coronavirus E protein/mitogen-activated protein kinase (MAPK)/phosphoinositide 3-kinase (PI3K)/protein kinase B/nuclear factor (NF)-κB signaling pathways respond to MERS-CoV infection and may serve as potential drug targets for therapeutic intervention strategies.36 , 37 Clinical studies have shown that SARS-CoV and SARS-CoV-2 cause olfactory dysfunction.38, 39, 40 The present study produced similar results, and the enrichment of multiple immune-related pathways in MERS-CoV-infected cells was shown by the KEGG, Hallmark, and Biocarta software analyses (Fig. 4). The PI3K/AKT pathway was activated in the KEGG analysis (Fig. 2B) and the NF-κB pathway in the KEGG and GO analyses of MERS-CoV-infected cells (Fig. 2B, E). External validation data in Fig. 3 and Supplementary Table 4 also indicate that these genes are upregulated during MERS-CoV infection. These data are consistent with previous research. For example, RNA-binding protein Roquin (RC3H1) is essential for human cytomegalovirus (HCMV) lytic production,41 NF-κB was correlated with SARS-CoV progression,42 cluster of differentiation 69 (CD69) serves as an activation marker of blood iNKT and MAIT cells in COVID-19 patients.43 TNFAIP3 responded to duck Tembusu viral infection,44 LEAP-2 plays a crucial role in immune activation as well as cytokine production for human immunodeficiency virus (HIV)/hepatitis C virus (HCV) infections.45 DUSP10 is an important regulator of rhinovirus infections,46 miR-449a enhances hepatitis B virus (HBV) replication via cAMP-responsive element binding protein 5 (CREB5).47 HIV enhancer binding protein 2 (HIVEP2) regulates neuronally mediated neuroinflammation,48 and CXCL2 serves as a pro-viral factor in early infection models of SARS-CoV-2.49 Alteration of olfactory or sensory gene signaling was detected in MERS-CoV-infected cells from the KEGG and GO analyses (Fig. 4). Our study used an enrichment analysis from gene sets of NCBI GEO datasets to explain clinical manifestations of patients and host cellular responses after viral infection.

There are several miRNAs involved in viral infection; for example, expressions of miR-1247-3p and its targeted genes increase in canine parvovirus-infected cells.50 miR-622 is upregulated in mosquitoes with dengue virus infection.51 Circulating miR-3180 was detected in patients with acute-phase HBV infection, which decreased during chronic infection.52 Other miRNAs are related to signal transduction, such as miR-127-5p activates PI3K/Akt signaling.53 The present study used a bioinformatics analysis and Venn diagrams to investigate miRNA expressions in MERS-CoV-infected cells from the GSE65574 and GSE139516 datasets. Common upregulated miRNAs included miR-1247-3p, miR-127-5p, miR-622, and miR-3180; miR-4660 is a serum marker of patients with oxalosis, but it is not involved in infection or immune regulation.54 We also found miRNAs without available data as to their molecular function, including miR-3180-3p and miR-3173-3p. These miRNAs were highly expressed in MERS-CoV-infected cells and may play important roles in disease development (Fig. 5A). We combined miRNA-targeted genes with upregulated mRNAs from the two datasets, and the intersection showed overexpression of the TCF4, NR3C1, and POU2F2 genes in MERS-CoV-infected cells (Fig. 5B). These three genes are reported in the viral infection-associated literature; for instance, Kaposi's sarcoma-associated herpesvirus reduces the binding affinity of the TCF4 protein to inhibit WNT signaling.55 , 56 Human immunodeficiency virus (HIV) is one RNA virus associated with TCF-4 and regulated by canonical Wnt/β-catenin signaling.57 , 58 Expression of NR3C1 is correlated with the serostatus of HIV.59 , 60 The OFT-2 and NF-κB genes were firmly upregulated after influenza A virus infection in a monocytic cell line.61 A nuclear antigen of the Epstein–Barr virus coincides with the POU2F2 protein to regulate transcription.62 Interacting networks of TCF4, NR3C1, and POU2F2 proteins in MERS-CoV-infected cells were established by STRING software (Fig. 5C). Our study discovered major altered miRNAs, and their downstream target genes are potential targets for new therapeutic agents against MERS-CoV infection; this study also sheds light on how cells respond to viral infection.

Through the CMap analysis, we revealed that compounds with highly negative correlations might serve as potential treatments for MERS-CoV-infected cells (Fig. 6). Some of these data were confirmed by previous research. Diethylcarbamazine (DEC) is an antihelmintic drug for filariasis. For patients coinfected with the filarial parasite, Wuchereria bancrofti, and HIV, DEC helps anti-HIV drugs suppress the viral load and increase the CD4 level.63 , 64 There is also some proposed evidence for using DEC in COVID-19.65 Harpagoside has potent anti-inflammatory effects and is active against the vesicular stomatitis virus in vitro.66 Ambroxol inhibits type 14 rhinovirus (RV14) infection in human tracheal epithelial cells and may serve as a potential therapeutic drug for severe influenza-associated complications.67 , 68 Bumetanide is a loop diuretic; it inhibited interactions of HIV and human protein lens epithelium-derived growth factor (LEDGF/p75) in a cellular study.69 Enalapril slows down the progression of HIV-associated kidney disease, and drug–drug interactions between enalapril and anti-HIV agents are possible.70 Valproic acid is an anticonvulsive drug and inhibits the replication of enveloped viruses in cultured cells. Valproic acid suppresses uptake of virus particles during herpes virus infection,71 and it induces antiviral action in SARS-CoV-2-infected lung epithelial cells.72 Fluticasone may be a potentially effective drug for SARS-CoV-2.73 Amitriptyline increased survival rates in an influenza A H5N1 virus-infected model.74 Streptomycin may demonstrate some inhibitory effects against SARS-CoV-275 and could significantly reduce coxsackievirus B3 (CVB3) shedding and pathogenesis.76 HIV-1 protease activity was inhibited by pepstatin.77 Estradiol may protect women from the most serious complications of COVID-19,78 and treatment with diphenhydramine in combination with ibuprofen provided therapeutic benefits in influenza A infection.79 Myricetin inhibited HIV-1 as well as coronavirus disease progression.80 , 81 Other compounds are also listed by CMap in Supplementary Table 5. These compounds might be potential therapeutic drugs against MERS-CoV infection.

Conclusions

The present study focused on MERS-CoV-infection-associated pathways. Although further studies are required to extend these results to SARS-CoV and SARS-CoV-2 infections, we demonstrated crucial roles of miRNAs and mRNAs in MERS-CoV-infected cells. These miRNA- and miRNA-targeted genes and upregulated mRNAs are potential targets for the treatment and prevention of MERS-CoV infection. These novel genes and drugs may guide experimental efforts towards developing vaccines against MERS-CoV and may further combat the coronavirus pandemic.

Funding

The study was supported by the Ministry of Science and Technology (MOST) of Taiwan (grants MOST105-2325-B-006-003 to M.-D.L., MOST 109-2314-B-006-018-MY3 to H.-P.H., and MOST109-2320-B-038-009-MY2 to C.-Y.W.), the National Cheng Kung University Hospital (grant NCKUH-10601002 to M.-D.L. and NCKUH-11002013 to H.-P.H.), Kaohsiung Medical University Hospital (KMUH108-8R72 to M.-C.Y.), and Taipei Medical University (grant TMU-108-AE1-B16 to C.-Y.W.).

Author contributions

Conceptualization, Y.H.W., H.P.H., C.Y.W., and M.D.L.; methodology and formal analysis, M.C.Y., I.J.Y., and J.H.H..; investigations, C.C.C. and C.F.C.; resources, N.N.P. and Z.S.; data curation, M.C.Y., I.J.Y., and J.H.H.; writing-original draft preparation Y.H.W and I.J.Y.; writing-review and editing, H.P.H., C.Y.W., and M.D.L.; project administration, H.P.H., C.Y.W., and M.D.L.; funding acquisition, M.C.Y., H.P.H., M.D.L., and C.Y.W. All authors have read and agreed to the published version of the manuscript.

Declaration of competing interest

The authors declare no conflict of interest.

Acknowledgments

Bioinformatics analyses and data mining were conducted at Taipei Medical University and the Bioinformatics Core at the National Cheng Kung University (Tainan, Taiwan). The authors give special thanks to EM695 from Scribendi (https://www.scribendi.com/) for excellent Academic Proofreading during revising and to Mr. Dan Chamberlin from the Office of Research and Development at Taipei Medical University for the edited first draft. This research was supported in part by the Higher Education Sprout Project, Ministry of Education to the Headquarters of University Advancement at NCKU. Meanwhile, we are grateful to the National Center for High-performance Computing in Taiwan for computer time and facilities (https://www.nchc.org.tw/).

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jmii.2021.03.007.

Appendix A. Supplementary data

The following is the Supplementary data to this article:

Multimedia component 1
mmc1.docx (135.9KB, docx)

References

  • 1.Uddin M.B., Hasan M., Harun-Al-Rashid A., Ahsan M.I., Imran M.A.S., Ahmed S.S.U. Ancestral origin, antigenic resemblance and epidemiological insights of novel coronavirus (SARS-CoV-2): global burden and Bangladesh perspective. Infect Genet Evol. 2020;84:104440. doi: 10.1016/j.meegid.2020.104440. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Lee Y.-L., Chu D., Chou S.-Y., Hu H.-Y., Huang S.-J., Yen Y.-F. Dental care and infection-control procedures during the COVID-19 pandemic: the experience in Taipei City Hospital, Taiwan. J Dent Sci. 2020;15(3):369–372. doi: 10.1016/j.jds.2020.05.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Tsai L., Hsieh S., Chang Y. Neurological manifestations in severe acute respiratory syndrome. Acta Neurol Taiwanica. 2005;14:113. [PubMed] [Google Scholar]
  • 4.Ou X., Liu Y., Lei X., Li P., Mi D., Ren L. Characterization of spike glycoprotein of SARS-CoV-2 on virus entry and its immune cross-reactivity with SARS-CoV. Nat Commun. 2020;11:1620. doi: 10.1038/s41467-020-15562-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Liang Y., Wang M.-L., Chien C.-S., Yarmishyn A.A., Yang Y.-P., Lai W.-Y. Highlight of immune pathogenic response and hematopathologic effect in SARS-CoV, MERS-CoV, and SARS-cov-2 infection. Front Immunol. 2020;11:1022. doi: 10.3389/fimmu.2020.01022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Tseng Y.H., Yang R.C., Lu T.S. Two hits to the renin-angiotensin system may play a key role in severe COVID-19. Kaohsiung J Med Sci. 2020;36(6):389–392. doi: 10.1002/kjm2.12237. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Menachery V.D., Mitchell H.D., Cockrell A.S., Gralinski L.E., Yount B.L., Jr., Graham R.L. MERS-CoV accessory ORFs play key role for infection and pathogenesis. mBio. 2017;8 doi: 10.1128/mBio.00665-17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Selinger C., Tisoncik-Go J., Menachery V.D., Agnihothram S., Law G.L., Chang J. Cytokine systems approach demonstrates differences in innate and pro-inflammatory host responses between genetically distinct MERS-CoV isolates. BMC Genom. 2014;15:1161. doi: 10.1186/1471-2164-15-1161. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Liu C., Chen Z., Hu Y., Ji H., Yu D., Shen W. Complemented palindromic small RNAs first discovered from SARS coronavirus. Genes. 2018;9 doi: 10.3390/genes9090442. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Messina F., Giombini E., Agrati C., Vairo F., Bartoli T.A., Al Moghazi S. COVID-19: viral–host interactome analyzed by network based-approach model to study pathogenesis of SARS-CoV-2 infection. J Transl Med. 2020;18:1–10. doi: 10.1186/s12967-020-02405-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Liu H.-L., Yeh I.-J., Phan N.N., Wu Y.-H., Yen M.-C., Hung J.-H. Gene signatures of SARS-CoV/SARS-CoV-2-infected ferret lungs in short-and long-term models. Infect Genet Evol. 2020;85:104438. doi: 10.1016/j.meegid.2020.104438. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Yeung M.-L., Yao Y., Jia L., Chan J.F.W., Chan K.-H., Cheung K.-F. MERS coronavirus induces apoptosis in kidney and lung by upregulating Smad7 and FGF2. Nat Microbiol. 2016;1 doi: 10.1038/nmicrobiol.2016.4. 16004-16004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Zhang X., Chu H., Wen L., Shuai H., Yang D., Wang Y. Competing endogenous RNA network profiling reveals novel host dependency factors required for MERS-CoV propagation. Emerg Microb Infect. 2020;9:733–746. doi: 10.1080/22221751.2020.1738277. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Ou K.C., Wang C.Y., Liu K.T., Chen Y.L., Chen Y.C., Lai M.D. Optimization protein productivity of human interleukin-2 through codon usage, gene copy number and intracellular tRNA concentration in CHO cells. Biochem Biophys Res Commun. 2014;454:347–352. doi: 10.1016/j.bbrc.2014.10.097. [DOI] [PubMed] [Google Scholar]
  • 15.Wang C.-Y., Chiao C.-C., Phan N.N., Li C.-Y., Sun Z.-D., Jiang J.-Z. Gene signatures and potential therapeutic targets of amino acid metabolism in estrogen receptor-positive breast cancer. Am J Canc Res. 2020;10:95. [PMC free article] [PubMed] [Google Scholar]
  • 16.Wang C.Y., Li C.Y., Hsu H.P., Cho C.Y., Yen M.C., Weng T.Y. PSMB5 plays a dual role in cancer development and immunosuppression. Am J Cancer Res. 2017;7:2103–2120. [PMC free article] [PubMed] [Google Scholar]
  • 17.Cheng L.-C., Chao Y.-J., Overman M.J., Wang C.-Y., Phan N.N., Chen Y.-L. Increased expression of secreted frizzled related protein 1 (SFRP1) predicts ampullary adenocarcinoma recurrence. Sci Rep. 2020;10:1–16. doi: 10.1038/s41598-020-69899-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Phan N.N., Liu S., Wang C.-Y., Hsu H.-P., Lai M.-D., Li C.-Y. Overexpressed gene signature of EPH receptor A/B family in cancer patients-comprehensive analyses from the public high-throughput database. Int J Clin Exp Pathol. 2020;13:1220. [PMC free article] [PubMed] [Google Scholar]
  • 19.Cheng L.C., Kao T.J., Phan N.N., Chiao C.C., Yen M.C., Chen C.F. Novel signaling pathways regulate SARS-CoV and SARS-CoV-2 infectious disease. Medicine (Baltim) 2021;100 doi: 10.1097/MD.0000000000024321. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Wu Y.H., Yeh I.J., Phan N.N., Yen M.C., Liu H.L., Wang C.Y. Severe acute respiratory syndrome coronavirus (SARS-CoV)-2 infection induces dysregulation of immunity: in silico gene expression analysis. Int J Med Sci. 2021;18:1143–1152. doi: 10.7150/ijms.52256. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Wang C.Y., Chao Y.J., Chen Y.L., Wang T.W., Phan N.N., Hsu H.P. Upregulation of peroxisome proliferator-activated receptor-α and the lipid metabolism pathway promotes carcinogenesis of ampullary cancer. Int J Med Sci. 2021;18:256–269. doi: 10.7150/ijms.48123. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Tian H., He Z. miR-200c targets nuclear factor IA to suppress HBV replication and gene expression via repressing HBV Enhancer I activity. Biomed Pharmacother. 2018;99:774–780. doi: 10.1016/j.biopha.2018.01.141. [DOI] [PubMed] [Google Scholar]
  • 23.Szklarczyk D., Gable A.L., Lyon D., Junge A., Wyder S., Huerta-Cepas J. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607–D613. doi: 10.1093/nar/gky1131. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Lamb J., Crawford E.D., Peck D., Modell J.W., Blat I.C., Wrobel M.J. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006;313:1929–1935. doi: 10.1126/science.1132939. [DOI] [PubMed] [Google Scholar]
  • 25.Lamb J. The Connectivity Map: a new tool for biomedical research. Nat Rev Canc. 2007;7:54. doi: 10.1038/nrc2044. [DOI] [PubMed] [Google Scholar]
  • 26.Chi H., Chiu N.C., Peng C.C., Lin C.H., Tai Y.L., Lee M.D. One-seventh of patients with COVID-19 had olfactory and gustatory abnormalities as their initial symptoms: a systematic review and meta-analysis. Life. 2020;10 doi: 10.3390/life10090158. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Parma V., Ohla K., Veldhuizen M.G., Niv M.Y., Kelly C.E., Bakke A.J. More than smell—COVID-19 is associated with severe impairment of smell, taste, and chemesthesis. Chem Senses. 2020;45:609–622. doi: 10.1093/chemse/bjaa041. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Hwang C.S. Olfactory neuropathy in severe acute respiratory syndrome: report of A case. Acta Neurol Taiwan. 2006;15:26–28. [PubMed] [Google Scholar]
  • 29.Jean S.-S., Hsueh P.-R. Old and re-purposed drugs for the treatment of COVID-19. Expert Rev Anti-infect Ther. 2020;18(9):843–847. doi: 10.1080/14787210.2020.1771181. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Tu Y.-F., Chien C.-S., Yarmishyn A.A., Lin Y.-Y., Luo Y.-H., Lin Y.-T. A review of SARS-CoV-2 and the ongoing clinical trials. Int J Mol Sci. 2020;21:2657. doi: 10.3390/ijms21072657. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Jean S.-S., Lee P.-I., Hsueh P.-R. Treatment options for COVID-19: the reality and challenges. J Microbiol Immunol Infect. 2020;53(3):436–443. doi: 10.1016/j.jmii.2020.03.034. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Cao Q., Chen Y.-C., Chen C.-L., Chiu C.-H. SARS-CoV-2 infection in children: transmission dynamics and clinical characteristics. J Formos Med Assoc. 2020;119:670–673. doi: 10.1016/j.jfma.2020.02.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Chan J.F.-W., Lau S.K.-P., Woo P.C.-Y. The emerging novel Middle East respiratory syndrome coronavirus: the “knowns” and “unknowns”. J Formos Med Assoc. 2013;112:372–381. doi: 10.1016/j.jfma.2013.05.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Sheahan T.P., Sims A.C., Leist S.R., Schafer A., Won J., Brown A.J. Comparative therapeutic efficacy of remdesivir and combination lopinavir, ritonavir, and interferon beta against MERS-CoV. Nat Commun. 2020;11:222. doi: 10.1038/s41467-019-13940-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Mehta P., McAuley D.F., Brown M., Sanchez E., Tattersall R.S., Manson J.J. COVID-19: consider cytokine storm syndromes and immunosuppression. Lancet. 2020;395:1033–1034. doi: 10.1016/S0140-6736(20)30628-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Kindrachuk J., Ork B., Hart B.J., Mazur S., Holbrook M.R., Frieman M.B. Antiviral potential of ERK/MAPK and PI3K/AKT/mTOR signaling modulation for Middle East respiratory syndrome coronavirus infection as identified by temporal kinome analysis. Antimicrob Agents Chemother. 2015;59:1088–1099. doi: 10.1128/AAC.03659-14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Yang C.W., Lee Y.Z., Hsu H.Y., Shih C., Chao Y.S., Chang H.Y. Targeting coronaviral replication and cellular JAK2 mediated dominant NF-kappaB activation for comprehensive and ultimate inhibition of coronaviral activity. Sci Rep. 2017;7:4105. doi: 10.1038/s41598-017-04203-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Yen Y.-F., Lai H.-H., Chan S.-Y., Su V.Y.-F., Chiu T.-F., Huang C.-Y. Olfactory disorder in patients infected with SARS-CoV-2. J Microbiol Immunol Infect. 2020 Aug 20 doi: 10.1016/j.jmii.2020.08.010. S1684-1182(20)30208-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Khare P., Chander A.M., Agrawal K., Jayant S.S., Mukherjee S., Yadav K. medRxiv; 2020. Utility of Olfactory test as screening tool for COVID-19: a pilot study. [Google Scholar]
  • 40.Lai C.-C., Ko W.-C., Lee P.-I., Jean S.-S., Hsueh P.-R. Extra-respiratory manifestations of COVID-19. Int J Antimicrob Agents. 2020:106024. doi: 10.1016/j.ijantimicag.2020.106024. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Song J., Lee S., Cho D.-Y., Lee S., Kim H., Yu N. Human cytomegalovirus induces and exploits Roquin to counteract the IRF1-mediated antiviral state. Proc Natl Acad Sci Unit States Am. 2019;116:18619–18628. doi: 10.1073/pnas.1909314116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Frieman M., Ratia K., Johnston R.E., Mesecar A.D., Baric R.S. Severe acute respiratory syndrome coronavirus papain-like protease ubiquitin-like domain and catalytic domain regulate antagonism of IRF3 and NF-κB signaling. J Virol. 2009;83:6689–6705. doi: 10.1128/JVI.02220-08. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Jouan Y., Guillon A., Gonzalez L., Perez Y., Boisseau C., Ehrmann S. Phenotypical and functional alteration of unconventional T cells in severe COVID-19 patients. J Exp Med. 2020;217 doi: 10.1084/jem.20200872. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Pan Y., Cheng A., Zhang X., Wang M., Chen S., Zhu D. Transcriptome analysis of duck embryo fibroblasts for the dynamic response to duck tembusu virus infection and dual regulation of apoptosis genes. Aging. 2020;12 doi: 10.18632/aging.103759. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Shata M.T.M., Abdel-Hameed E.A., Hetta H.F., Sherman K.E. Immune activation in HIV/HCV-infected patients is associated with low-level expression of liver expressed antimicrobial peptide-2 (LEAP-2) J Clin Pathol. 2013;66:967–975. doi: 10.1136/jclinpath-2013-201581. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Manley G.C., Stokes C.A., Marsh E.K., Sabroe I., Parker L.C. DUSP10 negatively regulates the inflammatory response to rhinovirus through interleukin-1β signaling. J Virol. 2019;93 doi: 10.1128/JVI.01659-18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Zhang X., Liu H., Xie Z., Deng W., Wu C., Qin B. Epigenetically regulated miR-449a enhances hepatitis B virus replication by targeting cAMP-responsive element binding protein 5 and modulating hepatocytes phenotype. Sci Rep. 2016;6:25389. doi: 10.1038/srep25389. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Sakurai T. Location, location, location: location of neuroinflammation is important in pathogenesis of schizophrenia. Brain Behav Immun. 2020;88:842–843. doi: 10.1016/j.bbi.2020.06.026. [DOI] [PubMed] [Google Scholar]
  • 49.Loganathan T., Ramachandran S., Shankaran P., Nagarajan D. Host transcriptome-guided drug repurposing for COVID-19 treatment: a meta-analysis based approach. PeerJ. 2020;8:e9357. doi: 10.7717/peerj.9357. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Chuammitri P., Vannamahaxay S., Sornpet B., Pringproa K., Patchanee P. Detection and characterization of microRNA expression profiling and its target genes in response to canine parvovirus in Crandell Reese Feline Kidney cells. PeerJ. 2020;8 doi: 10.7717/peerj.8522. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Su J., Wang G., Li C., Xing D., Yan T., Zhu X. Screening for differentially expressed miRNAs in Aedes albopictus (Diptera: Culicidae) exposed to DENV-2 and their effect on replication of DENV-2 in C6/36 cells. Parasites Vectors. 2019;12:44. doi: 10.1186/s13071-018-3261-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Chen J., Wu X., Cai B., Su Z., Li L., An Y. Circulating microRNAs as potential biomarkers of HBV infection persistence. Infect Genet Evol. 2017;54:152–157. doi: 10.1016/j.meegid.2017.06.023. [DOI] [PubMed] [Google Scholar]
  • 53.Liang J., Xu L., Zhou F., Liu A.M., Ge H.X., Chen Y.Y. MALAT1/miR-127-5p regulates osteopontin (OPN)-Mediated proliferation of human chondrocytes through PI3K/akt pathway. J Cell Biochem. 2018;119:431–439. doi: 10.1002/jcb.26200. [DOI] [PubMed] [Google Scholar]
  • 54.Tu X., Zhao Y., Li Q., Yu X., Yang Y., Shi S. Human MiR-4660 regulates the expression of alanine-glyoxylate aminotransferase and may be a biomarker for idiopathic oxalosis. Clin Exp Nephrol. 2019;23:890–897. doi: 10.1007/s10157-019-01723-8. [DOI] [PubMed] [Google Scholar]
  • 55.Cha S., Kang M.S., Seo T. KSHV vPK inhibits Wnt signaling via preventing interactions between beta-catenin and TCF4. Biochem Biophys Res Commun. 2018;497:381–387. doi: 10.1016/j.bbrc.2018.02.089. [DOI] [PubMed] [Google Scholar]
  • 56.Feng W., Zhou D., Meng W., Li G., Zhuang P., Pan Z. Growth retardation induced by avian leukosis virus subgroup J associated with down-regulated Wnt/beta-catenin pathway. Microb Pathog. 2017;104:48–55. doi: 10.1016/j.micpath.2017.01.013. [DOI] [PubMed] [Google Scholar]
  • 57.Aljawai Y., Richards M.H., Seaton M.S., Narasipura S.D., Al-Harthi L. β-Catenin/TCF-4 signaling regulates susceptibility of macrophages and resistance of monocytes to HIV-1 productive infection. Curr HIV Res. 2014;12:164–173. doi: 10.2174/1570162x12666140526122249. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Henderson L.J., Al-Harthi L. Role of β-catenin/TCF-4 signaling in HIV replication and pathogenesis: insights to informing novel anti-HIV molecular therapeutics. J Neuroimmune Pharmacol. 2011;6:247–259. doi: 10.1007/s11481-011-9266-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Bekhbat M., Mehta C.C., Kelly S.D., Vester A., Ofotokun I., Felger J. HIV and symptoms of depression are independently associated with impaired glucocorticoid signaling. Psychoneuroendocrinology. 2018;96:118–125. doi: 10.1016/j.psyneuen.2018.06.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Smith A.J., Li Q., Wietgrefe S.W., Schacker T.W., Reilly C.S., Haase A.T. Host genes associated with HIV-1 replication in lymphatic tissue. J Immunol. 2010;185:5417–5424. doi: 10.4049/jimmunol.1002197. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Bussfeld D., Bacher M., Moritz A., Gemsa D., Sprenger H. Expression of transcription factor genes after influenza A virus infection. Immunobiology. 1997;198:291–298. doi: 10.1016/S0171-2985(97)80049-6. [DOI] [PubMed] [Google Scholar]
  • 62.Jiang S., Willox B., Zhou H., Holthaus A.M., Wang A., Shi T.T. Epstein-Barr virus nuclear antigen 3C binds to BATF/IRF4 or SPI1/IRF4 composite sites and recruits Sin3A to repress CDKN2A. Proc Natl Acad Sci U S A. 2014;111:421–426. doi: 10.1073/pnas.1321704111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Nielsen N.O., Simonsen P.E., Dalgaard P., Krarup H., Magnussen P., Magesa S. Effect of diethylcarbamazine on HIV load, CD4%, and CD4/CD8 ratio in HIV-infected adult Tanzanians with or without lymphatic filariasis: randomized double-blind and placebo-controlled cross-over trial. Am J Trop Med Hyg. 2007;77:507–513. [PubMed] [Google Scholar]
  • 64.Bhumiratana A., Koyadun S., Srisuphanunt M., Satitvipawee P., Limpairojn N., Gaewchaiyo G. Border and imported bancroftian filariases: baseline seroprevalence in sentinel populations exposed to infections with Wuchereria bancrofti and concomitant HIV at the start of diethylcarbamazine mass treatment in Thailand. Southeast Asian J Trop Med Publ Health. 2005;36:390–407. [PubMed] [Google Scholar]
  • 65.Abeygunasekera A., Jayasinghe S. Is the anti-filarial drug diethylcarbamazine useful to treat COVID-19? Med Hypotheses. 2020:109843. doi: 10.1016/j.mehy.2020.109843. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 66.Bermejo P., Abad M.J., Diaz A.M., Fernandez L., De Santos J., Sanchez S. Antiviral activity of seven iridoids, three saikosaponins and one phenylpropanoid glycoside extracted from Bupleurum rigidum and Scrophularia scorodonia. Planta Med. 2002;68:106–110. doi: 10.1055/s-2002-20238. [DOI] [PubMed] [Google Scholar]
  • 67.Yamaya M., Nishimura H., Nadine L.K., Ota C., Kubo H., Nagatomi R. Ambroxol inhibits rhinovirus infection in primary cultures of human tracheal epithelial cells. Arch Pharm Res (Seoul) 2014;37:520–529. doi: 10.1007/s12272-013-0210-7. [DOI] [PubMed] [Google Scholar]
  • 68.Uchide N., Toyoda H. Antioxidant therapy as a potential approach to severe influenza-associated complications. Molecules. 2011;16:2032–2052. doi: 10.3390/molecules23100000. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Hu G., Li X., Sun X., Lu W., Liu G., Huang J. Identification of old drugs as potential inhibitors of HIV-1 integrase - human LEDGF/p75 interaction via molecular docking. J Mol Model. 2012;18:4995–5003. doi: 10.1007/s00894-012-1494-0. [DOI] [PubMed] [Google Scholar]
  • 70.Prakash J., Iqbal M., Patel P.S., Prakash S., Sundar S., Singh U. Outcome of HIV related kidney diseases treated with combined antiretroviral therapy (cART) J Assoc Phys India. 2019;67:18–22. [PubMed] [Google Scholar]
  • 71.Crespillo A.J., Praena B., Bello-Morales R., Lerma L., Vazquez-Calvo A., Martin-Acebes M.A. Inhibition of herpes virus infection in oligodendrocyte cultured cells by valproic acid. Virus Res. 2016;214:71–79. doi: 10.1016/j.virusres.2016.01.009. [DOI] [PubMed] [Google Scholar]
  • 72.Hachim M.Y., Al Heialy S., Hachim I.Y., Halwani R., Senok A.C., Maghazachi A.A. Interferon-induced transmembrane protein (IFITM3) is upregulated explicitly in SARS-CoV-2 infected lung epithelial cells. Front Immunol. 2020;11:1372. doi: 10.3389/fimmu.2020.01372. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73.Taguchi Y.H., Turki T. A new advanced in silico drug discovery method for novel coronavirus (SARS-CoV-2) with tensor decomposition-based unsupervised feature extraction. PloS One. 2020;15 doi: 10.1371/journal.pone.0238907. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Huang F., Zhang C., Liu Q., Zhao Y., Zhang Y., Qin Y. Identification of amitriptyline HCl, flavin adenine dinucleotide, azacitidine and calcitriol as repurposing drugs for influenza A H5N1 virus-induced lung injury. PLoS Pathog. 2020;16 doi: 10.1371/journal.ppat.1008341. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 75.Khadka S., Yuchi A., Shrestha D.B., Budhathoki P., Al-Subari S.M.M., Alhouzani T.M.Z. Repurposing drugs for COVID-19: an approach for treatment in the pandemic. Alternative Ther Health Med. 2020:AT6513. [PubMed] [Google Scholar]
  • 76.Robinson C.M., Acevedo M.A.W., McCune B.T., Pfeiffer J.K. Related enteric viruses have different requirements for host microbiota in mice. J Virol. 2019;93 doi: 10.1128/JVI.01339-19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Omoruyi B.E., Ighodaro D.I., Afolayan A.J., Bradley G. Inhibition of HIV-1 protease by Carpobrotus edulis (L.) Evid-Based Comp Altern Med. 2020;2020 doi: 10.1155/2020/9648056. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 78.Grandi G., Facchinetti F., Bitzer J. The gendered impact of coronavirus disease (COVID-19): do estrogens play a role? Eur J Contracept Reprod Health Care. 2020:1–2. doi: 10.1080/13625187.2020.1766017. [DOI] [PubMed] [Google Scholar]
  • 79.Cardoso F., Senkus E., Costa A., Papadopoulos E., Aapro M., Andre F. 4th ESO-ESMO international consensus guidelines for advanced breast cancer (ABC 4)dagger. Ann Oncol. 2018;29:1634–1657. doi: 10.1093/annonc/mdy192. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 80.Ortega J.T., Suárez A.I., Serrano M.L., Baptista J., Pujol F.H., Rangel H.R. The role of the glycosyl moiety of myricetin derivatives in anti-HIV-1 activity in vitro. AIDS Res Ther. 2017;14:57. doi: 10.1186/s12981-017-0183-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 81.Keum Y.-S., Jeong Y.-J. Development of chemical inhibitors of the SARS coronavirus: viral helicase as a potential target. Biochem Pharmacol. 2012;84:1351–1358. doi: 10.1016/j.bcp.2012.08.012. [DOI] [PMC free article] [PubMed] [Google Scholar]

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