Metabolic profiling of the traditional Chinese medicine formulation Yu Ping Feng San for the identification of constituents relevant for effects on expression of TNF-α, IFN-γ, IL-1β and IL-4 in U937 cells

Abstract

Yu Ping Feng San (YPFS) is a classical TCM formulation which has been traditionally used for treatment of immune system related diseases such as chronic bronchitis, allergic rhinitis and asthma. The formula is a mixture of Radix Saposhnikoviae (Fangfeng), Radix Astragali (Huangqi), and Rhizoma Atractylodis macrocephalae (Baizhu). TLC- and LC-DAD-ESI-MS/MS methods have been developed for the analysis of the metabolic profiles of the single herbs and of the formula. Decoctions and ASE extracts were analyzed in order to trace components of the individual herbs in YPFS. Nine constituents of Radix Saposhnikoviae, ten constituents of Radix Astragali and five constituents of Rhizoma Atractylodis macrocephalae have been assigned in the chemical profiles of the formula, which now allow the standardisation of YPFS. The pharmacological testing showed that all extracts significantly inhibited expression of TNF-α, IFN-γ, and IL-1β in U937 cells, while the inhibition of IL-4 was consistently low. Compared to conventional analyses which are focused on a limited set of compounds, metabolomics approaches, together with novel data processing tools, enable a more holistic comparison of the herbal extracts. In order to identify the constituents which are relevant for the immunomodulatory effects of the formula, metabolomics studies (PCA, OPLS-DA) have been performed using UPLC/QTOF MS data.

Establishment and application of a method for screening the therapeutic drugs of ethanol‐induced liver injury based on cellular metabonomics

“…OPLS-DA has an advantage over an unsupervised PCA method in that it separates the predictive variation from the orthogonal variation and can be studied and interpreted separately (Nikles et al, 2017). OPLS-DA has an advantage over an unsupervised PCA method in that it separates the predictive variation from the orthogonal variation and can be studied and interpreted separately (Nikles et al, 2017).…”

“…The differences in the metabolites between the VC group and the sample groups and the control group and the model group are shown as coefficient of variation plots. OPLS-DA has an advantage over an unsupervised PCA method in that it separates the predictive variation from the orthogonal variation and can be studied and interpreted separately (Nikles et al, 2017).…”

“…These metabolites are affected by external disturbances, and their changes can be used to screen for activity of the tested substance. Supervised models are subsequently generated using OPLS-DA to maximize the separation between classes (Nikles et al, 2017). Metabonomics is a comprehensive and fully quantitative analysis of low-molecular-weight molecules within a particular biological sample (Schicho et al, 2010).…”

“…Principal component analysis, which maps samples according to their spectral profile without using previous knowledge of class, is used to explore inherent groupings in the data and identify outliers. Supervised models are subsequently generated using OPLS-DA to maximize the separation between classes (Nikles et al, 2017).…”

Yupingfeng Granule Improves Th2‐Biased Immune State in Microenvironment of Hepatocellular Carcinoma through TSLP‐DC‐OX40L Pathway

“…ese results showed that the tumor microenvironment of HCC was 2-dominanted immunosuppression. YPF, a classical TCM formulation, has been traditionally used for treatment of immune systemrelated diseases such as chronic bronchitis, allergic rhinitis, and asthma [36]. A previous study has demonstrated that YPF efficiently inhibited 2 cytokines [15].…”