Identification of host transcriptome-guided repurposable drugs for SARS-CoV-1 infections and their validation with SARS-CoV-2 infections by using the integrated bioinformatics approaches

Abstract

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is one of the most severe global pandemic due to its high pathogenicity and death rate starting from the end of 2019. Though there are some vaccines available against SAER-CoV-2 infections, we are worried about their effectiveness, due to its unstable sequence patterns. Therefore, beside vaccines, globally effective supporting drugs are also required for the treatment against SARS-CoV-2 infection. To explore commonly effective repurposable drugs for the treatment against different variants of coronavirus infections, in this article, an attempt was made to explore host genomic biomarkers guided repurposable drugs for SARS-CoV-1 infections and their validation with SARS-CoV-2 infections by using the integrated bioinformatics approaches. At first, we identified 138 differentially expressed genes (DEGs) between SARS-CoV-1 infected and control samples by analyzing high throughput gene-expression profiles to select drug target key receptors. Then we identified top-ranked 11 key DEGs (SMAD4, GSK3B, SIRT1, ATM, RIPK1, PRKACB, MED17, CCT2, BIRC3, ETS1 and TXN) as hub genes (HubGs) by protein-protein interaction (PPI) network analysis of DEGs highlighting their functions, pathways, regulators and linkage with other disease risks that may influence SARS-CoV-1 infections. The DEGs-set enrichment analysis significantly detected some crucial biological processes (immune response, regulation of angiogenesis, apoptotic process, cytokine production and programmed cell death, response to hypoxia and oxidative stress), molecular functions (transcription factor binding and oxidoreductase activity) and pathways (transcriptional mis-regulation in cancer, pathways in cancer, chemokine signaling pathway) that are associated with SARS-CoV-1 infections as well as SARS-CoV-2 infections by involving HubGs. The gene regulatory network (GRN) analysis detected some transcription factors (FOXC1, GATA2, YY1, FOXL1, TP53 and SRF) and micro-RNAs (hsa-mir-92a-3p, hsa-mir-155-5p, hsa-mir-106b-5p, hsa-mir-34a-5p and hsa-mir-19b-3p) as the key transcriptional and post- transcriptional regulators of HubGs, respectively. We also detected some chemicals (Valproic Acid, Cyclosporine, Copper Sulfate and arsenic trioxide) that may regulates HubGs. The disease-HubGs interaction analysis showed that our predicted HubGs are also associated with several other diseases including different types of lung diseases. Then we considered 11 HubGs mediated proteins and their regulatory 6 key TFs proteins as the drug target proteins (receptors) and performed their docking analysis with the SARS-CoV-2 3CL protease-guided top listed 90 anti-viral drugs out of 3410. We found Rapamycin, Tacrolimus, Torin-2, Radotinib, Danoprevir, Ivermectin and Daclatasvir as the top-ranked 7 candidate-drugs with respect to our proposed target proteins for the treatment against SARS-CoV-1 infections. Then, we validated these 7 candidate-drugs against the already published top-ranked 11 target proteins associated with SARS-CoV-2 infections by molecular docking simulation and found their significant binding affinity scores with our proposed candidate-drugs. Finally, we validated all of our findings by the literature review. Therefore, the proposed candidate-drugs might play a vital role for the treatment against different variants of SARS-CoV-2 infections with comorbidities, since the proposed HubGs are also associated with several comorbidities.

Identifying novel host-based diagnostic biomarker panels for COVID-19: a whole-blood/nasopharyngeal transcriptome meta-analysis

“…In a study by Ahmed et al, a microarray dataset consisting of 10 and 4 PBMC samples of patients with SARS-CoV-1 infection and healthy controls, respectively was analyzed to find hub genes and remarkable signaling pathways in SARS-CoV-1 infection. The repurposable drugs for SARS-CoV-1 infections were then identified and validated for the treatment of SARS-CoV-2 infections (Ahmed et al 2022 ). Furthermore, Mosharaf et al analyzed an RNA-seq dataset consisting of 35 lung tissue samples infected with SARS-COV-2 (case) and 5 control samples to find differentially expressed genes (DEGs).…”

Exploration of the Link Between COVID-19 and Alcoholic Hepatitis from the Perspective of Bioinformatics and Systems Biology

“…The results showed that TFs (FOXC1, YY1, GATA2, SREBF1 and FOXL1) were associated with CXCR4, CXCL1, COL6A3, COL1A2 and ITGA6 and miRNAs (hsa-mir-1-3p, hsa-mir-26b-5p, hsa-mir-29b-3p, hsa-mir-105-5P and hsa-mir-4781-5P) were closely regulated with THBS2, SOX9, THY1, COL1A2 and TIMP. In previous bioinformatics analyses, Ahmed, Islam, and Lu Lu et al found FOXC1, YY1, GATA2, and FOXL1 are significant TFs for COVID-19 (6,49,50). Pharmacological studies have also found that SREBF1, as a key regulator of lipid-metabolizing enzymes connected closely with the procession of AH, is the transcription factor most activated by several effective antiviral drugs in vitro and may become A pivot therapeutic spot for both AH and COVID-19 (51,52).…”

Discovering common pathogenetic processes between COVID-19 and sepsis by bioinformatics and system biology approach

“…Our results showed that the regulatory relationship between TFs (FOXC1, YY1, GATA2, PPARG and FOXL1) and genes (FCGR1B, BCL6, CD1D, MS4A4A and LTF), as well as miRNAs (hsa-mir-27a-3p, hsa-mir-26a-5p, hsa-mir-124-3p, hsa-mir-146a-5p and hsa-mir-20a-5p) and genes ( SOCS3 , BCL6 , CXCR4 , and TNFSF13B ) that may play important roles in COVID-19 and sepsis. In previous bioinformatics analysis, Ahmed ( 72 ) and Islam et al. ( 73 ) both found that FOXC1, YY1, GATA2, and FOXL1 are important TFs for COVID-19.…”