4.4. Metabolite Profiling Analysis

In the present study, the root tissues of S. alopecuroides treated with 1.2% NaCl for 0, 24, 48, and 72 h were used to study the metabolic level of S. alopecuroides under salt stress. Each group had six biological replicates for metabolite determination. Tissues (100 mg) were individually ground with liquid nitrogen, and the homogenate was re-suspended with pre-chilled 80% methanol, and 0.1% formic acid by vortexing. The supernatant was injected into an LC-MS/MS system [80]. UHPLC-MS/MS analyses were performed using a Vanquish UHPLC system (ThermoFisher, Dreieich, Germany) coupled with an Orbitrap Q ExactiveTM HF mass spectrometer (ThermoFisher) at Novogene Co., Ltd. (Beijing, China). The raw data files generated by UHPLC-MS/MS were processed using the Compound Discoverer 3.1 (CD3.1; ThermoFisher) to perform peak alignment, peak picking, and quantitation analyses for each metabolite. The normalized data were used to predict the molecular formula based on the additive ions, molecular ion peaks, and fragment ions. Then, the peaks were matched with the mzCloud (https://www.mzcloud.org/, accessed on 23 October 2020), mzVault, and MassList databases to obtain accurate qualitative and quantitative results. Statistical analyses were performed using the statistical software R (R version R-3.4.3), Python (Python 2.7.6 version), and CentOS (CentOS release 6.6); when data were not normally distributed, normal transformations were attempted using the area normalization method.

These metabolites were annotated using the KEGG (https://www.genome.jp/kegg/pathway.html, accessed on 23 October 2020), HMDB (https://hmdb.ca/metabolites, accessed on 23 October 2020), and LIPIDMaps databases (http://www.lipidmaps.org/, accessed on 23 October 2020). Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed using metaX (a flexible and comprehensive piece of software for processing metabolomic data). We employed univariate analysis (t-test) to calculate the statistical significance (p-value). Metabolites with VIP > 1, a p-value < 0.05, and FC ≥ 2 or ≤ 0.5, were considered differential metabolites. Volcano plots were used to filter the metabolites of interest based on the log2 (FC) and log10 (p-value) of metabolites. The functions of these metabolites and metabolic pathways were analyzed using the KEGG database. Next, we performed the metabolic pathway enrichment of differential metabolites. When the ratios were satisfied by x/n > y/N, the metabolic pathway was considered as being enriched, while when the p-value of the metabolic pathway was <0.05, the enrichment of the metabolic pathway was considered as statistically significant.