Unraveling the relevance of SARS-Cov-2 infection and ferroptosis within the heart of COVID-19 patients

Abstract

Background: The coronavirus disease 2019 (COVID-19) was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which led to a huge mortality rate and imposed significant costs on the health system, causing severe damage to the cells of different organs such as the heart. However, the exact details and mechanisms behind this damage are not clarified. Therefore, we aimed to identify the cell and molecular mechanism behind the heart damage caused by SARS-Cov-2 infection.

Methods: RNA-seq data for COVID-19 patients' hearts was analyzed to obtain differentially expressed genes (DEGs) and differentially expressed ferroptosis-related genes (DEFRGs). Then, DEFRGs were used for analyzing GO and KEGG enrichment, and perdition of metabolites and drugs. we also constructed a PPI network and identified hub genes and functional modules for the DEFRGs. Subsequently, the hub genes were validated using two independent RNA-seq datasets. Finally, the miRNA-gene interaction networks were predicted in addition to a miRNA-TF co-regulatory network, and important miRNAs and transcription factors (TFs) were highlighted.

Findings: We found ferroptosis transcriptomic alterations within the hearts of COVID-19 patients. The enrichment analyses suggested the involvement of DEFRGs in the citrate cycle pathway, ferroptosis, carbon metabolism, amino acid biosynthesis, and response to oxidative stress. IL6, CDH1, AR, EGR1, SIRT3, GPT2, VDR, PCK2, VDR, and MUC1 were identified as the ferroptosis-related hub genes. The important miRNAs and TFs were miR-124-3P, miR-26b-5p, miR-183-5p, miR-34a-5p and miR-155-5p; EGR1, AR, IL6, HNF4A, SRC, EZH2, PPARA, and VDR.

Conclusion: These results provide a useful context and a cellular snapshot of how ferroptosis affects cardiomyocytes (CMs) in COVID-19 patients' hearts. Besides, suppressing ferroptosis seems to be a beneficial therapeutic approach to mitigate heart damage in COVID-19.

Keywords: Bioinformatics; COVID-19 heart samples; Ferroptosis; Gene regulatory network; SARS-CoV-2.

Fig. 1

Flow chart of the study. GEO: Gene Expression Omnibus; DEGs: differentially expressed genes; FRGs: ferroptosis related genes; DEFRGs: differentially expressed ferroptosis-related genes; PPI: protein-protein interaction; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes, and Genomes.

Fig. 2

Differentially expressed ferroptosis related genes (DEFRGs) between COVID-19 infected heart tissues, and non-infected controls. (A) Venn diagram showing the intersection between the differentially expressed genes (DEGs) in GSE169241, and ferroptosis genes. (B) Volcano plot showing the distribution of DEFRGs among the DEGs in COVID-19 patients, and non-COVID controls. (C) Heatmap showing the expression pattern of the 136 DEFRGs.

Fig. 3

Top 10 GO (Gene Ontology) terms, and Kyoto Encyclopedia of Genes, and Genomes (KEGG) pathways of the 136 DEFRGs between COVID-19 patients, and non-COVID controls. (A) Top 10 enriched terms of GO: biological process. (B) Top 10 enriched terms of GO: cellular component. (C) Top 10 enriched terms of GO: molecular function. (D) Top 10 enriched KEGG pathways (P-value <0.05).

Fig. 4

The protein-protein interaction network of the 136 DEFRGs. The DEFRGs are categorized into a cell tem plate, based on the assigned cellular location, using the Cytoscape plug-in boundaryLayout. The size of a node is proportional to the degree of the node.

Fig. 5

(A) Six MCODE modules (clusters) extracted from the protein-protein interaction (PPI) network of the 136 DEFRGs. Darker nodes indicate higher MCODE score. (B) Gene-disease association network of the six DEFRG clusters (degree >2). (C) The PPI network of the top 20 hub DEFRGs. Darker nodes show higher MCC score. (D) Spearman correlation analysis of the 20 hub DEFRGs. Blue, and red represent positive, and negative correlation, respectively (P-value <0.05). Circles with a deeper color indicate a stronger correlation index. The numbers in the plot represent spearman correlation coefficient. Blank represents not significant coefficient (P-value <0.05).

Fig. 6

(A) The miRNA-hub DEFRG interaction network (degree >2). The round nodes represent the hub DEFRGs, and the octagon nodes indicate the miRNAs. The size of the nodes is proportional to the degree of the node. DEFRGs with darker colors are regulated by higher number of miRNAs. (B) The TF-miRNA co-regulatory network of the hub DEFRGs (degree >4). The purple octagon nodes show the miRNAs, the blue diamond nods represent the TFs, and the rest of the nodes represent the hub DEFRGs. EGR1, AR, IL6, HNF4A, SRC, EZH2, PPARA, and VDR act both as hub DEFRGs, and TFs in this network. The size, and the shade of the nodes are proportional to the degree of the node.

Fig. 7

Validation of the of the hub DEFRGs in two external datasets (GSE156754, and GSE 151879). (A) Box plots depicting expression levels of the hub DEFRGs in human induced pluripotent stem cells (iPSCs)-derived cardiomyocytes (CMs) infected with SARS-CoV-2 (high MOI, n = 3) compared with the mock infection CMs (n = 3) in GSE156754. (B) Expression levels of the hub DEFRGs in adult human CMs infected with SARS-CoV-2 (n = 3) compared with the uninfected human CMs (n = 3) in GSE151879 (P-value <0.05).