# A Bioinformatic Approach Based on Systems Biology to Determine the Effects of SARS-CoV-2 Infection in Patients with Hypertrophic Cardiomyopathy

Authors: Xiao Han; Fei Wang; Ping Yang; Bin Di; Xiangdong Xu; Chunya Zhang; Man Yao; Yaping Sun; Yangyi Lin
Journal: Computational and Mathematical Methods in Medicine
 (2022)

---

## Abstract

Recently, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), has infected millions of individuals worldwide. While COVID-19 generally affects the lungs, it also damages other organs, including those of the cardiovascular system. Hypertrophic cardiomyopathy (HCM) is a common genetic cardiovascular disorder. Studies have shown that HCM patients with COVID-19 have a higher mortality rate; however, the reason for this phenomenon is not yet elucidated. Herein, we conducted transcriptomic analyses to identify shared biomarkers between HCM and COVID-19 to bridge this knowledge gap. Differentially expressed genes (DEGs) were obtained using the Gene Expression Omnibus ribonucleic acid (RNA) sequencing datasets, GSE147507 and GSE89714, to identify shared pathways and potential drug candidates. We discovered 30 DEGs that were common between these two datasets. Using a combination of statistical and biological tools, protein-protein interactions were constructed in response to these findings to support hub genes and modules. We discovered that HCM is linked to COVID-19 progression based on a functional analysis under ontology terms. Based on the DEGs identified from the datasets, a coregulatory network of transcription factors, genes, proteins, and microRNAs was also discovered. Lastly, our research suggests that the potential drugs we identified might be helpful for COVID-19 therapy.


---

## Body

## 1. Introduction

It has been determined that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel member of theCoronaviridae family and the class of Pisoniviricetes, causes mild and severe respiratory diseases in humans [1–4]. Even though SARS-CoV-2 infections primarily affect the respiratory tract, they frequently cause heart injuries in patients with moderate to severe coronavirus disease 2019 (COVID-19), particularly in those with underlying cardiovascular diseases [5–7]. Furthermore, growing evidence demonstrates a link between COVID-19 and increased mortality from heart failure and cardiovascular diseases [8].Hypertrophic cardiomyopathy (HCM) is one of the most prevalent inherited heart conditions associated with angiotensin-converting enzyme 2 (ACE2) deficiency in patients with heart failure [9, 10]. SARS-CoV-2 binds with ACE2 and accelerates its degradation, thereby decreasing its ability to counteract the activity of the renin-angiotensin system (RAS) protein [11]. Although the present results suggested that ACE2 expression increased with ACE inhibitor treatment in HCM patients’ tissues, they were not statistically significant [12]. Therefore, understanding the impact of SARS-CoV-2 infection in patients with HCM and developing therapeutic drugs that could decrease the odds of complications or death are essential. However, current efforts mainly focus on studying stress cardiomyopathies secondary to COVID-19, such as takotsubo cardiomyopathy [13, 14]. To date, no bioinformatic research on the impact of COVID-19 in patients with preexisting HCM at the molecular level has been reported.Herein, to bridge the knowledge gap, the cooccurrence of HCM and COVID-19 was examined using two datasets, GSE89714 (HCM) and GSE147507 (COVID-19), obtained from the Gene Expression Omnibus (GEO) database. We identified the differentially expressed genes (DEGs) in each dataset and searched for DEGs shared by the two diseases. These common DEGs, designated as the primary experimental genes, were also used to identify various transcriptional regulators. Then, the hub genes were extracted from these common DEGs using the specific algorithm in the Cytoscape programme. Additionally, the hub genes were used to predict potential therapeutic drugs. Overall, we predicted four agents that could be potentially therapeutic for HCM patients with COVID-19.


## 2. Materials and Methods

### 2.1. Study Datasets

The National Center for Biotechnology Information (https://www.ncbi.nlm.nih.gov/geo/) and GEO databases were used to obtain the COVID-19 and HCM ribonucleic acid sequencing (RNA-seq) datasets [15]. The following criteria were used to assess the quality of the eligible datasets: (1) case-control study; (2) high-throughput sequencing for expression profiling; (3) comparable experimental and control or untreated conditions; (4) more than three samples in each group; and (5) complete raw and processed microarray data was available. The high-throughput Illumina NextSeq 500 RNA sequencing platform was used to obtain the transcriptional profiles of lung biopsy samples from patients with COVID-19 for the GSE147507 [16]. RNA-seq data from heart tissue samples of four participants without HCM and five participants with HCM are included in the GSE89714 dataset. The HiSeq 2000 platform was used for the sequencing experiment. The CuffLinks programme was employed to assess gene expression. Table 1 summarises the two datasets.Table 1
A description of the two datasets with their GEO information.
Disease nameGEO accessionGEO platformTotal DEG countUpregulated DEG countDownregulated DEG countSARS-CoV-2GSE147507GPL1857317811390391HCMGSE89714GPL1115420713473Abbreviations: GEO: Gene Expression Omnibus; DEGs: differentially expressed genes; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; HCM: hypertrophic cardiomyopathy.The cut-off criteria were set atP<0.05 and ∣logFC∣≥1.0 to identify significant DEGs in each dataset using the DESEq2 R package. Jvenn online software was used to obtain the shared DEGs between GSE147507 and GSE89714 [17]. DEG expression was considered exclusive between the two datasets if statistically significant differences existed across different conditions [18].


### 2.2. Gene Ontology (GO) and Pathway Enrichment Analyses

Genome enrichment analysis helps determine the chromosome positions associated with various interrelated diseases [19]. We used an online tool, Enrichr (https://maayanlab.cloud/Enrichr/), to determine the possible molecular pathways and mechanisms involving the common DEGs. The shared pathways between HCM and COVID-19 were examined using four databases: BioCarta, WikiPathways, Reactome, and Kyoto Encyclopedia of Genes and Genomes (KEGG). A P value of < 0.05 was used as a standard metric in quantifying the top-ranked pathways.


### 2.3. Protein-Protein Interaction (PPI) Network Analysis

The interaction of different cellular proteins can indirectly reflect a protein’s functions and roles. Understanding PPI networks can therefore shed light on how proteins function across the board in cellular machinery [20–23]. The shared DEGs were uploaded to the STRING database (https://string-db.org/) [21] to illustrate potential protein connections between HCM and COVID-19. The common DEG PPI network was created using a low confidence score of 0.15. The obtained PPI network was viewed using Cytoscape software (v.3.8.0).


### 2.4. Hub Gene Extraction and Submodule Analysis

Cytohubba, a validated Cytoscape plugin, ranks and extracts central or targeted elements based on numerous network features. Maximal clique centrality is a commonly used algorithm in Cytohubba for analysing networks from various perspectives [24, 25]. The top 10 hub genes in the obtained PPI network were identified using this method. Additionally, we classified the shortest paths between hub genes based on the calculations from Cytohubba.


### 2.5. Recognition of Transcription Factors (TFs) and MicroRNAs (miRNAs)

A TF is a protein that binds to gene elements and regulates gene expression [26]. Candidate TFs that are topologically connected to mutual DEGs obtained from the JASPAR database were identified using the NetworkAnalyst platform, a popular web tool for the meta-analysis of gene expression data and viewing biological mechanisms, roles, and gene translation (https://www.networkanalyst.ca/) [27]. JASPAR provides open-access profiles of various TFs in six taxonomic groups [28]. In addition, TarBase and miRTarBase were used to analyse miRNA-targeted gene interactions to find miRNAs that potentially influence gene translation [29, 30]. These online tools can be used by researchers to filter high-degree miRNAs and identify the associated biochemical processes and characteristics to generate the most plausible hypothesis.


### 2.6. Prediction of Candidate Drugs

Predicting protein-drug interactions (PDIs) or identifying candidate drug molecules was a crucial aspect of this study. Enrichr was used to select potential drug molecules based on the identified DEGs in HCM and COVID-19 and the Drug Signatures database (DSigDB). Gene set libraries enabled by Enrichr allow users to study gene set enrichment at the genome-wide level [31]. Targeted drug substances connected to DEGs were identified using the DSigDB (https://maayanlab.cloud/Enrichr/) [32].


### 2.7. Gene and Disease Association Analysis

The DisGeNET database links various biomedical aspects of medical conditions with gene-disease relations. It focuses on our growing understanding of human genetic disorders (https://www.networkanalyst.ca/) [33]. We used this tool to determine various diseases related to the common DEGs and their chronic complications.


## 2.1. Study Datasets

The National Center for Biotechnology Information (https://www.ncbi.nlm.nih.gov/geo/) and GEO databases were used to obtain the COVID-19 and HCM ribonucleic acid sequencing (RNA-seq) datasets [15]. The following criteria were used to assess the quality of the eligible datasets: (1) case-control study; (2) high-throughput sequencing for expression profiling; (3) comparable experimental and control or untreated conditions; (4) more than three samples in each group; and (5) complete raw and processed microarray data was available. The high-throughput Illumina NextSeq 500 RNA sequencing platform was used to obtain the transcriptional profiles of lung biopsy samples from patients with COVID-19 for the GSE147507 [16]. RNA-seq data from heart tissue samples of four participants without HCM and five participants with HCM are included in the GSE89714 dataset. The HiSeq 2000 platform was used for the sequencing experiment. The CuffLinks programme was employed to assess gene expression. Table 1 summarises the two datasets.Table 1
A description of the two datasets with their GEO information.
Disease nameGEO accessionGEO platformTotal DEG countUpregulated DEG countDownregulated DEG countSARS-CoV-2GSE147507GPL1857317811390391HCMGSE89714GPL1115420713473Abbreviations: GEO: Gene Expression Omnibus; DEGs: differentially expressed genes; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; HCM: hypertrophic cardiomyopathy.The cut-off criteria were set atP<0.05 and ∣logFC∣≥1.0 to identify significant DEGs in each dataset using the DESEq2 R package. Jvenn online software was used to obtain the shared DEGs between GSE147507 and GSE89714 [17]. DEG expression was considered exclusive between the two datasets if statistically significant differences existed across different conditions [18].


## 2.2. Gene Ontology (GO) and Pathway Enrichment Analyses

Genome enrichment analysis helps determine the chromosome positions associated with various interrelated diseases [19]. We used an online tool, Enrichr (https://maayanlab.cloud/Enrichr/), to determine the possible molecular pathways and mechanisms involving the common DEGs. The shared pathways between HCM and COVID-19 were examined using four databases: BioCarta, WikiPathways, Reactome, and Kyoto Encyclopedia of Genes and Genomes (KEGG). A P value of < 0.05 was used as a standard metric in quantifying the top-ranked pathways.


## 2.3. Protein-Protein Interaction (PPI) Network Analysis

The interaction of different cellular proteins can indirectly reflect a protein’s functions and roles. Understanding PPI networks can therefore shed light on how proteins function across the board in cellular machinery [20–23]. The shared DEGs were uploaded to the STRING database (https://string-db.org/) [21] to illustrate potential protein connections between HCM and COVID-19. The common DEG PPI network was created using a low confidence score of 0.15. The obtained PPI network was viewed using Cytoscape software (v.3.8.0).


## 2.4. Hub Gene Extraction and Submodule Analysis

Cytohubba, a validated Cytoscape plugin, ranks and extracts central or targeted elements based on numerous network features. Maximal clique centrality is a commonly used algorithm in Cytohubba for analysing networks from various perspectives [24, 25]. The top 10 hub genes in the obtained PPI network were identified using this method. Additionally, we classified the shortest paths between hub genes based on the calculations from Cytohubba.


## 2.5. Recognition of Transcription Factors (TFs) and MicroRNAs (miRNAs)

A TF is a protein that binds to gene elements and regulates gene expression [26]. Candidate TFs that are topologically connected to mutual DEGs obtained from the JASPAR database were identified using the NetworkAnalyst platform, a popular web tool for the meta-analysis of gene expression data and viewing biological mechanisms, roles, and gene translation (https://www.networkanalyst.ca/) [27]. JASPAR provides open-access profiles of various TFs in six taxonomic groups [28]. In addition, TarBase and miRTarBase were used to analyse miRNA-targeted gene interactions to find miRNAs that potentially influence gene translation [29, 30]. These online tools can be used by researchers to filter high-degree miRNAs and identify the associated biochemical processes and characteristics to generate the most plausible hypothesis.


## 2.6. Prediction of Candidate Drugs

Predicting protein-drug interactions (PDIs) or identifying candidate drug molecules was a crucial aspect of this study. Enrichr was used to select potential drug molecules based on the identified DEGs in HCM and COVID-19 and the Drug Signatures database (DSigDB). Gene set libraries enabled by Enrichr allow users to study gene set enrichment at the genome-wide level [31]. Targeted drug substances connected to DEGs were identified using the DSigDB (https://maayanlab.cloud/Enrichr/) [32].


## 2.7. Gene and Disease Association Analysis

The DisGeNET database links various biomedical aspects of medical conditions with gene-disease relations. It focuses on our growing understanding of human genetic disorders (https://www.networkanalyst.ca/) [33]. We used this tool to determine various diseases related to the common DEGs and their chronic complications.


## 3. Results

### 3.1. Identification of DEGs and Common DEGs

Patients with COVID-19 exhibited a differential expression of 1,781 genes, including 1,390 upregulated and 391 downregulated genes after disease exposure. Similarly, various statistical analysis techniques were used to rank the DEGs identified for HCM. All DEGs were identified using a criterion ofP<0.05 and ∣logFC∣≥1. Using the Jvenn online platform, 30 common DEGs were identified between the two datasets (Figure 1). There was a close relationship between the two diseases as they shared several genes [34].Figure 1
Ribonucleic acid sequencing datasets for hypertrophic cardiomyopathy (HCM) (GSE89714) and coronavirus disease 2019 (COVID-19) (GSE147507) were used in this study. The integrated analysis identified 30 differentially expressed genes shared between COVID-19 and HCM.


### 3.2. GO and Pathway Enrichment Analyses

Using Enrichr, GO and pathway enrichment analyses were performed. Table2 summarises the top 10 GO terms in the biological processes, molecular functions, and cellular component categories. DEGs are listed in increasing order based on P value. Figure 2 summarises the linear comparison of the overall ontological analysis of each category. An organism’s active pathways reveal how it responds to its inherent modifications. It illustrates the interaction between diseases through basic molecular processes [35]. We examined four global databases, KEGG, WikiPathways, Reactome, and BioCarta, to determine the most important pathways involving the DEGs common to HCM and COVID-19. Table 3 summarises the critical pathways identified based on the examined datasets. Pathway enrichment analysis was performed on the datasets (Figure 3). DEGs are listed in increasing order based on P value. A P value of < 0.05 was used to determine the top functional items and pathways.Table 2
Gene ontology analysis of common differentially expressed genes between hypertrophic cardiomyopathy and coronavirus disease 2019.
CategoryGO IDTermP valuesGenesGO biological processGO:0006939Smooth muscle contraction1.10E-06ACTA2, EDNRA, and MYH11GO:0014829Vascular-associated smooth muscle contraction6.06E-05ACTA2, EDNRAGO:0048251Elastic fiber assembly6.06E-05MFAP4, MYH11GO:0030198Extracellular matrix organization7.69E-05COL1A2, BGN, CYP1B1, TIMP1, and LOXL1GO:0046466Membrane lipid catabolic process9.71E-05ENPP2, CYP1B1GO:0042310Vasoconstriction0.000118625ACTA2, EDNRAGO:0097435Supramolecular fiber organization0.000160702MFAP4, COL1A2, CYP1B1, MYH11, and LOXL1GO:0030199Collagen fibril organization0.000317018COL1A2, CYP1B1, and LOXL1GO:0085029Extracellular matrix assembly0.000588116MFAP4, MYH11GO:0055013Cardiac muscle cell development0.000588116MYH11, MYLK3GO molecular functionGO:0005105Type 1 fibroblast growth factor receptor binding0.007478193FGF18GO:0005111Type 2 fibroblast growth factor receptor binding0.007478193FGF18GO:0004528Phosphodiesterase I activity0.007478193ENPP2GO:0101020Estrogen 16-alpha-hydroxylase activity0.011939153CYP1B1GO:0002020Protease binding0.013479908COL1A2, TIMP1GO:0048407Platelet-derived growth factor binding0.016380734COL1A2GO:0031432Titin binding0.019331061ANKRD1GO:0008191Metalloendopeptidase inhibitor activity0.020803015TIMP1GO:0042288MHC class I protein binding0.025206075TUBB4BGO:0031690Adrenergic receptor binding0.025206075ARRDC3GO cellular componentGO:0062023Collagen-containing extracellular matrix6.41E-08MFAP4, COL1A2, ABI3BP, BGN, PLAT, AEBP1, THBS2, and LOXL1GO:0031091Platelet alpha granule0.000327618ISLR, TIMP1, and THBS2GO:0034774Secretory granule lumen0.001211585C3, ISLR, TIMP1, and TUBB4BGO:0005775Vacuolar lumen0.001772031C3, BGN, and TUBB4BGO:0031093Platelet alpha granule lumen0.004526565ISLR, TIMP1GO:0071953Elastic fiber0.007478193MFAP4GO:0035578Azurophil granule lumen0.008026254C3, TUBB4BGO:0005788Endoplasmic reticulum lumen0.008747528C3, COL1A2, and TIMP1GO:0001527Microfibril0.016380734MFAP4GO:0005859Muscle myosin complex0.022272833MYH11Figure 2
Gene ontology analysis of common differentially expressed genes shared between hypertrophic cardiomyopathy and coronavirus disease 2019 was performed using Enrichr. Terms were evaluated by categories: (a) biological processes, (b) molecular function, and (c) cellular components.
(a)(b)(c)Table 3
Pathway enrichment analysis of common differentially expressed genes between hypertrophic cardiomyopathy and coronavirus disease 2019.
CategoryPathwaysP valuesGenesWikiPathways humanIL-18 signaling pathway WP47544.84E-05ACTA2, BTG2, COL1A2, TIMP1, and IER3Endochondral ossification with skeletal dysplasias WP48080.000119283FRZB, FGF18, and PLATEndochondral ossification WP4740.000119283FRZB, FGF18, and PLATmiR-509-3p alteration of YAP1/ECM axis WP39670.000291693EDNRA, THBS2miRNA targets in ECM and membrane receptors WP29110.000493146COL1A2, THBS2Focal adhesion-PI3K-Akt-mTOR-signaling pathway WP39320.00103726COL1A2, FGF18, THBS2, FGF12PI3K-Akt signaling pathway WP41720.001585899COL1A2, FGF18, THBS2, and FGF12Focal adhesion WP3060.003186561COL1A2, THBS2, and MYLK3Complement and coagulation cascades WP5580.003412595C3, PLATGenotoxicity pathway WP42860.004013249ACTA2, BTG2BioCartaInhibition of matrix metalloproteinases h reckPathway0.011939153TIMP1BTG family proteins and cell cycle regulation h btg2Pathway0.013421829BTG2Alternative complement pathway h alternativePathway0.014902355C3Lectin induced complement pathway h lectinPathway0.019331061C3Platelet amyloid precursor protein pathway h plateletAppPathway0.020803015PLATClassical complement pathway h classicPathway0.022272833C3Fibrinolysis pathway h fibrinolysisPathway0.022272833PLATBeta-arrestins in GPCR desensitization h bArrestinPathway0.041187484EDNRAActivation of cAMP-dependent protein kinase, PKA h gsPathway0.042627715EDNRARole of Beta-arrestins in the activation and targeting of MAP kinases h barr-mapkPathway0.044065855EDNRAKEGG 2019 humanVascular smooth muscle contraction4.48E-05ACTA2, EDNRA, MYH11, and MYLK3Apelin signaling pathway0.001114898ACTA2, PLAT, and MYLK3Phagosome0.001503079C3, THBS2, and TUBB4BFocal adhesion0.003324312COL1A2, THBS2, and MYLK3Regulation of actin cytoskeleton0.004174068FGF18, MYH11, and MYLK3Calcium signaling pathway0.005454596EDNRA, FGF18, and MYLK3Complement and coagulation cascades0.007187738C3, PLATECM-receptor interaction0.007685775COL1A2;,THBS2Platelet activation0.014809355COL1A2, MYLK3PI3K-Akt signaling pathway0.015674766COL1A2, FGF18, and THBS2ReactomeExtracellular matrix organization R-HSA-14742445.84E-05MFAP4, COL1A2, BGN, TIMP1, and LOXL1Smooth muscle contraction R-HSA-4453550.0011157ACTA2, MYH11Elastic fiber formation R-HSA-15669480.001719859MFAP4, LOXL1Signaling by PDGF R-HSA-1867970.002034436FGF18, PLAT, THBS2, and IER3Assembly of collagen fibrils and other multimeric structures R-HSA-20220900.002965286COL1A2, LOXL1Muscle contraction R-HSA-3970140.003096721ACTA2, MYH11, and FGF12PI5P, PP2A, and IER3 regulate PI3K/AKT signaling R-HSA-68115580.006864228FGF18, IER3Collagen formation R-HSA-14742900.007187738COL1A2, LOXL1Diseases of glycosylation R-HSA-37818650.007685775BGN, THBS2Negative regulation of the PI3K/AKT network R-HSA-1994180.008026254FGF18, IER3Figure 3
Pathway enrichment analysis of the common differentially expressed genes between hypertrophic cardiomyopathy and coronavirus disease 2019 was performed using Enrichr. Different databases were used in the analysis: (a) WikiPathways, (b) BioCarta, (c) Reactome, and (d) Kyoto Encyclopedia of Genes and Genomes 2019 human database.
(a)(b)(c)(d)


### 3.3. Classification of Hub Proteins and Submodules

We predicted the interaction of DEGs by analysing the STRING PPI network using Cytoscape. The PPI network constructed using the common DEGs comprised 30 nodes and 124 edges (Figure4). Additionally, most of the interconnected nodes in the PPI network were identified as hub genes. Using the Cytohubba plugin, the top 10 DEGs were considered hub genes. This gene list includes thrombospondin 2 (THBS2), biglycan (BGN), collagen type I alpha 2 chain (COL1A2), actin alpha 2 (ACTA2), myosin heavy chain 11 (MYH11), adipocyte enhancer-binding protein 1 (AEBP1), immunoglobulin superfamily containing leucine-rich repeat (ISLR), frizzled-related protein (FRZB), microfibril-associated protein 4 (MFAP4), and lysyl oxidase homolog 1 (LOXL1). These hub genes might be used as biomarkers to identify diseases and develop new therapeutic approaches. To comprehend the connections between the hub genes, we also constructed a submodule network using the Cytohubba plugin (Figure 5).Figure 4
Protein-protein interaction (PPI) network of common differentially expressed genes (DEGs) between hypertrophic cardiomyopathy and coronavirus disease 2019. The circular nodes represent the DEGs, while the edges represent their interactions. The PPI network has 30 nodes and 124 edges.Figure 5
Determination of hub genes from the protein-protein interaction network using the latest maximal clique centrality procedure with the Cytohubba plugin in Cytoscape. The nodes highlighted in red or yellow represent the top 10 hub genes and their interactions with other molecules. There are 26 nodes and 119 edges in the network.


### 3.4. Determination of Regulatory Signatures

There is a network-based approach to identify the transcriptional changes, identify the regulatory TFs and miRNAs, and gain insights into the molecules that regulate hub proteins or common DEGs. Figure6 illustrates the interactions between the regulatory TFs and DEGs. Figure 7 illustrates the interactions between miRNA regulators and DEGs. According to the analyses of the TF-gene and miRNA-gene interaction networks, 41 TFs and 19 posttranscriptional miRNA signatures regulated more than one DEG, proving that they actively competed with one another.Figure 6
Differentially expressed gene-transcription factor (TF) regulatory interactions were constructed based on our analyses. The nodes in this diagram represent the TFs, while circular nodes are gene symbols that interact with the TFs.Figure 7
An interconnected network of differentially expressed genes and microRNAs (miRNAs). The circular node represents miRNAs, while the square nodes represent the interaction between genes and miRNAs.


### 3.5. Prediction of Candidate Drugs

Understanding the factors responsible for receptor sensitivity requires an assessment of PDIs [36, 37]. We used Enrich to identify four potential drug molecules for HCM and COVID-19 provided by DSigDB. Based on the P value, the top four candidate compounds were extracted. Table 4 lists the most effective drugs identified.Table 4
The candidate drugs for hypertrophic cardiomyopathy and coronavirus disease 2019.
NameP valueChemical formulaStructureDasatinib CTD 000043302.22E-06C22H26ClN7O2SRapamycin CTD 000073502.09E-04C51H79NO13Decitabine CTD 000007500.001005467C8H12N4O4Testosterone enanthate CTD 000001550.001963753C26H40O3


### 3.6. Determination of Disease Association

Similarities in gene expression between the two conditions can be used to infer disease association and correlation [36, 37]. The first step toward developing therapeutic intervention strategies for diseases is identifying gene-disease relationships [38]. We found that degenerative polyarthritis, hyperkyphosis, and platyspondyly were highly correlated with the hub genes of HCM and COVID-19 (Figure 8). These conditions are complex and multifactorial. Its pathophysiology is influenced by alterations in cell structure, barriers, and environmental factors.Figure 8
Figure showing the disease-gene association network. Circular nodes represent the gene symbols, and square nodes represent the disease.


## 3.1. Identification of DEGs and Common DEGs

Patients with COVID-19 exhibited a differential expression of 1,781 genes, including 1,390 upregulated and 391 downregulated genes after disease exposure. Similarly, various statistical analysis techniques were used to rank the DEGs identified for HCM. All DEGs were identified using a criterion ofP<0.05 and ∣logFC∣≥1. Using the Jvenn online platform, 30 common DEGs were identified between the two datasets (Figure 1). There was a close relationship between the two diseases as they shared several genes [34].Figure 1
Ribonucleic acid sequencing datasets for hypertrophic cardiomyopathy (HCM) (GSE89714) and coronavirus disease 2019 (COVID-19) (GSE147507) were used in this study. The integrated analysis identified 30 differentially expressed genes shared between COVID-19 and HCM.


## 3.2. GO and Pathway Enrichment Analyses

Using Enrichr, GO and pathway enrichment analyses were performed. Table2 summarises the top 10 GO terms in the biological processes, molecular functions, and cellular component categories. DEGs are listed in increasing order based on P value. Figure 2 summarises the linear comparison of the overall ontological analysis of each category. An organism’s active pathways reveal how it responds to its inherent modifications. It illustrates the interaction between diseases through basic molecular processes [35]. We examined four global databases, KEGG, WikiPathways, Reactome, and BioCarta, to determine the most important pathways involving the DEGs common to HCM and COVID-19. Table 3 summarises the critical pathways identified based on the examined datasets. Pathway enrichment analysis was performed on the datasets (Figure 3). DEGs are listed in increasing order based on P value. A P value of < 0.05 was used to determine the top functional items and pathways.Table 2
Gene ontology analysis of common differentially expressed genes between hypertrophic cardiomyopathy and coronavirus disease 2019.
CategoryGO IDTermP valuesGenesGO biological processGO:0006939Smooth muscle contraction1.10E-06ACTA2, EDNRA, and MYH11GO:0014829Vascular-associated smooth muscle contraction6.06E-05ACTA2, EDNRAGO:0048251Elastic fiber assembly6.06E-05MFAP4, MYH11GO:0030198Extracellular matrix organization7.69E-05COL1A2, BGN, CYP1B1, TIMP1, and LOXL1GO:0046466Membrane lipid catabolic process9.71E-05ENPP2, CYP1B1GO:0042310Vasoconstriction0.000118625ACTA2, EDNRAGO:0097435Supramolecular fiber organization0.000160702MFAP4, COL1A2, CYP1B1, MYH11, and LOXL1GO:0030199Collagen fibril organization0.000317018COL1A2, CYP1B1, and LOXL1GO:0085029Extracellular matrix assembly0.000588116MFAP4, MYH11GO:0055013Cardiac muscle cell development0.000588116MYH11, MYLK3GO molecular functionGO:0005105Type 1 fibroblast growth factor receptor binding0.007478193FGF18GO:0005111Type 2 fibroblast growth factor receptor binding0.007478193FGF18GO:0004528Phosphodiesterase I activity0.007478193ENPP2GO:0101020Estrogen 16-alpha-hydroxylase activity0.011939153CYP1B1GO:0002020Protease binding0.013479908COL1A2, TIMP1GO:0048407Platelet-derived growth factor binding0.016380734COL1A2GO:0031432Titin binding0.019331061ANKRD1GO:0008191Metalloendopeptidase inhibitor activity0.020803015TIMP1GO:0042288MHC class I protein binding0.025206075TUBB4BGO:0031690Adrenergic receptor binding0.025206075ARRDC3GO cellular componentGO:0062023Collagen-containing extracellular matrix6.41E-08MFAP4, COL1A2, ABI3BP, BGN, PLAT, AEBP1, THBS2, and LOXL1GO:0031091Platelet alpha granule0.000327618ISLR, TIMP1, and THBS2GO:0034774Secretory granule lumen0.001211585C3, ISLR, TIMP1, and TUBB4BGO:0005775Vacuolar lumen0.001772031C3, BGN, and TUBB4BGO:0031093Platelet alpha granule lumen0.004526565ISLR, TIMP1GO:0071953Elastic fiber0.007478193MFAP4GO:0035578Azurophil granule lumen0.008026254C3, TUBB4BGO:0005788Endoplasmic reticulum lumen0.008747528C3, COL1A2, and TIMP1GO:0001527Microfibril0.016380734MFAP4GO:0005859Muscle myosin complex0.022272833MYH11Figure 2
Gene ontology analysis of common differentially expressed genes shared between hypertrophic cardiomyopathy and coronavirus disease 2019 was performed using Enrichr. Terms were evaluated by categories: (a) biological processes, (b) molecular function, and (c) cellular components.
(a)(b)(c)Table 3
Pathway enrichment analysis of common differentially expressed genes between hypertrophic cardiomyopathy and coronavirus disease 2019.
CategoryPathwaysP valuesGenesWikiPathways humanIL-18 signaling pathway WP47544.84E-05ACTA2, BTG2, COL1A2, TIMP1, and IER3Endochondral ossification with skeletal dysplasias WP48080.000119283FRZB, FGF18, and PLATEndochondral ossification WP4740.000119283FRZB, FGF18, and PLATmiR-509-3p alteration of YAP1/ECM axis WP39670.000291693EDNRA, THBS2miRNA targets in ECM and membrane receptors WP29110.000493146COL1A2, THBS2Focal adhesion-PI3K-Akt-mTOR-signaling pathway WP39320.00103726COL1A2, FGF18, THBS2, FGF12PI3K-Akt signaling pathway WP41720.001585899COL1A2, FGF18, THBS2, and FGF12Focal adhesion WP3060.003186561COL1A2, THBS2, and MYLK3Complement and coagulation cascades WP5580.003412595C3, PLATGenotoxicity pathway WP42860.004013249ACTA2, BTG2BioCartaInhibition of matrix metalloproteinases h reckPathway0.011939153TIMP1BTG family proteins and cell cycle regulation h btg2Pathway0.013421829BTG2Alternative complement pathway h alternativePathway0.014902355C3Lectin induced complement pathway h lectinPathway0.019331061C3Platelet amyloid precursor protein pathway h plateletAppPathway0.020803015PLATClassical complement pathway h classicPathway0.022272833C3Fibrinolysis pathway h fibrinolysisPathway0.022272833PLATBeta-arrestins in GPCR desensitization h bArrestinPathway0.041187484EDNRAActivation of cAMP-dependent protein kinase, PKA h gsPathway0.042627715EDNRARole of Beta-arrestins in the activation and targeting of MAP kinases h barr-mapkPathway0.044065855EDNRAKEGG 2019 humanVascular smooth muscle contraction4.48E-05ACTA2, EDNRA, MYH11, and MYLK3Apelin signaling pathway0.001114898ACTA2, PLAT, and MYLK3Phagosome0.001503079C3, THBS2, and TUBB4BFocal adhesion0.003324312COL1A2, THBS2, and MYLK3Regulation of actin cytoskeleton0.004174068FGF18, MYH11, and MYLK3Calcium signaling pathway0.005454596EDNRA, FGF18, and MYLK3Complement and coagulation cascades0.007187738C3, PLATECM-receptor interaction0.007685775COL1A2;,THBS2Platelet activation0.014809355COL1A2, MYLK3PI3K-Akt signaling pathway0.015674766COL1A2, FGF18, and THBS2ReactomeExtracellular matrix organization R-HSA-14742445.84E-05MFAP4, COL1A2, BGN, TIMP1, and LOXL1Smooth muscle contraction R-HSA-4453550.0011157ACTA2, MYH11Elastic fiber formation R-HSA-15669480.001719859MFAP4, LOXL1Signaling by PDGF R-HSA-1867970.002034436FGF18, PLAT, THBS2, and IER3Assembly of collagen fibrils and other multimeric structures R-HSA-20220900.002965286COL1A2, LOXL1Muscle contraction R-HSA-3970140.003096721ACTA2, MYH11, and FGF12PI5P, PP2A, and IER3 regulate PI3K/AKT signaling R-HSA-68115580.006864228FGF18, IER3Collagen formation R-HSA-14742900.007187738COL1A2, LOXL1Diseases of glycosylation R-HSA-37818650.007685775BGN, THBS2Negative regulation of the PI3K/AKT network R-HSA-1994180.008026254FGF18, IER3Figure 3
Pathway enrichment analysis of the common differentially expressed genes between hypertrophic cardiomyopathy and coronavirus disease 2019 was performed using Enrichr. Different databases were used in the analysis: (a) WikiPathways, (b) BioCarta, (c) Reactome, and (d) Kyoto Encyclopedia of Genes and Genomes 2019 human database.
(a)(b)(c)(d)


## 3.3. Classification of Hub Proteins and Submodules

We predicted the interaction of DEGs by analysing the STRING PPI network using Cytoscape. The PPI network constructed using the common DEGs comprised 30 nodes and 124 edges (Figure4). Additionally, most of the interconnected nodes in the PPI network were identified as hub genes. Using the Cytohubba plugin, the top 10 DEGs were considered hub genes. This gene list includes thrombospondin 2 (THBS2), biglycan (BGN), collagen type I alpha 2 chain (COL1A2), actin alpha 2 (ACTA2), myosin heavy chain 11 (MYH11), adipocyte enhancer-binding protein 1 (AEBP1), immunoglobulin superfamily containing leucine-rich repeat (ISLR), frizzled-related protein (FRZB), microfibril-associated protein 4 (MFAP4), and lysyl oxidase homolog 1 (LOXL1). These hub genes might be used as biomarkers to identify diseases and develop new therapeutic approaches. To comprehend the connections between the hub genes, we also constructed a submodule network using the Cytohubba plugin (Figure 5).Figure 4
Protein-protein interaction (PPI) network of common differentially expressed genes (DEGs) between hypertrophic cardiomyopathy and coronavirus disease 2019. The circular nodes represent the DEGs, while the edges represent their interactions. The PPI network has 30 nodes and 124 edges.Figure 5
Determination of hub genes from the protein-protein interaction network using the latest maximal clique centrality procedure with the Cytohubba plugin in Cytoscape. The nodes highlighted in red or yellow represent the top 10 hub genes and their interactions with other molecules. There are 26 nodes and 119 edges in the network.


## 3.4. Determination of Regulatory Signatures

There is a network-based approach to identify the transcriptional changes, identify the regulatory TFs and miRNAs, and gain insights into the molecules that regulate hub proteins or common DEGs. Figure6 illustrates the interactions between the regulatory TFs and DEGs. Figure 7 illustrates the interactions between miRNA regulators and DEGs. According to the analyses of the TF-gene and miRNA-gene interaction networks, 41 TFs and 19 posttranscriptional miRNA signatures regulated more than one DEG, proving that they actively competed with one another.Figure 6
Differentially expressed gene-transcription factor (TF) regulatory interactions were constructed based on our analyses. The nodes in this diagram represent the TFs, while circular nodes are gene symbols that interact with the TFs.Figure 7
An interconnected network of differentially expressed genes and microRNAs (miRNAs). The circular node represents miRNAs, while the square nodes represent the interaction between genes and miRNAs.


## 3.5. Prediction of Candidate Drugs

Understanding the factors responsible for receptor sensitivity requires an assessment of PDIs [36, 37]. We used Enrich to identify four potential drug molecules for HCM and COVID-19 provided by DSigDB. Based on the P value, the top four candidate compounds were extracted. Table 4 lists the most effective drugs identified.Table 4
The candidate drugs for hypertrophic cardiomyopathy and coronavirus disease 2019.
NameP valueChemical formulaStructureDasatinib CTD 000043302.22E-06C22H26ClN7O2SRapamycin CTD 000073502.09E-04C51H79NO13Decitabine CTD 000007500.001005467C8H12N4O4Testosterone enanthate CTD 000001550.001963753C26H40O3


## 3.6. Determination of Disease Association

Similarities in gene expression between the two conditions can be used to infer disease association and correlation [36, 37]. The first step toward developing therapeutic intervention strategies for diseases is identifying gene-disease relationships [38]. We found that degenerative polyarthritis, hyperkyphosis, and platyspondyly were highly correlated with the hub genes of HCM and COVID-19 (Figure 8). These conditions are complex and multifactorial. Its pathophysiology is influenced by alterations in cell structure, barriers, and environmental factors.Figure 8
Figure showing the disease-gene association network. Circular nodes represent the gene symbols, and square nodes represent the disease.


## 4. Discussion

HCM is a common genetic cardiovascular disease that may lead to heart failure. SARS-CoV-2 also infected cardiac cells expressing ACE2, thereby advancing heart failure [39]. Individuals with cardiomyopathy are at high risk of SARS-CoV-2 infection. Herein, we identified molecular targets that could serve as COVID-19 biomarkers. Additionally, these markers might provide crucial details about how they contribute to diseases and conditions. In biomedicine and systems biology research, the expression profiling of high-throughput sequencing data is useful for identifying potential biomarkers [40]. Recently, RNA-seq, a new sequencing method, has significantly improved our ability to examine gene fusions, mutations/single nucleotide polymorphism posttranscriptional modifications, and differential gene expression analyses [41]. As advances in high-throughput sequencing technologies are made, it is becoming more challenging to cope with the increasing bioinformatics data obtained using traditional biological methods. All these limitations may be solved by approaches with artificial intelligence [42].In this study, our transcriptome analyses revealed that 30 DEGs share similar expression patterns between HCM and COVID-19. GO pathway analysis was performed to obtain insights into the biological significance of the common DEGs in disease progression The smooth muscle contraction pathway and vascular-associated smooth muscle contraction pathway were among the top GO terms identified for the biological process. There is a strong correlation between smooth muscle contraction and SARS-CoV-2 infection, according to several studies. Dysfunction endothelial cells prevent the release of adequate nitrogen oxide (NO), causing smooth muscle constriction [43] and reducing the cells’ ability to neutralise reactive oxygen species and release NO [44, 45]. The top two GO pathways identified in the molecular function category are types 1 and 2 fibroblast growth factor (FGF) receptor binders. Cardiac hypertrophy in the postnatal period has been linked to the FGF family, and activating mutations in FGF receptor-1 have been shown to cause HCM [46]. The release of proinflammatory cytokines, such as interleukin- (IL-) 9, IL-10, type 1 FGF, and type 2 FGF, was found in excessive and uncontrolled quantities in critically ill COVID-19 patients [47]. These cytokines are considered valuable biomarkers for evaluating disease progression and potential biological therapeutic targets currently being investigated. In the cellular component category, the top GO terms identified using the common DEGs were collagen-containing extracellular matrix (ECM) and platelet alpha granule. Similarly, the Reactome analysis of the DEGs was mainly enriched in ECM organization (R-HSA-1474244), smooth muscle contraction (R-HSA-445355), and elastic fiber formation (R-HSA-1566948). The ECM comprises fibrillar structures that are made of collagen. Cardiorespiratory disease has been linked to collagen dysfunction [48].We developed a PPI network based on the identified DEGs to understand how proteins behave biologically and predict potential drug targets. Herein, we used the topological metric (i.e., degree) to identify hub proteins that could serve as COVID-19 potential drug targets or biomarkers and could be linked to various pathological and cellular mechanisms. Most of the top hub proteins identified are associated with HCM and COVID-19 risk factors. These diseases have been linked to ten hub-protein products, including THBS2, BGN, COL1A2, ACTA2, MYH11, AEBP1, ISLR, FRZB, MFAP4, and LOXL1. In this study, a cut-off parameter of 12 degrees was used to identify hub proteins. Cardiorespiratory diseases are significantly impacted by the THBS family of proteins. The effects of circular RNA knockdown on the growth, migration, and necrosis of lung cancer cells are reversed by the overexpression ofTHBS2, a miR-590-5 target [49]. Additionally, this gene was linked to adenovirus infection [50] and could function as one of COVID-19’s possible therapeutic targets. Meanwhile, THBS1 and COL1A1 are genes involved in cardiac remodelling, a hallmark of cardiac hypertrophy [51]. Lastly, BGN ubiquitously exists in the intestinal ECM; thus, BGN could potentially serve as a therapeutic target for HCM patients with COVID-19.Herein, the TF-gene and miRNA interactions were also analysed to identify potential transcriptional regulators of the common DEGs. TFs and miRNAs regulate gene expression and posttranscriptional RNA silencing, two processes that are crucial to understanding disease development. We discovered connections between the common DEGs, TFs, and miRNAs. The identified TFs, such as the GATA-binding factor 2, histone H4 TF, TF AP-2 alpha, nuclear factor kappa B subunit 1,BGN, and forkhead box C 1, were found to relate to diffident types of developmental and hereditary diseases. Moreover, most of the miRNAs involved in various cancer types (e.g., hsa-mir-29c-3p, hsa-mir-1-3p, and hsa-mir-128-3p) [52–54] and immunity disorders (e.g., hsa-mir-129-2-3p, hsa-mir-16-5p, hsa-mir-182-5p, hsa-mir-27b-3p, and hsa-mir-124-3p) [55–59], as well as TFs related to the corresponding genes, target major proteins to alter their role in disease progression. For example, hsa-mir-29c-3p, hsa-mir-1-3p, and hsa-mir-129-2-3p have been found to target THBS2 [52, 53, 55]. Four miRNAs that we predicted—hsa-mir-376a-5p, hsa-mir-30a-5p, hsa-mir-23b-3p, and hsa-mir-27a-5p—were found to be associated with various HCM-related genes [60–63]. Many of the miRNAs identified are linked to several cancer types, especially lung cancer.The DEGs and their relation to various diseases were analysed using a gene-disease analysis. Our findings for COVID-19 revealed the involvement of several diseases, such as lung cancer, cardiovascular diseases, blood disorders, liver ailments, and blood coagulation disorders. According to some reviews, SARS-CoV-2 could exacerbate the pathological process of degenerative osteoarthritis. ACE2 expression, RAS imbalances, inflammation, and dysfunction at the molecular level have been suggested as the causative factors [64]. Based on the aforementioned reports, we speculate that systemic inflammation and ischaemia could aggravate cardiac injury in patients with HCM. Hence, anti-inflammatory therapy is particularly important for patients with COVID-19 and HCM.Herein, we identified dasatinib, a tyrosine kinase inhibitor used for leukaemia. Previous reports predicted that dasatinib could inhibit the binding of SARS-CoV-2 spike protein to ACE2 [65]. However, dasatinib has not yet been previously reported as a treatment option for patients with HCM. By boosting the activation of the mammalian target of rapamycin complex 2, rapamycin, another drug candidate discovered, may be used to reduce inflammation in patients with heart disease [66]. Meanwhile, another drug, decitabine, could increase neoantigen expression to enhance T cell-mediated toxicity against glioblastoma [67]. Testosterone enanthate replacement therapy is commonly used in patients with low testosterone [68]. Additionally, testosterone administration helps suppress the inflammatory response [69] and modulates the immune response, which would be more significant in female patients. We witnessed the first case of corticosteroid and tocilizumab application in reversing the severely reduced left ventricular systolic function due to myocardial depression caused by COVID-19 [70]. This partially demonstrates the clinical viability of our candidate drugs in patients with HCM and paves the way for future pharmaceutical studies. Although we could identify candidate drugs based on our bioinformatics analyses, the findings are also limited in that no experiments or further analytical validation were performed on the data obtained. These reasons could lead to unreliable and imprecise conclusions. Thus, further experiments or clinical trials are necessary to validate their effectiveness and safety.


## 5. Conclusions

As the COVID-19 vaccine becomes more widely used, more side effects are being reported [71]. Despite the ongoing development of numerous COVID-19 vaccines, mutant SARS-CoV-2 strains continue to appear. According to this study’s bioinformatics analysis, the 10 most important genes that HCM and COVID-19 have in common are THBS2, BGN, COL1A2, ACTA2, MYH11, AEBP1, ISLR, FRZB, MFAP4, and LOXL1. Each of these hub genes is essential for various functional mutation developments. Therefore, we used transcriptomic analysis to identify shared pathways and molecular biomarkers between HCM and COVID-19, which could aid in COVID-19 vaccine development and the discovery of novel therapeutic targets.



---

*Source: 5337380-2022-09-27.xml*