Fig 1.
Using circRNA based therapeutics to mitigate cytokine storm syndrome induced by SARS-CoV-2.
Fig 2.
Flow chart of the approach utilized in the present study for the construction of SARS-CoV-2 related circRNA-miRNA-mRNA regulator network.
Table 1.
Datasets used for retrieving expression profiles of circRNAs, miRNAs and mRNAs.
Table 2.
List of all softwares and tools utilized in the current study.
Fig 3.
Venn diagram of overlapped differentially expressed circRNAs among circRNAs of circRNA datasets, miRNA datasets and mRNA datasets.
a) Overlapped differentially expressed circRNAs among circRNAs of circRNA datasets, miRNA datasets and mRNA datasets (whole genes). b) Overlapped differentially expressed circRNAs among circRNAs of circRNA datasets, miRNA datasets and mRNA datasets (cytokine storm related mRNAs).
Table 3.
List of count of differentially expressed RNAs and their predicted targets.
Table 4.
Datasets used for the analysis of SARS-CoV-2 related cytokines.
Fig 4.
The SARS-CoV-2 induced cytokine storm related circRNA-miRNA-mRNA network visualized using Cytoscape software.
The circRNA-miRNA-mRNA network contains 81 nodes and 205 edges.
Table 5.
SARS-CoV-2 induced cytokine storm related circRNA-miRNA-mRNA regulatory axis.
Fig 5.
Gene ontology analysis of differentially expressed genes.
Top GO terms with lowest P-values in cellular component, molecular function, and biological process were shown, respectively.
Fig 6.
The cytoHubba plug-in in Cytoscape was used to search the list of top 10 genes from the PPI network with node degrees indicating hub differentially expressed genes, including STAT1, RSAD2, IFIT1, IFIT3, IFIT2, DDX58, OAS2, MX2, IFI44 and IFI44L.
Table 6.
KEGG analysis of 15 cytokine storm related genes.
Table 7.
Drug targets and their association with prioritized circRNAs during SARS-CoV-2 infection.
Fig 7.
Pathway analysis of COVID-19 pathogenesis (KEGG pathway ID: map05171).
Highlighted genes are targets of miRNAs and indirect targets of two prioritized circRNAs.