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J Biol Chem. 2015 Jul 31;290(31):19121-32. doi: 10.1074/jbc.M115.644146. Epub 2015 Jun 8.

Nephron Toxicity Profiling via Untargeted Metabolome Analysis Employing a High Performance Liquid Chromatography-Mass Spectrometry-based Experimental and Computational Pipeline.

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From the Department of Molecular Biology, Division of Chemistry and Bioanalytics, University of Salzburg, 5020 Salzburg, Austria.
the Applied Bioinformatics Group, Center for Bioinformatics, Quantitative Biology Center and Department of Computer Science, University of Tübingen, Tübingen 72076, Germany.
the Division of Physiology, Department of Physiology and Medical Physics, Medical University of Innsbruck, Innsbruck 6020, Austria.
Merck KGaA, Merck Serono, Nonclinical Safety, Darmstadt 64293, Germany, and.
the Department of Toxicology, University of Würzburg, Würzburg 97078, Germany.
From the Department of Molecular Biology, Division of Chemistry and Bioanalytics, University of Salzburg, 5020 Salzburg, Austria,


Untargeted metabolomics has the potential to improve the predictivity of in vitro toxicity models and therefore may aid the replacement of expensive and laborious animal models. Here we describe a long term repeat dose nephrotoxicity study conducted on the human renal proximal tubular epithelial cell line, RPTEC/TERT1, treated with 10 and 35 μmol·liter(-1) of chloroacetaldehyde, a metabolite of the anti-cancer drug ifosfamide. Our study outlines the establishment of an automated and easy to use untargeted metabolomics workflow for HPLC-high resolution mass spectrometry data. Automated data analysis workflows based on open source software (OpenMS, KNIME) enabled a comprehensive and reproducible analysis of the complex and voluminous metabolomics data produced by the profiling approach. Time- and concentration-dependent responses were clearly evident in the metabolomic profiles. To obtain a more comprehensive picture of the mode of action, transcriptomics and proteomics data were also integrated. For toxicity profiling of chloroacetaldehyde, 428 and 317 metabolite features were detectable in positive and negative modes, respectively, after stringent removal of chemical noise and unstable signals. Changes upon treatment were explored using principal component analysis, and statistically significant differences were identified using linear models for microarray assays. The analysis revealed toxic effects only for the treatment with 35 μmol·liter(-1) for 3 and 14 days. The most regulated metabolites were glutathione and metabolites related to the oxidative stress response of the cells. These findings are corroborated by proteomics and transcriptomics data, which show, among other things, an activation of the Nrf2 and ATF4 pathways.


KNIME; OpenMS; bioinformatics; chloroacetaldehyde; high performance liquid chromatography (HPLC); mass spectrometry (MS); metabolomics; oxidative stress; toxicity profiling

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