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. 2022 Nov 7:13:1052850.
doi: 10.3389/fimmu.2022.1052850. eCollection 2022.

Bioinformatics and systems biology approaches to identify molecular targeting mechanism influenced by COVID-19 on heart failure

Kezhen Yang  1 , Jipeng Liu  1 , Yu Gong  1 , Yinyin Li  1 , Qingguo Liu  1
Affiliations

Bioinformatics and systems biology approaches to identify molecular targeting mechanism influenced by COVID-19 on heart failure

Kezhen Yang et al. Front Immunol. .

Abstract

Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has emerged as a contemporary hazard to people. It has been known that COVID-19 can both induce heart failure (HF) and raise the risk of patient mortality. However, the mechanism underlying the association between COVID-19 and HF remains unclear. The common molecular pathways between COVID-19 and HF were identified using bioinformatic and systems biology techniques. Transcriptome analysis was performed to identify differentially expressed genes (DEGs). To identify gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways, common DEGs were used for enrichment analysis. The results showed that COVID-19 and HF have several common immune mechanisms, including differentiation of T helper (Th) 1, Th 2, Th 17 cells; activation of lymphocytes; and binding of major histocompatibility complex class I and II protein complexes. Furthermore, a protein-protein interaction network was constructed to identify hub genes, and immune cell infiltration analysis was performed. Six hub genes (FCGR3A, CD69, IFNG, CCR7, CCL5, and CCL4) were closely associated with COVID-19 and HF. These targets were associated with immune cells (central memory CD8 T cells, T follicular helper cells, regulatory T cells, myeloid-derived suppressor cells, plasmacytoid dendritic cells, macrophages, eosinophils, and neutrophils). Additionally, transcription factors, microRNAs, drugs, and chemicals that are closely associated with COVID-19 and HF were identified through the interaction network.

Keywords: COVID-19; bioinformatics analysis; heart failure; immunology; systems biology approaches.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Schematic diagram of the workflow in this study.
Figure 2
Figure 2
GO and KEGG pathway enrichment analysis of common DEGs. (A) Screening process of common DEGs. (B) KEGG enrichment analysis bar graph. Horizontal coordinates indicate the number of genes annotated to this pathway, and different colors indicate different pathway classifications. (C) KEGG enrichment analysis circle diagram. The first circle indicates the first 25 pathways, and the number of genes corresponds to the outer circle. The second circle indicates the number of genes and P-values in the genomic background. The third circle indicates the number of genes annotated to this pathway. The fourth circle indicates the enrichment factor for each KEGG term. (D–F) GO enrichment analysis bubble chart. The size of the dots corresponds to the number of genes annotated to this term, and the color of the dots corresponds to the magnitude of the P-value. GO, gene ontology; DEGs, differentially expressed genes; KEGG, Kyoto encyclopedia of genes and genomes.
Figure 3
Figure 3
Screening and validation of hub genes. (A) Screening process of hub genes. (B) Expression of hub genes in the COVID-19 dataset; blue and red indicate the healthy and COVID-19 groups, respectively. Expression of FCGR3A was significantly higher and that of CD69, IFNG, CCR7, CCL5, CCL4 was significantly lower in COVID-19 compared to expression levels in the healthy group. (C) AUC values of hub genes in the COVID-19 dataset. ROC curves and AUC statistics were used to assess the ability to distinguish COVID-19 from healthy controls with excellent sensitivity and specificity. COVID-19: coronavirus disease 2019. AUC, area under the curve; ROC, receiver operating characteristic.
Figure 4
Figure 4
Immune cell infiltration analysis. (A) Distribution of 28 immune cells in the healthy and COVID-19 groups. (B) Correlations between six hub genes and different immune cells. Red color indicates positive correlation; blue color indicates negative correlation. (C) Correlation between 28 immune cells. Red indicates positive correlations; blue indicates negative correlations. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001. COVID-19: coronavirus disease 2019.
Figure 5
Figure 5
TF–gene interaction network analysis. Round dots indicate hub genes; square dots indicate TFs. Darker colors indicate association with a greater number of hub genes and a higher degree of association. TF, transcription factor.
Figure 6
Figure 6
Gene–miRNA interaction network analysis. Round dots indicate hub genes; square dots indicate miRNAs. Darker colors indicate association with a greater number of hub genes and a higher degree of association. miRNA, microRNA.
Figure 7
Figure 7
Protein–drug and protein–chemical interaction network analyses. (A) Drugs connected to the protein; blue dots indicate the protein, and green dots indicate the drug. (B) Chemicals linked to proteins. The diagram only shows chemicals linked to at least three proteins, with darker colors and larger font sizes indicating linkage to a greater number of proteins.
Figure 8
Figure 8
Gene–disease interaction network analysis. Purple dots in the graph indicate hub gene, green dots indicate diseases associated with hub genes, and dark green dots indicate diseases associated with multiple hub genes.
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