Figure 1.

Schematic of workflow for “logical” identification of molecular properties for discrimination of unique OAT1 and OAT3 metabolites. A, OAs ultimately excreted by the kidney are cleared from the blood by the SLC transporters, OAT1 and OAT3, located in the basolateral membranes of proximal tubule cells of the kidney. Deletion of either of these transporters results in the plasma accumulation of OAs. Serum, obtained from WT and OatKOs, was subjected to untargeted, global LC-MS metabolomics analyses. B, resulting metabolomics data were used to identify Oat1 and Oat3 metabolites uniquely accumulating in each knockout mouse. Cheminformatics methods were used to identify over 60 molecular properties/features of the metabolites. C, data visualization and statistical analysis in Orange and Python libraries (Pandas, Matplotlib, Seaborn, and SQLAlchemy) of over 60 molecular properties for Oat1 and Oat3 unique metabolites were used to logically narrow down to a set of seven molecular properties to be used for machine-learning approaches to identify a set of features that classifies metabolites as uniquely Oat1 or uniquely Oat3.