## 3 Results
### Metabolomic Test Results and Sample Analysis  
In the metabolomic analysis, we designated _T. ganbajun_'s mycelium as GM, its primordium as GPM, and its fruiting body as GSM. While differences within groups were not pronounced, they were evident. PCA scores (Figure 4C) were then determined for each group, revealing that samples in the GM group were more discrete. However, samples in the GPM and GSM groups shared similar principal components, with each group being consistently categorized at the same level and devoid of anomalies. The statistics of the number of differentially expressed metabolites (DEMs) in each group are presented in Table 2. Volcano plots were constructed to visually depict the differences in metabolite expression levels between two groups (Figure 4A), along with the statistical significance of these differences. Notably, significant differences in metabolite expression were observed in all three developmental stages. Comparative analyses, including mycelium vs. primordium and mycelium vs. fruiting body, revealed more DEMs, with the majority of metabolites being up-regulated and displaying a dispersed pattern, mirroring the findings in the proteomic enrichment. Conversely, the number of DEMs during the primordium and fruiting body stages was significantly lower, and they were more concentrated compared to the previous stage, suggesting a stabilization of metabolic levels. In the heatmap of all metabolites (Figure 4B), samples from the same group are clustered, and samples from different groups are significantly differentiated. The redder the color, the higher the metabolite expression, and the greener the color, the lower the metabolites expression. GM during the nutrient growth period and GPM and GSM during the reproductive growth stage were highly differentiated, with correlation coefficients for the biological replicates in the same stage of 0.9, meaning the different stages were clearly separated. According to the Venn diagram of the enrichment results (Figure 4D), 1695 DEMs were common to GM vs. GPM and GM vs. GSM, 1181 DEMs were common to GM vs. GPM and GPM vs. GSM, 991 DEMs were common to GM vs. GSM and GPM vs. GSM, and 805 DEMs were enriched in all three groups. Figure 4: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Groups** & **All Diff** & **Down-Regulated** & **Up-Regulated** & **Down-Regulated** & **Up-Regulated** \\  & & **Regulated** & **Proportion (\%)** & **Proportion (\%)** & **Proportion (\%)** \\ \hline GM\_vs\_GPM & 3410 & 1129 & 2281 & 33.11 & 66.89 \\ GM\_vs\_GSM & 2925 & 1180 & 1745 & 40.34 & 59.66 \\ GPM\_vs\_GSM & 2235 & 1569 & 666 & 70.20 & 29.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistics of DEMs in different growth stages. Multiplicative Analysis of Variance and Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA)  
Following qualitative and quantitative analyses of the detected metabolites, the quantitative information on metabolite changes within each subgroup could be effectively reflected through the log2-processed multiplicity of differences. This highlights the variability in metabolite quantities across sample stages (Figure 5A). Metabolomics data are characterized by high dimensionality (numerous types of metabolites) and small samples (limited scale). They encompass both differential variables related to categorical variables  
Figure 4: Results of metabolome examination: (**A**) volcano plots of three stages; (**B**) heatmap of all metabolites; (**C**) PCA of DEMs; (**D**) Venn diagram of DEMs in different developmental stages. and a multitude of undifferentiated variables that may have correlations. Analyzing these high-dimensional variables with the PCA model often poses challenges. Inter-sample variance can be reflected in the principal components, which can fail to accurately reflect the specific numerical differences between samples. To address this, we employed the OPLS-DA statistical method to further process the PCA results.