URINE AND FECES METABOLOMICS-BASED ANALYSIS OF CAROB TREATED RATS

   Ceratonia siliqua L. Fabaceae, commonly known as the carob tree, is native to the eastern Mediterranean countries and its products are widely used in the diet of people living in Mediterranean Europe, Middle East and North Africa. Carobs are considered to be of high nutritional value, as they are virtually fat-free, rich in proteins, antioxidants, vitamins and contain several important minerals. Different types of carob products are available in the local market, such as carob syrup, powder, flour, snack, cream, etc. However, the potential positive health effects of carob-containing products are largely unknown and have not been extensively studied. The aim of this study was to determine significant urine and fecal metabolome alterations in 8 rats treated with carob powder for 15 days as compared to 8 non-treated ones (controls) using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and to underly specific metabolites that changed according to the treatment.
   Urine and fecal samples were collected in five time points during a 15 day period of treatment with carob powder throughout water consumption (10 g powder / L). A targeted HILIC-UPLC-MS/MS method was applied for the determination of 101 polar metabolites (sugars, amino acids, organic acids, amines, etc) in a single run of 40 min in both rat urine and feces. Chromatographic separation was performed on an Aquity BEH amide column (2.1 x 100 mm, i.d. 1.7 μm); the mobile phase was consisted of A: Acetonitrile:H₂O 95:5 v/v (+ 10 mM ammonium formate) and B: H₂O:Acetonitrile 70:30 v/v (+10 mM ammonium formate). The solvents flow rate was set at 0.5 mL/min. Mass spectrometry parameters were optimized for each of the 101 pre-selected analytes.
   Approximately 55 urinary and fecal metabolites were identified in both specimens. Data were further processed with multivariate (SIMCA 13) and univariate statistics (ANOVA). The differentiation of treated rats and controls was highlighted using discriminant multivariate models. 

Acknowledgements: The authors would like to thank the “Black Gold” project financially supported by the University of Cyprus.