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. 2021 Sep 15;1(1):20-28.
doi: 10.1097/EC9.0000000000000005. eCollection 2021 Sep.

Bioinformatics analysis of SARS-CoV-2 infection-associated immune injury and therapeutic prediction for COVID-19

Haomin Zhang  1 , Haoran Chen  2 , Jundong Zhang  1 , Ximeng Chen  3 , Bin Guo  4 , Peng Zhi  1   2 , Zhuoyang Li  2 , Geliang Liu  2 , Bo Yang  1 , Xiaohua Chi  5 , Yixing Wang  6 , Feng Cao  7 , Jun Ren  8 , Xuechun Lu  1
Affiliations

Bioinformatics analysis of SARS-CoV-2 infection-associated immune injury and therapeutic prediction for COVID-19

Haomin Zhang et al. Emerg Crit Care Med. .

Abstract

Background: Severe acute respiratory syndrome coronavirus 2 is a highly contagious viral infection, without any available targeted therapies. The high mortality rate of COVID-19 is speculated to be related to immune damage.

Methods: In this study, clinical bioinformatics analysis was conducted on transcriptome data of coronavirus infection.

Results: Bioinformatics analysis revealed that the complex immune injury induced by coronavirus infection provoked dysfunction of numerous immune-related molecules and signaling pathways, including immune cells and toll-like receptor cascades. Production of numerous cytokines through the Th17 signaling pathway led to elevation in plasma levels of cytokines (including IL6, NF-κB, and TNF-α) followed by concurrent inflammatory storm, which mediates the autoimmune response. Several novel medications seemed to display therapeutic effects on immune damage associated with coronavirus infection.

Conclusions: This study provided insights for further large-scale studies on the target therapy on reconciliation of immunological damage associated with COVID-19.

Keywords: Bioinformatics; COVID-19; Coronavirus; Drug prediction; Immune injury.

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Conflict of interest statement

Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article. Jun Ren is an Associate Editor of Emergency and Critical Care Medicine. The article was subject to the journal's standard procedures, with peer review handled independently of this Associate Editor and their research groups. The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Difference analysis of transcriptome data of SARS-CoV-2 infected cell lines. The threshold was set as FDR < 0.05, Log2FC > 1. Red indicates up-regulated, whereas green indicates down-regulated. FDR, false discovery rate; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.
Figure 2
Figure 2
Enrichment analysis results and immune-related signaling pathways. (A) GO enrichment analysis results. The x-axis represents the number of genes enriched in each GO, and the y-axis represents the GO name. (B) The top 20 KEGG enrichment analysis results. The x-axis represents the number of genes enriched in the pathway, and the y-axis represents the pathway name. (C) Some important signaling pathways are shown, wherein red indicates the up-regulated genes and green indicates the down-regulated genes among the differential genes. The displayed pathways included TNF signaling pathway and IL17 receptor signaling pathway that were speculated to be closely related to the increase in plasma cytokines after coronavirus infection and pulmonary fibrosis in severe patients. Go, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes 1 and Genomes.
Figure 3
Figure 3
Immune-damage-related gene PPI network and hub gene statistics. (A) Immune-damage-related gene PPI network. Red indicates up-regulated genes and blue indicate down-regulated genes. (B) The top 10 Hub gene statistics. PPI, protein-protein interaction.
Figure 4
Figure 4
Signaling pathways associated with plasma cytokine elevations. (A) Th17 signaling pathway. (B) TNF signaling pathway.
Figure 5
Figure 5
Cardiac, nerve-related signaling pathways. (A) Neuroactive ligand receptor interaction pathway. (B) Viral myocarditis pathway.
See this image and copyright information in PMC

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