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.
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.
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
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 (
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.
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.
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.
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).
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
GO study of COVID-19, COPD, and PAH co-DEGs.
Category | GO ID | Term | 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 | 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-194313 |
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 |
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.
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.
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 | 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 |
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.).
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).