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

@entity1 @entity137 @entity15205 @entity5 @entity2041 @entity36373 @entity13791 @entity1260
Untargeted metabolomics has the potential to improve the predictivity of in vitro @entity137 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 @entity1 renal proximal tubular epithelial cell line, RPTEC/TERT1, treated with 10 and 35  mol  liter(-1) of @entity36373 , a metabolite of the anti- @entity5 drug @entity1260 . 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 @entity137 profiling of @entity36373 , 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  mol  liter(-1) for 3 and 14 days. The most regulated metabolites were @entity2041 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 @entity13791 and @entity15205 pathways.
