Integrating targeted glycomics and untargeted metabolomics to investigate the processing chemistry of herbal medicines, a case study on Rehmanniae Radix

Zhou, Li; Xu, Jin–Di; Zhou, Shanshan; Mao, Qian; Kong, Ming; Shen, Hong; Li, Xiuyang; Duan, Su-Min; Xu, Jun; Li, Songlin

doi:10.1016/j.chroma.2016.10.043

Cited by 59 publications

(29 citation statements)

References 43 publications

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“…Well-developed analytical techniques such as the structural information provided by coupling mass spectrometry (MS) to LC is currently a more powerful analytical tool for saccharide analysis [ 20 ]. Zhou [ 21 ] et al also studied some saccharides of the raw RR and processed RR by ELSD and LC-MS, but only two types of RR—namely RRR and APRR—were studied. Meanwhile, the chemical conversion mechanism of saccharides in FRR and PRR were also not reported.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Analysis of Saccharides between Fresh and Processed Radix Rehmanniae by HPLC and UHPLC-LTQ-Orbitrap-MS with Multivariate Statistical Analysis

et al. 2018

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Radix Rehmanniae (RR) is a kind of herb which is widely used in the clinical and food processing industry. There are four forms of RR used in traditional Chinese medicine practice, which include fresh RR (FRR), raw RR (RRR), processed RR (PRR), and another processed RR (APRR), in which the APRR was processed by nine cycles of repeated steaming and drying. There are a large number of saccharides in RR. However, the differences in content were shown by different processing methods. In this study, an effective method using high-performance liquid chromatography (HPLC) and high-performance liquid chromatography-mass spectrometry (LC-MS) coupled with multivariate statistical analysis to rapidly distinguish different RR samples and validate the proposed chemical conversion mechanism. The datasets of the content of saccharides were subjected to principal component analysis (PCA) and one-way analysis of variance. The results showed that there different changes occurred in the contents of saccharides corresponding to the different processing methods, in which the contents of monosaccharides—namely arabinose, glucose, mannose, and galactose—had an increasing trend or remained relatively stable. However, the contents of fructose and oligosaccharides, including manninotriose, melibiose, sucrose, and raffinose, first increased and then reduced, or gradually decreased, yet the content of stachyose gradually decreased. The MSn data indicated that manninotriose, melibiose, and some monosaccharides were produced by the hydrolysis of oligosaccharides. In addition, the fragmentation pathways of 1-phenyl-3-methyl-5-pyrazolone (PMP) derivatization of monosaccharides were also found that its glycosidic bond was first broken and subsequently its inside ring broke, and the characteristic fragment ions were produced at m/z 511.22, 493.20, 373.16, and 175.08 in the PMP derivatization of monosaccharides. In conclusion, this study illustrates the change and chemical conversion mechanism of saccharides by processing in RR samples which might play a key role in further application of RR.

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Section: Introductionmentioning

confidence: 99%

Simultaneous Analysis of Saccharides between Fresh and Processed Radix Rehmanniae by HPLC and UHPLC-LTQ-Orbitrap-MS with Multivariate Statistical Analysis

et al. 2018

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“…MS-based plant metabolomics, by obtaining holistic information on the chemical composition of herbal medicines, have gained increasing interest [38]. The LC-MS-based metabolomics strategies have been applied in the investigation of the processing chemistry [39], the discrimination of plant species [22], and the discovery of bioactive components [40], etc. Untargeted metabolomics have the potential to provide a larger amount of chemical information.…”

Section: Discussionmentioning

confidence: 99%

Integrating Metabolomic Data With Machine Learning Approach for Discovery of Q-markers From Jinqi Jiangtang Preparation Against Type 2 Diabetes

Yang

Xue

Wei

et al. 2020

Preprint

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Background: Jinqi Jiangtang (JQJT) has been widely used in clinical practice to prevent and treat type 2 diabetes. However, little was known about its quality markers (Q-markers) associated with anti-diabetes bioactivity. In this study, a strategy combining mass spectrometry-based untargeted metabolomics with backpropagation artificial neural network (BP-ANN)-based machine learning approach was proposed to screen Q-markers from JQJT preparation. Methods: This strategy mainly involved chemical profiling of herbal medicines, statistic processing of metabolomic datasets, detection of different anti-diabetes activities and establishing of BP-ANN model. The chemical features of seventy-eight batches of JQJT extracts were first profiled using an untargeted UPLC-LTQ-Orbitrap metabolomic approach. The obtained chemical features associated with different anti-diabetes activities based on three modes of action were normalized, ranked, and then pre-selected by using ReliefF feature selection, respectively. BP-ANN model was then established and optimized to screen Q-markers based on mean impact value (MIV).Results: Optimized BP-ANN architecture was established with high accuracy of R > 0.9983 and relative low error of MSE < 0.0014, which showed better performance than that of PLSR calibration model (R2 < 0.5). Meanwhile, the BP-ANN model was subsequently applied to further screen potential bioactive components from the pre-selected chemical features by calculating their MIVs. Using this machine learning model, 15 potential Q-markers with bioactivity were discovered from JQJT. The tested anti-diabetes bioactivities of 78 batches of JQJT could be accurately predicted.Conclusions: The proposed artificial intelligence approach is suitable for quick and easy discovery of Q-markers with bioactivity from herbal medicines.

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“…Due to the changed chemical profiles, the finding of chemical markers that are pharmacologically relevant is essential for evaluating the processing practice. Several studies have demonstrated that “omics” studies are efficient and may at least partially represent holistic perspectives [ 116 – 119 ]. In a recent report, targeted glycomics and untargeted metabolomics were used to investigate the overall chemical characterization of Rehmanniae Radix [ 116 ].…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

“…Several studies have demonstrated that “omics” studies are efficient and may at least partially represent holistic perspectives [ 116 – 119 ]. In a recent report, targeted glycomics and untargeted metabolomics were used to investigate the overall chemical characterization of Rehmanniae Radix [ 116 ]. The obtained data were further processed by multivariate statistical analysis.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Seeing the unseen of Chinese herbal medicine processing (Paozhi): advances in new perspectives

Wang

et al. 2018

Chin Med

114

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Processing (Paozhi) represents a unique Chinese pharmaceutic technique to facilitate the use of Chinese herbal medicines (CHMs) for a specific clinical need in the guidance of Traditional Chinese Medicine (TCM) theory. Traditionally, most CHMs require a proper processing to meet the needs of specific clinical syndromes before being prescribed by TCM practitioners. During processing, significant changes in chemical profiles occur, which inevitably influence the associated pharmacological properties of a CHM. However, although processing is formed in a long-term practice, the underlying mechanisms remain unclear for most CHMs. The deepening understanding of the mechanism of processing would provide scientific basis for standardization of processing. This review introduced the role of processing in TCM and several typical methods of processing. We also summarized the up-to-date efforts on the mechanistic study of CHM processing. The processing mechanisms mainly include the following aspects: (i) directly reducing contents of toxic constituents; (ii) structural transformation of constituents; (iii) improving solubility of constituents; (iv) physically changing the existing form of constituents; (v) and influence by excipients. These progress may give new insights into future researches.

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Integrating targeted glycomics and untargeted metabolomics to investigate the processing chemistry of herbal medicines, a case study on Rehmanniae Radix

Cited by 59 publications

References 43 publications

Simultaneous Analysis of Saccharides between Fresh and Processed Radix Rehmanniae by HPLC and UHPLC-LTQ-Orbitrap-MS with Multivariate Statistical Analysis

Simultaneous Analysis of Saccharides between Fresh and Processed Radix Rehmanniae by HPLC and UHPLC-LTQ-Orbitrap-MS with Multivariate Statistical Analysis

Integrating Metabolomic Data With Machine Learning Approach for Discovery of Q-markers From Jinqi Jiangtang Preparation Against Type 2 Diabetes

Seeing the unseen of Chinese herbal medicine processing (Paozhi): advances in new perspectives

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