MetaboLights

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ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS11550/m_MTBLS11550_LC-MS_negative_reverse-phase_metabolite_profiling_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS11550/m_MTBLS11550_GC-MS_positive__metabolite_profiling_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS11550/m_MTBLS11550_LC-MS_positive_reverse-phase_metabolite_profiling_v2_maf.tsvftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS11550/s_MTBLS11550.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS11550/i_Investigation.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS11550/a_MTBLS11550_LC-MS_negative_reverse-phase_metabolite_profiling.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS11550/a_MTBLS11550_GC-MS_positive__metabolite_profiling.txtftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS11550/a_MTBLS11550_LC-MS_positive_reverse-phase_metabolite_profiling.txtprimaryOK200ftp://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS11550KEGG annotation and enrichment analysisIdentified metabolites were annotated using KEGG Compound database (http://www.kegg.jp/kegg/compound), annotated metabolites were then mapped to KEGG Pathway database (http://www.kegg.jp/kegg/pathway.html). Pathways with significantly regulated metabolites mapped to were then fed into MSEA (metabolite sets enrichment analysis), their significance was determined by hypergeometric test’s p-values. The computational formula of metabolomicsIn all the analysis content of metabolomics, data processing in the process of analysis mainly adopts two methods, which are calculated as follows:(1) UV (unit variance scaling)UV (unit variance scaling) is also called Z-score standardization/auto scaling. This method will standardize the data according to the mean and standard deviation of the original data. The processed data conform to the standard normal distribution, that is, the mean is 0 and the standard deviation is 1.Calculation method: Dividing the original data centralization by standard deviationThe formula is as follows: x, = (x−μ)/σ, µ is the mean and σ is the standard deviation.(2) Centered/Zero-centered (Ctr)Calculation method: the original data minus the mean value of the variableThe formula is as follows: x, = x−μ, µ is the mean. MetaboLightsPublicLiquid Chromatography MS - negative - reverse phaseGas Chromatography MS - - - Liquid Chromatography MS - - - Gas Chromatography MS - positiveLiquid Chromatography MS - positive - reverse phaseFor LC-MS: The sample extracts were analyzed using an UPLC-ESI-MS/MS system (UPLC, SHIMADZU Nexera X2, https://www.shimadzu.com.cn; MS, Applied Biosystems 4500 Q TRAP, https://www.thermofisher.cn/cn/zh/home/brands/applied-biosystems.html). The analytical conditions were as follows, UPLC: column, Agilent SB-C18 (1.8 µm, 2.1 mm x 100 mm); The mobile phase was consisted of solvent A, pure water with 0.1% formic acid, and solvent B, acetonitrile with 0.1% formic acid. Sample measurements were performed with a gradient program that employed the starting conditions of 95% A, 5% B. Within 9.0 min, a linear gradient to 5% A, 95% B was programmed, and a composition of 5% A, 95% B was kept for 1.0 min. Subsequently, a composition of 95% A, 5.0% B was adjusted within 1.1 min and kept for 2.9 min. The flow velocity was set as 0.35 mL/min; The column oven was set to 40 °C; The injection volume was 4 μL. The effluent was alternatively connected to an ESI-triple quadrupole-linear ion trap (QTRAP)-MS. For GC-MS: After sampling, desorption of the VOCs from the fibre coating was carried out in the injection port of the GC apparatus (Model 8890; Agilent) at 250 °C for 5 min in the splitless mode. The identification and quantification of VOCs was carried out using an Agilent Model 8890 GC and a 7000D mass spectrometer (Agilent), equipped with a 30 m x 0.25 mm x 0.25 μm DB-5MS (5% phenyl-polymethylsiloxane) capillary column. Helium was used as the carrier gas at a linear velocity of 1.2 mL/min. The injector temperature was kept at 250 °C and the detector at 280 °C. The oven temperature was programmed from 40 °C (3.5 min), increasing at 10 °C/min to 100 °C, at 7 °C/min to 180 °C, at 25 °C/min to 280 °C, hold for 5 min. Mass spectra was recorded in electron impact (EI) ionisation mode at 70 eV. The quadrupole mass detector, ion source and transfer line temperatures were set, respectively, at 150, 230 and 280 °C. The MS was selected ion monitoring (SIM) mode was used for the identification and quantification of analytes.The metabolite changes of Wolfberry (Lycium.) tea in different processing stages.National Wolfberry Engineering Research Center, Ningxia Academy of Agriculture and Forestry SciencesBo Zhangbudmass spectrometry assayFor LC-MS: Biological samples are freeze-dried by vacuum freeze-dryer (Scientz-100F). The freeze-dried sample was crushed using a mixer mill (MM 400, Retsch) with a zirconia bead for 1.5 min at 30 Hz. Dissolve 50 mg of lyophilized powder with 1.2 mL 70% methanol solution, vortex 30 s every 30 min for 6 times in total. Following centrifugation at 12,000 rpm for 3 min, the extracts were filtrated (SCAA-104, 0.22 μm pore size; ANPEL, Shanghai, China, http://www.anpel.com.cn) before UPLC-MS/MS analysis. For GC-MS: Materials were harvested, weighted, immediately frozen in liquid nitrogen and stored at -80 °C until needed. Samples were ground to a powder in liquid nitrogen. In total 500 mg (1 mL) of the powder was transferred immediately to a 20 mL head-space vial (Agilent, Palo Alto, CA, USA), containing NaCl saturated solution, to inhibit any enzyme reaction. The vials were sealed using crimp-top caps with TFE-silicone headspace septa (Agilent). At the time of SPME analysis, each vial was placed in 60 °C for 5 min, then a 120 µm DVB/CWR/PDMS fibre (Agilent) was exposed to the headspace of the sample for 15 min at 60 °C.Lycium barbarumhttps://www.ebi.ac.uk/metabolights/MTBLS11550bo zhang. Wolfberry Science Research institute, Ningxia Academy of Agriculture and Forestry Sciences. No.590 Huanghe East Road,Jinfeng District,Yinchuan City,Ningxia Hui Autonomous Region,china. zhang_bo_0309@126.com.Linyuan Duan. Wolfberry Science Research institute, Ningxia Academy of Agriculture and Forestry Sciences. No.590 Huanghe East Road,Jinfeng District,Yinchuan City,Ningxia Hui Autonomous Region. dly698013@163.com.Youlong Cao. Wolfberry Science Research institute, Ningxia Academy of Agriculture and Forestry Sciences. No.590 Huanghe East Road,Jinfeng District,Yinchuan City,Ningxia Hui Autonomous Region. youlongchk@163.com.Guanjun Pan. College of Horticulture,Fujian Agriculture and Forestry University. No.15 Shangdian Road,Cangshan District,Fuzhou City,Fujian Province,China. 3381039983@qq.com.Guoli Dai. Wolfberry Science Research institute, Ningxia Academy of Agriculture and Forestry Sciences. No.590 Huanghe East Road,Jinfeng District,Yinchuan City,Ningxia Hui Autonomous Region. dgl2006swfc@163.com.Jianhua Zhao. Wolfberry Science Research institute, Ningxia Academy of Agriculture and Forestry Sciences. No.590 Huanghe East Road,Jinfeng District,Yinchuan City,Ningxia Hui Autonomous Region. zhaojianhua0943@163.com.Zhilong Hao. College of Horticulture,Fujian Agriculture and Forestry University. No.15 Shangdian Road,Cangshan District,Fuzhou City,Fujian Province,China. haozhilong@126.com.Jinhong zhang. Beryl wolfberry Limited by Share Ltd. No.1-1 Decheng East Road,Helan Industrial Park,Yinchuan,Ningxia, China. 531jhz@163.com.Xinru He. Wolfberry Science Research institute, Ningxia Academy of Agriculture and Forestry Sciences. No.590 Huanghe East Road,Jinfeng District,Yinchuan City,Ningxia Hui Autonomous Region. hhexinru@163.com.ken Qin. Wolfberry Science Research institute, Ningxia Academy of Agriculture and Forestry Sciences. No.590 Huanghe East Road,Jinfeng District,Yinchuan City,Ningxia Hui Autonomous Region. qinken7@163.com.PCA analysisUnsupervised PCA (principal component analysis) was performed by statistics function within R (www.r-project.org). The data was unit variance scaled before unsupervised PCA. Hierarchical Cluster Analysis and Pearson Correlation CoefficientsThe HCA (hierarchical cluster analysis) results of samples and metabolites were presented as heatmaps with dendrograms, while Pearson correlation coefficients (PCC) between samples were calculated by the cor function in R and presented as only heatmaps. Both HCA and PCC were carried out by R package Complex Heatmap. For HCA, normalized signal intensities of metabolites (unit variance scaling) are visualized as a color spectrum. Differential metabolites selectedFor two-group analysis, differential metabolites were determined by VIP (VIP≥1) and absolute Log2FC (|Log2FC|≥1.0). VIP values were extracted from OPLS-DA result, which also contain score plots and permutation plots, was generated using R package MetaboAnalystR. The data was log transform (log2) and mean centering before OPLS-DA. In order to avoid overfitting, a permutation test (200 permutations) was performed.Tea processing stagezhang_bo_0309@126.comIn this study, the studied Wolfberry tea sample was Ningqicai No.1, planting in Ningxia (N38°39'56.7', E106°5'39.8'), China. We selected the different processing stages of Wolfberry shoots as the research object. The buds selection was 4 cm from top to bottom in shoots. The cultivation mode of deep furrow and wide ridge was adopted, with row spacing of 0.3 m and plant spacing of 0.2 m. All samples were collected from the 3 parallel samples, restored in liquid nitrogen for 1 h and stored at -80 °C before analysis. According to different processing stages, the samples of Wolfberry buds were divided into 5 groups. The buds of Wolfberry were Fresh group, named FWB. We put the buds of Wolfberry at room temperature from 18 to 24 h was the withering group, named WWS. We used the boiling water from 50 to 60 s fixation of the samples of Wolfberry was the fixation group, named FWS. The samples of Wolfberry were stir-firing at 80-220 °C, which was divided into primary and secondary stir-firing, named SFWS. We sealed and stored the samples of Wolfberry for 1 day at 200-300 °C for extracting fragrance, named EFWS. The preparation procedure of Wolfberry tea was shown in Scheme 1.MetabolomicsApplied Biosystems 4500 Q TRAPbudMetabolomicsFlavonoidsuntargeted analysisAgilent 7000D Triple QuadrupoleUPLC, ExionLCþ ADWolfberry teaFlavouring substanceLycium barbarumAgilent 8890 GCexperimental sampleApplied Biosystems 4500 Q TRAPbudMetabolomicsFlavonoidsuntargeted analysisAgilent 7000D Triple QuadrupoleUPLC, ExionLCþ ADWolfberry teaFlavouring substanceLycium barbarumexperimental sampleAgilent 8890 GCFor LC-MS: The ESI source operation parameters were as follows: source temperature 550 °C; ion spray voltage (IS) 5500 V (positive ion mode) / -4500 V (negative ion mode); ion source gas I (GSI), gas II(GSII) and curtain gas (CUR) were set at 50, 60 and 25 psi, respectively; the collision-activated dissociation (CAD) was high. QQQ scans were acquired as MRM experiments with collision gas (nitrogen) set to medium. DP (declustering potential) and CE (collision energy) for individual MRM transitions was done with further DP and CE optimization. A specific set of MRM transitions were monitored for each period according to the metabolites eluted within this period. For GC-MS: Mass spectra was recorded in electron impact (EI) ionisation mode at 70 eV. The quadrupole mass detector, ion source and transfer line temperatures were set, respectively, at 150, 230 and 280 °C. The MS was selected ion monitoring (SIM) mode was used for the identification and quantification of analytes.falseThe metabolite changes of Wolfberry (Lycium.) tea in different processing stagesWolfberry (Lycium.) tea has a long history of consumption in China and is an important cash crop. Wolfberry tea can be made through five processes: picking, withering, fixation, stir-firing and extracting fragrance, which will correspond to the FWB, WWS, FWS, SFWS and EFWS groups, respectively. There remains a lack of in-depth research on the analysis of the overall metabolism and changes in metabolic pathways of Wolfberry (Lycium.) tea at different processing stages. Thus, in this study, the dynamic changes of metabolites in wolfberry (Lycium.) tea during processing were investigated through metabolomic. The analysis of variability showed that the high temperature treatment in the frying procedure was the main reason for the differences between the groups. Compared with FWB, the metabolites that were up-regulated at different processing stages of wolfberry buds were mainly flavonoids, while the down-regulated metabolites were mainly lipids and amino acids and their derivatives. The volcano plots showed that the groups of SFWS and EFWS had the most significant differences of metabolites. Compared with FWB, the down-regulated amino acids and the derivatives were gradually increased as the processing, and that in the EFWS stage was up to 16. The hydrolysis of glycosides can produce monosaccharides, and the up-regulation of glycosides contributes to the formation of flavor of Wolfberry tea. This study is the first to investigate the changes of metabolic components in the production process of wolfberry tea, which will provide valuable data support for the further development of wolfberry tea in the future. In conclusion, at the different stages of Wolfberry tea making process, the metabolites of wolfberry tea could change obviously. Our research on the metabolites of Wolfberry tea is conducive to optimizing the tea making process of Wolfberry tea, improving the quality and yield of wolfberry tea, in order to meet the requirements of more Chinese and foreign consumers.2026-04-082025-06-18MTBLS11550