Fig 1.
The overview of this study.
Fig 2.
(a) Volcano plot of microarray data highlighting DEGs (blue: down regulated; red: up regulated; and black: insignificant), (b) Hierarchical clustering of DEGs to visualize up-regulated and down-regulated genes.
Fig 3.
The larger nodes highlighted with pink color indicate the HubGs.
Table 1.
Significantly enriched GO terms and KEGG pathways with DEGs by involving HubGs in four different databases that are involved in the pathogenetic processes of SARS-CoV infections (p-value <0.05).
Fig 4.
(a) miRNAs-HubGs interaction network based on TarBase and miRTarBase databases (b) Chemicals-HubGs interaction network based on comparative toxicogenomics database; where, Chemichal-1: (6-(4-(2-piperidin-1-ylethoxy)phenyl))-3-pyridin-4-ylpyrazolo(1,5-a)pyrimidine. (c) TFs-HubGs interaction network based on JASPAR database.
Fig 5.
Identification of comorbidities that may be influenced genetically by the SARS-CoV-1 infections.
The HubGs versus disease interaction network, where circle-shape with pink color indicates HubGs and circle-shape with other color indicates different diseases/comorbidities.
Fig 6.
The multivariate survival curves of lung cancer patients based on hub-DEGs.
Fig 7.
Phylogenetic analysis of SARS-CoV, MERS-CoV and SARS-CoV-2 (COVID-19) based on Neighbor-Joining method.
SARS-CoV and MERS-CoV highly pathogenic beta coronaviruses along with SARS-like bat coronaviruses closely linked to SARS-CoV-2. Number at nodes indicates support for bootstrap (100 replicates), and the bar of scale indicates the average number of substitutions per location. The alpha coronavirus HCoV-229E sequence were considered as the out-group.
Fig 8.
Molecular docking simulation results by AutoDock-Vina and PatchDock.
Red colors indicated the strong binding affinities between target proteins and drug agents, and green colors indicated their weak bindings. (a) Image of binding affinity scores (computed by AutoDock-Vina) based on the top listed ordered 90 anti-viral drug agents in X-axis and ordered 17 target proteins (proposed) corresponding to SARS-CoV-1 in Y-axis. (b) Image of binding scores (computed by PatchDock) based on the ordered top-ranked 20 anti-viral drug agents (from 7a) in X-axis and ordered 17 target proteins corresponding to SARS-CoV-1 (proposed) in Y-axis. (c) Image of binding affinity scores based on the ordered proposed 7 candidate-drugs in X-axis and ordered more common 11 existing target proteins (published) corresponding to SARS-CoV-2 in Y-axis.
Table 2.
Identification of top ranked target proteins associated with SARS-CoV-2 infections by literature review.
Fig 9.
Top 4 potential targets and top 3 lead compounds (drugs) based on AutoDock-Vina docking results.
Three candidate-drugs (Rapamycin, Tacrolimus, Torin-2) were selected based on their higher binding affinity scores. The 3D structure of hub protein with candidate-drugs is shown in the 4th column. The 2D Schematic diagram of hub protein with candidate-drugs interaction is given 5th column and neighbor residues (within 4 Å of the drug) are shown. Key interacting amino acids are shown in the last column.
Fig 10.
Top 4 potential targets and top 3 lead compounds (drugs) based on PatchDock docking results.
The 3D structures of complexes are shown in 2nd column. The 2D Schematic diagrams of complexes are given in 3rd column and neighbor interacting residues (within 4 Å of the drug) are shown in the last column.
Table 3.
PatchDock docking results corresponding to the most significant complexes between drugs and receptors.
Table 4.
Indications and mechanism of actions for the proposed repurposable drugs.