Dataset Information

Identification of Chemical Markers for the Discrimination of Radix Angelica sinensis Grown in Geoherb and Non-Geoherb Regions Using UHPLC-QTOF-MS/MS Based Metabolomics.

ABSTRACT: This research aimed to discover chemical markers for discriminating radix Angelica sinensis (RAS) from different regions and to explore the differences of RAS in the content of four active compounds and anti-inflammatory activities on lipopolysacchride (LPS)-induced RAW264.7 cells and calcium antagonists on the HEK 293T cells of RAS. Nine compounds were selected as characteristic chemical markers by ultra-high-performance liquid chromatography-quadrupole/time-of-flight mass spectrometry (UHPLC-QTOF-MS/MS), based on metabolomics, in order to rapidly discriminate RAS from geoherb and non-geoherb regions. The contents of senkyunolide I and butylidenephthalide in geoherb samples were higher than those in non-geoherb samples, but the contents of ferulic acid and levistolide A were lower in the geoherb samples. Furthermore, the geoherbs showed better nitric oxide (NO) inhibitory and calcium antagonistic activities than the non-geoherbs. These results demonstrate the diversity in quality of RAS between geoherbs and non-geoherbs.

SUBMITTER: Zhang K

PROVIDER: S-EPMC6804141 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Identification of Chemical Markers for the Discrimination of Radix <i>Angelica sinensis</i> Grown in Geoherb and Non-Geoherb Regions Using UHPLC-QTOF-MS/MS Based Metabolomics.

Zhang Kaixue K Yan Menglin M Han Shu S Cong Longfei L Wang Liyao L Zhang Liu L Sun Lili L Bai Haiying H Wei Guanhua G Du Hong H Jiang Min M Bai Gang G Yang Zhigang Z

Molecules (Basel, Switzerland) 20190930 19

This research aimed to discover chemical markers for discriminating radix <i>Angelica sinensis</i> (RAS) from different regions and to explore the differences of RAS in the content of four active compounds and anti-inflammatory activities on lipopolysacchride (LPS)-induced RAW264.7 cells and calcium antagonists on the HEK 293T cells of RAS. Nine compounds were selected as characteristic chemical markers by ultra-high-performance liquid chromatography-quadrupole/time-of-flight mass spectrometry ( ...[more]

PMID: 31574916

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Project description:High-grain diets are strongly associated with metabolic disorders in beef steers. Metabolomics can be used to explore the relationship between diet and metabolic changes, but no study has reported rumen and plasma metabolomics profiling associated with increasing dietary corn proportions in the diet of beef steers. Therefore, 12 steers paired according to similar body weights and body condition scores were randomly allocated to one of two diets: a low-corn (28.76%) diet (LCD) with a 40:60 ratio of concentrate to roughage and a high-corn (48.76%) diet (HCD) with a 60:40 ratio of concentrate to roughage. Metabolomics profiling by ultra-high-performance liquid tandem chromatography quadrupole time of flight mass spectrometry (UHPLC-QTOF/MS) showed that steers fed the HCD had increased rumen and plasma carbohydrate metabolites and amino acids, which contributed to the growth of the beef steers. However, the rumen acidity and ruminal and plasma lipopolysaccharide (LPS) concentrations significantly increased with the increase amounts of corn in the diet. In total, 717 rumen metabolites and 386 plasma metabolites were identified. By multivariate analysis, 144 rumen and 56 plasma metabolites were further identified that were significantly different between the two groups (P < 0.05 and variable influence on projection > 1). The differential metabolites in the rumen and plasma were associated with different metabolic pathways, and phenylalanine, tyrosine and tryptophan biosynthesis and phenylalanine metabolism were common key metabolic pathways for the two biofluids. In conclusion, the high-corn diet improved the growth performance of beef steers but decreased the ruminal pH and increased the LPS and harmful metabolites in the rumen and blood, which has implications for the incidence of metabolic diseases. The identified differential metabolites in both the rumen and plasma and the related metabolic pathways may contribute to the exploration of potential biomarkers for high-corn diet-based metabolic diseases.

Project description:Traumatic brain injury (TBI) is a critical public health and socioeconomic problem worldwide. The herb pair Astragali Radix (AR)-Radix Angelica Sinensis (RAS) is a common prescribed herbal formula or is added to other Chinese medicine prescriptions for traumatic brain injury (TBI) treatment. However, the underlying mechanisms are unclear. In this study, we aimed to explore the active ingredients and action targets of AR-RAS based on the combined methods of network pharmacology prediction and experimental verification. Furthermore, the corresponding potential mechanisms of "multicomponents, multitargets, and multipathways" were disclosed. Methods. A network pharmacology approach including ADME (absorption, distribution, metabolism, and excretion) filter analysis, target prediction, known therapeutic targets collection, Gene Ontology (GO), pathway enrichment analysis, and network construction was used in this study. Further verification experiments were performed to reveal the therapeutic effects of AR-RAS in a rat model of TBI. Results. The comprehensive systematic approach was to successfully identify 14 bioactive ingredients in AR-RAS, while 33 potential targets hit by these ingredients related to TBI. Based on GO annotation analysis, multiple biological processes were significantly regulated by AR-RAS. In addition, 89 novel signaling pathways (P<0.05) underlying the effects of AR-RAS for TBI treatment were identified by DAVID. The neurotrophin signaling pathway was suggested as the major related pathway targeted by AR-RAS to improve axonal growth. The animal experiment confirmed that AR-RAS significantly induced tissue recovery and improved neurological deficits on the 14th day (P<0.01). Treatment with AR-RAS markedly reduced the protein and mRNA expression level of NogoA in the hippocampus of TBI rats. Conclusion. Our work illuminates the "multicompounds, multitargets, and multipathways" curative action of AR-RAS in the treatment of TBI by network pharmacology. The animal experiment verifies the effects of AR-RAS on neurological function improvement and axonal outgrowth via downregulation of NogoA expression, providing a theoretical basis for further research on treatment of TBI.

Project description:(1) Background: The ability to determine the age of ginseng is very important because the price of ginseng depends on the cultivation period. Since morphological observation is subjective, a new scientific and systematic method for determining the age of ginseng is required. (2) Methods: Three techniques were used for a metabolomics approach. High-resolution magic-angle-spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy was used to analyze powdered ginseng samples without extraction. Ultrahigh-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) and gas chromatography quadrupole time-of-fight mass spectrometry (GC-TOF/MS) were used to analyze the extracts of 4-, 5-, and 6-year-old ginseng. (3) Results: A metabolomics approach has the potential to discriminate the age of ginseng. Among the primary metabolites detected from NMR spectroscopy, the levels of fumarate and choline showed moderate prediction with an area under the curve (AUC) value of more than 0.7. As a result of UPLC-QTOF/MS-based profiling, 61 metabolites referring to the VIP (variable importance in the projection) score contributed to discriminating the age of ginseng. The results of GC×GC-TOF/MS showed clear discrimination of 4-, 5-, and 6-year-old ginseng using orthogonal partial least-squares discriminant analysis (OPLS-DA) to 100% of the discrimination rate. The results of receiver operating characteristic (ROC) analysis, 16 metabolites between 4- and 5-year-old ginseng, and 18 metabolites between 5- and 6-year-old ginseng contributed to age discrimination in all regions. (4) Conclusions: These results showed that metabolic profiling and multivariate statistical analyses can distinguish the age of ginseng. Especially, it is meaningful that ginseng samples from different areas had the same metabolites for age discrimination. In future studies, it will be necessary to identify the unknown variables and to collaboratively study with other fields the biochemistry of aging in ginseng.

Dataset Information

Identification of Chemical Markers for the Discrimination of Radix Angelica sinensis Grown in Geoherb and Non-Geoherb Regions Using UHPLC-QTOF-MS/MS Based Metabolomics.

Publications

Identification of Chemical Markers for the Discrimination of Radix <i>Angelica sinensis</i> Grown in Geoherb and Non-Geoherb Regions Using UHPLC-QTOF-MS/MS Based Metabolomics.

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