Influence of drought stress on increasing bioactive compounds of pomegranate (Punica granatum L.) juice. Exploratory study using LC–MS-based untargeted metabolomics approach

Abstract

Water deficit is one of the most important abiotic factors that reduce the agricultural productivity. The impact of irrigation levels on bioactive compounds of one commercial pomegranate cultivar (‘Mollar de Elche’) was studied through LC–MS-based untargeted metabolomics approach. Two treatments were applied to explore crop responses to the timing and the regime of the irrigation applications. The main goal of the present study was to validate the untargeted metabolomics approach as analytical tool to find new markers related with the water deficit. Robust classification and prediction model was built with the use of supervised technique, partial least squares discriminant analysis (PLS-DA). The results revealed that pomegranate juices obtained from plants grown under DIr conditions experimented physiological stress, which significantly increased their bioactive compounds (polyphenols, phenylpropanoids, peptides, benzoic acid, tannins, and phospholipids) in comparison with the Control treatments. The results indicate that controlled irrigation stress would increase the production of bioactive compounds by increasing the phenylpropanoids metabolism. New metabolites were tentatively identified in pomegranate juice by the untargeted metabolomics techniques. This study showed, for the first time, the satisfactory application of the untargeted metabolomics approach using an UHPLC-QTOF system to identify bioactive secondary metabolites from pomegranate juices to discriminate among different irrigation conditions.

Untargeted Metabolomics Analysis Based on LC-QTOF-MS to Investigate the Phenolic Composition of Red and White Wines Elaborated from Sonicated Grapes

“…Non-targeted metabolomics was approached as previously reported using an Agilent 1290 Infinity series LC system coupled to a 6550 I-Funnel Accurate-Mass QTOF (Agilent Technologies, Waldbronn, Germany) with a dual electrospray ionization interface (ESI-Jet Stream Technology, Waldbronn, Germany). The samples were injected into a reverse-phase Poroshell 120 EC-C18 column (Agilent) following the operating conditions described by Gomez-Bellot et al [ 27 ]. As there were no specific target analytes (untargeted analysis), generic settings were applied to obtain as many compounds as possible.…”

“…All samples were injected in the same batch, in a randomized order to avoid sample bias. A mixture with one replicate of each group of samples was used as ‘quality control’ and was injected at the beginning and at the end of the batch [ 27 ].…”

“…Detection was performed in both positive and negative ionization modes, and the data were acquired using the Mass Hunter Workstation software (version B.08.00, Service Pack 1, Agilent). Feature extraction statistical analysis and tentative identification were carried out according to previous publications [ 27 , 28 ].…”

Identifying Bioactive Compounds in Common Bean (Phaseolus vulgaris L.) Plants under Water Deficit Conditions

“…Then, the samples were sonicated for 30 min in an ultrasonic bath, centrifuged at 11,500× g for 15 min, and filtered through a 0.45 µm filter (Agilent Technologies, Santa Clara, CA, USA). The samples were injected into a reverse-phase Poroshell 120 EC-C18 column (Agilent Technologies, Santa Clara, CA, USA), according to the operating conditions that were previously described [23] Detection was performed in both positive and negative ionization modes, and the data were acquired using Mass Hunter Workstation software (version B.08.00, Service Pack 1, Agilent). Feature extraction statistical analysis and tentative identification were carried out according to our previous publications [23][24][25][26].…”

“…According to previous publications [23][24][25][26], pre-processed CEF files were exported into the Mass Profiler Professional (MPP) software package (revision B.14.09.01, Agilent Technologies, Santa Clara, CA, USA) for statistical analysis. The final list of features was used for metabolite identification with purchased METLIN databases and according to the exact mass [26].…”