Meta-Data Analysis to Explore the Hub of the Hub-Genes That Influence SARS-CoV-2 Infections Highlighting Their Pathogenetic Processes and Drugs Repurposing

Mosharaf, Md. Parvez; Kibria, Md. Kaderi; Hossen, Md. Bayazid; Islam, Md. Ariful; Reza, Md. Selim; Mahumud, Rashidul Alam; Alam, Khorshed; Gow, Jeff; Mollah, Md. Nurul Haque

doi:10.3390/vaccines10081248

Cited by 6 publications

(4 citation statements)

References 129 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…VEGFA , and TP53 were found to be key DEGs in the network, which had a high connectivity degree. The level of VEGFA was increased during infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), thus promoting inflammation by facilitating recruitment of inflammatory cells [ 37 ]. A study identified the gene regulatory network regulated by Vegfa/Nrp1/Flt1 during psoriasis development [ 38 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of differentially methylated genes for severe acne by genome-wide DNA methylation and gene expression analysis

et al. 2023

View full text Add to dashboard Cite

Severe acne is a chronic inflammatory skin condition that is affected by both genetic and environmental factors. DNA methylation is associated with a variety of inflammatory skin diseases, but its role in severe acne is unclear. In this study, we conducted a two-stage epigenome correlation study using 88 blood samples to identify disease-related differential methylation sites. We found close associations between the DNA methylation at 23 differentially methylated sites (DMSs) and severe acne, including PDGFD, ARHGEF10 , etc. Further analysis revealed that differentially methylated genes ( PARP8 and MAPKAPK2 ) were also expressed differently between severe acne and health control groups. These findings lead us to speculation that epigenetic mechanisms may play an important role in the pathogenesis of severe acne.

show abstract

Section: Discussionmentioning

confidence: 99%

Identification of differentially methylated genes for severe acne by genome-wide DNA methylation and gene expression analysis

et al. 2023

View full text Add to dashboard Cite

show abstract

“…To explore repurposable candidate drug molecules for the treatments against SARS-CoV-2 infections with IPF disease as comorbidity risk, we considered our proposed 10 cHubGs and its regulatory 2 key TFs-proteins as the drug target receptors and performed their docking analysis with 185 meta-drug agents [see Table S1(I, II)]. Then we selected top-ranked 10 drugs (Tegobuvir [127][128][129][130][131] , Nilotinib [132][133][134][135][136][137][138] , Digoxin [139][140][141][142][143][144][145] , Proscillaridin [146][147][148][149][150][151] , Simeprevir [152][153][154][155] , Sorafenib 113,[156][157][158] ., Torin 2 159 , Rapamycin 160,161 , Vancomycin 162 and Hesperidin [163][164][165][166][167][168] as the candidate drug molecules (see Fig. 6A), wh...…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics-based investigation on the genetic influence between SARS-CoV-2 infections and idiopathic pulmonary fibrosis (IPF) diseases, and drug repurposing

Islam

Kibria

Hossen

et al. 2023

Sci Rep

Self Cite

View full text Add to dashboard Cite

Some recent studies showed that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and idiopathic pulmonary fibrosis (IPF) disease might stimulate each other through the shared genes. Therefore, in this study, an attempt was made to explore common genomic biomarkers for SARS-CoV-2 infections and IPF disease highlighting their functions, pathways, regulators and associated drug molecules. At first, we identified 32 statistically significant common differentially expressed genes (cDEGs) between disease (SARS-CoV-2 and IPF) and control samples of RNA-Seq profiles by using a statistical r-package (edgeR). Then we detected 10 cDEGs (CXCR4, TNFAIP3, VCAM1, NLRP3, TNFAIP6, SELE, MX2, IRF4, UBD and CH25H) out of 32 as the common hub genes (cHubGs) by the protein–protein interaction (PPI) network analysis. The cHubGs regulatory network analysis detected few key TFs-proteins and miRNAs as the transcriptional and post-transcriptional regulators of cHubGs. The cDEGs-set enrichment analysis identified some crucial SARS-CoV-2 and IPF causing common molecular mechanisms including biological processes, molecular functions, cellular components and signaling pathways. Then, we suggested the cHubGs-guided top-ranked 10 candidate drug molecules (Tegobuvir, Nilotinib, Digoxin, Proscillaridin, Simeprevir, Sorafenib, Torin 2, Rapamycin, Vancomycin and Hesperidin) for the treatment against SARS-CoV-2 infections with IFP diseases as comorbidity. Finally, we investigated the resistance performance of our proposed drug molecules compare to the already published molecules, against the state-of-the-art alternatives publicly available top-ranked independent receptors by molecular docking analysis. Molecular docking results suggested that our proposed drug molecules would be more effective compare to the already published drug molecules. Thus, the findings of this study might be played a vital role for diagnosis and therapies of SARS-CoV-2 infections with IPF disease as comorbidity risk.

show abstract

“…Then we picked up the top-ranked 10 drugs (Nilotinib, Tegobuvir, Digoxin, Proscillaridin, Olysio, Simeprevir, Hesperidin, Oleanolic Acid, Naltrindole, and Danoprevir) as the candidate drug agents based on their strong binding affinities with all the target proteins (Fig 5A). These drug molecules are also supported by other individual studies for the treatment against SARS-CoV-2 infections which includes Nilotinib [97][98][99][100][101][102], Tegobuvir [103][104][105][106][107][108][109], Digoxin [110][111][112][113][114][115], Proscillaridin [116][117][118][119][120][121], Olysio [109,122,123], Simeprevir [119,[124][125][126][127][128], Hesperidin [129][130][131][132][133], Oleanolic Acid [133][134][135][136], Danoprevir [6,…”

Section: Plos Onementioning

confidence: 91%

Identification of host genomic biomarkers from multiple transcriptomics datasets for diagnosis and therapies of SARS-CoV-2 infections

et al. 2023

Self Cite

View full text Add to dashboard Cite

The pandemic of COVID-19 is a severe threat to human life and the global economy. Despite the success of vaccination efforts in reducing the spread of the virus, the situation remains largely uncontrolled due to the random mutation in the RNA sequence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which demands different variants of effective drugs. Disease-causing gene-mediated proteins are usually used as receptors to explore effective drug molecules. In this study, we analyzed two different RNA-Seq and one microarray gene expression profile datasets by integrating EdgeR, LIMMA, weighted gene co-expression network and robust rank aggregation approaches, which revealed SARS-CoV-2 infection causing eight hub-genes (HubGs) including HubGs; REL, AURKA, AURKB, FBXL3, OAS1, STAT4, MMP2 and IL6 as the host genomic biomarkers. Gene Ontology and pathway enrichment analyses of HubGs significantly enriched some crucial biological processes, molecular functions, cellular components and signaling pathways that are associated with the mechanisms of SARS-CoV-2 infections. Regulatory network analysis identified top-ranked 5 TFs (SRF, PBX1, MEIS1, ESR1 and MYC) and 5 miRNAs (hsa-miR-106b-5p, hsa-miR-20b-5p, hsa-miR-93-5p, hsa-miR-106a-5p and hsa-miR-20a-5p) as the key transcriptional and post-transcriptional regulators of HubGs. Then, we conducted a molecular docking analysis to determine potential drug candidates that could interact with HubGs-mediated receptors. This analysis resulted in the identification of top-ranked ten drug agents, including Nilotinib, Tegobuvir, Digoxin, Proscillaridin, Olysio, Simeprevir, Hesperidin, Oleanolic Acid, Naltrindole and Danoprevir. Finally, we investigated the binding stability of the top-ranked three drug molecules Nilotinib, Tegobuvir and Proscillaridin with the three top-ranked proposed receptors (AURKA, AURKB, OAS1) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore, the findings of this study might be useful resources for diagnosis and therapies of SARS-CoV-2 infections.

show abstract

Meta-Data Analysis to Explore the Hub of the Hub-Genes That Influence SARS-CoV-2 Infections Highlighting Their Pathogenetic Processes and Drugs Repurposing

Cited by 6 publications

References 129 publications

Identification of differentially methylated genes for severe acne by genome-wide DNA methylation and gene expression analysis

Identification of differentially methylated genes for severe acne by genome-wide DNA methylation and gene expression analysis

Bioinformatics-based investigation on the genetic influence between SARS-CoV-2 infections and idiopathic pulmonary fibrosis (IPF) diseases, and drug repurposing

Identification of host genomic biomarkers from multiple transcriptomics datasets for diagnosis and therapies of SARS-CoV-2 infections

Contact Info

Product

Resources

About