From Logs to Bags: A Metabolic Blueprint of Sanghuang Cultivation Revealed by UPLC-Q-TOF-MS/MS and Amino Acid Profiling

Abstract

Sanghuang (SH), a natural fungal resource used for food and medicinal purposes, has drawn considerable attention due to its pharmacological effects and efficacy. This study focused on Wild Sanghuang (WS) and Sanghuang cultivated using two different methods: Duanmu Sanghuang and Mycelium Sanghuang. Using UPLC-O-TOF-MS, we conducted an in-depth analysis of the secondary metabolites present in SH. The content of 18 amino acids was measured using an automated amino acid analyzer. The results demonstrated major differences in secondary metabolites, including flavonoids, organic acids, amino acids, and their derivatives, between WS and cultivated Sanghuang (CS). The total amino acid content in WS surpassed that of CS, with segmental trunk SH exhibiting a total amino acid content 1.3 times (p < 0.05) greater than that of bag material SH. This variation may be linked to the biosynthetic pathways of valine, leucine, isoleucine, and flavonoids. By comparing the metabolomic and amino acidomic differences between WS and artificially CS, this study aims to provide a scientific basis for understanding the nutritional and medicinal value of various cultivation methods for SH and offer theoretical support for the future development of SH-related products.

Keywords: Sanghuang; amino acid; antioxidant; bioactivity; metabolite.

Figure 1

Primary classification pie chart of secondary metabolites cultivated in three different ways.

Figure 2

Metabolomic analysis of wild and cultivated Sanghuang; (A) Principal component analysis of Sanghuang metabolite data: ★ indicates MS, ▲ indicates WS, and ■ indicates DS; (B) WS_vs_DS_vs_MS Hierarchical clustering heatmap; (C) OPLS-DA plot for WS, MS, and DS metabolite data: ★ indicates MS, ▲ indicates WS, and ■ indicates DS; (D) Venn diagram illustrating pairwise comparisons of “WS_vs_CS,” “WS_vs_DS,” and “WS_vs_MS”.

Figure 3

Pairwise comparative analysis of differential metabolites among WS, DS, and MS. (A) Volcano plot for “DS_vs_MS”; (B) Volcano plot for “WS_vs_MS”; (C) Volcano plot for “WS_vs_DS”; (D) PCA plot for “MS” (▲) and “DS” (■).

Figure 4

(A) Single-omic correlation network of differential amino acid derivatives in WS, DS, and MS; (B) VENN diagram of differential amino acid derivatives in WS, DS, and MS.