| Description |
Samples stored at -80 were thawed at room temperature. Soil: Weigh 500 mg of sample and place it in a 2 mL centrifuge tube. Add two small steel beads followed by 1 mL of methanol-water (V:V=1:1, with L-2-chlorophenylalanine at 2 μg/mL). Pre-cool the sample at -40 for 2 min. Grind using a grinder at 60 Hz for 2 min. Transfer the homogenized sample to a 15 mL centrifuge tube and rinse the tube wall residue with 1 mL of methanol-water (V:V=1:1, containing L-2-chlorophenylalanine at 2 μg/mL). Repeat the procedure once. Centrifuge for 10 min at 7700 rpm and 4. Transfer 2.5 mL of the supernatant to a 5 mL centrifuge tube and freeze-dry. Re-dissolve the sample in 400 μL of methanol-water (V:V=1:4), vortex for 1 min, ultrasonic for 3 min, and transfer to a 1.5 mL centrifuge tube. Nutrient Solution: Pass 1 mL of the sample through an SPE solid-phase cartridge. Collect 3 mL of methanol eluate and dry using a nitrogen blower. After drying, add 300 μL of methanol-water (V:V=4:1, with L-2-chlorophenylalanine at 4 μg/mL) to re-dissolve. Vortex for 1 min and ultrasonic in an ice-water bath for 10 min. After completing the above procedures for both soil and nutrient solution samples, let them stand at -40 for 30 min. Centrifuge for 10 min at 12000 rpm and 4, draw 150 μL of the supernatant using a syringe, filter through a 0.22 μm organic phase needle filter, transfer to an LC sample vial, and store at -80 until LC-MS analysis. Quality control samples (QC) are prepared by combining equal volumes of extracts from all samples. The metabolomic data analysis was conducted by Shanghai Luming Biological Technology Co., Ltd., located in Shanghai, China. An ACQUITY UPLC I-Class Plus system (Waters Corporation, Milford, USA), equipped with a Q-Exactive mass spectrometer and a heated electrospray ionization (ESI) source (Thermo Fisher Scientific, Waltham, MA, USA), was employed for metabolic profiling in both positive and negative ESI ion modes. An ACQUITY UPLC HSS T3 column was used in both positive and negative ion modes. The binary gradient elution system, consisting of (A) water (with 0.1% formic acid, v/v) and (B) acetonitrile (with 0.1% formic acid, v/v), The mass range covered m/z 100 to 1,000. The resolution was set to 70,000 for full MS scans and 17,500 for HCD MS/MS scans. Collision energy settings were 10, 20, and 40 eV. The mass spectrometer settings were: spray voltage at 3800 V + and 3200 V ; sheath gas flow rate at 35 units; auxiliary gas flow rate at 8 units; capillary temperature at 320; auxiliary gas heater temperature at 350; and S-lens RF level set to 50. The original LC-MS data were processed using Progenesis QI V2.3 software (Nonlinear Dynamics, Newcastle, UK) for baseline filtering, peak identification, integration, retention time correction, peak alignment, and normalization. Parameters included a 5 ppm precursor tolerance, 10 ppm product tolerance, and a 5% product ion threshold. Compound identification was based on the precise mass-to-charge ratio (m/z), secondary fragments, and isotopic distribution using The Human Metabolome Database, Lipidmaps (V2.3), Metlin, and custom-built databases. The extracted data underwent further processing: peaks with more than 50% missing values (ion intensity = 0) were removed, zero values were replaced with half of the minimum value, and compounds were screened based on qualitative results. Compounds with scores below 36 out of 60 were considered inaccurate and excluded. A combined data matrix was generated from both positive and negative ion data.utilized the following gradient: 0.01 min, 5% B; 2 min, 5% B; 4 min, 30% B; 8 min, 50% B; 10 min, 80% B; 14 min, 100% B; 15 min, 100% B; 15.1 min, 5% B; 16 min, 5% B. The flow rate was set at 0.35 mL/min, and the column temperature was maintained at 45. During the analysis, all samples were maintained at 10. An injection volume of 2 μL was used. The data matrix was imported into R for Principle Component Analysis to visualize the overall sample distribution and assess the stability of the analysis process. Orthogonal Partial Least-Squares-Discriminant Analysis and Partial Least-Squares-Discriminant Analysis were employed to identify differing metabolites between groups. To prevent overfitting, the model's quality was assessed using 7-fold cross-validation and 200 Response Permutation Testing. Variable Importance of Projection values from the OPLS-DA model ranked each variable's contribution to group discrimination. |
