Chroma tof 4.3x software

GC-TOF-MS analysis was conducted [25 (link)]. Chroma TOF 4.3X Software (Leco), (Carl Schultz, America) equipped with the Leco-Fiehn Rtx5 database was used to identify metabolites. All data analysis was performed with SPSS software version 22.0 and the results were reported as the mean ± standard error (S.E.) of three replicates. One-way analysis of variance (ANOVA) was carried out to analyze the results, which included mycelial growth assay. Differences between different treatments were subjected to the LSD test by Duncan’s multiple comparison (p < 0.05). Commercial databases including KEGG http://www.genome.jp/kegg/ (accessed on 1 March 2019) and MetaboAnalyst http://www.metaboanalyst.ca/ (accessed on 1 March 2019) were utilized to search for the pathways of metabolites.

GC–TOFMS analysis was performed using an Agilent 7890 gas chromatograph system coupled with a Pegasus HT time-of-flight mass spectrometer. The system utilized a DB-5MS capillary column coated with 5% diphenyl cross-linked with 95% dimethylpolysiloxane (30 m × 250 μm inner diameter, 0.25 μm film thickness; J&W Scientific, Folsom, CA, USA). A 1 μL aliquot of the analyte was injected in splitless mode. Helium was used as the carrier gas, the front inlet purge flow was 3 mL/min, and the gas flow rate through the column was 1 mL/min. The initial temperature was kept at 50 °C for 1 min, then raised to 310 °C at a rate of 20 °C/min, then kept for 5 min at 310 °C. The injection, transfer line, and ion source temperatures were 280, 270, and 220 °C, respectively. The energy was − 70 eV in electron impact mode. The mass spectrometry data were acquired in full-scan mode with the m/z range of 50–500 at a rate of 20 spectra per second after a solvent delay of 455 s.
Chroma TOF 4.3X software of LECO Corporation and LECO-Fiehn Rtx5 database were used for raw peaks exacting, the data baselines filtering and calibration of the baseline, peak alignment, deconvolution analysis, peak identification and integration of the peak area. The RI (retention time index) method was used in the peak identification, and the RI tolerance was 5000. Remove metabolic features detected in < 50% of QC samples.

The detailed process of GC-TOF/MS analysis follows the method of Yang et al. (31 (link)). In short: (i) metabolite extraction was performed on six samples in each group, and l-2-chlorophenylalanine was added as an internal standard; (ii) metabolite derivatization uses the methoxyamine hydrochloride and the BSTFA reagent; (iii) the Agilent 7,890 gas chromatograph system coupled with a Pegasus HT time-of-flight mass spectrometer was used to detect metabolites. The mass spectrometry data were acquired with an m/z range of 50–500 at a rate of 20 spectra per second after a solvent delay of 6.04 min (−70 eV, full-scan mode).
Chroma TOF4.3X software of the LECO Corporation and the LECO-Fiehn Rtx5 database were used for data preprocessing. Then, the SIMCA14 software package (Umetrics, Umea, Sweden) was used to perform principal component analysis (PCA) and orthogonal projections to latent structures-discriminant analysis (OPLS-DA).

Fecal samples were centrifuged, and supernatant (0.28 ml) was obtained and (28 (link)) analyzed by gas chromatography/time-of-flight mass spectrometry (GC-TOF-MS) using a 7890 Gas Chromatograph System (Agilent Technologies, Santa Clara, CA, USA) coupled with a Pegasus™ HT TOF Mass Spectrometer (LECO, Saint Joseph, MI, USA). Chroma TOF 4.3x software (LECO) and LECO-Fiehn Rtx5 database were used for extracting raw peaks, filtering, calibrating baselines, aligning peaks, performing deconvolution analysis, identifying peaks, and integrating peak area (29 (link)). Retention time index (RI) was used for peak identification, with an RI tolerance of 5,000. Metabolic features detected in <50% of quality control (QC) samples were removed (30 (link)). The identified differential metabolites were further validated by searching in the Kyoto Encyclopedia of Genes and Genomes (KEGG). Principal component analysis (PCA), enrichment analysis for the differential metabolites, and construction of random forest models were performed on the online platform MetaboAnalyst 4.0 (31 (link)).