MetaboLights

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ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS1965/m_MTBLS1965_LC-MS_positive_reverse-phase_metabolite_profiling_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS1965/m_MTBLS1965_LC-MS_negative_reverse-phase_metabolite_profiling_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS1965/a_MTBLS1965_LC-MS_negative_reverse-phase_metabolite_profiling.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS1965/a_MTBLS1965_LC-MS_positive_reverse-phase_metabolite_profiling.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS1965/s_MTBLS1965.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS1965/i_Investigation.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS1965/FILES/DERIVED_FILES/1132_d_2_MRM.mzMLftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS1965/FILES/DERIVED_FILES/1133_3_MRM755.mzMLftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS1965/FILES/DERIVED_FILES/Col-0_B3_d_1.mzMLftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS1965/FILES/DERIVED_FILES/RSS1133_2_MRM386_207_757_529.mzMLftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS1965/FILES/DERIVED_FILES/1132_d_4_MRM771.mzMLftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS1965/FILES/DERIVED_FILES/1131_4_2nd_MRM739.mzMLftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS1965/FILES/DERIVED_FILES/RSS1131_2_2nd_1Hz_MRM741mz.mzMLftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS1965/FILES/DERIVED_FILES/1097_d_1.mzMLprimaryOK200ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS1965(Partial) identification of metabolitesMetabolites were putatively and partially identified on the basis of ion types, accurate m/z values, and intensities of parent and MS/MS fragment ions. Sugar moieties were identified on the basis of accurate calculations of neutral losses; this allowed differentiation between a hexosyl (neutral loss: 162.0528 Da) and a caffeoyl (162.0317 Da) moiety as well as between a deoxyhexosyl (146.0579 Da) and a p-coumaroyl (146.0368 Da) moiety. Since deoxyhexosyl substitutions in flavonoids are most commonly rhamnosyl moieties, we assumed rhamnosyl groups as deoxyhexosyl substitutions/neutral losses. For structural formula prediction, in-silico fragmentation with MetFrag [1] was applied to the ESI+ fragments, using the PubChem database [2]; this was accompanied by spectral matching with entries in the MassBank of North America (https://mona.fiehnlab.ucdavis.edu/). Abbreviations: Hex, hexosyl; Rha, rhamnosyl Published MS/MS spectraFor identification, samples with high intensities of the interesting features (i.e., the main candidate products and substrates of the investigated BGLU enzymes) were selected, ensuring that also MS/MS spectra were available in these samples. These MS/MS spectra can be found in the files uploaded to MetaboLights in the following files at the following spectra numbers:BGLU1:feature with m/z 741 in ESI+ mode: sample RSS1131_2_2nd_1Hz_MRM741mz.mzML, spectra 746-758feature with m/z 739 in ESI– mode: sample 1131_4_2nd_MRM739.mzML, spectrum 1028BGLU3:feature with m/z 866 in ESI+ mode: sample Col-0_B3_d_1.mzML, spectrum 3387feature with m/z 704 in ESI+ mode: sample 1097_d_1.mzML, spectrum 3759feature with m/z 781 in ESI+ mode: sample 1097_d_1.mzML, spectrum 4411feature with m/z 773 in ESI+ mode: sample 1132_d_2_MRM.mzML, spectrum 1231feature with m/z 771 in ESI– mode: sample 1132_d_4_MRM771.mzML, spectra 1007-1013BGLU4:feature with m/z 757 in ESI+ mode: sample RSS1133_2_MRM386_207_757_529.mzML, spectrum 687feature with m/z 755 in ESI– mode: sample 1133_3_MRM755.mzML, spectra 1080-1082 References:[1] Ruttkies, C., Schymanski, E.L., Wolf, S., Hollender, J., Neumann, S., 2016. MetFrag relaunched: incorporating strategies beyond in silico fragmentation. J. Cheminformatics 8, 3.[2] Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., Li, Q., Shoemaker, B.A., Thiessen, P.A., Yu, B., Zaslavsky, L., Zhang, J., Bolton, E.E., 2019. PubChem 2019 update: improved access to chemical data. Nucleic Acids Res. 47, D1102–D1109.MetaboLightsPublicLiquid Chromatography MS - negative - reverse phaseLiquid Chromatography MS - positive - reverse phaseSamples were analyzed using an ultra-high performance liquid chromatograph (Dionex UltiMate 3000, Thermo Fisher Scientific). Separation was done on a Kinetex XB-C18 column (1.7 μm, 150 mm × 2.1 mm, with guard column; Phenomenex) at 45 °C with a flow rate of 0.5 mL/min. As mobile phases, 0.1% (v:v) formic acid (~98%, LC-MS grade, Honeywell Research Chemicals, Fluka) in H2OMilliQ (eluent A) and 0.1% formic acid in acetonitrile (LC-MS grade; Fisher Scientific or HiPerSolv CHROMANORM, VWR) (eluent B) were used, with a gradient increasing linearly from 2% to 30% B within 20 min and to 75% B within 9 min, followed by column cleanup and equilibration.Metabolic fingerprinting reveals roles of Arabidopsis thaliana BGLU1, BGLU3, and BGLU4 in glycosylation of various flavonoids. 10.1016/j.phytochem.2024.114338. PMID:39603578Department of Chemical Ecology, Bielefeld UniversityRabea Schweigerrosetteseedmass spectrometry assayExtraction and analysis of (semi)-polar metabolites were performed as described [1] with some modifications. Samples were extracted threefold in ice-cold 90% (v:v) methanol (LC-MS grade; Fisher Scientific UK Limited or Th. Geyer GmbH & Co. KG), supplemented with luteolin 7-O-glucoside (Extrasynthese) as internal standard. Pooled supernatants were filtered using Phenex™ syringe filters (0.2 μm, Phenomenex®). One blank was prepared for each set of ten samples. References:[1] Schrieber, K., Schweiger, R., Kröner, L., Müller, C., 2019. Inbreeding diminishes herbivore-induced metabolic responses in native and invasive plant populations. J. Ecol. 107, 923–936.Arabidopsis thalianahttps://www.ebi.ac.uk/metabolights/MTBLS1965Bernd Weisshaar. Department of Genetics and Genomics of Plants, Bielefeld University.Boas Pucker. Department of Genetics and Genomics of Plants, Bielefeld University.Ralf Stracke. Department of Genetics and Genomics of Plants, Bielefeld University.Rabea Schweiger. Department of Chemical Ecology, Bielefeld University. rabea.schweiger@uni-bielefeld.de.Lennart Sielmann. Department of Genetics and Genomics of Plants, Bielefeld University.Caroline Müller. Department of Chemical Ecology, Bielefeld University.Jana-Freja Frommann. Department of Genetics and Genomics of Plants, Bielefeld University.Mass axis recalibration using the Na(HCOO) calibrant and picking of metabolic features (each characterized by a RT and m/z), including spectral background subtraction, were performed in Compass Data-Analysis v4.4 (Bruker Daltonics). The 'Find Molecular Features' algorithm of the Bruker DataAnalysis software was used for feature picking in MS mode, with the following settings: signal-to-noise threshold 3 (or 1 if measured at 1 Hz), correlation coefficient threshold 0.75; depending on the spectra rates, minimum compound lengths were set to 5–22 spectra and smoothing widths to 0–6. Using Compass ProfileAnalysis v2.3 (Bruker Daltonics), metabolic features likely to belong to the same metabolite (i.e., [M+H]+ ions, common adducts, and fragments with corresponding isotopes and charge states) were grouped together in so-called buckets. The split-buckets-with-multiple-compounds option was selected to separate the internal standard from a peak with similar RT and m/z in seed samples. Each bucket was reduced to the feature with the highest intensity in that bucket and this feature was used for quantification via its peak height. These features were aligned across samples, allowing RT deviations of 0.1 or 0.2 min and m/z deviations of 6 mDa, respectively. Features within the injection peak and those with peak heights above detector saturation were excluded. Peak heights were related to the height of the [M+H]+ ion of the internal standard. Based on the resulting values, features were retained in the data set of the corresponding gene, if their mean intensity in at least one genotype of a sample set (bglu mutant, wt, 2×35S::BGLU line) was at least 50 times higher than the corresponding intensity in the blanks. Moreover, features had to be present in at least three of the four biological replicates in at least one genotype. Finally, the feature intensities were divided by the sample dry weight. Screening for metabolitesTo screen for metabolites that may represent products and substrates of the enzymes encoded by BGLU1, BGLU3, and BGLU4, fold changes (FCs) were calculated. For this, the mean intensities of all metabolic features in the BGLU expression variant lines were divided by the corresponding mean intensities in the wt if at least one genotype in the pairwise combination showed peaks in at least three replicates. Candidate product features were selected based on higher peaks in 2×35S::BGLU than in wt samples (FC ≥ 1.5 or present only in 2×35S::BGLU but not in wt samples) and/or lower peaks in bglu than in wt samples (FC ≤ 0.67 or only occurring in wt but not in bglu samples). Candidate substrate features were selected by screening for the opposite peak intensity patterns. Extracted ion chromatograms of the m/z belonging to the features of interest were manually reviewed and features were considered relevant if the peak intensity patterns across genotypes resembled the transcript expression patterns from semi-quantitative reverse transcriptase PCR (RT-PCR) (candidate products) or showed the opposite pattern (candidate substrates). This was based on the assumption that the levels of metabolic products and substrates of an enzyme correlate with the transcript levels of the corresponding gene. The peak areas of the features of interest were determined in ESI+ MS mode by manual integration in DataAnalysis and divided by the peak area of the manually integrated m/z trace of the [M+H]+ ion of the internal standard and the sample dry weight to calculate more accurate (i.e., peak area-based) FCs.TreatmentBGLU experimentExpression variantrabea.schweiger@uni-bielefeld.deGeneral experimental procedures Plant materialSeeds of A. thaliana were obtained from the Nottingham Arabidopsis Stock Center, including the wt Col-0 accession and the loss-of-function T-DNA insertion mutants bglu1-1 (At1g45191, GK-341B12, N432664), bglu3-2 (At4g22100, GK-853H01, N481877; [1]), and bglu4-2 (At1g60090, SALK_029729, N25045; [2]). Homozygous plants were selected based on PCR-based genotyping and kept as mutant lines by selfing. The insertion alleles and genomic integrity of the T-DNA insertion mutants were verified by long-read sequencing (see below). Overexpression lines, based on the double enhancer cauliflower mosaic virus 35S (2×35S) promoter [3], were generated in Col-0 wild type (wt) as described below. Plant growth conditionsWe worked with seed from all the lines used, produced in parallel, so that all the lines were grown from seed of the same age (harvest date). Unless stated otherwise, plants were grown in the greenhouse under long day conditions (about 14 h light) at 23 °C and 70% relative humidity (rH). They were grown in 9 × 9 cm pots on compost “Sondermischung Max Planck Institute 19277634” consisting of 70% white peat (finely ground), 20% vermiculite® and 10% sand with pH 6.5; the substrate contained 1 kg/m³ Osmocote® Start 8 Weeks (starting fertilizer); 1 kg/m³ Triabon® (depot fertilizer) and 0.25 kg/m³ Fe-EDDHA. Plants were fertilized with Wuxal® Super (Manna) and watered as required. Until flowering, pots of the different genotypes were placed in random order. To prevent cross-pollination at the flowering stage, pots of the same genotype were placed in trays that were randomized daily. For seed formation, plants were grown under short day conditions (8 h light, 22 °C, about 55% rH) for two months before being transferred to long day conditions. For seed ripening, the temperature was raised to 24 °C. Leaf samples for gene expression and metabolic studies were obtained from 6-week-old plants, which were grown for 8 days on 0.5x Murashige and Skoog medium, followed by 5 weeks of growth in a growth chamber (Percival) with 13 h light at 24 °C and 80% rH (18 °C and 65% rH at night). Generation of BGLU overexpression linesThe full-length BGLU1, BGLU3, or BGLU4 coding sequences (CDSs) from the entry constructs were introduced into the binary expression vector pLEELA [4] using GATEWAY® LR clonase. The T-DNA from the resulting plasmid constructs with 2×35S::BGLU::35S-polyA expression cassettes was transferred into A. thaliana via Agrobacterium tumefaciens-mediated (Agrobacterium, GV101:pMP90RK; [5]) gene transfer by floral dip [6]. Positive lines were identified by BASTA-selection and confirmed by PCR-based genotyping. Transgene expression was analyzed in rosette leaves for the 2×35S::BGLU1, 2×35S::BGLU3, and 2×35S::BGLU4 lines by RT-PCR to select lines with the highest BGLU transcript levels. Metabolic analyses Untargeted metabolic fingerprintingMetabolic fingerprinting was used to screen for candidate product and substrate metabolites of the biosynthetic reactions catalyzed by the investigated enzymes. For each investigated BGLU gene, a sample set containing (i) the bglu loss-of-function mutant (bglu1-1, bglu3-2, or bglu4-2), (ii) the wt, and (iii) the overexpression line (2×35S::BGLU1 6, 2×35S::BGLU3 44, or 2×35S::BGLU4 91) was prepared. The plant part with the highest BGLU gene expression in the wt (see above) was used for the metabolic analyses. For BGLU1 samples, four to six rosette leaves of 6-week-old plants were harvested 6 h after artificial sunrise. For BGLU3 samples, 150 mg dry mature seeds were used, and for BGLU4 samples 120 mg seeds, which were sown on wet filter paper to soak with water for 24 h in the dark (all seeds were 5–6 months old, derived from a pool of plants). Four biological replicates were prepared for each genotype. Samples were flash frozen in liquid nitrogen, stored at -80 °C, lyophilized, and ground. References:[1] Kleinboelting, N., Huep, G., Kloetgen, A., Viehoever, P., Weisshaar, B., 2012. GABI-Kat SimpleSearch: new features of the Arabidopsis thaliana T-DNA mutant database. Nucleic Acids Res. 40, D1211–D1215.[2] Alonso, J.M., Stepanova, A.N., Leisse, T.J., Kim, C.J., Chen, H., Shinn, P., Stevenson, D.K., Zimmerman, J., Barajas, P., Cheuk, R., Gadrinab, C., Heller, C., Jeske, A., Koesema, E., Meyers, C.C., Parker, H., Prednis, L., Ansari, Y., Choy, N., Deen, H., Geralt, M., Hazari, N., Hom, E., Karnes, M., Mulholland, C., Ndubaku, R., Schmidt, I., Guzman, P., Aguilar-Henonin, L., Schmid, M., Weigel, D., Carter, D.E., Marchand, T., Risseeuw, E., Brogden, D., Zeko, A., Crosby, W.L., Berry, C.C., Ecker, J.R., 2003. Genome-wide insertional mutagenesis of Arabidopsis thaliana. Science 301, 653–657.[3] Kay, R., Chan, A., Daly, M., McPherson, J., 1987. Duplication of CaMV 35S promoter sequences creates a strong enhancer for plant genes. Science 236, 1299–1302.[4] Jakoby, M., Wang, H.-Y., Reidt, W., Weisshaar, B., Bauer, P., 2004. FRU (BHLH029) is required for induction of iron mobilization genes in Arabidopsis thaliana. FEBS Lett. 577, 528–534.[5] Koncz, C., Schell, J., 1986. The promoter of TL-DNA gene 5 controls the tissue-specific expression of chimaeric genes carried by a novel type of Agrobacterium binary vector. Mol. Genet. Genomics 204, 383–396.[6] Clough, S.J., Bent, A.F., 1998. Floral dip: a simplified method for Agrobacterium-mediated transformation of Arabidopsis thaliana. Plant J. 16, 735–743.MetabolomicsArabidopsis thalianaBGLUGlycosyltransferaseMetabolic fingerprintingGlycosylationBrassicaceaeflavonoidsArabidopsis thalianaBGLUGlycosyltransferaseMetabolic fingerprintingGlycosylationBrassicaceaeflavonoidsSamples were analyzed using a quadrupole time-of-flight mass spectrometer (compact, Bruker Daltonics) in positive electrospray ionization (ESI+) mode. A nebulizer (N2) pressure of 3 bar, an end plate offset of 500 V, a capillary voltage of 4500 V and N2 as drying gas (275 °C, flow rate: 12 l/min) were used. A Na(HCOO)-based calibration solution was introduced to the ESI sprayer before or after each sample. Line mass spectra were recorded in the mass-to-charge (m/z) range of 50–1300 at 1–8 Hz, depending on the type of sample (plant part) and peak heights; the same spectra rate was used for samples to be compared (see below). The MS parameters were: 4 eV quadrupole ion energy, a low mass with an m/z value of 90, 7 eV collision energy, 75 μs transfer time, and 6 μs pre-pulse storage. To obtain MS/MS spectra of the ions with the highest intensities, the Auto-MS/MS mode was used with N2 as collision gas and the isolation widths and collision energies increased with the m/z of the precursors. To aid in metabolite identification, some samples were additionally measured at low spectra rates (1–3 Hz) and using multiple reaction monitoring to specifically fragment certain ions, sometimes using different collision energies. Some samples were also measured in negative electrospray ionization (ESI–) mode (capillary voltage 3000 V) for aglycon identification; since the MS/MS spectra were taken from single samples and selected data points within the MS/MS chromatogram traces, the retention times (RTs) in ESI+ and ESI– modes may slightly differ.Flavonoids are specialized metabolites that play important roles in plants, including interactions with the environment. The high structural diversity of this metabolite group is largely due to enzyme-mediated modifications of flavonoid core skeletons. In particular, glycosylation with different sugars is very common. In this study, the functions of the Arabidopsis thaliana glycoside hydrolase family 1-type glycosyltransferase proteins BGLU1, BGLU3, and BGLU4 were investigated, using a reverse genetics approach and untargeted metabolic fingerprinting. We screened for metabolic differences between A. thaliana wild type, loss-of-function mutants, and overexpression lines and partially identified differentially accumulating metabolites, which are putative products and/or substrates of the BGLU enzymes. Our study revealed that the investigated BGLU proteins are glycosyltransferases involved in the glycosylation of already glycosylated flavonoids using different substrates. While BGLU1 appears to be involved in the rhamnosylation of a kaempferol diglycoside in leaves, BGLU3 and BGLU4 are likely involved in the glycosylation of quercetin diglycosides in A. thaliana seeds. In addition, we present evidence that BGLU3 is a multifunctional enzyme that catalyzes other metabolic reactions with more complex substrates. This study deepens our understanding of the metabolic pathways and enzymes that contribute to the high structural diversity of flavonoids.Metabolic fingerprinting reveals roles of Arabidopsis thaliana BGLU1, BGLU3, and BGLU4 in glycosylation of various flavonoids.Frommann Jana-Freja JF, Pucker Boas B, Sielmann Lennart Malte LM, Müller Caroline C, Weisshaar Bernd B, Stracke Ralf R, Schweiger Rabea RfalseMetabolic fingerprinting reveals roles of Arabidopsis thaliana BGLU1, BGLU3, and BGLU4 in glycosylation of various flavonoidsFlavonoids are specialized metabolites that play important roles in plants, including interactions with the environment. The high structural diversity of this metabolite group is largely due to enzyme-mediated modifications of flavonoid core skeletons. In particular, glycosylation with different sugars is very common. In this study, the functions of the Arabidopsis thaliana glycoside hydrolase family 1-type glycosyltransferase proteins BGLU1, BGLU3, and BGLU4 were investigated, using a reverse genetics approach and untargeted metabolic fingerprinting. We screened for metabolic differences between A. thaliana wild type, loss-of- function mutants, and overexpression lines and partially identified differentially accumulating metabolites, which are putative products and/or substrates of the BGLU enzymes. Our study revealed that the investigated BGLU proteins are glycosyltransferases involved in the glycosylation of already glycosylated flavonoids using different substrates. While BGLU1 appears to be involved in the rhamnosylation of a kaempferol diglycoside in leaves, BGLU3 and BGLU4 are likely involved in the glycosylation of quercetin diglycosides in A. thaliana seeds. In addition, we present evidence that BGLU3 is a multifunctional enzyme that catalyzes other metabolic reactions with more complex substrates. This study deepens our understanding of the metabolic pathways and enzymes that contribute to the high structural diversity of flavonoids.2025-02-252020-07-29MTBLS196539603578