Dataset Information

Omics-Based Platform for Studying Chemical Toxicity Using Stem Cells.

ABSTRACT: The new strategy for chemical toxicity testing and modeling is to use in vitro human cell-based assays in conjunction with quantitative high-throughput screening (qHTS) technology, to identify molecular mechanisms and predict in vivo responses. Stem cells are more physiologically relevant than immortalized cell lines because of their unique proliferation and differentiation potentials. We established a robust two stem cells-two lineages assay system, encompassing human mesenchymal stem cells (hMSCs) along osteogenesis and human induced pluripotent stem cells (hiPSCs) along hepatogenesis. We performed qHTS phenotypic screening of LOPAC1280 and identified 38 preliminary hits for hMSCs. This was followed by validation of a selected number of hits and determination of their IC50 values and mechanistic studies of idarubicin and cantharidin treatments using proteomics and bioinformatics. In general, hiPSCs were more sensitive than hMSCs to chemicals, and differentiated progenies were less sensitive than their progenitors. We showed that chemical toxicity depends on both stem cell types and their differentiation stages. Proteomics identified and quantified over 3000 proteins for both stem cells. Bioinformatics identified apoptosis and G2/M as the top pathways conferring idarubicin toxicity. Our Omics-based assays of stem cells provide mechanistic insights into chemical toxicity and may help prioritize chemicals for in-depth toxicological evaluations.

SUBMITTER: Han Y

PROVIDER: S-EPMC5756105 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Omics-Based Platform for Studying Chemical Toxicity Using Stem Cells.

Han Yan Y Zhao Jinghua J Huang Ruili R Xia Menghang M Wang Daojing D

Journal of proteome research 20171220 1

The new strategy for chemical toxicity testing and modeling is to use in vitro human cell-based assays in conjunction with quantitative high-throughput screening (qHTS) technology, to identify molecular mechanisms and predict in vivo responses. Stem cells are more physiologically relevant than immortalized cell lines because of their unique proliferation and differentiation potentials. We established a robust two stem cells-two lineages assay system, encompassing human mesenchymal stem cells (hM ...[more]

PMID: 29261316

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Project description:Read-across has become a primary approach to fill data gaps for chemical safety assessments. Chemical similarity based on structure, reactivity, and physic-chemical property information is a traditional approach applied for read-across toxicity studies. However, toxicity mechanisms are usually complicated in a biological system, so only using chemical similarity to perform the read-across for new compounds was not satisfactory for most toxicity endpoints, especially when the chemically similar compounds show dissimilar toxicities. This study aims to develop an enhanced read-across method for chemical toxicity predictions. To this end, we used two large toxicity datasets for read-across purposes. One consists of 3979 compounds with Ames mutagenicity data, and the other contains 7332 compounds with rat acute oral toxicity data. First, biological data for all compounds in these two datasets were obtained by querying thousands of PubChem bioassays. The PubChem bioassays with at least five compounds from either of these two datasets showing active responses were selected to generate comprehensive bioprofiles. The read-across studies were performed by using chemical similarity search only and also by using a hybrid similarity search based on both chemical descriptors and bioprofiles. Compared to traditional read-across based on chemical similarity, the hybrid read-across approach showed improved accuracy of predictions for both Ames mutagenicity and acute oral toxicity. Furthermore, we could illustrate potential toxicity mechanisms by analyzing the bioprofiles used for this hybrid read-across study. The results of this study indicate that the new hybrid read-across approach could be an applicable computational tool for chemical toxicity predictions. In this way, the bottleneck of traditional read-across studies can be overcome by introducing public biological data into the traditional process. The incorporation of bioprofiles generated from the additional biological data for compounds can partially solve the "activity cliff" issue and reveal their potential toxicity mechanisms. This study leads to a promising direction to utilize data-driven approaches for computational toxicology studies in the big data era.

Dataset Information

Omics-Based Platform for Studying Chemical Toxicity Using Stem Cells.

Publications

Omics-Based Platform for Studying Chemical Toxicity Using Stem Cells.

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