Integrated metabolomics and transcriptomics study of traditional herb Astragalus membranaceus Bge. var. mongolicus (Bge.) Hsiao reveals global metabolic profile and novel phytochemical ingredients

Abstract

Background Astragalus membranaceus Bge. var. mongolicus (Bge.) Hsiao is one of the most common herbs widely used in South and East Asia, to enhance people’s health and reinforce vital energy. Despite its prevalence, however, the knowledge about phytochemical compositions and metabolite biosynthesis in Astragalus membranaceus Bge. var. mongolicus (Bge.) Hsiao is very limited. Results An integrated metabolomics and transcriptomics analysis using state-of-the-art UPLC-Q-Orbitrap mass spectrometer and advanced bioinformatics pipeline were conducted to study global metabolic profiles and phytochemical ingredients/biosynthesis in Astragalus membranaceus Bge. var. mongolicus (Bge.) Hsiao. A total of 5435 metabolites were detected, from which 2190 were annotated, representing an order of magnitude increase over previously known. Metabolic profiling of Astragalus membranaceus Bge. var. mongolicus (Bge.) Hsiao tissues found contents and synthetic enzymes for phytochemicals were significantly higher in leaf and stem in general, whereas the contents of the main bioactive ingredients were significantly enriched in root, underlying the value of root in herbal remedies. Using integrated metabolomics and transcriptomics data, we illustrated the complete pathways of phenylpropanoid biosynthesis, flavonoid biosynthesis, and isoflavonoid biosynthesis, in which some were first reported in the herb. More importantly, we discovered novel flavonoid derivatives using informatics method for neutral loss scan, in addition to inferring their likely synthesis pathways in Astragalus membranaceus Bge. var. mongolicus (Bge.) Hsiao. Conclusions The current study represents the most comprehensive metabolomics and transcriptomics analysis on traditional herb Astragalus membranaceus Bge. var. mongolicus (Bge.) Hsiao. We demonstrated our integrated metabolomics and transcriptomics approach offers great potentials in discovering novel metabolite structure and associated synthesis pathways. This study provides novel insights into the phytochemical ingredients, metabolite biosynthesis, and complex metabolic network in herbs, highlighting the rich natural resource and nutritional value of traditional herbal plants.

Nutritional Characterization and Untargeted Metabolomics of Oyster Mushroom Produced Using Astragalus membranaceus var. mongolicus Stems and Leaves as Substrates

“…The metabolites with VIP > 1, P < 0.05 and fold change > 2 or < 0.5 were considered statistically significant. To explain the physical and chemical properties and biological functions of metabolites, the online METLIN, 1 Human Metabolome, 2 KEGG databases (using MetaboAnalyst) 3 and metabolomic data (Wu et al, 2020) of A. membranaceus var. mongolicus (published data annotated by MassBank and Bio-PubChem) were used for identification and annotation.…”

“…In metabolomic studies (Li et al, 2019;Wu et al, 2020), a large number of pythometabolites were identified in AMM herbs. In our study, we also found an abundance of phytometabolites from P. ostreatus var.…”

UHPLC–MS/MS Analysis on Flavonoids Composition in Astragalus membranaceus and Their Antioxidant Activity

“…Phenolics, including flavonoids, have been judged as the marker for quality evaluation and standardization of A. membranaceus and its processed products [ 7 ]. Methoxylated flavonoids and their glycosides, such as calycosin and calycosin-7- O - β -D-glucoside, were the major bioactive constituents of A. membranaceus due to their superior bioactivities [ 8 , 9 ]. After glycosylation and methylation, the reactivity and solubility of the flavonoids usually improved and thereby so did their absorption and bioactivity [ 10 ].…”

Mining plant metabolomes: Methods, applications, and perspectives

“…In the peakonly algorithm, the convolutional neural network-based deep learning method is used to detect true positive peaks in the raw data ( Melnikov et al., 2020 ). Except for these useful methods, a hierarchical five-step filtering approach ( Duan et al., 2016 ) is considered as one of the most efficient methods ( Ju et al., 2019 ), and has been used in some recently published works ( Wu et al., 2018 , 2020 ; Li et al., 2019 ).…”