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Chen J, Li Y, Gu X, Wu T, Du H, Bai C, Yang J, Hu K. Identifying Anti-NSCLC Bioactive Compounds in Scutellaria via 2D NMR-Based Metabolomic Analysis of Pharmacologically Classified Crude Extracts. Chem Biodivers 2024; 21:e202400258. [PMID: 38581076 DOI: 10.1002/cbdv.202400258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 03/03/2024] [Accepted: 04/05/2024] [Indexed: 04/07/2024]
Abstract
We presented a strategy utilizing 2D NMR-based metabolomic analysis of crude extracts, categorized by different pharmacological activities, to rapidly identify the primary bioactive components of TCM. It was applied to identify the potential bioactive components from Scutellaria crude extracts that exhibit anti-non-small cell lung cancer (anti-NSCLC) activity. Four Scutellaria species were chosen as the study subjects because of their close phylogenetic relationship, but their crude extracts exhibit significantly different anti-NSCLC activity. Cell proliferation assay was used to assess the anti-NSCLC activity of four species of Scutellaria. 1H-13C HSQC spectra were acquired for the chemical profiling of these crude extracts. Based on the pharmacological classification (PCA, OPLS-DA and univariate hypothesis test) were performed to identify the bioactive constituents in Scutellaria associated with the anti-NSCLC activity. As a result, three compounds, baicalein, wogonin and scutellarin were identified as bioactive compounds. The anti-NSCLC activity of the three potential active compounds were further confirmed via cell proliferation assay. The mechanism of the anti-NSCLC activity by these active constituents was further explored via flow cytometry and western blot analyses. This study demonstrated 2D NMR-based metabolomic analysis of pharmacologically classified crude extracts to be an efficient approach to the identification of active components of herbal medicine.
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Affiliation(s)
- Jialuo Chen
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 611137, China
| | - Yanping Li
- School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, No.1166 Liutai Avenue, Chengdu, Sichuan, 611137, China Tel
| | - Xiu Gu
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, No.1166 Liutai Avenue, Chengdu, Sichuan, 611137, China Tel
| | - Tianren Wu
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 611137, China
| | - Huan Du
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
- School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, 611137, China
| | - Caihong Bai
- School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, No.1166 Liutai Avenue, Chengdu, Sichuan, 611137, China Tel
| | - Jiahui Yang
- School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, No.1166 Liutai Avenue, Chengdu, Sichuan, 611137, China Tel
| | - Kaifeng Hu
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, No.1166 Liutai Avenue, Chengdu, Sichuan, 611137, China Tel
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Du H, Gu X, Chen J, Bai C, Duan X, Hu K. GIPMA: Global Intensity-Guided Peak Matching and Alignment for 2D 1H- 13C HSQC-Based Metabolomics. Anal Chem 2023; 95:3195-3203. [PMID: 36728684 DOI: 10.1021/acs.analchem.2c03323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Two-dimensional (2D) 1H-13C heteronuclear single quantum coherence (HSQC) has been increasingly applied to metabolomics studies because it can greatly improve the resolving capability compared with one-dimensional (1D) 1H NMR. However, preprocessing methods such as peak matching and alignment tools for 2D NMR-based metabolomics have lagged behind similar methods for 1D 1H NMR-based metabolomics. Correct matching and alignment of 2D NMR spectral features across multiple samples are particularly important for subsequent multivariate data analysis. Considering different intensity dynamic ranges of a variety of metabolites and the chemical shift variation across the spectra of multiple samples, here, we developed an efficient peak matching and alignment algorithm for 2D 1H-13C HSQC-based metabolomics, called global intensity-guided peak matching and alignment (GIPMA). In GIPMA, peaks identified in all spectra are pooled together and sorted by intensity. Chemical shift of a stronger peak is regarded to be more accurate and reliable than that of a weaker peak. The strongest undesignated peak is chosen as the reference of a new cluster if it is not located within the chemical shift tolerance of any existing peak cluster (PC), or otherwise it is matched to an existing PC and the aligned chemical shift of the PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the cluster. Setting an optimum chemical shift tolerance (Δδo) is critical for the peak matching and alignment across multiple samples. GIPMA dynamically searches for and intelligently selects the Δδo for peak matching to maximize the number of valid peak clusters (vPC), that is, spectral features, among multiple samples. By GIPMA, fully automatic peakwise matching and alignment do not require any spectrum as initial reference, while the chemical shift of each PC is updated as the intensity-weighted average of the chemical shifts of all peaks in the same PC, which is warranted to be statistically more accurate. Accurate chemical shifts for each representative spectral feature will facilitate subsequent peak assignment and are essential for correct metabolite identification and result interpretation. The proposed method was demonstrated successfully on the spectra of six model mixtures consisting of seven typical metabolites, yielding correct matching of all known spectral features. The performance of GIPMA was also demonstrated on 2D 1H-13C HSQC spectra of 87 real extracts of 29 samples of five Dendrobium species. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) of the 87 matched and aligned spectra by GIPMA generates correct classification of the 29 samples into five groups. In summary, the proposed algorithm of GIPMA provided a practical peak matching and alignment method to facilitate 2D NMR-based metabolomics studies.
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Affiliation(s)
- Huan Du
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China
| | - Xiu Gu
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China
| | - Jialuo Chen
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China
| | - Caihong Bai
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China
| | - Xiaohui Duan
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China.,School of Pharmacy, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Kaifeng Hu
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.,Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, China
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Effect of Different Processing Methods on the Chemical Constituents of Scrophulariae Radix as Revealed by 2D NMR-Based Metabolomics. Molecules 2022; 27:molecules27154687. [PMID: 35897871 PMCID: PMC9331298 DOI: 10.3390/molecules27154687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 07/18/2022] [Accepted: 07/20/2022] [Indexed: 12/04/2022] Open
Abstract
Scrophulariae Radix (SR) is one of the oldest and most frequently used Chinese herbs for oriental medicine in China. Before clinical use, the SR should be processed using different methods after harvest, such as steaming, “sweating”, and traditional fire-drying. In order to investigate the difference in chemical constituents using different processing methods, the two-dimensional (2D) 1H-13C heteronuclear single quantum correlation (1H-13C HSQC)-based metabolomics approach was applied to extensively characterize the difference in the chemical components in the extracts of SR processed using different processing methods. In total, 20 compounds were identified as potential chemical markers that changed significantly with different steaming durations. Seven compounds can be used as potential chemical markers to differentiate processing by sweating, hot-air drying, and steaming for 4 h. These findings could elucidate the change of chemical constituents of the processed SR and provide a guide for the processing. In addition, our protocol may represent a general approach to characterizing chemical compounds of traditional Chinese medicine (TCM) and therefore might be considered as a promising approach to exploring the scientific basis of traditional processing of TCM.
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Gu X, Zhu S, Du H, Bai C, Duan X, Li Y, Hu K. Comprehensive multi-component analysis for authentication and differentiation of 6 Dendrobium species by 2D NMR-based metabolomic profiling. Microchem J 2022. [DOI: 10.1016/j.microc.2022.107225] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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The importance of method validation in herbal drug research. J Pharm Biomed Anal 2022; 214:114735. [PMID: 35344789 DOI: 10.1016/j.jpba.2022.114735] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 03/03/2022] [Accepted: 03/18/2022] [Indexed: 12/13/2022]
Abstract
There are countless scientific publications on herbal drugs, but unfortunately many of them do not correctly report their chemical, biological and pharmacological aspects, including the composition and stability of the herbal/extract preparations, therefore their safety, efficacy and consistency could not be proven. For developing a modern drug from herbal drug(s), complete chemical and pharmacological characterizations of their bioactive metabolites need to be well established. Reproducible results require the development, assessment, and standardization of the chemical, biological and pharmacological methods based on the current state of the art. Therefore, all methods used in research must be properly validated before its routine applications. This present review will describe and discuss the important aspects of method validation (chemical, biological and pharmacological) in herbal drug research according to the newest current Pharmacopeia, official Guidelines and related recent publications.
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Bertho G, Lordello L, Chen X, Lucas-Torres C, Oumezziane IE, Caradeuc C, Baudin M, Nuan-Aliman S, Thieblemont C, Baud V, Giraud N. Ultrahigh-Resolution NMR with Water Signal Suppression for a Deeper Understanding of the Action of Antimetabolic Drugs on Diffuse Large B-Cell Lymphoma. J Proteome Res 2022; 21:1041-1051. [PMID: 35119866 DOI: 10.1021/acs.jproteome.1c00914] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Ultrahigh-resolution NMR has recently attracted considerable attention in the field of complex samples analysis. Indeed, the implementation of broadband homonuclear decoupling techniques has allowed us to greatly simplify crowded 1H spectra, yielding singlets for almost every proton site from the analyzed molecules. Pure shift methods have notably shown to be particularly suitable for deciphering mixtures of metabolites in biological samples. Here, we have successfully implemented a new pure shift pulse sequence based on the PSYCHE method, which incorporates a block for solvent suppression that is suitable for metabolomics analysis. The resulting experiment allows us to record ultrahigh-resolution 1D NOESY 1H spectra of biofluids with suppression of the water signal, which is a crucial step for highlighting metabolite mixtures in an aqueous phase. We have successfully recorded pure shift spectra on extracellular media of diffuse large B-cell lymphoma (DLBCL) cells. Despite a lower sensitivity, the resolution of pure shift data was found to be better than that of the standard approach, which provides a more detailed vision of the exo-metabolome. The statistical analyses carried out on the resulting metabolic profiles allow us to successfully highlight several metabolic pathways affected by these drugs. Notably, we show that Kidrolase plays a major role in the metabolic pathways of this DLBCL cell line.
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Affiliation(s)
- Gildas Bertho
- Laboratoire de Chimie et de Biochimie Pharmacologiques et Toxicologiques, Université de Paris, CNRS, F-75006 Paris, France
| | - Leonardo Lordello
- NF-κB, Différenciation et Cancer, Université de Paris, F-75006 Paris, France
| | - Xi Chen
- Laboratoire de Chimie et de Biochimie Pharmacologiques et Toxicologiques, Université de Paris, CNRS, F-75006 Paris, France
| | - Covadonga Lucas-Torres
- Laboratoire de Chimie et de Biochimie Pharmacologiques et Toxicologiques, Université de Paris, CNRS, F-75006 Paris, France
| | - Imed Eddine Oumezziane
- Laboratoire de Chimie et de Biochimie Pharmacologiques et Toxicologiques, Université de Paris, CNRS, F-75006 Paris, France
| | - Cédric Caradeuc
- Laboratoire de Chimie et de Biochimie Pharmacologiques et Toxicologiques, Université de Paris, CNRS, F-75006 Paris, France
| | - Mathieu Baudin
- Laboratoire de Chimie et de Biochimie Pharmacologiques et Toxicologiques, Université de Paris, CNRS, F-75006 Paris, France.,Laboratoire des Biomolécules, LBM, Département de chimie, École normale supérieure, PSL University, Sorbonne Université, CNRS, 75005 Paris, France
| | | | - Catherine Thieblemont
- NF-κB, Différenciation et Cancer, Université de Paris, F-75006 Paris, France.,AP-HP, Hôpital Saint-Louis, Service Hémato-Oncologie, F-75010 Paris, France
| | - Véronique Baud
- NF-κB, Différenciation et Cancer, Université de Paris, F-75006 Paris, France
| | - Nicolas Giraud
- Laboratoire de Chimie et de Biochimie Pharmacologiques et Toxicologiques, Université de Paris, CNRS, F-75006 Paris, France
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Li G, Lin P, Wang K, Gu CC, Kusari S. Artificial intelligence-guided discovery of anticancer lead compounds from plants and associated microorganisms. Trends Cancer 2021; 8:65-80. [PMID: 34750090 DOI: 10.1016/j.trecan.2021.10.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 10/02/2021] [Accepted: 10/08/2021] [Indexed: 12/20/2022]
Abstract
Plants and associated microorganisms are essential sources of natural products against human cancer diseases, partly exemplified by plant-derived anticancer drugs such as Taxol (paclitaxel). Natural products provide diverse mechanisms of action and can be used directly or as prodrugs for further anticancer optimization. Despite the success, major bottlenecks can delay anticancer lead discovery and implementation. Recent advances in sequencing and omics-related technology have provided a mine of information for developing new therapeutics from natural products. Artificial intelligence (AI), including machine learning (ML), has offered powerful techniques for extensive data analysis and prediction-making in anticancer leads discovery. This review presents an overview of current AI-guided solutions to discover anticancer lead compounds, focusing on natural products from plants and associated microorganisms.
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Affiliation(s)
- Gang Li
- Department of Natural Medicinal Chemistry and Pharmacognosy, School of Pharmacy, Qingdao University, Qingdao 266071, People's Republic of China.
| | - Ping Lin
- Department of Natural Medicinal Chemistry and Pharmacognosy, School of Pharmacy, Qingdao University, Qingdao 266071, People's Republic of China
| | - Ke Wang
- Department of Natural Medicinal Chemistry and Pharmacognosy, School of Pharmacy, Qingdao University, Qingdao 266071, People's Republic of China
| | - Chen-Chen Gu
- Department of Natural Medicinal Chemistry and Pharmacognosy, School of Pharmacy, Qingdao University, Qingdao 266071, People's Republic of China
| | - Souvik Kusari
- Center for Mass Spectrometry, Faculty of Chemistry and Chemical Biology, Technische Universität Dortmund, Dortmund 44227, Germany.
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