MetaboLights

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ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14820/m_MTBLS14820_LC-MS_negative_reverse-phase_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14820/i_Investigation.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14820/s_MTBLS14820.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14820/a_MTBLS14820_LC-MS_negative_reverse-phase.txtprimary200OKftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS14820Significant biomarkers from the generated S-plot and those from the Variable Importance in Projection (VIP) score plots were annotated using their respective spectral features and retention times. The databases used for annotation included PubChem, MassBank, COCONUT, ChemSpider, Human Metabolome Database (HMDB), KEGG compound, KNApSAcK, and Sirius version 5.8.5. To confirm the annotations, previous literature related to this study was used. Pathway analysis was done using MetaboAnalyst version 6.0 to encapsulate the map of the metabolism for the wheat cultivars on the annotated metabolites under investigation when infested with RWASA5. This toolkit employs already established KEGG metabolic pathways to accomplish pathway mapping.Please update this protocol descriptionMetaboLightsPublicLiquid Chromatography MS - negative - reverse-phaseSample extracts from the leaves of the two cultivars (infested and non-infested) were analysed on a liquid chromatography-quadrupole time-of-flight tandem MS instrument (LCMS-9030 qTOF, Shimadzu Corporation, Kyoto, Japan). To perform chromatographic separation, a Shim-pack Velox C18 column (100 mm × 2.1 mm with particle size of 2.7 µm) (Shimadzu Corporation, Kyoto, Japan) was used at a temperature of 55 °C. From each sample, 5 μL was taken and analysed using a 13-minute method with the following gradient conditions: solvent A: 0.1% formic acid in Milli-Q water (both HPLC grade, Merck, Darmstadt, Germany) and solvent B: Methanol (UHPLC grade, Romil Ltd, Cambridge, UK) with 0.1% formic acid. The flow rate was maintained at 0.45 mL/min throughout the set gradient. The metabolites were then separated by setting the following separation conditions: 10% B was equilibrated for 2 min, an increase from 10-60% B was generated over 3-5 min, the conditions were changed from 60-90% B (5-8 min), and then the gradient was maintained at 90% for 3 minutes (8-11min). The conditions were then returned to the initial conditions of 10% B from 11-12 min. Thereafter, the column was re-equilibrated for 1 min.Metabolic analysis of two wheat (Triticum aestivum) cultivars in response to Russian wheat aphid (Diuraphis noxia Kurdjumov) infestation.Phumzile SibisiUniversity Of South Africaleafmass spectrometry assayMetabolites were extracted from the powdered wheat samples. This was conducted following the method outlined by Makhumbila et al. (2023), with slight modifications. The fine powdered material (50 mg) was added to a 2 ml Eppendorf tube, and each sample was extracted using 80% (HP-LC grade) methanol (Merck, Darmstadt, Germany). The resulting sample mixtures were then subjected to 30 seconds of vortexing and 2 hours of sonication in a sonicating water bath (Branson CPX, Fischer Scientific, Waltham, MA, USA). Afterwards, the sample mixtures were then centrifuged for 5 min at 4507 rpm. The supernatant was then transferred to a clean 2 ml Eppendorf tube. Nylon filters (0.22 μm) were used to filter the supernatants into chromatography glass vials with 500 μL inserts (Alwsci Technologies, 6 × 31 mm). Three replicates for each sample group were prepared for analysis and stored at 4 °C.Please update this protocol descriptionTriticum aestivumhttps://www.ebi.ac.uk/metabolights/MTBLS14820Phumzile Sibisi. University Of South Africa. sibispp@unisa.ac.za.The data was extracted as mzML files from the LCMS-9030 qTOF and pre-processed using XCMS online. The data pre-processing was performed with UPLC/UHD-qTOF parameters using the CentWave feature detection method, a maximum tolerated m/z was set at 15 ppm, a signal-to-noise ratio set to 6, and prefilters for noise, peaks and intensity at 0, 3, and 100, respectively. The retention time correction was carried out using the obiwarp method with a profStep of 1. The alignment of the minimum fraction of samples was 0.5, whilst the width of overlapping m/z was 0.015 m/z. The Welch t-test was applied to the data, resulting in a feature table with 3434 features. The data matrix was exported to SIMCA version 17.0 software (Sartorius, South Africa) and then normalised and Pareto scaled before model application. Principal Component Analysis (PCA) and Orthogonal Projection to Latent Structures- Discriminant Analysis (OPLS-DA) models were employed. Partial Least Squares Discriminant Analysis (PLS-DA) was used to generate the loading S-plot.Please update this protocol descriptionColletion timepointGenotypesibispp@unisa.ac.zaExperimental siteThe experiment was conducted at the University of Johannesburg (Auckland Park Campus) in a controlled environment of the greenhouse. Metabolite extraction was done at UNISA (Florida campus), whilst the analysis was conducted at the University of Venda. Experimental designA randomised complete block design (RCBD) was implemented for the design of the study. Two Triticum aestivum cultivars (Tugela and Tugela DN) were infested with twenty RWASA5. Cages (150 ×150 cm) with an aperture of 160 µm (fine nylon mesh) were used to cover each plant to prevent cross-infestation. Three biological repetitions were performed, and the plants were autoclaved to kill all the aphids upon termination of the experiment. RWASA5 colony maintenanceThe Russian wheat aphids were obtained from the Agricultural Research Council - Small Grain Institute (ARC-SGI) in Bethlehem, South Africa. The aphid colonies were kept on RWASA5 susceptible PAN 3434 (Pannar) cultivar in a controlled environment at 22 ºC and exposed to light for 12 hours each day. Irrigation occurred three times a week. Wheat growth and infestation trialFor each wheat cultivar, five seeds were planted in 10 separate plastic pots. Sterilised potting soil and compost mixed at a ratio of 2:1 were used as a growing medium, and the plants were grown in a controlled environment at 22 ºC with a 12-hour photoperiod and irrigated three times a week. Following the two-leaf stage, each leaf was infested with 20 RWASA5. After infestation, the leaves were harvested at 0, 4, 8, 24, and 48 hours post infestation (hpi), placed immediately in liquid nitrogen, ground into fine powder using a mortar and pestle, and then stored at -80 ºC until further use. Metabolite extractionMetabolites were extracted from the powdered wheat samples. This was conducted following the method outlined by Makhumbila et al. (2023), with slight modifications. The fine powdered material (50 mg) was added to a 2 ml Eppendorf tube, and each sample was extracted using 80% (HP-LC grade) methanol (Merck, Darmstadt, Germany). The resulting sample mixtures were then subjected to 30 seconds of vortexing and 2 hours of sonication in a sonicating water bath (Branson CPX, Fischer Scientific, Waltham, MA, USA). Afterwards, the sample mixtures were then centrifuged for 5 min at 4507 rpm. The supernatant was then transferred to a clean 2 ml Eppendorf tube. Nylon filters (0.22 μm) were used to filter the supernatants into chromatography glass vials with 500 μL inserts (Alwsci Technologies, 6 × 31 mm). Three replicates for each sample group were prepared for analysis and stored at 4 °C. LC-MS qTOF analysisSample extracts from the leaves of the two cultivars (infested and non-infested) were analysed on a liquid chromatography-quadrupole time-of-flight tandem MS instrument (LCMS-9030 qTOF, Shimadzu Corporation, Kyoto, Japan). To perform chromatographic separation, a Shim-pack Velox C18 column (100 mm × 2.1 mm with particle size of 2.7 µm) (Shimadzu Corporation, Kyoto, Japan) was used at a temperature of 55 °C. From each sample, 5 μL was taken and analysed using a 13-minute method with the following gradient conditions: solvent A: 0.1% formic acid in Milli-Q water (both HPLC grade, Merck, Darmstadt, Germany) and solvent B: Methanol (UHPLC grade, Romil Ltd, Cambridge, UK) with 0.1% formic acid. The flow rate was maintained at 0.45 mL/min throughout the set gradient. The metabolites were then separated by setting the following separation conditions: 10% B was equilibrated for 2 min, an increase from 10-60% B was generated over 3-5 min, the conditions were changed from 60-90% B (5-8 min), and then the gradient was maintained at 90% for 3 minutes (8-11min). The conditions were then returned to the initial conditions of 10% B from 11-12 min. Thereafter, the column was re-equilibrated for 1 min. A qTOF high-definition mass spectrometer was used to conduct chromatographic analysis. This spectrometer was set to negative electrospray ionisation for data acquisition. The parameters were set following the procedure by Makhumbila et al. (2023). They were set as interface voltage (-3 kV), interface temperature (300°C), nebulisation and dry gas flow (3 L/min), detector voltage (1.8 kV), heat block (400°C), desolvation line (280°C), and flight tube (42°C) temperatures. Ion fragmentation was performed using argon gas for collision with a collision energy of 30 eV and a spread of 5 eV. Data pre-processing and analysisThe data was extracted as mzML files from the LCMS-9030 qTOF and pre-processed using XCMS online. The data pre-processing was performed with UPLC/UHD-qTOF parameters using the CentWave feature detection method, a maximum tolerated m/z was set at 15 ppm, a signal-to-noise ratio set to 6, and prefilters for noise, peaks and intensity at 0, 3, and 100, respectively. The retention time correction was carried out using the obiwarp method with a profStep of 1. The alignment of the minimum fraction of samples was 0.5, whilst the width of overlapping m/z was 0.015 m/z. The Welch t-test was applied to the data, resulting in a feature table with 3434 features. The data matrix was exported to SIMCA version 17.0 software (Sartorius, South Africa) and then normalised and Pareto scaled before model application. Principal Component Analysis (PCA) and Orthogonal Projection to Latent Structures- Discriminant Analysis (OPLS-DA) models were employed. Partial Least Squares Discriminant Analysis (PLS-DA) was used to generate the loading S-plot. Metabolite annotation and pathway analysisSignificant biomarkers from the generated S-plot and those from the Variable Importance in Projection (VIP) score plots were annotated using their respective spectral features and retention times. The databases used for annotation included PubChem, MassBank, COCONUT, ChemSpider, Human Metabolome Database (HMDB), KEGG compound, KNApSAcK, and Sirius version 5.8.5. To confirm the annotations, previous literature related to this study was used. Pathway analysis was done using MetaboAnalyst version 6.0 to encapsulate the map of the metabolism for the wheat cultivars on the annotated metabolites under investigation when infested with RWASA5. This toolkit employs already established KEGG metabolic pathways to accomplish pathway mapping.MetabolomicsLeavesMetabolomicsShimadzu LCMS-9030Triticum AestivumAgilent 6546 LC/Q-TOFuntargeted analysisDiuraphis noxiaPlant DefencemzML formatuntargeted metabolite profilingexperimental sampledata-independent acquisitionLeavesMetabolomicsShimadzu LCMS-9030Triticum AestivumAgilent 6546 LC/Q-TOFuntargeted analysisDiuraphis noxiaPlant DefencemzML formatexperimental sampleuntargeted metabolite profilingdata-independent acquisitionA qTOF high-definition mass spectrometer was used to conduct chromatographic analysis. This spectrometer was set to negative electrospray ionisation for data acquisition. The parameters were set following the procedure by Makhumbila et al. (2023). They were set as interface voltage (-3 kV), interface temperature (300°C), nebulisation and dry gas flow (3 L/min), detector voltage (1.8 kV), heat block (400°C), desolvation line (280°C), and flight tube (42°C) temperatures. Ion fragmentation was performed using argon gas for collision with a collision energy of 30 eV and a spread of 5 eV.Please update this protocol descriptionPlease update this protocol descriptionfalseMetabolic analysis of two wheat (Triticum aestivum) cultivars in response to Russian wheat aphid (Diuraphis noxia Kurdjumov) infestationWheat is one of the most widely consumed cereal grains globally and plays a critical role in food security. As a staple crop in many households, it provides essential nutrients and contributes significantly to daily caloric intake. Although wheat is attacked by multiple pests, the Russian wheat aphid (RWA), Diuraphis noxia, remains one of the most damaging, causing considerable yield losses. While resistance breeding has long been the preferred control strategy, newly emerging RWA biotypes have increasingly overcome previously effective resistance genes. This highlights the need for alternative approaches, with metabolomics emerging as a valuable tool for identifying metabolic biomarkers that can support faster and more effective resistance breeding. This study therefore aimed to investigate the effect of the South African Russian wheat aphid biotype 5 (RWASA5) on the metabolic profiles of two wheat cultivars, Tugela and Tugela DN. The cultivars were infested with RWASA5 and harvested at 0, 4, 8, 24, and 48 hours post-infestation, followed by untargeted liquid chromatography–mass spectrometry (LC-MS) profiling. A total of 19 metabolites were identified, of which 12 were upregulated and 7 were downregulated. Phenolic compounds were the dominant class, with secoisolariciresinol diglucoside showing a significant increase in Tugela DN following RWASA5 infestation. Flavonoids formed the most abundant phenolic subgroup. Enriched pathways included glycerophospholipid, linolenic acid, and arachidonic acid metabolism. These findings indicate that RWA infestation triggers distinct metabolic shifts in wheat and provide insights into wheat–RWA interactions for crop improvement.2026-06-232026-06-22MTBLS14820