Exploring potential chemical markers by metabolomics method for studying the processing mechanism of traditional Chinese medicine using RPLC-Q-TOF/MS: a case study of Radix Aconiti
© Li et al; licensee Chemistry Central Ltd. 2013
Received: 24 December 2012
Accepted: 15 February 2013
Published: 22 February 2013
Pao zhi is a common traditional approach that usually occurs before most herbs are prescribed whereby during processing, secondary plant metabolites are transformed, thus helping to increase potency, reduce toxicity and altering their effects. Using Radix Aconiti (Chuan Wu, CW) as a model herb, suitable chemical markers are crucial for studying the processing mechanisms of these herbs.
In this study, the comprehensive metabolomic characters of CW and Prepared CW (ZCW) by RPLC-Q-TOF/MS were investigated to guarantee clinical safety. Multivariate analyses successfully identified specific metabolite changes between CW and ZCW. In addition, 22 key biomarkers responsible for the detoxifying actions of pao zhi were discovered. The processing mechanism of CW were discussed according to the identified metabolites. This method is efficient, providing more accurate characterisations of traditional pao zhi detoxification.
The proposed strategy proves that RPLC-Q-TOF/MS-based metabolomic analysis does not only explore chemical markers but can also provide a comprehensive understanding of the transformation mechanisms underlying pao zhi.
KeywordsMetabolomics Radix Aconiti Chuan Wu Reversed phase liquid chromatography/quadrupole time-of-flight tandem mass spectrometry
Radix Aconiti (Chuan Wu, CW) is the dried mother root of Aconitum carmichaeli Debx. This root is an essential drug in Traditional Chinese Medicine (TCM) and has been used for thousands of years. The herb is widely distributed in Sichuan Province (located in southwestern China), and has a wide range of pharmacological effects. Although CW has a limited therapeutic range, it is commonly used to treat various diseases such as collapse, syncope, rheumatic fever, painful joints, gastroenteritis, diarrhoea, oedema, bronchial asthma, and several tumors [1–4]. Prepared CW (ZCW) is traditionally manufactured by boiling raw CW at 100°C for 8 h before drying it. More than 20 commonly used proprietary herbal products from both historical medical literature and modern clinical research reports contain CW or ZCW as main ingredient or auxiliary ingredient. These products include ‘Wutou Tang’, ‘Chuanfu Wan’, ‘Wufu Jiaojiang Tang’, ‘Zhentongning Injection’ and ‘Fengshigutong Jiaonang’ etc.. In TCM, CW and ZCW have different uses and potential toxicity. CW is strongly toxic and is used externally; whereas ZCW has a ‘warning toxicology’, and is taken orally or injected. Therefore, consuming the wrong form of herb may lead to undesirable clinical outcomes. Hence, quality control of this herb is paramount.
Pao zhi is a common approach that usually occurs before most herbs are prescribed whereby during processing. The role of pao zhi is to strengthen the effect, eliminate or reduce the toxicity, facilitate the preparation and storage of drugs. During processing, secondary plant metabolites are transformed, thus helping to increase potency and reduce toxicity, and altering their effects . As a detoxifying measure, Paozhi is necessary to remove the poisonous Aconitum alkaloids mainly deriving from the diester diterpene alkaloids (DDAs) including aconitine, mesaconitine and hypaconitine . They can be decomposed into less or non-toxic derivatives through Paozhi that plays an essential role in detoxification. The main mechanisms underlying herb processing were found to be mainly related to changes in composition and/or activity of herb components [5, 6]. However, the difference in global metabolomic characters between CW and ZCW remains unclear. This difference restricts further application of ZCW in a clinical environment.
Metabolomics is a branch of science concerned with the total metabolome of integrated biological systems and dynamic responses to alterations of endogenous and/or exogenous factors . The objective of ‘nontargeted’ metabolic profiling analysis is to detect as many metabolites as possible in a certain sample. Several papers have illustrated that metabolomics has been used in evaluating the pharmacological and toxicological effects of aconite products [8, 9]. With the development of accurate, precision and new analytical techniques, metabolomics can provide global, comprehensive, detailed and reliable pieces of evidence for further studies and determination on efficacy/toxicity of CWs. Several methods have been developed for analysing aconitine alkaloids in CWs. These methods include high-performance liquid chromatography (HPLC), ultraviolet spectrophotometry (UV) and reversed phase liquid chromatography/quadrupole time-of-flight tandem mass spectrometry (RPLC-Q-TOF/MS) [10–12]. However, research is still limited for content changes of several main alkaloids, while could not exploring potential chemical markers for studying the processing mechanism of CW.
In this study, an approach that uses RPLC-Q-TOF/MS and pattern recognition analysis was developed to rapidly find potential chemical markers for studying the processing mechanism of Radix Aconiti. The protocol was executed using three steps. Firstly, this proposed strategy used RPLC-Q-TOF/MS to scan the full metabolic profiling of raw and processed Radix Aconiti. Secondly, a multivariate statistical analysis by principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) of the mass spectrometry (MS) spectra based on all chemical information was conducted to find potential chemical markers. Lastly, the underlying regulations of pao zhi perturbed metabolic pathways were discussed and the processing mechanism of CW was elucidated according to the results of chemical markers for CW and ZCW. This novel method can be valuable for rapidly exploring potential chemical markers and studying processing mechanisms of herbs.
Results and discussion
Acquisition and processing of metabolic profile data
Identified 22 potential biomarkers between CW and ZCW by RPLC-Q-TOF/MS in positive ESI mode a
m/z [M + H]+b
648[M + H]+
556[M + H–C3H8O3]+
538[M + H–C3H10O4]+
528[M + H–C4H8O4]+
105[M + H–C26H41NO11]+
632[M + H]+
572[M + H–C2H4O2]+
540[M + H–C3H8O3]+
512[M + H–C4H8O4]+
105[M + H–C26H41NO10]+
662[M + H]+
602[M + H–C2H4O2]+
570[M + H–C3H8O3]+
542[M + H–C4H8O4]+
105[M + H–C27H43NO11]+
616[M + H]+
584[M + H–CH4O]+
556[M + H–C2H4O2]+
524[M + H–C3H8O3]+
105[M + H–C26H41NO9]+
630[M + H]+
570[M + H–C2H4O2]+
538[M + H–C3H8O3]+
510[M + H–C4H8O4]+
105[M + H–C27H43NO9]+
606[M + H]+
574[M + H–CH4O]+
556[M + H–CH6O2]+
524[M + H–C2H10O3]+
105[M + H–C25H43NO9]+
620[M + H]+
602[M + H–H2O]+
570[M + H–CH6O2]+
538[M + H–C2H10O3]+
105[M + H–C25H45NO10]+
590[M + H]+
572[M + H–H2O]+
558[M + H–CH4O]+
540[M + H–CH6O2]+
105[M + H–C24H39NO9]+
604[M + H]+
586[M + H–H2O]+
572[M + H–CH4O]+
554[M + H–CH6O2]+
105[M + H–C25H41NO9]+
574[M + H]+
542[M + H–CH4O]+
510[M + H–C2H8O2]+
105[M + H–C24H39NO8]+
602[M + H]+
584[M + H–H2O]+
570[M + H–CH4O]+
552[M + H–CH6O2]+
572[M + H]+
554[M + H–H2O]+
540[M + H–CH4O]+
522[M + H–CH6O2]+
586[M + H]+
554[M + H–CH4O]+
536[M + H–CH6O2]+
570[M + H]+
552[M + H–H2O]+
520[M + H–CH6O2]+
850[M + H]+
572[M + H–C18H30O2]+
852[M + H]+
570[M + H–C18H34O2]+
836[M + H]+
556[M + H–C18H32O2]+
812[M + H]+
556[M + H–C16H32O2]+
838[M + H]+
556[M + H–C18H34O2]+
826[M + H]+
570[M + H–C16H32O2]+
358[M + H]+
340[M + H–H2O]+
464[M + H]+
446[M + H–H2O]+
The processing mechanism of CW
Chemicals, reference compounds and samples
Acetonitrile (ACN, HPLC-MS grade) from Merck (Darmstadt, Germany), formic acid (HPLC grade) from Sigma-Aldrich (Steinheim, Germany) and sodium formate from Sigma-Aldrich (St. Louis, MO, USA) were purchased. Ultra-pure water was prepared using a Milli-Q SP system (Millipore, Bedford, MA, USA). Other solvents and chemicals were of analytical grade. CWs were collected from Jiangyou in Sichuan Province, which is the indigenous cultivating region for CW. The identity of all CW samples (root and rhizome) was authenticated to be dried using morphological and histological methods by Dr. Lu Zhang. Preparation of processed CW was carried out according to Chinese Pharmacopoeia (CP) (2010). Voucher specimens of Aconitum carmichaeli Debx. and samples used in this study were deposited at Tianjin University of Traditional Chinese Medicine. Six reference compounds were purchased from the National Institute for the Control of Pharmaceutical and Biological Products, China.
Liquid chromatography was performed with an Agilent 1200 system (Agilent Corp., MA, USA), equipped with a binary solvent delivery system and an autosampler. A mobile phase consisting of water (A) and acetonitrile (B) (each containing 0.1% formic acid) was used. In addition, separation was performed on an RP-C18 column (Agilent Zorbax SB-Aq, 2.1 mm × 100 mm, 1.8 μm particle size). RPLC elution condition was optimised as follows: 2% to 6% B (0 min to 5 min), 6% to 13% B (5 min to 10 min), 13% to 15% B (10 min to 15 min), 15% to 20% B (15 min to 20 min), 20% to 28% B (20 min to 25 min), 28% to 40% B (25 min to 30 min), 40% to 85% B (30 min to 35 min), 85% B (35 min to 40 min), 85% to 2% B (40 min to 42 min), isocratic at 2% B (42 min to 60 min) and finally, washing and reconditioning of the column. Flow rate was set at 0.2 mL/min. The column and autosampler were maintained at 25°C and 10°C, respectively. The injection volume of reference compounds and samples was 2 μL.
Mass spectrometry analysis was carried out on a time-of-flight mass spectrometer Micro-TOF-QII (Bruker Daltonik GmbH, Germany) using the following setting of tuning parameters: capillary voltage 4.5 kV, drying temperature 180°C, nitrogen flow rate 6 L/min and pressure 0.8 bar. The external calibration with sodium formate was clustered before individual measurements. Mass spectra were acquired in positive electrospray ionization (ESI) mode in a scan range from 100 m/z to 1000 m/z at a sampling rate of 2 Hz. Reference mass was scanned once every five scans for positive data collection.
Sample preparation of CW and ZCW
Eight samples of raw Aconitum carmichaeli Debx. were collected from Jiangyou, Sichuan Province. ZCW was obtained by boiling raw CW at 100°C for 8 h, and then drying it according to CP (2010). The samples were pulverised, passed through a 0.30 mm sieve and stored in a desiccator.
Powder (1.0 g) was extracted with 70% ethanol (10 mL and 8 mL) by extracting it twice before being filtered and combined. The supernatant diluted to 100 mL with deionised water was then passed through a 0.22 mm-filter. The filtrate was stored at 4°C in a refrigerator before being used for RPLC analysis.
Data processing and pattern recognition analysis
Raw data acquired from RPLC-Q-TOF/MS were pretreated using DataAnalysis 4.0 software (Bruker Daltonics) to find characteristic compounds with molecular features. Furthermore, mass data were exported to ProfileAnalysis 1.1 software (Bruker), which allowed for peak alignment, background noise subtraction and data reduction. Results provided a table of mass and retention time pairs with associated intensities for all detected peaks . The main parameters were set as follows: retention time range 2 min to 55 min, mass range 100 to 1000, mass window 0.5, retention time window 1 min and signal-to-noise (S/N) ratio threshold 5. Variables that did not exist in 80% of participants in one group were filtered . To correct MS response shift during long analysis duration and different sample enrichment factors, data of each sample were normalised, thus ensuring that each sample was represented by a collection of variables to characterise its metabolic pattern before multivariate data analysis.
Normalised data were further exported to SIMCA-P 11.5 demo version software (Umetrics AB, Sweden) for multivariate data analysis . Both PCA and PLS-DA were applied to investigate the metabolic profiles of the samples. PCA is an unsupervised data analysis technique that allows original data to be reduced to a few principal components whilst retaining features that mostly contribute to the variance . By contrast, PLS-DA, is a supervised extension of PCA that uses class information to maximise separation among observation classes. Close sample clustering indicates their compositional similarity, whereas distant sample clustering suggests their diverse metabolomic compositions . The significance of between-group differences for these metabolites was examined by the student’s t-test using the computer software SPSS 13.0 (SPSS Inc., Chicago, USA). P-values less than 0.05 were selected to indicate statistical significance.
In this study, the comprehensive metabolomic characters of CW and ZCW by RPLC-Q-TOF/MS were investigated to guarantee clinical safety. Multivariate analyses successfully identified specific metabolite changes between CW and ZCW. In addition, 22 key biomarkers responsible for the detoxifying actions of pao zhi were discovered. The processing mechanism of CW were discussed according to the identified metabolites. This method is efficient, providing more accurate characterisations of traditional pao zhi detoxification.
Traditional Chinese medicine
High-performance liquid chromatography
Reversed phase liquid chromatography/quadrupole time-of-flight tandem mass spectrometry
Principal component analysis
Partial least squares discriminant analysis
Variable importance parameters
Diester diterpene alkaloids
Monoester diterpene alkaloids
Amine diterpenoid alkaloids.
This project was financially supported by the National Basic Research Program of China (973 Program) (2011CB505300, 2011CB505302) and the National Key Technology R&D Program (No. 2011BAI07B08).
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