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Interaction of Biochemical Processes between Chronic Obstructive Pulmonary Disease (COPD), Pulmonary Arterial Hypertension (PAH), and Coronavirus Disease 2019 (COVID-19)

 y    | 14 jun 2023

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Introduction

Coronavirus disease 2019 (COVID-19) is the infectious respiratory illness brought on by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Despite extensive precautions, COVID-19 has resulted in unfathomable losses throughout the world. With a genomic sequence of 29,903 positive-sense singlestranded RNA (ss-RNA) nucleotides and a very high mutation rate, SARS-CoV-2 is a member of the coronavirus genus (Yi et al. 2021). SARS-CoV-2 attaches to host cells’ angiotensin-converting enzyme 2 (ACE2) receptor. S protein is a crucial protein that mediates virus invasion of host cells, and, therefore, it is a key target for antibody production. Currently, SARS-CoV-2 mutations in the receptor binding domain (RBD), where the S protein interacts with ACE2, pose significant difficulties in controlling and preventing epidemics. Although some people are asymptomatic, COVID-19 symptoms include fever, cough, dyspnea, and loss of taste or smell. Symptomatic patients can be divided into mild and severe cases; mild ones with common coldlike discomfort and severe ones with life-threatening sudden acute viral pneumonia.

The information links preexisting illnesses such as chronic obstructive pulmonary disease (COPD), diabetes, and cardiovascular disease to severe symptoms and higher death after COVID-19 infection (Aghagoli et al. 2020; Alqahtani et al. 2020; Huang et al. 2020; Wu et al. 2020). Given the lung damage caused by SARS-CoV-2, we are concerned that underlying chronic respiratory disease may increase the risk of developing COVID-19. Patients with COPD have higher levels of ACE2 in their airways, which may increase their risk for severe COVID-19 infection (Leung et al. 2020). The development of COVID-19 and greater susceptibility to viral infection may also be influenced by interferon production and other innate immune responses in COPD. Patients with COVID-19 who are hospitalized for COPD should get cautious observation.

Pulmonary hypertension and pulmonary arteriolar vascular remodeling are two signs of pulmonary arterial hypertension (PAH) (Zhang et al. 2020b). Patients with severe COVID-19 had 20% higher PAH levels (Mishra et al. 2020). Transthoracic echocardiography reportedly revealed a higher incidence of PAH in 91 patients with moderate COVID-19 infection who were treated and released (Tudoran et al. 2021). This study hypothesized that the severity of PAH, a frequent side effect of COVID-19 infection, appears to be correlated with the degree of initial lung injury and inflammatory response. There is an assumption that the number of PAH found in COVID-19 patients did not increase in the hardest-hit areas; however, it is also assumed that areas struck by the pandemic did not show an increase in the number of PAH among COVID-19 patients (Farha and Heresi 2020). The ACE2 prevalence is downregulated in PAH (Zhang et al. 2018), although whether this is advantageous or detrimental to COVID-19 patients is unclear.

This research sought to identify the biochemical processes underlying COVID-19, COPD, PAH, and their interactions. The GSE147507, GSE106986, and GSE15197 were selected from the GEO database, representing COVID-19, COPD, and PAH. The data set was first screened for DEGs to identify co-DEGs among the three diseases. In genome-based expression investigations, these co-DEGs were employed for pathway and enrichment analyses to comprehend biological processes. Co-DEGs were examined using the PPI network, and the regulatory and interaction networks between TF and miRNA. Finally, candidate drugs were selected.

Experimental
Materials and Methods
Dataset filtering

The GSE147507 dataset was provided by Blanco-Melo et al. (2020). Data from this dataset came from both human cell experiments (GPL18573) and animal experiments (GPL28369), and we analyzed the experimental cell data. The GSE106986 data set, provided by Heinbockel et al. (2018), included 14 COPD tissue samples and five non-smoking patient tissue samples for identifying target genes in the lung tissues of COPD patients. RNA was isolated from 18 PAH patients and 13 healthy controls from fresh-frozen lung tissue specimens. The GSE15197 dataset obtained by microarray analysis was used to evaluate the transcriptome differences between PAH and healthy control samples (White et al. 2011). The mind map of the research process is shown in Fig. 1.

Fig. 1.

The mind map of the research process (https://smart.servier.com, https://www.freeimages.com).

Finding co-DEGs and identifying DEGs in COVID-19, COPD, and PAH

GEO is a public functional genomics data repository supporting MIAME-compliant data submissions. It is only a significant database where data for particular subjects, derived from array and sequence-based data and tools, are provided to help users query and download experiments and to curate gene expression profiles. The introduction of GSE147507 data basic information in the GEO database was downloaded, the meaning of the specimens was verified, and the data of healthy person specimens screened (lung biopsy for healthy controls) and COVID-19 patient specimens (lung samples from postmortem COVID-19 patients) in the original counts’ data. The data were loaded into R software (version 3.6.3) and ran the DESeq2 package (15) for standardized visualization and differential analysis. The sample groups were compared using the GEO query and limma R programs in GEO2R, and all DEGs were chosen based on padj < 0.05 and |log FC| > 1. The intersection of the DEGs from the three datasets was taken to find co-DEGs, the ggplot2 package in R language (version 3.6.3) was loaded, and a Veen diagram was created.

GO and pathway enrichment analysis

Molecular functions (MF), cellular elements (CC), and biological processes (BP) associated with co-DEGs were available through the GO analysis. In the R language program (version 3.6.3) and the org.Hs.eg.db package (version 3.10.0) was loaded for ID conversion, and the cluster Profiler package (version 3.14.3) was used for enrichment analysis. KEGG/WikiPathways/BioCarta/ Reactome pathway analysis was performed using Enrichr (http://amp.pharm.mssm.edu/Enrichr) (Love et al. 2014; Xie et al. 2021).

PPI network of co-DEGs

To build a PPI network, co-DEGs were searched in the STRING database (Szklarczyk et al. 2023). This experiment’s lowest needed interaction score was set at 0.402, equivalent to a medium degree of confidence. As the line thickness increased, so did the strength of the interaction between the proteins. The maximum number of interactive objects displayed was 50. Among the sources of interaction were text mining, experimental results, database collection, adjacent genomic genes, co-expressed genes, and cross-linked genes. The text output was imported into Cytoscape (Shannon et al. 2003), which could be combined into a visual network.

Networks regulating TF-miRNA and TF-DEGs interact with one another

The Network Analyst was used to analyze the TF-DEGs interaction and TF-miRNA regulation networks. TF-DEGs interaction network and TF-miRNA regulatory network were both visualized using Cytoscape.

Potential drug candidates

The DSigDB (Yoo et al. 2015) contains 22,527 gene sets covering 19,531 genes, of which 17,389 are unique compounds. The Enrichr portal predicted the targeted drugs related to COVID-19, COPD, and PAH based on the DSigDB database. The novel Pfizer agent PAXLOVID™ (PF-07321332) was an investigational COVID-19 oral antiviral candidate. Prostacyclin analogs, phosphodiesterase-5 inhibitors, and endothelin receptor antagonists (ERAs) are drugs for PAH.

Results
Co-DEGs found in COVID-19, COPD, and PAH patients

Eight hundred fourteen COVID-19 patient DEGs, including 419 up-regulated genes and 395 down-regulated genes, were found in the GSE147507 dataset. There were 2,569 COPD patient DEGs in the GSE106986 dataset, of which 1,189 were up-regulated, and 1,380 were down-regulated. The data collection yielded GSE15197 2,417 DEGs of PAH patients, of which 1,149 were up-regulated, and 1,268 were down-regulated. Eleven co-DEGs were found after the intersection of these three datasets, which were LUZP1, GBP3, PGF, RALBP1, TNS1, SP140, MICAL3, ABLIM3, TNXB, TFPI2, and ARSE (Fig. 2).

Fig. 2.

The Veen diagram of data in different data sets.

GO/KEGG/Wikipathways/Reactome/BioCarta analysis

GO database was used to comprehensively describe gene attributes in co-DEGs, and functional annotations of co-DEGs in BP, CC, and MF were obtained. The top 10 GO terms in each category were displayed according to p-value, as shown in Table I. KEGG, Wikipathways, Reactome, and BioCarta pathway analysis are depicted in Table II. Fig. 3 displays the outcomes of the GO analysis and KEGG/Wiki-pathways/Reactome/BioCarta pathway enrichment. The focal adhesion was most significant in the KEGG pathway. The ras signaling pathway was significantly demonstrated according to BioCarta. The VEGF ligand-receptor interactions were different in the Reactome.

Fig. 3.

A – Bar graph of GO analysis of co-DEGs of COVID-19, COPD, PAH. BP, CC, MF-related GO terms ranked by p-values. B – Bar graph showing pathway enrichment analysis for COVID-19, COPD, and PAH. Identifying of the examination of pathways in KEGG/Reactome/Wikipathways/BioCarta database by p-value ranking.

GO study of COVID-19, COPD, and PAH co-DEGs.

Category GO ID Term p-values Genes
GO-BP GO: 0060754 positive regulation of mast cell chemotaxis 2.75E-03 PGF
GO: 0060753 regulation of mast cell chemotaxis 3.30E-03 PGF
GO: 0032489 regulation of Cdc42 protein signal transduction 4.39E-03 RALBP1
GO: 0048251 elastic fiber assembly 4.39E-03 TNXB
GO: 0031401 positive regulation of protein modification process 5.88E-03 RALBP1/PGF
GO: 0030042 actin filament depolymerization 6.03E-03 MICAL3
GO: 0050930 induction of positive chemotaxis 6.03E-03 PGF
GO: 0038084 vascular endothelial growth factor signaling pathway 7.67E-03 PGF
GO: 0042327 positive regulation of phosphorylation 8.13E-03 RALBP1/PGF
GO: 0001932 regulation of protein phosphorylation 8.95E-03 RALBP1/PGF
GO-CC GO: 0015629 actin cytoskeleton 1.61E-01 ABLIM3
GO: 0062023 collagen-containing extracellular matrix 1.90E-01 TNXB
GO: 0005925 focal adhesion 1.93E-01 TNS1
GO: 0030055 cell-substrate junction 1.97E-01 TNS1
GO: 0005856 cytoskeleton 2.85E-01 ABLIM3
GO:0005634 nucleus 7.44E-01 SP140/MICAL3
GO:0043231 intracellular membrane-bounded organelle 8.22E-01 SP140/MICAL3
GO-MF GO: 0005172 vascular endothelial growth factor receptor binding 6.58E-03 PGF
GO: 0071949 FAD binding 1.37E-02 MICAL3
GO: 0042056 chemoattractant activity 1.75E-02 PGF
GO: 0016709 oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen, NAD(P)H as one donor, and incorporation of one atom of oxygen 1.95E-02 MICAL3
  GO: 0050660 flavin adenine dinucleotide binding 2.98E-02 MICAL3
GO: 0004867 serine-type endopeptidase inhibitor activity 3.25E-02 TFPI2
GO: 0008083 growth factor activity 4.68E-02 PGF
GO: 0070851 growth factor receptor binding 5.63E-02 PGF
GO: 0005126 cytokine receptor binding 5.63E-02 PGF
GO: 0004866 endopeptidase inhibitor activity 6.15E-02 TFPI2

Pathway enrichment analysis of co-DEGs of COVID-19, COPD and PA.

Database type Pathways p-values Genes
KEGG Focal adhesion 5.21E-03 TNXB/PGF
Ras signaling pathway 6.88E-03 RALBP1/PGF
PI3K-Akt signaling pathway 1.55E-02 TNXB/PGF
Pathways in cancer 3.30E-02 RALBP1/PGF
Pancreatic cancer 4.10E-02 RALBP1
ECM-receptor interaction 4.74E-02 TNXB
NOD-like receptor signaling pathway 9.52E-02 GBP3
Axon guidance 9.57E-02 ABLIM3
Rap1 signaling pathway 1.10E-01 PGF
MAPK signaling pathway 1.50E-01 PGF
WikiPathways Focal Adhesion WP306 5.06E-03 TNXB/PGF
RalA downstream regulated genes WP2290 6.58E-03 RALBP1
Estrogen metabolism WP697 9.86E-03 ARSE
Focal Adhesion-PI3K-Akt-mTOR-signaling pathway WP3932 1.15E-02 TNXB/PGF
miRNA targets in ECM and membrane receptors WP2911 1.20E-02 TNXB
PI3K-Akt signaling pathway WP4172 1.43E-02 TNXB/PGF
VEGFA-VEGFR2 Signaling Pathway WP3888 2.25E-02 TNXB/PGF
Hematopoietic Stem Cell Differentiation WP2849 2.98E-02 TNXB
Pancreatic adenocarcinoma pathway WP4263 4.79E-02 RALBP1
Integrin-mediated Cell Adhesion WP185 5.42E-02 TNS1
BioCarta Ras Signaling Pathway Homo sapiens h rasPathway 1.09E-03 RALBP1
ADP-Ribosylation Factor Homo sapiens h arapPathway 1.58E-03 RALBP1
Erk and PI-3 Kinase Are Necessary for Collagen Binding in Corneal Epithelia Homo sapiens h ecmPathway 1.69E-02 TNS1
Rho cell motility signaling pathway Homo sapiens h rhoPathway 1.75E-02 RALBP1
Integrin Signaling Pathway Homo sapiens h integrinPathway 1.80E-02 TNS1
Rac 1 cell motility signaling pathway Homo sapiens h rac1Pathway 1.96E-02 RALBP1
T Cell Receptor Signaling Pathway Homo sapiens h TCR pathway 2.98E-02 RALBP1
Reactome VEGF ligand-receptor interactions Homo sapiens R-HSA-194313VEGF binds to VEGFR leading to receptor dimerization 4.39E-03 PGF
Homo sapiens R-HSA-195399 4.39E-03 PGF
The activation of arylsulfatases Homo sapiens R-HSA-1663150 7.13E-03 ARSE
DCC mediated attractive signaling Homo sapiens R-HSA-418885 7.67E-03 ABLIM3
Gamma carboxylation, hypusine formation and arylsulfatase activation Homo sapiens R-HSA-163841 2.12E-02 ARSE
Glycosphingolipid metabolism Homo sapiens R-HSA-1660662 2.23E-02 ARSE
Netrin-1 signaling Homo sapiens R-HSA-373752 2.29E-02 ABLIM3
ECM proteoglycans Homo sapiens R-HSA-3000178 2.98E-02 TNXB
Sphingolipid metabolism Homo sapiens R-HSA-428157 4.00E-02 ARSE
Interferon gamma signaling Homo sapiens R-HSA-877300 5.00E-02 GBP3
PPI network

The files obtained were imported into Cytoscape to construct the PPI network (Fig. 4). The PPI network contained 59 nodes and 555 edges. The size of the nodes was adjusted according to a degree, and the edges’ thickness was adjusted according to Combined_score.

Fig. 4.

PPI network of COVID-19, COPD and PAH in DEGs. The network contains 59 nodes and 555 edges.

Regulation characteristics

Two major types of regulatory elements control gene expression: TF and microRNAs (Gholaminejad et al. 2021). The interaction between TFs and co-DEGs is shown in Fig. 5. The network consisted of 53 edges and 51 nodes, including 46 TFs. Twenty TF genes controlled RALBP1, twenty TF genes controlled ABLIM3, eight TF genes – TNS1, three TF genes – TNXB, and two TF genes – GBP3. Part of TF regulates the formation of miRNA, while part of miRNA affects the translation of TF. Therefore, TF and miRNA constitute a complex regulatory network (Marson et al. 2008). The cooperative regulatory network of TF-miRNA-DEGs is illustrated in Fig. 6, which includes 172 nodes and 200 edges. Sixty-eight TF genes and 94 miRNAs interact with co-DEGs, forming a complex network.

Fig. 5.

The network of co-DEG and TF gene interactions. Yellow nodes stand in for TF genes, while red nodes represent co-DEGs.

Fig. 6.

Cooperative TF-miRNA-DEGs network. Yellow nodes stand in for TF genes, blue nodes for miRNAs, and red nodes for co-DEGs.

Potential drug candidates

Using the protein-drug interaction data from the DsigDB database, 10 possible drug molecules were identified with the co-DEGs of COVID-19, COPD, and PAH as potential drug targets (Table III). These compounds were detected according to the common DEGs, which might also be potential drug candidates for treating the above three diseases.

Recommended drugs for COVID-19, COPD, PAH.

Name p-value Chemical formula Structure
Acetaminophen CTD 00005295 2.40E-03 C8H9NO2
dmnq CTD 00002569 3.21E-03 C12H10O4
2,2’,4,4’,5,5’-hexachlorobiphenyl CTD 00000731 5.57E-03 C12H4Cl6
Hexylene glycol TTD 00008431 6.58E-03 C6H14O2
Tetradioxin CTD 00006848 8.61E-03 C12H4Cl4O2
Bisindolylmaleimide IV CTD 00003116 9.31E-03 C20H13N3O2
Leukotriene C4 CTD 00007223 9.31E-03 C30H47N3O9S
Azacyclonol HL60 UP 9.55E-03 C18H21NO
Corbadrine PC3 UP 1.26E-02 C9H13NO3
Mecamylamine CTD 00006250 1.48E-02 C11H21N
Discussion

COVID-19 has made the world face a series of challenges, and this study has combined COVID-19, COPD, and PAH. It is worth exploring a series of complications caused by COVID-19. Research on miRNA-targeted drugs and vaccines has been carried out worldwide, and further analysis of TFs and miRNAs listed in this paper may unearth additional drugs.

COVID-19 can cause severe illness in people with COPD, which is concerning. SARS-CoV-2 enters host cells and binds to ACE2 to cause COVID-19 (Bhuiyan et al. 2018; Saheb Sharif-Askari et al. 2020). A study investigated airway ACE2 gene expression levels in patients with and without COPD, which manifested that ACE2 expression was significantly increased in the former group. Additionally, the gene expression level of ACE2 was found to be negatively correlated with individual forced expiratory volume 1st (FEV1) (Gholaminejad et al. 2021). Although upregulating of ACE2 may help protect against acute lung injury, coronaviruses use this receptor to enter epithelial cells, thereby increasing their chances of infecting the host. The pathogenesis and complications of COVID-19 are well documented, but there are few reports on PAH. Deng et al. (2020) found that the incidence of PAH in patients with severe COVID-19 was significantly higher than in those without severe COVID-19, but they did not describe the specific situation of PAH and its relationship with the clinic, and did not rule out PAH caused by previous diseases, pulmonary embolism or other causes. Patients with COVID-19 may experience pulmonary vasoconstriction and impaired lung function due to hypoxemia and inflammation brought on by pathological lung alterations (Vicenzi et al. 2020), leading to PAH. ACE2 can convert angiotensin ii into angiotensin to play a vasodilator role (Tang et al. 202) and effectively reduce pulmonary vascular resistance, thus, delaying or reducing the occurrence of PAH. SARS-CoV-2, on the other hand, binds to ACE2 and suppresses its expression by endocytosis and proteolytic enzyme cleavage (Ovali et al. 2020). More research is required to determine whether PAH in severe and critical COVID-19 patients is brought on by the depletion of ACE2 in lung tissue.

This study’s goal was to examine COVID-19, COPD, and PAH crossover using bioinformatics-related methods. As putative molecular targets of COVID-19, 11 common DEGs were discovered from the GSE147507, GSE106986, and GSE15197 databases. Drug candidate screening, GO analysis, pathway analysis, PPI network, TF-gene interaction, and TF-miRNA-gene synergy network were also done. The BP, RALBP1, and PGF genes appeared many times, and they were mostly enriched in processes that positively regulated protein modification and phosphorylation. A more significant part of COVID-19’s quick infection is the phosphorylation modification. On the Vero E6 cell model infected with SARS-CoV-2, the viral protein concentration began to increase 8 hours after infection, and their host protein abundance changed only slightly within 24 hours. Protein phosphorylation, however, was drastically altered. Furthermore, some viral proteins could be phosphorylated according to the results of phosphotomics. Sequence analysis revealed that host CK2, CDK, and PKC are upstream kinases leading to the phosphorylation of these viral proteins, which might contribute to virus infection. At the host level, by cluster analysis of phosphorylation sites and kinase prediction, it was found that the activities of P38 MAPK, CK2, CAMK2G, and other kinases were up-regulated. In contrast, CDK, AKT, and Rho family kinase activities were down-regulated (Aghagoli et al. 2020). ERK1/2, p38, and eIF4E were considerably more phosphorylated in the platelets of COVID-19 individuals in the ICU ward, activating the MAPK pathway (Manne et al. 2020). Patients recovered from COVID-19 showed significant inhibition of the MAPK pathways (FOS, JUN, JunB, and DUSP1), indicating that inhibition of these pathways might be a sign of restoration (Zhang et al. 2020a). Compared with non-smokers, COPD patients have increased p38 MAPK activity in alveolar wall macrophages and other cells (Banerjee et al. 2012), p38 MAPK inhibitors may help target airway inflammation by reducing cytokine production by alveolar macrophages. Additionally, MAPK activation is positively associated with PAH occurrence and development (Wei et al. 2019). It suggests that SARS-CoV-2 infection can encourage the activation of P38 MAPK and worsen COPD and PAH patients’ conditions. Vascular endothelial growth factor receptor binding was the MF term that was statistically the most significant. Vascular endothelial growth factor, which increases vascular permeability, results in tissue hypoxia by causing pulmonary vascular leakage, plasma extravasation, and pulmonary edema (Kaner et al. 2000). Thus, improving oxygen perfusion, the anti-inflammatory response, and reducing clinical symptoms in COVID-19 patients, can be achieved by limiting the signal transduction mediated by the vascular endothelial growth factor and its receptor (Pang et al. 2012). Some researchers claim that SARS-CoV-2 infection can alter the extracellular milieu in the lung, suggesting a potential pathophysiologic mechanism (Leng et al. 2020). Activation of the RAS signaling system, oxidative stress and cell death, cytokine storm, and endothelial dysfunction is hypothesized to be the four pathways that lead to COVID-19. One important anti-COVID-19 mechanism of NSAIDs is the inhibition of RAS signaling, which inhibits tissue and/or cellular proinflammatory stimuli (Oh et al. 2021).

TF is a crucial regulator at the transcriptional level, and miRNA primarily controls gene expression at the post-transcriptional point. As a result of their interaction, target gene expression is also synergistically regulated, making the gene expression regulatory network more accurate. As our results show, the degree value in the network of TF-gene interactions for RALBP1 and ABLIM3 is 20, and they often interact with other TF genes. IRF1, one of these TFs, had a degree value of 3 in the network of TF-gene interactions and significantly interacted with co-DEGs. Transcriptional factors such as IRF regulate the expression of genes that encode IFN. A high expression of antiviral genes has been found in bat cells and tissues; often, these are associated with IFNα signaling, and IRF contributes to the expression of IFN-I ligands (Irving et al. 2020). More significantly, IFN-I signaling induces explicitly the transcription factor IRF1, which stimulates the transcription of proinflammatory cytokine genes (Lazear et al. 2019). Additionally, Rusmini et al. (2021) discovered that COVID-19 was positively related to an intronic mutation of IRF1 (rs17622656), which may result in decreased carnitine absorption and impaired immune response to SARS-CoV-2.

The TF-miRNA co-regulation network contained 68 TF genes and 94 miRNAs. The majority of these miRNAs are linked to cancer tissue and contribute to various cancers in people, particularly lung cancer. In TFs, the most substantial interactions are NFKB1 and JUN, with values of 4 and 3, respectively. Among the miRNAs, the strongest interactions are has-miR-506 and hsa-miR-942, both with a degree value of 2. Patients with COVID-19 experience a cytokine storm as a result of TNF-induced NFKB1 activation. CD4+ T cells are involved in viral infection. Wen et al. (2020) discovered that the expression of the Jun gene (inflammatory response) was high in CD4+ T cells from COVID-19 survivors. Anti-inflammatory genes linked to CD4+ T cells were found to be downregulated in COVID-19 individuals compared to healthy controls. Individuals with mild to moderate COPD and those without COPD had higher levels of c-JUN in their epithelial cells than patients with severe or very severe COPD (Reddy et al. 2012). Lack of c-JUN increases the severity of lymphocyte infiltration and smoking-induced lung inflammation (Hogg et al. 2004). miR-506-3p overexpression prevents cancer cell proliferation, migration, and invasion while its expression is downregulated in non-small cell lung cancer tissues and cells (Guo et al. 2017.).

Conclusions

COVID-19 is currently not treated with a specific drug, so reusing existing drugs is considered a critical step toward a faster recovery (Yan et al. 2021). Some antiviral activity against SARS-CoV-2 has been demonstrated for chloroquine, remdesivir, nitazoxanide, interferon, lopinavir-ritonavir, ribavirin, favipiravir, hydroxychloroquine, camostat, and convalescent plasma carrying antibodies (Beigel et al. 2019; Cunningham et al. 2020; Dong et al. 2020; Li et al. 2020; Sheahan et al. 2020; Verma et al. 2020). Based on 10 co-EDGs, the DSigDB database recommends the top 10 drug candidates. Most COVID-19 patients are known to have a fever while in the hospital. It is recommended that COVID-19 patients use acetaminophen instead of NSAIDs as a symptom reliever (Park et al. 2021).

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Life Sciences, Microbiology and Virology