Table1 metabolites and metabolic pathways

Each run consisted of 5 μL of sample, and the column eluent was analysed directly on the MS [24].The cecal contents (0.2g) was mixed with 4 volumes of methanol/acetonitrile (1:1, v/v) for analysis. After shaking for 30 seconds, all samples were ultrasonically treated for 10 minutes in an ice-cold water bath. A 2-hour incubation at -20°C was followed by 15 minutes at 4°C centrifugation with 13,000 x g to facilitate protein precipitation.

A supernatant of cecal contents was collected, dried under vacuum, and centrifuged at 4°C. Following resuspension in 200 μL of methanol/acetonitrile (1:1), shaking for 30 s, and centrifugation at 13,000 x g for 15 minutes at 4°C, the aliquots were dried. The supernatant was analysed on a UHPLC system with a Q-TOF system (Agilent 6545, USA) using approximately 150 microliters. A quality control (QC) test was performed after each analysis of five samples.Data were obtained using the auto MS/MS mode from 50 to 1100 m/z.

Collision energies for collision-induced dissociation were 20 V and 40 V. MS parameters were set as follows: ion source dry gas temperature, 320°C; N2 gas flow, 8L/min; sheath gas temperature, 350°C; sheath gas flow, 12 L/min; ion spray voltage, 4000 V (positive ion) and 3500 V (negative ion).The Analysis Base File Converter was used to convert data files from the Q-TOF-MS system to .abf format. The following parameters were used for peak detection, chromatogram deconvolution, and other data analyses: alignment-MS1 tolerance, 0.01 Da; retention time tolerance, 0.2 min; identification accurate mass tolerance (MS1), 0.005 Da; MS2, 0.05Da; identification score cut off, 60%.

Metabolites and metabolic pathways were identified using HMDB (hmdb.ca/), METLIN (metlin.scripps.edu/), Massbank (massbank.jp) and KEGG (kegg.com/) databases. In order to enhance precision, the identified metabolites were cross-validated utilizing a laboratory standard library.SIMCA-P software (version 14.1, Umetrics, Umea, Sweden) was used for the multivariate statistical analysis.

A principal component analysis (PCA) was performed on the matrix imported into R to observe the overall distribution of samples and assess the stability of the whole process. Metabolites were distinguished between groups using partial least squares discriminant analysis (PLS-DA). Seven-fold cross-validation and 200 permutation tests (response permutation testing [RPT]) were performed to evaluate the model's quality.

A permutation test was used to assess the validity of the model and its degree of overfitting. A VIP >1.0 and a p-value of 0.05 indicate that there is a significant difference. A p-value of .05 indicates that there is a significant difference between two groups compared using the Mann–Whitney U test.

For multiple groups, the false discovery rate (FDR) was applied (p < 0.10). 24.        Zhao, X., et al., Serum metabolomics study of polycystic ovary syndrome based on liquid chromatography-mass spectrometry. J Proteome Res, 2014. 13(2): p. 1101-11.