Use of in vitro metabolomics in NRK cells to help predicting nephrotoxicity and differentiating the MoA of nephrotoxicants.

We describe a strategy using an in vitro metabolomics assay with tubular rat NRK-52E cells to investigate the Modes of Action (MoAs) of nephrotoxic compounds. Chemicals were selected according to their MoAs based on literature information: acetaminophen, 4-aminophenol and S-(trichlorovinyl-)L-cysteine (TCVC), (covalent protein binding); gentamycin, vancomycin, polymycin B and CdCl2 (lysosomal overload) and tenofovir and cidofovir (mitochondrial DNA-interaction). After treatment and harvesting of the cells, intracellular endogenous metabolites were quantified relative to vehicle control. Metabolite patterns were evaluated in a purely data-driven pattern generation process excluding published information. This strategy confirmed the assignment of the chemicals to the respective MoA except for TCVC and CdCl2. Finally, TCVC was defined as unidentified and CdCl2 was reclassified to the MoA "covalent protein binding". Hierarchical cluster analysis of 58 distinct metabolites from the patterns enabled a clear visual separation of chemicals in each MoA. The assay reproducibility was very good and metabolic responses were consistent. These results support the use of metabolome analysis in NRK-52E cells as a suitable tool for understanding and investigating the MoA of nephrotoxicants. This assay could enable the early identification of nephrotoxic compounds and finally reduce animal testing.

Metabolic Drug Response Phenotyping in Colorectal Cancer Organoids by LC-QTOF-MS.

As metabolic rewiring is crucial for cancer cell proliferation, metabolic phenotyping of patient-derived organoids is desirable to identify drug-induced changes and trace metabolic vulnerabilities of tumor subtypes. We established a novel protocol for metabolomic and lipidomic profiling of colorectal cancer organoids by liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) facing the challenge of capturing metabolic information from a minimal sample amount (<500 cells/injection) in the presence of an extracellular matrix (ECM). The best procedure of the tested protocols included ultrasonic metabolite extraction with acetonitrile/methanol/water (2:2:1, v/v/v) without ECM removal. To eliminate ECM-derived background signals, we implemented a data filtering procedure based on the p-value and fold change cut-offs, which retained features with signal intensities >120% compared to matrix-derived signals present in blank samples. As a proof-of-concept, the method was applied to examine the early metabolic response of colorectal cancer organoids to 5-fluorouracil treatment. Statistical analysis revealed dose-dependent changes in the metabolic profiles of treated organoids including elevated levels of 2'-deoxyuridine, 2'-O-methylcytidine, inosine and 1-methyladenosine and depletion of 2'-deoxyadenosine and specific phospholipids. In accordance with the mechanism of action of 5-fluorouracil, changed metabolites are mainly involved in purine and pyrimidine metabolism. The novel protocol provides a first basis for the assessment of metabolic drug response phenotypes in 3D organoid models.