Modulation of the Tomato Fruit Metabolome by LED Light (GCMS and LCMS datasets)
Ntagkas, Nikolaos et al. (2021), Modulation of the Tomato Fruit Metabolome by LED Light (GCMS and LCMS datasets), Dryad, Dataset, https://doi.org/10.5061/dryad.5qfttdz2j
Metabolic profiles of tomatoes change during ripening and light can modulate the activity of relevant biochemical pathways. We investigated the effects of light directly supplied to the fruits, on the metabolome of the fruit pericarp during ripening. Mature green tomatoes were exposed to well-controlled conditions with light as the only varying factor; control fruits were kept in darkness. In Experiment 1 the fruits were exposed to either white light or darkness for 15 days. In Experiment 2 fruits were exposed to different light spectra (blue, green, red, far-red, white) added to white background light for 7 days. Changes in the global metabolome of the fruit pericarp were monitored using LCMS and GCMS (554 compounds in total). Health-beneficial compounds (carotenoids, flavonoids, tocopherols and phenolic acids) accumulated faster under white light compared to darkness, while alkaloids and chlorophylls decreased faster. Light also changed the levels of taste-related metabolites including glutamate and malate. The light spectrum treatments indicated that the addition of blue light was the most effective treatment in altering the fruit metabolome. We conclude that light during ripening of tomatoes can have various effects on the metabolome and may help shaping the levels of key compounds involved in various fruit quality characteristics.
Metabolite extractions and analyses were all performed using 100 mg fresh weight of frozen tomato pericarp powder per extraction. In short, lipid-soluble compounds including carotenoids, tocopherols and chlorophylls were extracted using water/chloroform/methanol with 0.1% butylhydroxytoluene as antioxidant and Sudan I as internal standard. The lipid phase was dried, compounds re-dissolved in 500 µl ethylacetate and analyzed in a targeted manner by C30-reversed phase HPLC (Waters) coupled to photodiode array (PDA) detection for both carotenoids and chlorophylls, and fluorescence (Fl) detection for tocopherols (in short: HPLC-PDA-Fl). These lipid-soluble compounds were quantified using calibration curves from authentic standards. Semi-polar compounds, including flavonoids, phenylpropanoids, alkaloids and various volatile-glycosides, were extracted in 75% methanol containing 0.1% formic acid and by C18-reversed phase HPLC (Acquity system, Waters) coupled to both PDA detection (Waters 2996) and a LTQ-Orbitrap FTMS hybrid system (Thermo) with electrospray ionization (ESI) in negative mode, a mass resolution of 70,000 FWHM and a m/z range of 90-1350 (in short: LC-MS). Polar compounds, including sugars, organic acids and amino acids, were extracted using water-methanol-chloroform containing ribitol as internal standard. The polar phase was dried, derivatized with both methoxyamine and N-methyl-N-(trimethylsilyl) trifluoroacetamide, and mixed with 1 µl of an alkane series (C10-C30) using an online derivatization/injection robot (CTC Analytics) and analyzed by an Agilent 6890 GC coupled to Pegasus III TOF MS with 70 eV electron impact (EI) ionization (in short: GC-MS).
Samples from both Exp.1 and Exp.2 were all extracted and analyzed in a single series in a random order. The processing of data from the targeted HPLC-PDA-Fl analysis of lipid-soluble compounds (isoprenoids) was performed using Empower software (Waters); these lipid-soluble compounds were annotated and quantified using authentic standards. Both LC-MS and GC-MS data were processed in an untargeted manner, using Metalign software for automatic noise estimation, unbiased peak picking (with the intensity based on peak height) and alignment. Settings are provided in Supplemental Figure 3. The resulting datasets were then further processed by grouping all mass features putatively derived from the same compound (mass clusters), based on their similarity in both retention time (scan number) and relative abundance across samples, thus removing mass signal redundancy and retaining a single representative value (i.e. the total of ion counts of clustered mass signals) per metabolite, using MSClust software. In cases when metabolite intensities did not exceed the detection threshold, their levels were assumed as zero. In addition, the resulting mass clusters actually represent low energy-ESI (pseudo) mass spectra in the case of LC-MS and high energy-EI mass spectra in the case of GC-MS; these mass spectra were used for annotation of selected compounds. For LC-MS, the molecular ion of selected compounds was manually checked in the pseudo spectrum and annotated based on comparisons of retention time, accurate mass, isotopic pattern and (pseudo)mass spectrum information, and the corresponding PDA spectrum if available, with in-house databases and on-line available metabolite databases such as KNApSAcK, HMDB and MassBank. For GC-MS, the mass spectra from MSClust and calculated retention indices, based on the retention of the added alkane series, was compared with available EI-spectral libraries such as the NIST2014 and the Golm spectral database (http://gmd.mpimp-golm.mpg.de/), as well as an in-house library of derivatized standards.
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