Data acquisition and pre-processing and metabolite identification were performed using ChromaTOF software (version 3.34, LECO) and the EI-MS and Fiehnlib databases. The missing data values were removed and replaced by half the smallest positive value of all raw data; area-based normalisation was performed. Principal component analysis (PCA), orthogonal partial least-squares discrimination analysis (OPLS-DA) and partial least-squares discriminant analysis were carried out using SIMCA-P14.1 software (Umetrica) and variable importance projection (VIP) values were calculated. SPSS software (Version18.0, IBM) was used to perform t-tests and calculate p-values. Differential metabolites were screened on the basis of VIP > 1, p < 0.05 along with similarity values > 500. Data values are expressed as mean ± standard deviation of four biological replicates. Data were analysed using SAS 9.4 version at p < 0.05 and p < 0.01.Based on Visio software (OfficeVisio2013, 15.0, MicrosoftUSA), KEGG (www.genome.jp/kegg) and Metaboanalyst 3.0 (www.metaboanalyst.ca) databases to perform pathway analysis and metabolic network mapping.
"],"repository":["MetaboLights"],"study_status":["Public"],"ptm_modification":[""],"instrument_platform":["Liquid Chromatography MS - negative - reverse-phase","Liquid Chromatography MS - positive - reverse-phase"],"chromatography_protocol":["Samples were analysed on an Agilent 7890 gas chromatography-mass spectrometry (GC-MS) system (Agilent Technologies). There were four biological replicates for each experimental material and each treatment.
"],"publication":["Ionic homeostasis, carbohydrate metabolism, and oxidative balance underlie wild soybean resistance to low potassium stress."],"submitter_name":["Sunchen Pan"],"submitter_affiliation":["ChangChun Normal University"],"organism_part":["leaf"],"technology_type":["mass spectrometry assay"],"disease":[""],"extraction_protocol":["The freeze-dried samples were crushed with beads for 30 s at 60 Hz. 25 mg aliquot of individual samples were accurately weighed and were transferred to an Eppendorf tube. 1000 μL of extract solution (methanol/water=4:1, containing internal standard) and beads were added. After 30 s vortex, the mixed samples were homogenized (45 Hz, 4 min) and sonicated for 5 min in 4 ℃ water bath, the step was repeated for three times. The samples were incubated for 1 h at -40 ℃ to precipitate proteins. Then samples were centrifuged at 12000 rpm for 15 min at 4℃. The supernatant was carefully filtered through a 0.22 μm microporous membrane and transferred to 2 mL glass vials for LCMS analysis. Take equal volumes of each sample and mix them to form QC samples.
"],"organism":["Glycine max"],"full_dataset_link":["https://www.ebi.ac.uk/metabolights/MTBLS14256"],"author":["Ming Li. changchun normal university. limingxia@ccsfu.edu.cn. 86-15526863715.","Sun Pan. changchun normal university. 2584048237@qq.com. 86-13357839146."],"data_transformation_protocol":["Data acquisition and pre-processing and metabolite identification were performed using ChromaTOF software (version 3.34, LECO) and the EI-MS and Fiehnlib databases. The missing data values were removed and replaced by half the smallest positive value of all raw data; area-based normalisation was performed. Principal component analysis (PCA), orthogonal partial least-squares discrimination analysis (OPLS-DA) and partial least-squares discriminant analysis were carried out using SIMCA-P14.1 software (Umetrica) and variable importance projection (VIP) values were calculated. SPSS software (Version18.0, IBM) was used to perform t-tests and calculate p-values. Differential metabolites were screened on the basis of VIP > 1, p < 0.05 along with similarity values > 500. Data values are expressed as mean ± standard deviation of four biological replicates. Data were analysed using SAS 9.4 version at p < 0.05 and p < 0.01.Based on Visio software (OfficeVisio2013, 15.0, MicrosoftUSA), KEGG (www.genome.jp/kegg) and Metaboanalyst 3.0 (www.metaboanalyst.ca) databases to perform pathway analysis and metabolic network mapping.
"],"study_factor":["Treatment"],"submitter_email":["2584048237@qq.com"],"sample_collection_protocol":["Two weeks after low potassium stress treatment, four pots were randomly selected for metabolomic testing.
"],"omics_type":["Metabolomics"],"study_design":["Thermo Scientific Vanquish UHPLC System","soybean","Metabolomics","LC-MS data","untargeted analysis","quercetin","untargeted metabolites","Glycine soja","Stellar","leaf","Salt Stress","Glycine max"],"curator_keywords":["Thermo Scientific Vanquish UHPLC System","soybean","Metabolomics","LC-MS data","untargeted analysis","quercetin","untargeted metabolites","Glycine soja","Stellar","leaf","Salt Stress","Glycine max"],"mass_spectrometry_protocol":["Samples were analysed on an Agilent 7890 gas chromatography-mass spectrometry (GC-MS) system (Agilent Technologies). There were four biological replicates for each experimental material and each treatment.
"],"additional_accession":[]},"is_claimable":false,"name":"Ionic homeostasis, carbohydrate metabolism, and oxidative balance underlie wild soybean resistance to low potassium stress","description":"The scarcity of potassium resources in farmland soils poses a major challenge to global food security. Wild soybean (Glycine soja), a valuable wild germplasm related to cultivated soybeans, is known for its high-stress resistance and adaptability. This study comprehensively compares two wild soybean ecotypes in terms of growth parameters, photosynthetic physiology, mineral ions and metabolite contents, and gene expression, aiming to clarify the regulatory mechanisms of low potassium stress tolerance in wild soybean seedlings' leaves. Results show that in barren-tolerant wild soybean (GS2), genes involved in potassium ion transport were significantly upregulated. This promotes potassium absorption and transport, maintaining a high K+ concentration and K+/Na+ ratio. Carbohydrate synthesis is enhanced in GS2, with increased sucrose and raffinose accumulation and a more active tricarboxylic acid (TCA) cycle. GS2 also strengthens the ascorbic acid-glutathione (ASA-GSH) cycle, along with promoting salicylic acid and 4-aminobutyric acid GABA synthesis, which boosts antioxidant capacity and reactive oxygen species (ROS) scavenging, maintaining oxidative balance. Under low potassium stress, GS2 accumulates unsaturated fatty acids, enhancing cell-membrane fluidity and providing an stress-resistant structural barrier. Overall, this study provides a basis for developing high-quality wild soybean resources and exploring genes for low potassium stress tolerance, which could contribute to improving cultivated soybeans' adaptability to potassium-deficient soils and ensuring global food production stability.
","dates":{"publication":"2026-04-12","submission":"2026-04-11"},"accession":"MTBLS14256","cross_references":{}}