## 4 Materials and Methods
### Widely Targeted Metabolic Profiling and Statistical Analysis  
Metabolite profiling was carried out using a widely targeted metabolome method by Wuhan Metware Biotechnology Co., Ltd. (Wuhan, China) ([[http://www.metware.cn/](http://www.metware.cn/)]([http://www.metware.cn/](http://www.metware.cn/)), accessed on 1 July 2020) [43]. Freeze-dried roots were crushed at 30 Hz for 1.5 min using a mixed mill with zirconia beads (MM 400, Retsch, Haan, Germany). A 100 mg portion of the obtained powder was extracted with 1.0 mL aqueous methanol (70:30 methanol/water) at 4 \({}^{\circ}\)C overnight. After centrifugation at 10,000 g for 10 min, the extract was passed through a CNWBOND Carbon-GCB SPE column (250 mg, 3 mL; ANPEL, Shanghai, China) and filtered before HPLC-MS analysis (SCAA-104, 0.22 pore size (\(\mu\)m); ANPEL, Shanghai, China). The sample extracts were analyzed using an LC-electrospray ionization-MS/MS system. Details of the analytical conditions for HPLC and ESI-Q TRAP-MS/MS have been provided elsewhere [44].  
Software Analyst 1.6.3 was used to process mass spectrum data. Based on MWDB (Metware Database) and public metabolite information databases, the primary and secondary mass spectra data were analyzed qualitatively. In qualitative analyses of some substances, the isotope signals, some repeated signals (containing K\({}^{+}\), Na\({}^{+}\), NH4\({}^{+}\)), and fragments of other substances with larger molecular weight were removed. The structure analysis of metabolites was conducted by consulting public mass spectrum databases, such as MassBank ([[http://www.massbank.jp/](http://www.massbank.jp/)]([http://www.massbank.jp/](http://www.massbank.jp/)), accessed on 1 July 2020), KNAPSAcK ([[http://kanaya.naist.jp/KNApSAcK/](http://kanaya.naist.jp/KNApSAcK/)]([http://kanaya.naist.jp/KNApSAcK/](http://kanaya.naist.jp/KNApSAcK/)), accessed on 1 July 2020), HMDB ([[http://www.hmdb.ca/](http://www.hmdb.ca/)]([http://www.hmdb.ca/](http://www.hmdb.ca/)), accessed on 1 July 2020), MoTo DB ([[http://www.ab.wur.nl/moto/](http://www.ab.wur.nl/moto/)]([http://www.ab.wur.nl/moto/](http://www.ab.wur.nl/moto/))), and METLIN ([[http://metlin.scripps.edu/index.php](http://metlin.scripps.edu/index.php)]([http://metlin.scripps.edu/index.php](http://metlin.scripps.edu/index.php)), accessed on 1 July 2020). The metabolites were quantified by multiple reaction monitoring (MRM) using triple quadrupole mass spectrometry [45]. The characteristic ion of each substance was screened by triple quadrupole LC-MS/MS, and the signal intensity (CPS) of the characteristic ion was measured by the detector. MultiQuant software was used to manage the mass spectrometry files and to integrate and correct chromatographic peaks. The peak area (Area) of each chromatographic peak represented the relative content of the corresponding substance (as a proportion of the total of all peak areas in each sample).  
Before analyses of significant differences in metabolite accumulation among samples, a PCA was performed for all samples (including quality control samples) to visualize the overall metabolic differences between the groups and within each group. Orthogonal partial least squares discriminant analysis (OPLS-DA) was used to decompose the X matrix information into two types of information related to Y and irrelevant information. The difference variables were screened by removing irrelevant differences. Differentially accumulated metabolites were screened using the following criteria: variable importance in project (VIP) \(\geq\) 1 and fold change \(\geq\)2 or \(\leq\)0.5.