RESUMO
Calystegines are potent glycosidase inhibitors with therapeutic potential and are constituents of food and feed with potential toxic effects. This study aims to target calystegines and other nitrogenous substances in food plants. Hydroalcoholic extracts from Solanum tuberosum, Ipomoea batatas, S. lycocarpum, and fruit from S. lycopersicum, S. aethiopicum, S. paniculatum, S. crinitum, and S. acanthodes were analyzed by liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) using an acidic HILIC column. The dereplication approach included data processing using MZMine2, FBMN-GNPS, and structure elucidation and interpretation of the organized data. The calystegines A3, A5, B2, and C1 were identified, and several potential new calystegine analogues: three may correspond to new calystegines of the A-group, one glycosyl derivative of calystegine A3, and two glycosyl derivatives of the B-group. These findings help to direct the search for new calystegines. In addition, the dereplication approach enabled the annotation of 22 other nitrogen compounds.
Assuntos
Solanum , Plantas Comestíveis , Espectrometria de Massas em Tandem , Frutas , BrasilRESUMO
One of the initial and critical procedures for the analysis of metabolomics data using liquid chromatography and mass spectrometry is feature detection. Feature detection is the process to detect boundaries of the mass surface from raw data. It consists of detected abundances arranged in a two-dimensional (2D) matrix of mass/charge and elution time. MZmine 2 is one of the leading software environments that provide a full analysis pipeline for these data. However, the feature detection algorithms provided in MZmine 2 are based mainly on the analysis of one-dimension at a time. We propose GridMass, an efficient algorithm for 2D feature detection. The algorithm is based on landing probes across the chromatographic space that are moved to find local maxima providing accurate boundary estimations. We tested GridMass on a controlled marker experiment, on plasma samples, on plant fruits, and in a proteome sample. Compared with other algorithms, GridMass is faster and may achieve comparable or better sensitivity and specificity. As a proof of concept, GridMass has been implemented in Java under the MZmine 2 environment and is available at http://www.bioinformatica.mty.itesm.mx/GridMass and MASSyPup. It has also been submitted to the MZmine 2 developing community.