DOI: 10.5061/dryad.5qfttdz2j
title: Modulation of the Tomato Fruit Metabolome by LED Light (GCMS and LCMS datasets)

description: 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.
description: 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.