## 3 Results  
### Identification of Metabolites  
The effect of the fungal elicitor on metabolites in the ET and WET groups was investigated through metabolome analysis. The results showed a significant overlap in the total ion chromatogram for metabolite detection, with consistent signals of the mass spectrum. The sample quality was homogeneous and stable, meeting the criteria for follow-up experiments. Principal component analysis (PCA) was performed on _S. sandhuang_ samples from ET and WET groups, with PC1 and PC2 accounting for 71.46% and 5.73%, respectively, as shown in Figure 1A. An orthogonal partial least-squares discriminant analysis (OPLS-DA) of the metabolite profiles of _S. sandhuang_ was also performed, with R2X, R2Y, and Q2 values of 0.766, 0.999, and 0.995, respectively (Figure 1B), indicating a stable and credible OPLS-DA model with Q2 > 0.9. The PCA and OPLS-DA score plots showed a clear separation between ET and WET, suggesting that the polysaccharide elicitors had an effect on metabolism in _S. sandhuang_.  
A total of 128 metabolites were detected and identified in the ET and WET groups, mainly including flavonoids, phenolic acids, and alkaloids (Figure 1C). Based on the OPLS-DA results, multivariate analysis was initially performed on the metabolites of different samples using the variable importance in prediction (VIP) obtained from the OPLS-DA model. Univariate analysis was then combined with the _p_-value or fold change to further screen for differentially accumulated metabolites (DAMs). Significantly different metabolites were screened using a statistical difference multiplier value of fold change \(\geq\) 2 or fold change \(\leq\) 0.5, combined with a VIP value of \(\geq\)1 in the OPLS-DA model. Among these, 48 metabolites were identified as significantly differential secondary metabolites, with 25 upregulated and 23 downregulated (Table S2 and Figure 1D).  
The results showed that the significantly different metabolites mainly included flavonoids, phenolic acids, and terpenoids (Table S2). Among them, dihydroxyoleanolic acid, a terpenoid, was significantly upregulated with a difference fold of 114.672, while betulinic acid showed a difference fold of 2.625, significantly higher than the control group (Additional File S2, Table S2). Three biphenylquinone and other terpenoid-quinone bioactivity-related triterpenoids were detected, including betulinol, which was downregulated, and betulinic acid and 2-hydroxyoleanolic acid, which were upregulated (Figure 2). These metabolites showed obvious differences between the ET and WET groups.  
Figure 1: _S. singhuang_ samples from ET and WET groups were analyzed via UPLC\(-\)MS\(/\)MS to produce PCA and OPLS\(-\)DA score plots. (**A**) PCA score plots for ET (SH\(-\)CL) and WET (SH\(-\)CK) groups. The \(x\)-axis represents PC1, and the \(y-\)axis represents PC2. (**B**) {1}: the first predicted principal; to[1]: the first orthogonal principal component. Plot of the OPLS\(-\)DA scores of presumptive annotated metabolites of ET and WET. The \(x-\)axis represents the scores of the main components in the orthogonal signal correction, and the difference between the groups can be determined from the orientation of the \(x-\)axis. The \(y-\)axis represents the fraction of orthogonal components in the orthogonal signal correction, and the difference between the groups can be determined from the orientation of the \(y-\)axis. (**C**) Metabolites classification statistical graphs. (**D**) Differential metabolite volcano map. In the graph, the green dots represent the downregulated differentially expressed metabolites; red dots represent the upregulated differentially expressed metabolites; and gray points represent the inspected metabolites, which were not significantly different.