Drug repurposing for COVID-19 using computational screening: Is Fostamatinib/R406 a potential candidate?

Saha, Sovan; Bandyopadhyay, Soumyendu Sekhar; Chatterjee, Piyali; Nasipuri, Mita; Bose, Debdas; Basu, Subhadip

doi:10.1016/j.ymeth.2021.08.007

Cited by 21 publications

(15 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other critical hub-high traffic genes in the blue module included EP300 ( 146 , 147 ), CDK1 ( 4 , 148 ), VEGFA ( 149 – 151 ), CTNNB1 ( 151 , 152 ), IL10 ( 9 , 153 – 155 ), SOCS1 , SOCS3 ( 156 ), SIRT1 ( 157 , 158 ), TFRC ( 159 – 161 ), HSP90AB1 ( 162 ), CYCS ( 163 ), EZR ( 164 ), TNFAIP3 , ICAM1 ( 10 ), and PIK3R1 ( 139 ) as well as TFs such as ATF4 ( 165 ), ATF3 ( 166 , 167 ), and BCL6 ( 152 ) which have important roles in pathogenesis of SARS-CoV-2, and some of which are potential therapeutic targets. For instance, SOCS1 / 3 antagonists may be prophylactic or therapeutic against the COVID-19 pandemic ( 156 ).…”

Section: Discussionmentioning

confidence: 99%

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

et al. 2021

View full text Add to dashboard Cite

BackgroundThe recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches.MethodsRNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules.ResultsBased on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19’s main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis.ConclusionThis study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.

show abstract

Section: Discussionmentioning

confidence: 99%

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Multiple studies recently suggested the possible connection between such neurodegenerative diseases and COVID-19 [ 32 , 33 , 34 , 35 , 36 ]. Several kinases, such as NTRK1 (10,495th → 29th), FYN (3902nd → 46th), ABL1 (7261st → 91st), and SRC (8965th → 56th), are being studied for repurposing several kinase inhibitors for COVID-19 treatment [ 37 , 38 ]. Furthermore, STAT1 (21,435th → 32nd), STAT2, EGFR , and IRF9 (2455th → 36th) are involved in interferon signaling and have been suggested in numerous COVID-19 studies [ 39 , 40 , 41 , 42 , 43 ].…”

Section: Resultsmentioning

confidence: 99%

COVID-GWAB: A Web-Based Prediction of COVID-19 Host Genes via Network Boosting of Genome-Wide Association Data

2022

View full text Add to dashboard Cite

Host genetics affect both the susceptibility and response to viral infection. Searching for host genes that contribute to COVID-19, the Host Genetics Initiative (HGI) was formed to investigate the genetic factors involved in COVID-19 via genome-wide association studies (GWAS). The GWAS suffer from limited statistical power and in general, only a few genes can pass the conventional significance thresholds. This statistical limitation may be overcome by boosting weak association signals through integrating independent functional information such as molecular interactions. Additionally, the boosted results can be evaluated by various independent data for further connections to COVID-19. We present COVID-GWAB, a web-based tool to boost original GWAS signals from COVID-19 patients by taking the signals of the interactome neighbors. COVID-GWAB takes summary statistics from the COVID-19 HGI or user input data and reprioritizes candidate host genes for COVID-19 using HumanNet, a co-functional human gene network. The current version of COVID-GWAB provides the pre-processed data of releases 5, 6, and 7 of the HGI. Additionally, COVID-GWAB provides web interfaces for a summary of augmented GWAS signals, prediction evaluations by appearance frequency in COVID-19 literature, single-cell transcriptome data, and associated pathways. The web server also enables browsing the candidate gene networks.

show abstract

“…The majority of drugs currently in clinical trials for COVID-19 treatment present either antiviral or immunomodulating properties, with the aim of targeting the viral life cycle and alleviating the lung-damaging symptoms [ 18 , 39 ]. Kinases play an important role in many of these biological processes, and therefore, different kinase inhibitors have been proposed for COVID-19 treatment [ 40 , 41 ]. These compounds show pharmacodynamic properties allowing the dual goal of mitigating both host immunological response and antiviral activity [ 40 ].…”

Section: Resultsmentioning

confidence: 99%

Computationally prioritized drugs inhibit SARS-CoV-2 infection and syncytia formation

Serra

Fratello

Federico

et al. 2021

Briefings in Bioinformatics

View full text Add to dashboard Cite

The pharmacological arsenal against the COVID-19 pandemic is largely based on generic anti-inflammatory strategies or poorly scalable solutions. Moreover, as the ongoing vaccination campaign is rolling slower than wished, affordable and effective therapeutics are needed. To this end, there is increasing attention toward computational methods for drug repositioning and de novo drug design. Here, multiple data-driven computational approaches are systematically integrated to perform a virtual screening and prioritize candidate drugs for the treatment of COVID-19. From the list of prioritized drugs, a subset of representative candidates to test in human cells is selected. Two compounds, 7-hydroxystaurosporine and bafetinib, show synergistic antiviral effects in vitro and strongly inhibit viral-induced syncytia formation. Moreover, since existing drug repositioning methods provide limited usable information for de novo drug design, the relevant chemical substructures of the identified drugs are extracted to provide a chemical vocabulary that may help to design new effective drugs.

show abstract

Drug repurposing for COVID-19 using computational screening: Is Fostamatinib/R406 a potential candidate?

Cited by 21 publications

References 56 publications

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

COVID-GWAB: A Web-Based Prediction of COVID-19 Host Genes via Network Boosting of Genome-Wide Association Data

Computationally prioritized drugs inhibit SARS-CoV-2 infection and syncytia formation

Contact Info

Product

Resources

About