Major bioactive chemical compounds in Astragali Radix samples from different vendors vary greatly

Identify production area, growth mode, species, and grade of Astragali Radix using metabolomics "big data" and machine learning

BACKGROUND

Astragali Radix (AR) is a widely used herbal medicine. The quality of AR is influenced by several key factors, including the production area, growth mode, species, and grade. However, the markers currently used to distinguish these factors primarily focus on secondary metabolites, and their validation on large-scale samples is lacking.

PURPOSE

This study aims to discover reliable markers and develop classification models for identifying the production area, growth mode, species, and grade of AR.

METHODS

A total of 366 batches of AR crude slices were collected from six provinces in China and divided into learning (n = 191) and validation (n = 175) sets. Three ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) methods were developed and validated for determining 22 primary and 10 secondary metabolites in AR methanol extract. Based on the quantification data, seven machine learning algorithms, such as Nearest Neighbors and Gradient Boosted Trees, were applied to screen the potential markers and build the classification models for identifying the four factors associated with AR quality.

RESULTS

Our analysis revealed that secondary metabolites (e.g., astragaloside IV, calycosin-7-O-β-D-glucoside, and ononin) played a crucial role in evaluating AR quality, particularly in identifying the production area and species. Additionally, fatty acids (e.g., behenic acid and lignoceric acid) were vital in determining the growth mode of AR, while amino acids (e.g., alanine and phenylalanine) were helpful in distinguishing different grades. With both primary and secondary metabolites, the Nearest Neighbors algorithm-based model was constructed for identifying each factor of AR, achieving good classification accuracy (>70%) on the validation set. Furthermore, a panel of four metabolites including ononin, astragaloside II, pentadecanoic acid, and alanine, allowed for simultaneous identification of all four factors of AR, offering an accuracy of 86.9%.

CONCLUSION

Our findings highlight the potential of integrating large-scale targeted metabolomics and machine learning approaches to accurately identify the quality-associated factors of AR. This study opens up possibilities for enhancing the evaluation of other herbal medicines through similar methodologies, and further exploration in this area is warranted.

Pseudotargeted metabolomics-based random forest model for tracking plant species from herbal products

BACKGROUND

The "one-to-multiple" phenomenon is prevalent in medicinal herbs. Accurate species identification is critical to ensure the safety and efficacy of herbal products but is extremely challenging due to their complex matrices and diverse compositions.

PURPOSE

This study aimed to identify the determinable chemicalome of herbs and develop a reasonable strategy to track their relevant species from herbal products.

METHODS

Take Astragali Radix-the typical "one to multiple" herb, as a case. An in-house database-driven identification of the potentially bioactive chemicalome (saponins and flavonoids) in AR was performed. Furthermore, a pseudotargeted metabolomics method was first developed and validated to obtain high-quality semi-quantitative data. Then based on the data matrix, the random forest algorithm was trained to predict Astragali Radix species from commercial products.

RESULTS

The pseudotargeted metabolomics method was first developed and validated to obtain high-quality semi-quantitative data (including 56 saponins and 49 flavonoids) from 26 batches of AR. Then the random forest algorithm was well-trained by importing the valid data matrix and showed high performance in predicting Astragalus species from ten commercial products.

CONCLUSION

This strategy could learn species-special combination features for accurate herbal species tracing and could be expected to promote the traceability of herbal materials in herbal products, contributing to manufacturing standardization.

Molecular Mechanisms Underlying Qi-Invigorating Effects in Traditional Medicine: Network Pharmacology-Based Study on the Unique Functions of Qi-Invigorating Herb Group

Qi-invigorating herbs (QIHs) are a group of herbs that invigorate Qi, the most vital force for maintaining the physiological functions of the human body in traditional medicine. However, the mechanism underlying the Qi-invigorating effects remains unclear. This study aimed to elucidate the unique mechanisms of QIHs based on unique compounds, using a network pharmacology approach. QIHs and their compounds were identified using existing literature and the TCMSP database, respectively. Subsequently, a method was proposed to screen for unique compounds that are common in QIHs but rare in other traditional herbs. Unique compounds’ targets were predicted using the TCMSP, BATMAN-TCM, and SwissTargetPrediction databases. Finally, enriched GO and KEGG pathways were obtained using DAVID to uncover the biomolecular functions and mechanisms. Thirteen unique compounds, mainly including amino acids and vitamins that participate in energy metabolism and improve Qi deficiency syndrome, were identified among the eight QIHs. GO and KEGG pathway analyses revealed that these compounds commonly participate in neuroactive ligand–receptor interaction and the metabolism of amino acids, and are related to the components of mitochondria and neuronal cells. Our results appropriately reflect the characteristics of traditional Qi-invigorating effects; therefore, this study facilitates the scientific interpretation of Qi functions and provides evidence regarding the treatment effectiveness of QIHs.

Elicitation of (E)-2-Hexenal and 2,3-Butanediol on the Bioactive Compounds in Adventitious Roots of Astragalus membranaceus var. mongholicus

This study aimed to investigate the elicitation of volatile organic compounds (E)-2-hexenal and 2,3-butanediol on bioactive metabolites in Astragalus membranaceus var. mongholicus adventitious root cultures by adding them into the medium. The experiment was performed for 72 h and the roots were dynamically sampled for quantification of representative astragaloside IV, calycosin-7-O-β-d-glucoside (CG), ononin, and the gene expression. Compared with the controls, the combination of 2,3-butanediol and (E)-2-hexenal advanced the peak accumulation of astragaloside IV and was the most effective, but their individual application delayed it. Meanwhile, 2,3-butanediol and (E)-2-hexenal had no obviously promoting effect on the production of CG and ononin but chronologically changed their accumulation patterns. The underlying mechanism was uncovered by the correlation analysis between the metabolites and the gene expression, as did the identification of the target genes. Collectively, 2,3-butanediol and (E)-2-hexenal were important cues shaping the production of bioactive products in the herbal plant.

Effects of Physical Properties and Processing Methods on Astragaloside IV and Flavonoids Content in Astragali radix

The aim of this study was to investigate the effects of the physical properties (diameter size, powder particle size, composition of bark- and wood-tissue, and turnover rate) and processing methods on the content of active ingredients in Astragali radix (AR), a popular Chinese herbal medicine. The astragaloside IV and flavonoid contents increased with decreasing diameter size. Bark-tissue had significantly higher astragaloside IV and formononetin content than that in the wood-tissue. As a higher proportion of bark-tissue is associated with decreasing diameter, a strong correlation was also shown between bark- to wood-tissue ratio and active ingredients' content. Furthermore, an increase in astragaloside IV content was observed in thin powder as compared to coarse powder ground from the whole root. However, this association between active ingredients' content and powder particle size was abolished when isolating bark- and wood-tissue individually. Moreover, AR stir-frying with refined honey, a typical processing method of AR, increased formononetin content. The turnover rate of active constituents upon decoction ranged from 61.9-81.4%. Assessing the active constituent contents using its physical properties and processing methods allows for a more comprehensive understanding of optimizing and strengthening the therapeutic potentials of AR used in food and herbal supplements.