UPLC-ESI-MS/MS-based widely targeted metabolomics reveals differences in metabolite composition among four Ganoderma species

Front Nutr. 2024 Mar 18:11:1335538. doi: 10.3389/fnut.2024.1335538. eCollection 2024.

Abstract

The Chinese name "Lingzhi" refers to Ganoderma genus, which are increasingly used in the food and medical industries. Ganoderma species are often used interchangeably since the differences in their composition are not known. To find compositional metabolite differences among Ganoderma species, we conducted a widely targeted metabolomics analysis of four commonly used edible and medicinal Ganoderma species based on ultra performance liquid chromatography-electrospray ionization-tandem mass spectrometry. Through pairwise comparisons, we identified 575-764 significant differential metabolites among the species, most of which exhibited large fold differences. We screened and analyzed the composition and functionality of the advantageous metabolites in each species. Ganoderma lingzhi advantageous metabolites were mostly related to amino acids and derivatives, as well as terpenes, G. sinense to terpenes, and G. leucocontextum and G. tsugae to nucleotides and derivatives, alkaloids, and lipids. Network pharmacological analysis showed that SRC, GAPDH, TNF, and AKT1 were the key targets of high-degree advantage metabolites among the four Ganoderma species. Analysis of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes demonstrated that the advantage metabolites in the four Ganoderma species may regulate and participate in signaling pathways associated with diverse cancers, Alzheimer's disease, and diabetes. Our findings contribute to more targeted development of Ganoderma products in the food and medical industries.

Keywords: Ganoderma; Ganoderma leucocontextum; Ganoderma lingzhi; Ganoderma sinense; Ganoderma tsugae; widely targeted metabolomics.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was funded by the National Natural Science Foundation of China (No. 32370008), the Science and Technology Plan Project of Liaoning Province (2022-MS-418), Key Research and Development Plan of Tibet (XZ202201ZY0010N).