Computational identification of host genomic biomarkers highlighting their functions, pathways and regulators that influence SARS-CoV-2 infections and drug repurposing

Abstract

The pandemic threat of COVID-19 has severely destroyed human life as well as the economy around the world. Although, the vaccination has reduced the outspread, but people are still suffering due to the unstable RNA sequence patterns of SARS-CoV-2 which demands supplementary drugs. To explore novel drug target proteins, in this study, a transcriptomics RNA-Seq data generated from SARS-CoV-2 infection and control samples were analyzed. We identified 109 differentially expressed genes (DEGs) that were utilized to identify 10 hub-genes/proteins (TLR2, USP53, GUCY1A2, SNRPD2, NEDD9, IGF2, CXCL2, KLF6, PAG1 and ZFP36) by the protein–protein interaction (PPI) network analysis. The GO functional and KEGG pathway enrichment analyses of hub-DEGs revealed some important functions and signaling pathways that are significantly associated with SARS-CoV-2 infections. The interaction network analysis identified 5 TFs proteins and 6 miRNAs as the key regulators of hub-DEGs. Considering 10 hub-proteins and 5 key TFs-proteins as drug target receptors, we performed their docking analysis with the SARS-CoV-2 3CL protease-guided top listed 90 FDA approved drugs. We found Torin-2, Rapamycin, Radotinib, Ivermectin, Thiostrepton, Tacrolimus and Daclatasvir as the top ranked seven candidate drugs. We investigated their resistance performance against the already published COVID-19 causing top-ranked 11 independent and 8 protonated receptor proteins by molecular docking analysis and found their strong binding affinities, which indicates that the proposed drugs are effective against the state-of-the-arts alternatives independent receptor proteins also. Finally, we investigated the stability of top three drugs (Torin-2, Rapamycin and Radotinib) by using 100 ns MD-based MM-PBSA simulations with the two top-ranked proposed receptors (TLR2, USP53) and independent receptors (IRF7, STAT1), and observed their stable performance. Therefore, the proposed drugs might play a vital role for the treatment against different variants of SARS-CoV-2 infections.

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

“…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). By constructing a protein–protein interaction network, key genes and signaling pathways in SARS-COV-2 infection were determined to be used as targets for drug repurposing in COVID-19 (Mosharaf et al 2022 ). However, recent developments in "omics" technologies, along with advances in computer sciences, have provided an opportunity to integrate and analyze multi-cohort datasets using systems biology approaches and machine learning (ML) methods, leading to decreased heterogeneity of publicly available single population-based transcriptome datasets (Tavassolifar et al 2020 ).…”

Disclosing Potential Key Genes, Therapeutic Targets and Agents for Non-Small Cell Lung Cancer: Evidence from Integrative Bioinformatics Analysis

“…To reach the goal of this study, we analyzed a publicly available gene expression dataset by using integrated bioinformatics approaches [ 16 , 28 , 29 ]. The global working flowchart of this study is displayed in Figure 1 .…”

“…[ 23 , 24 , 25 , 26 ]. Computationally, mutated genes (potential key genes) are predicted by the analysis of differential gene expression patterns [ 16 , 17 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. Therefore, in this study, an attempt was made to explore NSCLC-causing key genes from the publicly available gene expression profiles, highlighting their functions, pathways, and regulators, which yield relevant information for diagnosis, prognosis, and therapies of NSCLC, by using the integrated bioinformatics approaches.…”

Metadata analysis to explore hub of the hub-genes highlighting their functions, pathways and regulators for cervical cancer diagnosis and therapies

“…The initial energy minimization process of each simulation system, consisting of of 55,410 ± 10, 72,287 ± 10, and 96,252 ± 10 atoms for CDK1_Docetaxel, CHEK1_Temsirolimus, and TOP2A_Paclitaxel complexes were performed by a simulated annealing method respectively, using the steepest gradient approach (5000 cycles). For the details of MD simulation methods see our previous paper [ 14 , 15 ]. The trajectories were recorded every 250 ps for further analysis, and subsequent analysis was implemented by default script of YASARA [ 120 ] macro and SciDAVis software available at http://scidavis.sourceforge.net/ .…”