Decoding HiPSC-CM's Response to SARS-CoV-2: mapping the molecular landscape of cardiac injury

Abstract

Background: Acute cardiac injury caused by coronavirus disease 2019 (COVID-19) increases mortality. Acute cardiac injury caused by COVID-19 requires understanding how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) directly infects cardiomyocytes. This study provides a solid foundation for related studies by using a model of SARS-CoV-2 infection in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) at the transcriptome level, highlighting the relevance of this study to related studies. SARS-CoV-2 infection in hiPSC-CMs has previously been studied by bioinformatics without presenting the full molecular biological process. We present a unique bioinformatics view of the complete molecular biological process of SARS-CoV-2 infection in hiPSC-CMs.

Methods: To validate the RNA-seq datasets, we used GSE184715 and GSE150392 for the analytical studies, GSE193722 for validation at the cellular level, and GSE169241 for validation in heart tissue samples. GeneCards and MsigDB databases were used to find genes associated with the phenotype. In addition to differential expression analysis and principal component analysis (PCA), we also performed protein-protein interaction (PPI) analysis, functional enrichment analysis, hub gene analysis, upstream transcription factor prediction, and drug prediction.

Results: Differentially expressed genes (DEGs) were classified into four categories: cardiomyocyte cytoskeletal protein inhibition, proto-oncogene activation and inflammation, mitochondrial dysfunction, and intracellular cytoplasmic physiological function. Each of the hub genes showed good diagnostic prediction, which was well validated in other datasets. Inhibited biological functions included cardiomyocyte cytoskeletal proteins, adenosine triphosphate (ATP) synthesis and electron transport chain (ETC), glucose metabolism, amino acid metabolism, fatty acid metabolism, pyruvate metabolism, citric acid cycle, nucleic acid metabolism, replication, transcription, translation, ubiquitination, autophagy, and cellular transport. Proto-oncogenes, inflammation, nuclear factor-kappaB (NF-κB) pathways, and interferon signaling were activated, as well as inflammatory factors. Viral infection activates multiple pathways, including the interferon pathway, proto-oncogenes and mitochondrial oxidative stress, while inhibiting cardiomyocyte backbone proteins and energy metabolism. Infection limits intracellular synthesis and metabolism, as well as the raw materials for mitochondrial energy synthesis. Mitochondrial dysfunction and energy abnormalities are ultimately caused by proto-oncogene activation and SARS-CoV-2 infection. Activation of the interferon pathway, proto-oncogene up-regulation, and mitochondrial oxidative stress cause the inflammatory response and lead to diminished cardiomyocyte contraction. Replication, transcription, translation, ubiquitination, autophagy, and cellular transport are among the functions that decline physiologically.

Conclusion: SARS-CoV-2 infection in hiPSC-CMs is fundamentally mediated via mitochondrial dysfunction. Therapeutic interventions targeting mitochondrial dysfunction may alleviate the cardiovascular complications associated with SARS-CoV-2 infection.

Keywords: Biological significance; Mitochondrial dysfunction; Molecular mechanisms; SARS-CoV-2; Transcriptome analysis; hiPSC-CMs.

Fig. 1

Flow chart of this study. CMI: cardiomyocyte injury; GEO: Gene Expression Omnibus; GO: gene ontology; PPI: protein-protein interaction; ROC: receiver operating characteristic; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; TF: transcription factor

Fig. 2

Differential expression analysis and PPI network construction clustered by MCODE plugin of Cytoscape software. A Heatmap of DEGs of GSE184715 and GSE150392. B Venn diagram of DEGs of GSE184715 and GSE150392, as well as SARS-CoV-2 and CMI related genes from GeneCards database. C PPI network of genes of D|S|C cluster 1 and D|S|C cluster 2. D PPI network of genes of D|S cluster 1, D|S cluster 2 and D|S cluster 3. E PPI network of genes of D|C cluster 1 and D|C cluster 2. F PPI network of genes of D cluster 1, D cluster 2 and D cluster 3. DEGs: differently expressed genes; C: CMI; D: common DEGs of GSE184715 and GSE150392 excluding genes associated with SARS-CoV-2 and CMI; S: SARS-CoV-2; other abbreviations as in Fig. 1

Fig. 3

Integrated analysis of Group 1. A Expression visualization of Group 1 genes. B PPI network of Group 1 genes, and the size and color of nodes represent the degree values calculated by Cytoscape software. C GO and pathway analysis of Group 1 genes. D Expression visualization, functional similarity analysis and correlation analysis of top 10 hub genes among Group 1, and top 10 hub genes were calculated by the MCC method in cytoHubba plugin of Cytoscape software. E ROC curves of top 10 hub genes among Group 1 in GSE184715, GSE193722 and GSE169241. BP: biological process; CC: cellular component; KEGG: Kyoto Encyclopedia of Genes and Genomes; MF: molecular function; Group 1: combination of D|S|C cluster 2 and D|C cluster 1; other abbreviations as in Figs. 1, 2

Fig. 4

Integrated analysis of Group 2. A Expression visualization of Group 2 genes. B PPI network of Group 2 genes, and the size and color of nodes represent the degree values calculated by Cytoscape software. C GO and pathway analysis of Group 2 genes. D Expression visualization, functional similarity analysis and correlation analysis of top 10 hub genes among Group 2, and top 10 hub genes were calculated by the MCC method in cytoHubba plugin of Cytoscape software. E ROC curves of top 10 hub genes among Group 2 in GSE184715, GSE193722 and GSE169241. Group 2: combination of D|S|C cluster 1 and D|C cluster 2; other abbreviations as in Figs. 1, 2 and 3

Fig. 5

Integrated analysis of Group 3. A Expression visualization of Group 3 genes. B PPI network of Group 3 genes, and the size and color of nodes represent the degree values calculated by Cytoscape software. C GO and pathway analysis of Group 3 genes. D Expression visualization, functional similarity analysis and correlation analysis of top 10 hub genes among Group 3, and top 10 hub genes were calculated by the MCC method in cytoHubba plugin of Cytoscape software. E ROC curves of top 10 hub genes among Group 3 in GSE184715, GSE193722 and GSE169241. Group 3: combination of D|S cluster 1, D|S cluster 2, D cluster 2 and D cluster 3; other abbreviations as in Figs. 1, 2 and 3

Fig. 6

Integrated analysis of Group 4. A Expression visualization of Group 4 genes. B PPI network of Group 4 genes, and the size and color of nodes represent the degree values calculated by Cytoscape software. C GO and pathway analysis of Group 4 genes. D Expression visualization, functional similarity analysis and correlation analysis of top 10 hub genes among Group 4, and top 10 hub genes were calculated by the MCC method in cytoHubba plugin of Cytoscape software. E ROC curves of top 10 hub genes among Group 4 in GSE184715, GSE193722 and GSE169241. Group 4: combination of D|S cluster 3 and clusters of D other than cluster 2 and cluster 3; other abbreviations as in Figs. 1, 2 and 3

Fig. 7

TF-gene regulatory network construction by JASPAR database in NetworkAnalyst online tool. The regulatory network between upstream TFs and top 10 hub genes among A Group 1, B Group 2, C Group 3, and D Group 4. E PPI network of common upstream TFs of top 10 hub genes among 4 groups, the size and color of nodes represent the degree values calculated by Cytoscape software, and top 5 TFs in the center were calculated by the MCC method in cytoHubba plugin of Cytoscape software. F GO and pathway analysis of common upstream TFs. Abbreviations as in Figs. 1, 2 and 3

Fig. 8

Identification of candidate drugs by Coremine Medical database. Predicted drugs of top 5 hub genes among A Group 1, B Group 2, C Group 3, and D Group 4. E Venn diagram of predicted drugs of top 5 hub genes among 4 groups. F Predicted drugs of top 5 common upstream TFs of top 10 hub genes among 4 groups. Abbreviations as in Figs. 1, 2

Fig. 9

Pathophysiological process of SARS-CoV-2 infecting hiPSC-CMs. ACE2: angiotensin-converting enzyme 2; ATP: adenosine triphosphate; ETC: electron transport chain; hiPSC-CMs: human induced pluripotent stem cell-derived cardiomyocytes; ROS: reactive oxygen species; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2