Inflammatory activation and immune cell infiltration are main biological characteristics of SARS-CoV-2 infected myocardium.
Rui ZhangXi ChenWenjie ZuoZhenjun JiYangyang QuYamin SuMingming YangPengfei ZuoGenshan MaYongjun LiPublished in: Bioengineered (2022)
The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) can target cardiomyocytes (CMs) to directly invade the heart resulting in high mortality. This study aims to explore the biological characteristics of SARS-CoV-2 infected myocardium based on omics by collecting transcriptome data and analyzing them with a series of bioinformatics tools. Totally, 86 differentially expressed genes (DEGs) were discovered in SARS-CoV-2 infected CMs, and 15 miRNAs were discovered to target 60 genes. Functional enrichment analysis indicated that these DEGs were mainly enriched in the inflammatory signaling pathway. After the protein-protein interaction (PPI) network was constructed, several genes including CCL2 and CXCL8 were regarded as the hub genes. SRC inhibitor saracatinib was predicted to potentially act against the cardiac dysfunction induced by SARS-CoV-2. Among the 86 DEGs, 28 were validated to be dysregulated in SARS-CoV-2 infected hearts. Gene Set Enrichment Analysis (GSEA) analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) showed that malaria, IL-17 signaling pathway, and complement and coagulation cascades were significantly enriched. Immune infiltration analysis indicated that 'naive B cells' was significantly increased in the SARS-CoV-2 infected heart. The above results may help to improve the prognosis of patients with COVID-19.
Keyphrases
- sars cov
- respiratory syndrome coronavirus
- genome wide
- signaling pathway
- genome wide identification
- bioinformatics analysis
- protein protein
- heart failure
- oxidative stress
- dna methylation
- genome wide analysis
- coronavirus disease
- type diabetes
- epithelial mesenchymal transition
- pi k akt
- coronary artery disease
- cell proliferation
- atrial fibrillation
- risk factors
- wastewater treatment
- cardiovascular events
- machine learning
- transcription factor