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High Throughput Screening Identifies a Novel Compound Protecting Cardiomyocytes from Doxorubicin-Induced Damage.

ABSTRACT: Antracyclines are effective antitumor agents. One of the most commonly used antracyclines is doxorubicin, which can be successfully used to treat a diverse spectrum of tumors. Application of these drugs is limited by their cardiotoxic effect, which is determined by a lifetime cumulative dose. We set out to identify by high throughput screening cardioprotective compounds protecting cardiomyocytes from doxorubicin-induced injury. Ten thousand compounds of ChemBridge's DIVERSet compound library were screened to identify compounds that can protect H9C2 rat cardiomyocytes against doxorubicin-induced cell death. The most effective compound proved protective in doxorubicin-treated primary rat cardiomyocytes and was further characterized to demonstrate that it significantly decreased doxorubicin-induced apoptotic and necrotic cell death and inhibited doxorubicin-induced activation of JNK MAP kinase without having considerable radical scavenging effect or interfering with the antitumor effect of doxorubicin. In fact the compound identified as 3-[2-(4-ethylphenyl)-2-oxoethyl]-1,2-dimethyl-1H-3,1-benzimidazol-3-ium bromide was toxic to all tumor cell lines tested even without doxorubicine treatment. This benzimidazole compound may lead, through further optimalization, to the development of a drug candidate protecting the heart from doxorubicin-induced injury.

SUBMITTER: Gergely S

PROVIDER: S-EPMC4475553 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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High Throughput Screening Identifies a Novel Compound Protecting Cardiomyocytes from Doxorubicin-Induced Damage.

Gergely Szabolcs S Hegedűs Csaba C Lakatos Petra P Kovács Katalin K Gáspár Renáta R Csont Tamás T Virág László L

Oxidative medicine and cellular longevity 20150607

Antracyclines are effective antitumor agents. One of the most commonly used antracyclines is doxorubicin, which can be successfully used to treat a diverse spectrum of tumors. Application of these drugs is limited by their cardiotoxic effect, which is determined by a lifetime cumulative dose. We set out to identify by high throughput screening cardioprotective compounds protecting cardiomyocytes from doxorubicin-induced injury. Ten thousand compounds of ChemBridge's DIVERSet compound library wer ...[more]

PMID: 26137186

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Project description:BACKGROUND: Previous studies from our laboratory and others have demonstrated that in addition to altering chromatin acetylation and conformation, histone deacetylase inhibitors (HDACi) disrupt the acetylation status of numerous transcription factors and other proteins. A whole genome yeast deletion library screen was used to identify components of the transcriptional apparatus that modulate the sensitivity to the hydroxamic acid-based HDACi, CG-1521. RESULTS: Screening 4852 haploid Saccharomyces cerevisiae deletion strains for sensitivity to CG-1521 identifies 407 sensitive and 80 resistant strains. Gene ontology (GO) enrichment analysis shows that strains sensitive to CG-1521 are highly enriched in processes regulating chromatin remodeling and transcription as well as other ontologies, including vacuolar acidification and vesicle-mediated transport. CG-1521-resistant strains include those deficient in the regulation of transcription and tRNA modification. Components of the SAGA histone acetyltransferase (HAT) complex are overrepresented in the sensitive strains, including the catalytic subunit, Gcn5. Cell cycle analysis indicates that both the wild-type and gcn5? strains show a G1 delay after CG-1521 treatment, however the gcn5? strain displays increased sensitivity to CG-1521-induced cell death compared to the wild-type strain. To test whether the enzymatic activity of Gcn5 is necessary in the response to CG-1521, growth assays with a yeast strain expressing a catalytically inactive variant of the Gcn5 protein were performed and the results show that this strain is less sensitive to CG-1521 than the gcn5? strain. CONCLUSION: Genome-wide deletion mutant screening identifies biological processes that affect the sensitivity to the HDAC inhibitor CG-1521, including transcription and chromatin remodeling. This study illuminates the pathways involved in the response to CG-1521 in yeast and provides incentives to understand the mechanisms of HDAC inhibitors in cancer cells. The data presented here demonstrate that components of the SAGA complex are involved in mediating the response to CG-1521. Additional experiments suggest that functions other than the acetyltransferase activity of Gcn5 may be sufficient to attenuate the effects of CG-1521 on cell growth.

Project description:Target-based high-throughput compound screening dominates conventional one-drug-one-gene drug discovery process. However, the readout from the chemical modulation of a single protein is poorly correlated with phenotypic response of organism, leading to high failure rate in drug development. Chemical-induced gene expression profile provides an attractive solution to phenotype-based screening. However, the use of such data is currently limited by their sparseness, unreliability, and relatively low throughput. Several methods have been proposed to impute missing values for gene expression datasets. However, few existing methods can perform de novo chemical compound screening. In this study, we propose a mechanism-driven neural network-based method named DeepCE (Deep Chemical Expression) which utilizes graph convolutional neural network to learn chemical representation and multi-head attention mechanism to model chemical substructure-gene and gene-gene feature associations. In addition, we propose a novel data augmentation method which extracts useful information from unreliable experiments in L1000 dataset. The experimental results show that DeepCE achieves the superior performances not only in de novo chemical setting but also in traditional imputation setting compared to state-of-the-art baselines for the prediction of chemical-induced gene expression. We further verify the effectiveness of gene expression profiles generated from DeepCE by comparing them with gene expression profiles in L1000 dataset for downstream classification tasks including drug-target and disease predictions. To demonstrate the value of DeepCE, we apply it to patient-specific drug repurposing of COVID-19 for the first time, and generate novel lead compounds consistent with clinical evidences. Thus, DeepCE provides a potentially powerful framework for robust predictive modeling by utilizing noisy omics data as well as screening novel chemicals for the modulation of systemic response to disease.

Dataset Information

High Throughput Screening Identifies a Novel Compound Protecting Cardiomyocytes from Doxorubicin-Induced Damage.

Publications

High Throughput Screening Identifies a Novel Compound Protecting Cardiomyocytes from Doxorubicin-Induced Damage.

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