Evaluation of DNA microarray results in the Toxicogenomics Project (TGP) consortium in Japan

Nakatsu, Noriyuki; Igarashi, Yoshinobu; Ono, Atsushi; Yamada, Hiroshi; Ohno, Yasuo; Urushidani, Tetsuro

doi:10.2131/jts.37.791

Cited by 12 publications

(5 citation statements)

References 23 publications

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“…In recent years, several massive toxicological analyses have yielded large volumes of microarray data sets. Such undertakings as the chemical effects in biological systems (CEBS) project ( 1 ), a study of the effects of 2,4,6-trinitrotoluene (TNT) on the liver ( 2 ) and the TGP Consortium in Japan ( 3 ) have taken on the challenge of systematically measuring, analyzing and understanding the toxic effects of chemicals on the human body, as well as developing new bioinformatics methods for mining large-scale toxicology data ( 4 , 5 ). However, due to legal, ethical and/or religious concerns that prohibit direct testing on the human body, chemical toxicity studies performed over the last few decades have had to use animals as human models.…”

Section: Introductionmentioning

confidence: 99%

Prediction of developmental chemical toxicity based on gene networks of human embryonic stem cells

et al. 2016

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Predictive toxicology using stem cells or their derived tissues has gained increasing importance in biomedical and pharmaceutical research. Here, we show that toxicity category prediction by support vector machines (SVMs), which uses qRT-PCR data from 20 categorized chemicals based on a human embryonic stem cell (hESC) system, is improved by the adoption of gene networks, in which network edge weights are added as feature vectors when noisy qRT-PCR data fail to make accurate predictions. The accuracies of our system were 97.5–100% for three toxicity categories: neurotoxins (NTs), genotoxic carcinogens (GCs) and non-genotoxic carcinogens (NGCs). For two uncategorized chemicals, bisphenol-A and permethrin, our system yielded reasonable results: bisphenol-A was categorized as an NGC, and permethrin was categorized as an NT; both predictions were supported by recently published papers. Our study has two important features: (i) as the first study to employ gene networks without using conventional quantitative structure-activity relationships (QSARs) as input data for SVMs to analyze toxicogenomics data in an hESC validation system, it uses additional information of gene-to-gene interactions to significantly increase prediction accuracies for noisy gene expression data; and (ii) using only undifferentiated hESCs, our study has considerable potential to predict late-onset chemical toxicities, including abnormalities that occur during embryonic development.

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Section: Introductionmentioning

confidence: 99%

Prediction of developmental chemical toxicity based on gene networks of human embryonic stem cells

et al. 2016

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“…Data quality is checked by evaluating inter- and intra-laboratory reproducibility of biological replicates by clustering, Spearman correlation and determining the overlap in lists of differentially expressed genes (Noriyuki et al 2012 ). Further quality control is performed using the ArrayAnalysis Quality Control pipeline ( www.arrayanalysis.org/ ).…”

Section: Workhop Presentations and Discussionmentioning

confidence: 99%

Workshop report: Identifying opportunities for global integration of toxicogenomics databases, 26–27 June 2013, Research Triangle Park, NC, USA

et al. 2014

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A joint US-EU workshop on enhancing data sharing and exchange in toxicogenomics was held at the National Institute for Environmental Health Sciences. Currently, efficient reuse of data is hampered by problems related to public data availability, data quality, database interoperability (the ability to exchange information), standardization and sustainability. At the workshop, experts from universities and research institutes presented databases, studies, organizations and tools that attempt to deal with these problems. Furthermore, a case study showing that combining toxicogenomics data from multiple resources leads to more accurate predictions in risk assessment was presented. All participants agreed that there is a need for a web portal describing the diverse, heterogeneous data resources relevant for toxicogenomics research. Furthermore, there was agreement that linking more data resources would improve toxicogenomics data analysis. To outline a roadmap to enhance interoperability between data resources, the participants recommend collecting user stories from the toxicogenomics research community on barriers in data sharing and exchange currently hampering answering to certain research questions. These user stories may guide the prioritization of steps to be taken for enhancing integration of toxicogenomics databases.

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“…To develop our strategy, we used gene expression data from the Open TG-GATEs database and we built condition-specific metabolic networks with a partial enumeration method adapted from the DEXOM [ 34 ] approach. Open TG-GATEs has been created from two projects spanning several years: Toxicogenomics Project One [ 38 ] (TGP1: 2004–2007) and Toxicogenomics Project Two [ 39 ] (TGP2: 2010–2011). Open TG-GATEs contains gene expression data generated on Crl:CD Sprague–Dawley rats and on primary human hepatocytes (PHH) and primary rat hepatocytes, after exposure to low cytotoxic doses of 150 pharmaceutical molecules.…”

Section: Introductionmentioning

confidence: 99%

A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques

Fresnais,

Perin,

Riu

et al. 2024

BMC Bioinformatics

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Background The growing abundance of in vitro omics data, coupled with the necessity to reduce animal testing in the safety assessment of chemical compounds and even eliminate it in the evaluation of cosmetics, highlights the need for adequate computational methodologies. Data from omics technologies allow the exploration of a wide range of biological processes, therefore providing a better understanding of mechanisms of action (MoA) related to chemical exposure in biological systems. However, the analysis of these large datasets remains difficult due to the complexity of modulations spanning multiple biological processes. Results To address this, we propose a strategy to reduce information overload by computing, based on transcriptomics data, a comprehensive metabolic sub-network reflecting the metabolic impact of a chemical. The proposed strategy integrates transcriptomic data to a genome scale metabolic network through enumeration of condition-specific metabolic models hence translating transcriptomics data into reaction activity probabilities. Based on these results, a graph algorithm is applied to retrieve user readable sub-networks reflecting the possible metabolic MoA (mMoA) of chemicals. This strategy has been implemented as a three-step workflow. The first step consists in building cell condition-specific models reflecting the metabolic impact of each exposure condition while taking into account the diversity of possible optimal solutions with a partial enumeration algorithm. In a second step, we address the challenge of analyzing thousands of enumerated condition-specific networks by computing differentially activated reactions (DARs) between the two sets of enumerated possible condition-specific models. Finally, in the third step, DARs are grouped into clusters of functionally interconnected metabolic reactions, representing possible mMoA, using the distance-based clustering and subnetwork extraction method. The first part of the workflow was exemplified on eight molecules selected for their known human hepatotoxic outcomes associated with specific MoAs well described in the literature and for which we retrieved primary human hepatocytes transcriptomic data in Open TG-GATEs. Then, we further applied this strategy to more precisely model and visualize associated mMoA for two of these eight molecules (amiodarone and valproic acid). The approach proved to go beyond gene-based analysis by identifying mMoA when few genes are significantly differentially expressed (2 differentially expressed genes (DEGs) for amiodarone), bringing additional information from the network topology, or when very large number of genes were differentially expressed (5709 DEGs for valproic acid). In both cases, the results of our strategy well fitted evidence from the literature regarding known MoA. Beyond these confirmations, the workflow highlighted potential other unexplored mMoA. Conclusion The proposed strategy allows toxicology experts to decipher which part of cellular metabolism is expected to be affected by the exposition to a given chemical. The approach originality resides in the combination of different metabolic modelling approaches (constraint based and graph modelling). The application to two model molecules shows the strong potential of the approach for interpretation and visual mining of complex omics in vitro data. The presented strategy is freely available as a python module (https://pypi.org/project/manamodeller/) and jupyter notebooks (https://github.com/LouisonF/MANA).

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Evaluation of DNA microarray results in the Toxicogenomics Project (TGP) consortium in Japan

Cited by 12 publications

References 23 publications

Prediction of developmental chemical toxicity based on gene networks of human embryonic stem cells

Prediction of developmental chemical toxicity based on gene networks of human embryonic stem cells

Workshop report: Identifying opportunities for global integration of toxicogenomics databases, 26–27 June 2013, Research Triangle Park, NC, USA

A strategy to detect metabolic changes induced by exposure to chemicals from large sets of condition-specific metabolic models computed with enumeration techniques

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