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. 2021 Mar 22;22(2):1254-1266.
doi: 10.1093/bib/bbaa235.

Network-based identification genetic effect of SARS-CoV-2 infections to Idiopathic pulmonary fibrosis (IPF) patients

Tasnimul Alam Taz  1 , Kawsar Ahmed  2 , Bikash Kumar Paul  3 , Md Kawsar  1 , Nargis Aktar  4 , S M Hasan Mahmud  5 , Mohammad Ali Moni  6
Affiliations

Network-based identification genetic effect of SARS-CoV-2 infections to Idiopathic pulmonary fibrosis (IPF) patients

Tasnimul Alam Taz et al. Brief Bioinform. .

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is accountable for the cause of coronavirus disease (COVID-19) that causes a major threat to humanity. As the spread of the virus is probably getting out of control on every day, the epidemic is now crossing the most dreadful phase. Idiopathic pulmonary fibrosis (IPF) is a risk factor for COVID-19 as patients with long-term lung injuries are more likely to suffer in the severity of the infection. Transcriptomic analyses of SARS-CoV-2 infection and IPF patients in lung epithelium cell datasets were selected to identify the synergistic effect of SARS-CoV-2 to IPF patients. Common genes were identified to find shared pathways and drug targets for IPF patients with COVID-19 infections. Using several enterprising Bioinformatics tools, protein-protein interactions (PPIs) network was designed. Hub genes and essential modules were detected based on the PPIs network. TF-genes and miRNA interaction with common differentially expressed genes and the activity of TFs are also identified. Functional analysis was performed using gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathway and found some shared associations that may cause the increased mortality of IPF patients for the SARS-CoV-2 infections. Drug molecules for the IPF were also suggested for the SARS-CoV-2 infections.

Keywords: SARS-CoV-2; differentially expressed genes; drug molecule; gene ontology; hub gene; idiopathic pulmonary fibrosis; protein–protein interactions.

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Figures

Figure 1
Figure 1
Methodical workflow for the current investigation. Two type of samples (control cells, SARS-CoV-2 infected cells) were collected from SARS-CoV-2 infected lung epithelial cells and both are included in the GSE147507 dataset. GSE147507 dataset contains a sample of SARS-CoV-2 infected lung epithelial cells and the GSE35145 dataset contains IPF affected lung samples. Common DEGs were identified from both the datasets using the R programming language. From the common DEGs, GO identification, KEGG pathway, PPIs network, TF and miRNA analysis, hub gene identification and module analysis was designed and based on those analysis drug molecule identification was performed.
Figure 2
Figure 2
Common differentially expressed genes representation through a Venn diagram. Eleven genes were found common from the 108 differentially expressed genes of SARS-CoV-2 infection and 359 differentially expressed genes of IPF patients. The common differentially expressed genes were 2.4% among total 467 differentially expressed genes.
Figure 3
Figure 3
(A) Biological process, molecular function and cellular component related GO terms identification result according to combined score. The higher the enrichment score, the higher number of genes are involved in a certain ontology. (B) Pathway analysis result identification through KEGG, WikiPathways, Reactome and BioCarta. The results of the pathway terms were identified through the combined score.
Figure 4
Figure 4
Protein–protein interactions (PPIs) network for identified common differentially expressed genes that are shared by two diseases (COVID-19 and IPF). Nodes in orange color indicate common differentially expressed genes and edges specify the interconnection in the middle of two genes. The analyzed network holds 60 nodes and 403 edges.
Figure 5
Figure 5
Detection of hub genes from the PPIs network of common differentially expressed genes. The highlighted five genes are VEGFA, AKT1, MMP9, ICAM1 and CD44. These five genes are considered as hub genes according to their degree value. The network has 53 nodes and 378 edges. According to topological analysis, the degree value of VEGFA and AKT1 was 38. The degree value of MMP9, ICAM1 and CD44 were 34, 29 and 27, respectively.
Figure 6
Figure 6
Module analysis network obtained from Figure 4 PPIs network. ICAM1 and MMP9 are highlighted in red color as these two hub nodes are common between GSE147507 and GSE35145. The network represents highly interconnected regions of the PPIs network. The network holds 16 nodes and 106 edges.
Figure 7
Figure 7
Network for TF-gene interaction with common differentially expressed genes. The highlighted blue color node represents the common genes and other nodes represent TF-genes. The network consists of 142 nodes and 180 edges.
Figure 8
Figure 8
The network presents the TF-miRNA coregulatory network. The network consists of 101 nodes and 131 edges including 53 TF-genes, 39 miRNA and nine differentially expressed genes. The nodes in pink color are the differentially expressed genes, a yellow node represents miRNA and other nodes indicate TF-genes.
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