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. 2020 Dec:21:100956.
doi: 10.1016/j.genrep.2020.100956. Epub 2020 Nov 4.

Bioinformatics analyses of significant genes, related pathways, and candidate diagnostic biomarkers and molecular targets in SARS-CoV-2/COVID-19

Basavaraj Vastrad  1 , Chanabasayya Vastrad  2 , Anandkumar Tengli  3
Affiliations

Bioinformatics analyses of significant genes, related pathways, and candidate diagnostic biomarkers and molecular targets in SARS-CoV-2/COVID-19

Basavaraj Vastrad et al. Gene Rep. 2020 Dec.

Abstract

Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infection is a leading cause of pneumonia and death. The aim of this investigation is to identify the key genes in SARS-CoV-2 infection and uncover their potential functions. We downloaded the expression profiling by high throughput sequencing of GSE152075 from the Gene Expression Omnibus database. Normalization of the data from primary SARS-CoV-2 infected samples and negative control samples in the database was conducted using R software. Then, joint analysis of the data was performed. Pathway and Gene ontology (GO) enrichment analyses were performed, and the protein-protein interaction (PPI) network, target gene - miRNA regulatory network, target gene - TF regulatory network of the differentially expressed genes (DEGs) were constructed using Cytoscape software. Identification of diagnostic biomarkers was conducted using receiver operating characteristic (ROC) curve analysis. 994 DEGs (496 up regulated and 498 down regulated genes) were identified. Pathway and GO enrichment analysis showed up and down regulated genes mainly enriched in the NOD-like receptor signaling pathway, Ribosome, response to external biotic stimulus and viral transcription in SARS-CoV-2 infection. Down and up regulated genes were selected to establish the PPI network, modules, target gene - miRNA regulatory network, target gene - TF regulatory network revealed that these genes were involved in adaptive immune system, fluid shear stress and atherosclerosis, influenza A and protein processing in endoplasmic reticulum. In total, ten genes (CBL, ISG15, NEDD4, PML, REL, CTNNB1, ERBB2, JUN, RPS8 and STUB1) were identified as good diagnostic biomarkers. In conclusion, the identified DEGs, hub genes and target genes contribute to the understanding of the molecular mechanisms underlying the advancement of SARS-CoV-2 infection and they may be used as diagnostic and molecular targets for the treatment of patients with SARS-CoV-2 infection in the future.

Keywords: Bioinformatics; CBL, Cbl proto-oncogene; DEGs, differentially expressed genes; Diagnosis; GO, Gene ontology; ISG15, ISG15 ubiquitin like modifier; Key genes; NEDD4, NEDD4 E3 ubiquitin protein ligase; PML, promyelocyticleukemia; PPI, protein-protein interaction; Pathways; REL, REL proto-oncogene, NF-kB subunit; ROC, receiver operating characteristic; SARS-CoV-2 infection; SARS-CoV-2, Severe acute respiratory syndrome corona virus 2.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Box plots of the normalized data. (A) 54 negative control samples (B) 430 SARS-CoV-2 infected samples. Horizontal axis represents the sample symbol and the vertical axis represents the gene expression values. The black line in the box plot represents the median value of gene expression. (A1 – A54 = negative control samples (red color box); B1 – B430 = SARS-CoV-2 infected samples (blue color box)). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 2
Fig. 2
Volcano plot of differentially expressed genes. Genes with a significant change of more than two-fold were selected. Green dot represented up regulated significant genes and red dot represented down regulated significant genes. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 3
Fig. 3
Heat map of up regulated differentially expressed genes. Legend on the top left indicate log fold change of genes. (A1 – A54 = negative control samples (red color box); B1 – B430 = SARS-CoV-2 infected samples (blue color box)). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 4
Fig. 4
Heat map of down regulated differentially expressed genes. Legend on the top left indicate log fold change of genes. (A1 – A54 = negative control samples (red color box); B1 – B430 = SARS-CoV-2 infected samples (blue color box)). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 5
Fig. 5
Protein–protein interaction network of up regulated genes. Green nodes denotes up regulated genes. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 6
Fig. 6
Scatter plot for up regulated genes. (A- Node degree; B- Betweenness centrality; C- Stress centrality; D-Closeness centrality; E- Clustering coefficient).
Fig. 7
Fig. 7
Protein–protein interaction network of down regulated genes. Red nodes denotes down regulated genes. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 8
Fig. 8
Scatter plot for down regulated genes. (A- Node degree; B- Betweenness centrality; C- Stress centrality; D-Closeness centrality; E- Clustering coefficient).
Fig. 9
Fig. 9
Modules in PPI network. The green nodes denote the up regulated genes. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 10
Fig. 10
Modules in PPI network. The red nodes denote the down regulated genes. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 11
Fig. 11
The network of up regulated genes and their related miRNAs. The green circles nodes are the up regulated genes, and purple diamond nodes are the miRNAs. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 12
Fig. 12
The network of down regulated genes and their related miRNAs. The red circles nodes are the down regulated genes, and blue diamond nodes are the miRNAs. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 13
Fig. 13
The network of up regulated genes and their related TFs. The green circles nodes are the up regulated genes, and purple triangle nodes are the TFs. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 14
Fig. 14
The network of down regulated genes and their related TFs. The green circles nodes are the down regulated genes, and blue triangle nodes are the TFs. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Fig. 15
Fig. 15
ROC curve validated the sensitivity, specificity of hub genes as a predictive biomarker for SARS-CoV-2 infection. A) CBL B) ISG15 C) NEDD4 D) PML E) REL F) CTNNB1 G) ERBB2 H) JUN I) RPS8 J) STUB1.
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