Analyst work station software

The SCIEX Analyst Work Station Software (Version 1.6.3) was used for data collection and processing. MSconverter was used to convert MS raw data (.wiff) to TXT format. Peak detection and annotation were performed using an in-house R program and database (Zhang et al., 2015 (link)). SIMCA (V16.0.2) was used to perform principal component analysis (PCA) and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) (Wiklund et al., 2008 (link)).
The value of variable importance in the projection (VIP) of the first principal component in OPLS-DA analysis was obtained to visualize group separation and identify significantly changed metabolites. It summarizes the contribution of each variable to the model. Metabolites with VIP > 1 and fold change ≥ 2 or ≤ 0.5 and P-value < 0.05 (Student’s t-test) were considered DAMs. Commercial databases, including KEGG and MetaboAnalyst,¹ were used for KEGG enrichment analysis.

The samples were extracted by 500 μL extraction solution (methanol/acetonitrile/water, 2:2:1, precooling at −40°C). The upon mixture was vortexed for 30s and sonicated for 5 min at 0°C, followed by homogenization for 4 min at 40 Hz. The homogenate and sonicate circle was repeated three times. After the above procedure, the sample was incubated at −40°C for an hour and centrifuged at 12,000 rpm for 15 min at 4°C. The UHPLC separation was carried out using an ExionLC-Sciex (Sciex), equipped with a Waters ACQUITY UPLC BEH Amide (100 × 2.1 mm, 1.7 μm, Waters). The mobile phase A was 5 mM ammonium acetate& 0.1% acetic acid in water, and the mobile phase B was acetonitrile. A SCIEX 6500 QTRAP + triple quadrupole mass spectrometer (Sciex), equipped with an IonDrive Turbo V electrospray ionization (ESI) interface, was applied for assay development. Typical ion source parameters were: Curtain Gas = 40 psi, IonSpray Voltage = ±4500 V, temperature = 475°C, Ion Source Gas 1 = 30 psi, Ion Source Gas 2 = 30 psi. The MRM parameters for each of the targeted analytes were optimized using flow injection analysis and were performed by injecting the standard solutions of the individual analytes into the API source of the mass spectrometer. SCIEX Analyst Work Station Software (Version 1.6.3) and Sciex MultiQuant™ 3.0.3 were employed for MRM data acquisition and processing.

SCIEX Analyst Workstation Software (Version 1.6.3) was employed for MRM data acquisition and processing. MS raw data (.wiff) files were converted to the .txt format using msConvert. The R program and database were applied for peak detection and annotation (Smith et al., 2006 (link); Kuhl et al., 2012 (link); Zhang et al., 2015 (link)). In the ion chromatogram, the target compounds showed symmetrical chromatographic peaks, and the chromatographic separation of the target compounds was well achieved. After data processing, orthogonal partial least squares-discriminant analysis (OPLS-DA) and principal component analysis were performed on metabolite data before and after inoculation, and the results were visualized by scatter plots (Mi et al., 2019 (link); Yang et al., 2020 (link)). Differentially accumulated metabolites (DAMs) between the pre-E. cristatum and post-E. cristatum groups were determined according to t-test value of p <0.05 and VIP >1. Lastly, the KEGG pathway database was used to enrich the metabolic pathways of DAMs, and R was used to visualize the results.

SCIEX Analyst Work Station Software (Version 1.6.3, Framingham, MA, USA) was employed for MRM data acquisition and processing. MS (Mass spectrometry) raw data files (.wiff format) were converted to the TXT format using MSconventer. Then, in-house R program and self-compiled database (Shanghai Biotree Biotech Co., Ltd., Shanghai, China) were applied to peak detection and annotation [42 (link),43 (link)].

Data acquisition and processing for Multiple-reaction-monitoring (MRM) was carried out using SCIEX Analyst Work Station Software (Version 1.6.3). MS-converter was used to change MS raw data (.wiff) files to the TXT format. An in-house R program and database were applied for peak detection and annotation. The data were standardized using the internal standard normalization method.