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Zheng X, Zhan Y, Peng M, Xu W, Deng G. Metabolite Profiling Analysis of the Tongmai Sini Decoction in Rats after Oral Administration through UHPLC-Q-Exactive-MS/MS. Metabolites 2024; 14:333. [PMID: 38921468 PMCID: PMC11205536 DOI: 10.3390/metabo14060333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 05/28/2024] [Accepted: 06/03/2024] [Indexed: 06/27/2024] Open
Abstract
Tongmai Sini decoction (TSD), the classical prescriptions of traditional Chinese medicine, consisting of three commonly used herbal medicines, has been widely applied for the treatment of myocardial infarction and heart failure. However, the absorbed components and their metabolism in vivo of TSD still remain unknown. In this study, a reliable and effective method using ultra-performance liquid chromatography coupled with hybrid quadrupole-Orbitrap mass spectrometry (UHPLC-Q-Exactive-MS/MS) was employed to identify prototype components and metabolites in vivo (rat plasma and urine). Combined with mass defect filtering (MDF), dynamic background subtraction (DBS), and neutral loss filtering (NLF) data-mining tools, a total of thirty-two major compounds were selected and investigated for their metabolism in vivo. As a result, a total of 82 prototype compounds were identified or tentatively characterized in vivo, including 41 alkaloids, 35 phenolic compounds, 6 saponins. Meanwhile, A total of 65 metabolites (40 alkaloids and 25 phenolic compounds) were tentatively identified. The metabolic reactions were mainly hydrogenation, demethylation, hydroxylation, hydration, methylation, deoxylation, and sulfation. These findings will be beneficial for an in-depth understanding of the pharmacological mechanism and pharmacodynamic substance basis of TSD.
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Affiliation(s)
- Xianhui Zheng
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou 510006, China; (X.Z.); (Y.Z.); (M.P.); (W.X.)
- Guangdong Provincial Hospital of Chinese Medicine, Guangzhou 510120, China
| | - Yingying Zhan
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou 510006, China; (X.Z.); (Y.Z.); (M.P.); (W.X.)
| | - Mengling Peng
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou 510006, China; (X.Z.); (Y.Z.); (M.P.); (W.X.)
| | - Wen Xu
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou 510006, China; (X.Z.); (Y.Z.); (M.P.); (W.X.)
- Guangdong Provincial Hospital of Chinese Medicine, Guangzhou 510120, China
| | - Guanghai Deng
- The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou 510006, China; (X.Z.); (Y.Z.); (M.P.); (W.X.)
- Guangdong Provincial Hospital of Chinese Medicine, Guangzhou 510120, China
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2
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Cai TY, Ji JB, Wang X, Xing J. Targeted screening of the synergistic components in Artemisia annua L. leading to enhanced antiplasmodial potency of artemisinin based on a "top down" PD-PK approach. JOURNAL OF ETHNOPHARMACOLOGY 2024; 322:117612. [PMID: 38135228 DOI: 10.1016/j.jep.2023.117612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/09/2023] [Accepted: 12/15/2023] [Indexed: 12/24/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Artemisinin (ART) showed enhanced antimalarial potency in the herb Artemisia annua L. (A. annua), from which ART is isolated. Increased absorption of ART with inhibited metabolism in the plant matrix is an underlying mechanism. Several synergistic components have been reported based on a "bottom-up" approach, i.e., traditional isolation followed by pharmacokinetic and/or pharmacodynamic evaluation. AIM OF THE STUDY In this study, we employed a "top-down" approach based on in vivo antimalarial and pharmacokinetic studies to identify synergistic components in A. annua. MATERIALS AND METHODS Two A. annua extracts in different chemical composition were obtained by extraction using ethyl acetate (EA) and petroleum ether (PE). The synergistic antimalarial activity of ART in two extracts was compared both in vitro (Plasmodium falciparum) and in vivo (murine Plasmodium yoelii). For the PD-PK correlation analysis, the pharmacokinetic profiles of ART and its major metabolite (ART-M) were investigated in healthy rats after a single oral administration of pure ART (20 mg/kg) or equivalent ART in each A. annua extract. A liquid chromatography-tandem high-resolution mass spectrometry (LC-HRMS)-based analytical strategy was then applied for efficient component classification and structural characterization of the differential components in the targeted extract with a higher antimalarial potency. Major components isolated from the targeted extract were then evaluated for their synergistic effect in the same proportion. RESULTS Compared with pure ART (ED50, 5.6 mg/kg), ART showed enhanced antimalarial potency in two extracts in vivo (ED50 of EA, 2.9 mg/kg; ED50 of PE, 1.6 mg/kg), but not in vitro (IC50, 15.0-20.0 nM). A significant increase (1.7-fold) in ART absorption (AUC0-t) was found in rats after a single oral dose of equivalent ART in PE but not in EA; however, no significant change in the metabolic capability (AUCART-M/AUCART) was found for ART in either extract. The differential component analysis of the two extracts showed a higher composition of sesquiterpene compounds, especially component AB (3.0% in PE vs. 0.9% in EA) and component AA (14.1% in PE vs. 5.1% in EA). Two target sesquiterpenes were isolated and identified as arteannuin B (AB) and artemisinic acid (AA). The synergism between ART and AB/AA in the same proportion with PE extract (20:1.6:7.6, mg/kg) was verified by a pharmacokinetic study in rats. CONCLUSIONS A "top-down" strategy based on PD-PK studies was successfully employed to identify synergistic components for ART in A. annua. Two sesquiterpene compounds (arteannuin B and artemisinic acid) could enhance the antimalarial potency of ART by increasing its absorption.
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Affiliation(s)
- Tian-Yu Cai
- School of Pharmaceutical Sciences, Shandong University, Jinan, China
| | - Jian-Bo Ji
- School of Pharmaceutical Sciences, Shandong University, Jinan, China
| | - Xin Wang
- School of Pharmaceutical Sciences, Shandong University, Jinan, China
| | - Jie Xing
- School of Pharmaceutical Sciences, Shandong University, Jinan, China.
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Zeng J, Li Y, Wang C, Fu S, He M. Combination of in silico prediction and convolutional neural network framework for targeted screening of metabolites from LC-HRMS fingerprints: A case study of "Pericarpium Citri Reticulatae - FructusAurantii". Talanta 2024; 269:125514. [PMID: 38071769 DOI: 10.1016/j.talanta.2023.125514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 11/26/2023] [Accepted: 12/01/2023] [Indexed: 01/05/2024]
Abstract
In this study, a novel approach is introduced, merging in silico prediction with a Convolutional Neural Network (CNN) framework for the targeted screening of in vivo metabolites in Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS) fingerprints. Initially, three predictive tools, supplemented by literature, identify potential metabolites for target prototypes derived from Traditional Chinese Medicines (TCMs) or functional foods. Subsequently, a CNN is developed to minimize false positives from CWT-based peak detection. The Extracted Ion Chromatogram (EIC) peaks are then annotated using MS-FINDER across three levels of confidence. This methodology focuses on analyzing the metabolic fingerprints of rats administered with "Pericarpium Citri Reticulatae - Fructus Aurantii" (PCR-FA). Consequently, 384 peaks in positive mode and 282 in negative mode were identified as true peaks of probable metabolites. By contrasting these with "blank serum" data, EIC peaks of adequate intensity were chosen for MS/MS fragment analysis. Ultimately, 14 prototypes (including flavonoids and lactones) and 40 metabolites were precisely linked to their corresponding EIC peaks, thereby providing deeper insight into the pharmacological mechanism. This innovative strategy markedly enhances the chemical coverage in the targeted screening of LC-HRMS metabolic fingerprints.
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Affiliation(s)
- Jun Zeng
- Department of Pharmaceutical Engineering, School of Chemical Engineering, Xiangtan University, Xiangtan 411105, China
| | - Yaping Li
- Department of Quality Control, Xiangtan Central Hospital, Xiangtan 411100, China
| | - Chuanlin Wang
- Department of Pharmaceutical Engineering, School of Chemical Engineering, Xiangtan University, Xiangtan 411105, China
| | - Sheng Fu
- Hunan prevention and treatment institute for occupational disease, Changsha 410007, China
| | - Min He
- Department of Pharmaceutical Engineering, School of Chemical Engineering, Xiangtan University, Xiangtan 411105, China.
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4
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Zhang H, Jiang X, Zhang D, Yang Y, Xie Q, Wu C. An integrated approach for studying the metabolic profiling of herbal medicine in mice using high-resolution mass spectrometry and metabolomics data processing tools. J Chromatogr A 2024; 1713:464505. [PMID: 37976901 DOI: 10.1016/j.chroma.2023.464505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/08/2023] [Accepted: 11/10/2023] [Indexed: 11/19/2023]
Abstract
Analysis of exposure to traditional Chinese medicine (TCM) in vivo based on mass spectrometry is helpful for the screening of effective ingredients of TCM and the development of new drugs. The method of screening biomarkers through metabolomics technology is a nontargeted research method to explore the differential components between two sets of biological samples. By taking this advantage, this study aims to takes Forsythia suspensa, which is a TCM also known as Lian Qiao (LQ), as the research object and to study its in vivo exposure by using metabolomics technology. By comparing the significant differences between biological samples before and after administration, it could be focused on the components that were significantly upregulated, where a complete set of analysis strategies for nontargeted TCM in vivo exposure mass spectrometry was established. Furthermore, the threshold parameters for peak extraction, parameter selection during statistical data analysis, and sample concentration multiples in this method have also been optimized. More interestingly, by using the established analysis strategy, we found 393 LQ-related chemical components in mice after administration, including 102 prototypes and 291 LQ-related metabolites, and plotted their metabolic profiles in vivo. In short, this study has obtained a complete mass spectrum of LQ exposure in mice in vivo for the first time, which provides a reference for research on the active ingredients of LQ in vivo. More importantly, compared with other methods, the analysis strategy of nontargeted exposure of TCM in vivo-based mass spectrometry, constructed by using this research method, has good universality and does not require self-developed postprocessing software. It is worth mentioning that, for the identification and characterization of trace amounts of metabolites in vivo, this analysis strategy has no discrimination and has a detection capability similar to that of highly exposed components.
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Affiliation(s)
- Hairong Zhang
- Fujian Provincial Key Laboratory of Innovative Drug Target Research and State Key Laboratory of Cell Stress Biology, School of Pharmaceutical Sciences, Xiamen University, Xiamen, Fujian 361102, China
| | - Xiaojuan Jiang
- Fujian Provincial Key Laboratory of Innovative Drug Target Research and State Key Laboratory of Cell Stress Biology, School of Pharmaceutical Sciences, Xiamen University, Xiamen, Fujian 361102, China
| | - Dandan Zhang
- Fujian Provincial Key Laboratory of Innovative Drug Target Research and State Key Laboratory of Cell Stress Biology, School of Pharmaceutical Sciences, Xiamen University, Xiamen, Fujian 361102, China
| | - Yuexin Yang
- Fujian Provincial Key Laboratory of Innovative Drug Target Research and State Key Laboratory of Cell Stress Biology, School of Pharmaceutical Sciences, Xiamen University, Xiamen, Fujian 361102, China
| | - Qiang Xie
- Department of Cardiology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen 361003, China.
| | - Caisheng Wu
- Fujian Provincial Key Laboratory of Innovative Drug Target Research and State Key Laboratory of Cell Stress Biology, School of Pharmaceutical Sciences, Xiamen University, Xiamen, Fujian 361102, China.
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Lin IS, Chuang CYA, Shih CL. Dose-response technique combined with stable isotope tracing for drug metabolite profiling by using high-resolution mass spectrometry. Front Pharmacol 2023; 14:1293540. [PMID: 38155901 PMCID: PMC10753831 DOI: 10.3389/fphar.2023.1293540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 11/27/2023] [Indexed: 12/30/2023] Open
Abstract
Background: Mass spectrometry metabolomics-based data-processing approaches have been developed for drug metabolite profiling. However, existing approaches cannot be used to comprehensively identify drug metabolites with high efficacy. Methods: Herein, we propose a two-stage data-processing approach for effective and comprehensive drug metabolite identification. The approach combines dose-response experiments with stable isotope tracing (SIT). Rosiglitazone (ROS), commonly used to treat type 2 diabetes, was employed as a model drug. Results: In the first stage of data processing, 1,071 features exhibited a dose-response relationship among 22,597 features investigated. In the second stage, these 1,071 features were screened for isotope pairs, and 200 features with isotope pairs were identified. In time-course experiments, a large proportion of the identified features (69.5%: 137 out of 200 features) were confirmed to be possible ROS metabolites. We compared the validated features identified using our approach with those identified using a previously reported approach [the mass defect filter (MDF) combined with SIT] and discovered that most of the validated features (37 out of 42) identified using the MDF-SIT combination were also successfully identified using our approach. Of the 143 validated features identified by both approaches, 74 had a proposed structure of an ROS-structure-related metabolite; the other 34 features that contained a specific fragment of ROS metabolites were considered possible ROS metabolites. Interestingly, numerous ROS-structure-related metabolites were identified in this study, most of which were novel. Conclusion: The results reveal that the proposed approach can effectively and comprehensively identify ROS metabolites.
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Affiliation(s)
- I-Shou Lin
- Department of Anesthesiology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chia-Yi City, Taiwan
| | | | - Chia-Lung Shih
- Clinical Research Center, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chia-Yi City, Taiwan
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Murray KJ, Villalta PW, Griffin TJ, Balbo S. Discovery of Modified Metabolites, Secondary Metabolites, and Xenobiotics by Structure-Oriented LC-MS/MS. Chem Res Toxicol 2023; 36:1666-1682. [PMID: 37862059 DOI: 10.1021/acs.chemrestox.3c00209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
Exogenous compounds and metabolites derived from therapeutics, microbiota, or environmental exposures directly interact with endogenous metabolic pathways, influencing disease pathogenesis and modulating outcomes of clinical interventions. With few spectral library references, the identification of covalently modified biomolecules, secondary metabolites, and xenobiotics is a challenging task using global metabolomics profiling approaches. Numerous liquid chromatography-coupled mass spectrometry (LC-MS) small molecule analytical workflows have been developed to curate global profiling experiments for specific compound groups of interest. These workflows exploit shared structural moiety, functional groups, or elemental composition to discover novel and undescribed compounds through nontargeted small molecule discovery pipelines. This Review introduces the concept of structure-oriented LC-MS discovery methodology and aims to highlight common approaches employed for the detection and characterization of covalently modified biomolecules, secondary metabolites, and xenobiotics. These approaches represent a combination of instrument-dependent and computational techniques to rapidly curate global profiling experiments to detect putative ions of interest based on fragmentation patterns, predictable phase I or phase II metabolic transformations, or rare elemental composition. Application of these methods is explored for the detection and identification of novel and undescribed biomolecules relevant to the fields of toxicology, pharmacology, and drug discovery. Continued advances in these methods expand the capacity for selective compound discovery and characterization that promise remarkable insights into the molecular interactions of exogenous chemicals with host biochemical pathways.
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Affiliation(s)
- Kevin J Murray
- Department of Biochemistry, Molecular Biology, and Biophysics, College of Biological Science, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Peter W Villalta
- Department of Medicinal Chemistry, College of Pharmacy, University of Minnesota, Minneapolis, Minnesota 55455, United States
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology, and Biophysics, College of Biological Science, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Silvia Balbo
- Division of Environmental Health Sciences, School of Public Health, University of Minnesota, Minneapolis, Minnesota 55455, United States
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota 55455, United States
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7
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Zhu R, Chen H, Liu M, Xu Y, Jiang W, Si X, Yi L, Gu Y, Ren D, Wang J. Nontargeted screening of aldehydes and ketones by chemical isotope labeling combined with ultra-high performance liquid chromatography-high resolution mass spectrometry followed by hybrid filtering of features. J Chromatogr A 2023; 1708:464332. [PMID: 37703764 DOI: 10.1016/j.chroma.2023.464332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 08/19/2023] [Accepted: 08/24/2023] [Indexed: 09/15/2023]
Abstract
Aldehydes and ketones are important carbonyl compounds that are widely present in foodstuffs, biological organisms and human living environment. However, it is still challenging to comprehensively detect and capture them using liquid chromatography - mass spectrometry. In this work, a chemical isotope labeling (CIL) coupled with ultra-high performance liquid chromatography - high resolution mass spectrometry (UHPLC-HRMS) strategy was developed for the capture and detection of this class of compounds. 2,4-Dinitrophenylhydrazine (DNPH) and isotope-labeled DNPH (DNPH-d3) were utilized to selectively label the target analytes. To address the difficulties in processing UHPLC-HRMS data, a post-acquisition data processing method called MSFilter was proposed to facilitate the screening and identification aldehydes and ketones in complex matrices. The MSFilter consists of four independent filters, namely statistical characteristic-based filtering, mass defect filtering, CIL paired peaks filtering, and diagnostic fragmentation ion filtering. These filters can be used individually or in combination to eliminate unrelated interfering MS features and efficiently detect DNPH-labeled aldehydes and ketones. The results of a mixture containing 48 model compounds showed that although all individual filtering methods could significantly reduce more than 95% of the raw MS features with acceptable recall rates above 85%, but they had relatively high false positive ratios of over 90%. In comparison, the hybrid filtering method combining four filters is able to eliminate massive interfering features (> 99.5%) with a high recall rate of 81.25% and a much lower false positive ratio of 15.22%. By implementing the hybrid filtering method in MSFilter, a total of 154 features were identified as potential signals of CCs from the original 45,961 features of real tobacco samples, of which 70 were annotated. We believe that the proposed strategy is promising to analyze the potential CCs in complex samples by UHPLC-HRMS.
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Affiliation(s)
- Ruizhi Zhu
- Yunnan Key Laboratory of Tobacco Chemistry, R&D Center of China Tobacco Yunnan Industrial Co., Ltd., Kunming 650231, China
| | - Han Chen
- Yunnan Key Laboratory of Tobacco Chemistry, R&D Center of China Tobacco Yunnan Industrial Co., Ltd., Kunming 650231, China; Faculty of Agriculture and Food, Kunming University of Science and Technology, Kunming, 650500, China
| | - Meiyan Liu
- Yunnan Key Laboratory of Tobacco Chemistry, R&D Center of China Tobacco Yunnan Industrial Co., Ltd., Kunming 650231, China; Faculty of Agriculture and Food, Kunming University of Science and Technology, Kunming, 650500, China
| | - Yanqun Xu
- Yunnan Key Laboratory of Tobacco Chemistry, R&D Center of China Tobacco Yunnan Industrial Co., Ltd., Kunming 650231, China
| | - Wei Jiang
- Yunnan Key Laboratory of Tobacco Chemistry, R&D Center of China Tobacco Yunnan Industrial Co., Ltd., Kunming 650231, China
| | - Xiaoxi Si
- Yunnan Key Laboratory of Tobacco Chemistry, R&D Center of China Tobacco Yunnan Industrial Co., Ltd., Kunming 650231, China
| | - Lunzhao Yi
- Faculty of Agriculture and Food, Kunming University of Science and Technology, Kunming, 650500, China
| | - Ying Gu
- Faculty of Agriculture and Food, Kunming University of Science and Technology, Kunming, 650500, China
| | - Dabing Ren
- Faculty of Agriculture and Food, Kunming University of Science and Technology, Kunming, 650500, China.
| | - Juan Wang
- College of Arts and Sciences·Kunming, Kunming, 650221, China.
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8
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Liu Y, Li J, Xiao S, Liu Y, Bai M, Gong L, Zhao J, Chen D. Revolutionizing Precision Medicine: Exploring Wearable Sensors for Therapeutic Drug Monitoring and Personalized Therapy. BIOSENSORS 2023; 13:726. [PMID: 37504123 PMCID: PMC10377150 DOI: 10.3390/bios13070726] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/02/2023] [Accepted: 07/08/2023] [Indexed: 07/29/2023]
Abstract
Precision medicine, particularly therapeutic drug monitoring (TDM), is essential for optimizing drug dosage and minimizing toxicity. However, current TDM methods have limitations, including the need for skilled operators, patient discomfort, and the inability to monitor dynamic drug level changes. In recent years, wearable sensors have emerged as a promising solution for drug monitoring. These sensors offer real-time and continuous measurement of drug concentrations in biofluids, enabling personalized medicine and reducing the risk of toxicity. This review provides an overview of drugs detectable by wearable sensors and explores biosensing technologies that can enable drug monitoring in the future. It presents a comparative analysis of multiple biosensing technologies and evaluates their strengths and limitations for integration into wearable detection systems. The promising capabilities of wearable sensors for real-time and continuous drug monitoring offer revolutionary advancements in diagnostic tools, supporting personalized medicine and optimal therapeutic effects. Wearable sensors are poised to become essential components of healthcare systems, catering to the diverse needs of patients and reducing healthcare costs.
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Affiliation(s)
- Yuqiao Liu
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Junmin Li
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Shenghao Xiao
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Yanhui Liu
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Mingxia Bai
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Lixiu Gong
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Jiaqian Zhao
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Dajing Chen
- School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310007, China
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9
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Liu M, Xu X, Wang X, Wang H, Mi Y, Gao X, Guo D, Yang W. Enhanced Identification of Ginsenosides Simultaneously from Seven Panax Herbal Extracts by Data-Dependent Acquisition Including a Preferred Precursor Ions List Derived from an In-House Programmed Virtual Library. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:13796-13807. [PMID: 36239255 DOI: 10.1021/acs.jafc.2c06781] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Data-dependent acquisition (DDA) is widely utilized for metabolite identification in natural product research and food science, which, however, can suffer from low coverage. A potential solution to improve DDA coverage is to include the precursor ions list (PIL). Here, we aimed to construct a PIL-containing DDA strategy based on an in-house library of ginsenosides (VLG) and identify ginsenosides simultaneously from seven Panax herbal extracts. VLG, combined with mass defect filtering, could efficiently screen the ginsenoside precursors and elaborate the separate PIL involved in DDA for each ginseng extract. Consequently, we could characterize 500 ginsenosides, including 176 ones with unknown masses. Using the Panax ginseng extract, the superiority of this strategy was embodied in targeting more known ginsenoside masses and newly acquiring the MS2 spectra of 13 components. Conclusively, knowledge-based large-scale molecular prediction and PIL-DDA can represent a powerful targeted/untargeted strategy beneficial to novel natural compound discovery.
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Affiliation(s)
- Meiyu Liu
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
| | - Xiaoyan Xu
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
| | - Xiaoyan Wang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
| | - Hongda Wang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
| | - Yueguang Mi
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
| | - Xiumei Gao
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Key Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
| | - Dean Guo
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Shanghai Research Center for Modernization of Traditional Chinese Medicine, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 501 Haike Road, Shanghai 201203, China
| | - Wenzhi Yang
- State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
- Key Laboratory of Pharmacology of Traditional Chinese Medical Formulae, Ministry of Education, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Jinghai, Tianjin 301617, China
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