Dataset Information

Target RNA modification for epigenetic drug repositioning in neuroblastoma: computational omics proximity between repurposing drug and disease.

ABSTRACT: RNA modifications modulate most steps of gene expression. However, little is known about its role in neuroblastoma (NBL) and the inhibitors targeting it. We analyzed the RNA-seq (n=122) and CNV data (n=78) from NBL patients in Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. The NBL sub-clusters (cluster1/2) were identified via consensus clustering for expression of RNA modification regulators (RNA-MRs). Cox regression, principle component analysis and chi-square analysis were used to compare differences of survival, transcriptome, and clinicopathology between clusters. Cluster1 showed significantly poor prognosis, of which RNA-MRs' expression and CNV alteration were closely related to pathologic stage. RNA-MRs and functional related prognostic genes were obtained using spearman correlation analysis, and queried in CMap and L1000 FWD database to obtain 88 inhibitors. The effects of 5 inhibitors on RNA-MRs were confirmed in SH-SY5Y cells. The RNA-MRs exhibited two complementary regulation functions: one conducted by TET2 and related to translation and glycolysis; another conducted by ALYREF, NSUN2 and ADARB1 and related to cell cycle and DNA repair. The perturbed proteomic profile of HDAC inhibitors was different from that of others, thus drug combination overcame drug resistance and was potential for NBL therapy with RNA-MRs as therapeutic targets.

SUBMITTER: Ma P

PROVIDER: S-EPMC7732279 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Target RNA modification for epigenetic drug repositioning in neuroblastoma: computational omics proximity between repurposing drug and disease.

Ma Pei P Yue Lifeng L Zhang Sen S Hao Dacheng D Wu Zhihong Z Xu Lijia L Du Guanhua G Xiao Peigen P

Aging 20201012 19

RNA modifications modulate most steps of gene expression. However, little is known about its role in neuroblastoma (NBL) and the inhibitors targeting it. We analyzed the RNA-seq (n=122) and CNV data (n=78) from NBL patients in Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. The NBL sub-clusters (cluster1/2) were identified via consensus clustering for expression of RNA modification regulators (RNA-MRs). Cox regression, principle component analysis and chi- ...[more]

PMID: 33044945

Similar Datasets

Project description:Drug repositioning (also referred to as drug repurposing) is the method of exploring novel therapeutic indications for Food and Drug Administration-approved clinically implemented drugs. The unique strategy of drug repositioning is used to boost the drug development process since drug discovery is an expensive, arduous, cumbersome, and high-risk procedure. Recently, several pharmaceutical firms have used the drug repositioning technique in their drug discovery and development programs to develop new medications based on the identification of new therapeutic targets. This technique is extremely effective, saves time, is comparatively economical, and has a low chance of failure. Developing appropriate treatment measures to inhibit the spread of Coronavirus disease-2019 (COVID-19) is currently a top priority. As a result, several studies were conducted to build novel therapeutic molecules using diverse strategies of drug repurposing to discover drug candidates against COVID-19 infection that can act as substantial inhibitors against virus particles. By implementing virtual screening of drug libraries, it is possible to identify potential drugs through drug repurposing. A molecular docking approach and calculation of binding free energy are used to estimate binding affinity and drug–receptor interactions. Drug-repurposing methodologies can be divided into three categories: target-oriented, drug-oriented, and disease-oriented, based on the gathered data about the various physicochemical, pharmacokinetic and pharmacological features of a drug candidate. Using computational methods such as homology modeling and molecular similarity, this methodology aids in determining the binding interaction of drug molecules with the target protein of the virus. In this book chapter, we explore a typical set of currently utilized computational techniques for identifying repurposable drug molecules for COVID-19, as well as their supporting databases. We also assess promising drugs anticipated by computational approaches to drugs currently being evaluated in clinical trials. Moreover, we also examine the takeaways from the evaluated research efforts, such as how to competently combine bioinformatics tools with experimental work and suggest a fully integrated drug-repurposing approach to combat the deadly COVID-19 infection.

Project description:BackgroundHerpes viruses are important human pathogens that can cause mild to severe lifelong infections with high morbidity. They remain latent in the host cells and can cause recurrent infections that might prove fatal. These viruses are known to infect the host cells by causing the fusion of viral and host cell membrane proteins. Fusion is achieved with the help of conserved fusion machinery components, glycoproteins gB, heterodimer gH-gL complex along with other non-conserved components. Whereas, another important glycoprotein gD without which viral entry to the cell is not possible, acts as a co-activator for the gB-gH-gL complex formation. Thus, this complex formation interface is the most promising drug target for the development of novel anti-herpes drug candidates. In the present study, we propose a model for binding of gH-gL to gB glycoprotein leading from pre to post conformational changes during gB-gH-gL complex formation and reported the key residues involved in this binding activity along with possible binding site locations. To validate the drug targetability of our proposed binding site, we have repositioned some of the most promising in vitro, in vivo validated anti-herpes molecules onto the proposed binding site of gH-gL complex in a computational approach.MethodsHex 6.3 standalone software was used for protein-protein docking studies. Arguslab 4.0.1 and Accelrys® Discovery Studio 3.1 Visualizer softwares were used for semi-flexible docking studies and visualizing the interactions respectively. Protein receptors and ethno compounds were retrieved from Protein Data Bank (PDB) and Pubchem databases respectively. Lipinski's Filter, Osiris Property Explorer and Lazar online servers were used to check the pharmaceutical fidelity of the drug candidates.ResultsThrough protein-protein docking studies, it was identified that the amino acid residues VAL342, GLU347, SER349, TYR355, SER388, ASN395, HIS398 and ALA387 of gH-gL complex play an active role in its binding activity with gB. Semi flexible docking analysis of the most promising in vitro, in vivo validated anti-herpes molecules targeting the above mentioned key residues of gH-gL complex showed that all the analyzed ethno medicinal compounds have successfully docked into the proposed binding site of gH-gL glycoprotein with binding energy range between -10.4 to -6.4 K.cal./mol.ConclusionsSuccessful repositioning of the analyzed compounds onto the proposed binding site confirms the drug targetability of gH-gL complex. Based on the free binding energy and pharmacological properties, we propose (3-chloro phenyl) methyl-3,4,5 trihydroxybenzoate as worth a small ethno medicinal lead molecule for further development as potent anti-herpes drug candidate targeting gB-gH-gL complex formation interface.

Dataset Information

Target RNA modification for epigenetic drug repositioning in neuroblastoma: computational omics proximity between repurposing drug and disease.

Publications

Target RNA modification for epigenetic drug repositioning in neuroblastoma: computational omics proximity between repurposing drug and disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets