MetaboLights

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metabolite identification was performed within Progenesis QI v3.0 by matching accurate mass and MS/MS spectra against the Human Metabolome Database (HMDB, http://www.hmdb.ca/), Metlin (https://metlin.scripps.edu/), and an in-house database. The mass tolerance for precursor ions was set to <10 ppm. Final assignments were based on combined MS and MS/MS matching scores. The final output included metabolite names, database identifiers (HMDB, KEGG, LipidMaps), m/z, retention time, and integrated peak areas for all samples.MetaboLightsPublicLiquid Chromatography MS - negative - reverse-phaseLiquid Chromatography MS - positive - reverse-phaseChromatographic separation was performed on a Vanquish Horizon UHPLC system (Thermo Scientific). The analytical column was an ACQUITY UPLC HSS T3 column (100 mm x 2.1 mm i.d., 1.8 um particle size; Waters, Milford, USA). This is a reverse-phase column (C18 bonded). The column temperature was maintained at 40 degC. Mobile phase A consisted of 95% water + 5% acetonitrile with 0.1% formic acid, and mobile phase B consisted of 47.5% acetonitrile + 47.5% isopropanol + 5% water with 0.1% formic acid. The injection volume was 3 uL.Microbiome network remodeling drives soil lipid metabolism under safflower cropping systems at different sites: An omics-based dissection.Department of Art, Tangshan UniversityJia WeijiaSoilmass spectrometry assayEach precisely weighed sample (1000 +/- 5 mg) was transferred to a 2 mL centrifuge tube containing one 6 mm grinding bead. A volume of 1000 uL of chilled extraction solvent (methanol:water = 4:1, v/v) spiked with four internal standards (including 0.02 mg/mL L-2-chlorophenylalanine) was added. The mixture was homogenized using a frozen tissue grinder (Wonbio-96c) at -10 degC and 50 Hz for 6 min, followed by ultrasonic extraction at 5 degC and 40 kHz for 30 min. The homogenate was then kept at 20 degC for 30 min to precipitate proteins. After centrifugation at 13,000 x g for 15 min at 4 degC, the supernatant was evaporated to dryness under a nitrogen stream. The residue was reconstituted in 120 uL of acetonitrile:water (1:1, v/v), vortexed for 30 s, and subjected to a second ultrasonic extraction (5 degC, 40 kHz, 5 min). After a final centrifugation (13,000 x g, 4 degC, 15 min), the clear supernatant was transferred to autosampler vials. A pooled quality control (QC) sample was prepared by mixing 20 uL of supernatant from each individual sample.soil erosionhttps://www.ebi.ac.uk/metabolights/MTBLS15129Jia Weijia. Department of Art, Tangshan University. jwj0618202106@163.com.Raw LC-MS data were processed using Progenesis QI v3.0 (Waters Corporation). The workflow included baseline filtering, peak detection, peak alignment, and retention time correction. The resulting data matrix contained retention time, m/z, and peak intensity for each feature. QC samples were injected every 5–15 analytical runs to monitor system stability, and internal standard z-scores were calculated to evaluate data reproducibility.Treatmentjwj0618202106@163.comA total of 18 biological samples were received for this study. Detailed sample information was recorded on a separate sample information sheet. Upon receipt, all samples were stored under appropriate conditions until processing. For the extraction process, each sample was accurately weighed to 1000±5 mg1000 mg.MetabolomicsThermo Scientific Vanquish UHPLC SystemMetabolomicsThermo Scientific softwareuntargeted analysisThermo Scientific Q Exactive HF-XOpenMSSoilsoil erosionWaters softwareexperimental sampleThermo Scientific Vanquish UHPLC SystemMetabolomicsThermo Scientific softwareuntargeted analysisThermo Scientific Q Exactive HF-XOpenMSSoilsoil erosionWaters softwareexperimental sampleMass detection was carried out on a Q-Exactive HF-X mass spectrometer (Thermo Scientific) equipped with an electrospray ionization (ESI) source. The instrument is a quadrupole-Orbitrap mass analyzer. Data were acquired in both positive and negative ion modes. The full scan m/z range was set from 70 to 1050. Key parameters were: sheath gas flow 50 arb, auxiliary gas 13 arb, heater temperature 425 degC, capillary temperature 325 degC, S-Lens RF level 50, and normalized collision energy 20/40/60 eV. Spray voltage was +3500 V (positive) and -3500 V (negative). Full MS resolution was 60,000 and MS² resolution was 7,500.falseMicrobiome network remodeling drives soil lipid metabolism under safflower cropping systems at different sites: An omics-based dissectionThis study investigated the effects of three planting pattern–location combinations (PLMEs) on safflower rhizosphere soil microbiota and metabolic functions: soybean-safflower rotation at Chenghai (CH), tobacco–safflower rotation at Longpan (ZYHY), and apple orchard intercropping at Lijiang(LJ). Each PLME is set up with 6 plots. Bacterial α-diversity was significantly higher in the CH group, whereas fungal α-diversity peaked under the ZYHY pattern. Correlation analysis showed that bacterial α-diversity was negatively correlated with available potassium, while fungal α-diversity was negatively correlated with catalase activity. Microbial community structures significantly varied under the different planting patterns, with redundancy analysis indicating that bacterial variation was mainly driven by electrical conductivity, while fungal variation was mainly driven by available phosphorus. Co occurrence network analysis showed that the bacterial network under CH exhibited greater topological complexity and stability, while the fungal network under ZYHY was more complex than the others, although no significant differences in fungal network stability were detected among treatments. Although dominant microbial taxa were unchanged, their relative abundances varied notably. Non-targeted metabolomics analysis identified significant shifts in glycerophospholipid metabolism, with five key metabolites, including L-serine, phosphatidylethanolamine, and lecithin, serving as biomarkers strongly correlated with genera such as Gaiella, Microlunatus, and Mortierella. Structural equation modeling suggested that PLME indirectly influenced glycerophospholipid metabolism by regulating rhizosphere microbes. Bacterial α-diversity and network complexity had significant positive effects, while fungal network complexity had a negative effect. Overall, the CH optimized the rhizosphere microenvironment by enhancing bacterial diversity, stabilizing microbial networks, and positively regulating key metabolic pathways, thereby providing more favorable soil conditions for safflower growth.2026-07-222026-07-22MTBLS15129