Metabolomics study on Fuzi and its processed products using ultra-performance liquid-chromatography/electrospray-ionization synapt high-definition mass spectrometry coupled with pattern recognition analysis

Abstract

The lateral root of Aconitum carmichaelii Debx is named “Fuzi” which is widely distributed across Asia and North America and has been used to relieve joint pain and treat rheumatic diseases for over two thousand years. However, it has very narrow therapeutic ranges and despite the toxicological risk, its usage remains very high. A traditional Chinese processing approach (Paozhi, detoxifying measure) 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. Processed Fuzi is mainly focused on the three main forms of Yanfuzi (YFZ), Heishunpian (HSP) and Baifupian (BFP) which are highly desirable in order to guarantee the clinical safety and their low toxicity in decoctions. The difference in metabolomic characters between Fuzi and its processed preparations is still completely unclear. Therefore, this paper was designed to investigate a comprehensive metabolome of Fuzi and its processed products by ultra-performance liquid-chromatography/electrospray-ionization synapt high-definition mass spectrometry (UPLC-Q-TOF-HDMS) combined with pattern recognition methods. The difference in metabolic profiles between Fuzi and its processed preparations was well observed by the principal component analysis (PCA) of the MS spectra. Significant changes of 19 metabolite biomarkers were detected in the Fuzi samples and three preparations. The underlying regulations of Paozhi-perturbed metabolic pathways were also discussed according to the identified metabolites. The present study proves that UPLC-Q-TOF-HDMS based metabolomic analysis greatly contributes to the investigation of Fuzi metabolism through Paozhi techniques, and provides useful information to further comprehensively understand the pharmacological activity and potential toxicity of processed Fuzi in a clinical environment.

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1. Introduction

Traditional Chinese medicines (TCMs) have gained increasing acceptance worldwide in recent years and are generally considered as being natural and harmless.^1–3 The Aconitum species, an essential drug in TCM, have been used for several thousand years and widely distributed across Asia and North America. As a widely used Chinese herbal medicine, the roots of Aconitum have a wide range of pharmacological effects although it has very narrow therapeutic ranges, commonly applied for various diseases, i.e., collapse, syncope, rheumatic fever, painful joints, gastroenteritis, diarrhea, oedema, bronchial asthma, various tumors, and some endocrinal disorders like irregularmenstruation.^4,5 In Chinese Pharmacopoeia (CP) 2010, Aconitum carmichaeli Debx. (Fig. 1A) are recorded, extensively distributed in the Sichuan Province of China. The mother root of Aconitum carmichaelii Debx is named “Chuanwu” (CW, Fig. 1B), while the daughter or lateral root of Aconitum carmichaelii Debx is “Fuzi” (FZ, Fig. 1C). As one of the most crucial TCM drugs, FZ has been more frequently prescribed than CW to treat rheumatic diseases. It was firstly recorded in the earliest Chinese medicinal classic, Shennong's Materia Medica. There are more than 20 commonly used proprietary herbal products for muscular disorders, joint pain and arthritis, from both historical literature and modern clinical reports containing processed Fuzi as a main ingredient, such as ‘Yin Chen Si Ni Tang’ ‘Fuzi Lizhong Wan’, ‘Guifulizhong Wan’, ‘Jinguishenqi Wan’, “Shenfu Injection”.^6,7

Fig. 1 Aconitum carmichaeli Debx. (A); the mother root of Aconitum carmichaelii Debx. (B); the daughter or lateral root of Aconitum carmichaelii Debx. (C).

However, the toxicity of Fuzi leads to a potentially high risk of severe problems that are sometimes even life-threatening.⁸ At present, a comparison of the metabolic profiles revealed that four major groups of alkaloids – monoester diterpene alkaloids (MDAs), amine diterpenoid alkaloids (ADAs), diester diterpene alkaloids (DDAs), and lipoalkaloids – were found in the roots of Aconitum.⁹ They share a common C₁₉-norditerpenoid skeleton, in which the C₈ position is occupied by a hydroxyl, an acetoxy or a fatty acid acyl group, respectively.^10,11 The toxicity of Aconitum mainly derives from the DDAs including aconitine, mesaconitine, and hypaconitine.¹² As we know, TCM herbal processing approaches, namely “Paozhi”, by means of the transformation of secondary plant metabolites help to reduce the toxicity of the drug, and might exert a large maximal therapeutic efficacy with minimal adverse effects.¹³ As a traditional processing form, Paozhi could remarkably reduce the toxicity of the Fuzi by decomposing the DDAs to the relatively lower toxic MDA. All of the processed Fuzi must be used. According to the ways of the processing form, it is converted into Fuzi preparations including Yanfuzi (YFZ), Heishunpian (HSP) and Baifupian (BFP), which are recorded in CP 2010. The Paozhi approach is a good detoxifying measure to remove the poisonous Aconitumalkaloids. However, the difference in metabolomic characters between Fuzi and its processed products is still completely unclear, restricting the further application of processed Fuzi in the clinical environment.

Metabolomics is an ‘omics’ approach that aims to analyze all metabolites in a biological sample comprehensively.^14,15 The detailed metabolite profiling of thousands of the secondary metabolites has great potential for directly elucidating plant metabolic processes.¹⁶ It has brought enormous opportunities for improved detection and discovery of biomarkers, and adopts a ‘top-down’ strategy to reflect the function of organisms from terminal symptoms of the metabolic network and understand metabolic changes of a complete system caused by interventions in a holistic context.^17–21 Metabolomics not only will assist in understanding the nature of plant responses to environmental change but also will provide unique insights into the fundamental nature of plant phenotypes in relation to development, physiology, resistance, biodiversity, etc. Several studies have shown that metabolomics have been used in the evaluation of differences in toxicological and pharmacological actions of aconite products when applying different TCM processing methods.^22,23 A clear understanding of the differentiation mechanism of the Aconitum is essential to evaluate drug safety and for the safe use in the clinic. However, the research is still limited for content changes of several alkaloids to determine its efficacy/toxicity, while ignoring the crude and processed products as a complex group of plant metabolites. Moreover, little is known about the global change of the metabolome of processed Fuzi. With the development of new analytical techniques, metabolomics as a research platform, could provide comprehensive, detailed, reliable evidence for further study on efficacy/toxicity of processed Fuzi. The objective of ‘non-targeted’ metabolic profiling analysis can determine ‘global’ metabolites.²⁴ Of the various profiling techniques, UPLC/MS has now become a promising tool for investigating the metabolome of plants. It is believed that metabolomics analysis will play an important role as an effective tool in terms of high-throughput elucidation of metabolic phenotypes of crude and processed preparations. Thus, this paper was designed to investigate comprehensive metabolomic characters of Fuzi and its processed products by UPLC-Q-TOF-HDMS.

2. Experimental

2.1 Chemicals and materials

Acetonitrile and methanol (HPLC grade) was purchased from Merck (Darmstadt, Germany). Distilled water was purchased from Watson's Food & Beverage Co., Ltd. (Guangzhou, China), and formic acid (HPLC grade) was purchased from the Beijing Reagent Company (Beijing, China). Leucine enkephalin was supplied by Sigma-Aldrich (MO, UK). Other chemicals, except as noted, were analytical grade. FZ were collected from the mianyang county in Sichuan province (South, China) in July 2009. They were authenticated by Prof. Xijun Wang, Department of Pharmacognosy of Heilongjiang University of Chinese Medicine (Harbin, China). The processed preparation of Fuzi was carried out according to CP 2010.⁵

2.2 Sample preparation

The crude Fuzi and processed roots were pulverized into a powder, passed through a 0.45 mm sieve, and stored in a desiccator. The powder (1.0 g) was extracted with 75% methanol two times (5.0 ml and 10 min each time) by sonication at room temperature, and then vortexed for 2 min respectively. The mixture was centrifuged for 5 min at 13 000 rpm at 4 °C. The supernatant diluted 50 times with methanol was then filtered through a 0.22 μm-filter, the filtrate was used for UPLC analysis.

2.3 Ultra-performance liquid chromatography

Chromatographic analysis was performed in a Waters ACQUITY UPLC system controlled with Masslynx (V4.1, Waters Corporation, Milford, USA). An aliquot of 2 μL of sample solution was injected onto an ACQUITY UPLCTM BEH C₁₈ column(50 mm × 2.1 mm i.d., 1.7 μm, Waters Corp, Milford, USA) held at 45 °C and the flow rate was 0.5 mL min⁻¹. The mobile phase consisted of a linear gradient system of A (0.1% formic acid in water) and B (0.1% formic acid in acetonitrile), 0–1.5 min, B 10–35%; 1.5–2.5 min, B 35–60%; 2.5–3.0 min, B 60–67%, 3.0–4.5 min B 99%; 4.5–5.0 min, B 99–1%; 5.0–7.0 min, B 1%.

2.4 Mass spectrometry

Mass spectrometry detection was performed using a Waters Micromass Q-tof micro™ Synapt High Definition Mass Spectrometer (Manchester, UK) equipped with electrospray ionization. The optimal operating conditions were as follows: the source temperature, 110 °C; desolvation gas temperature, 300 °C; cone gas flow, 50 L h⁻¹; desolvation gas flow, 500 L h⁻¹; capillary voltage, 2.5 kV; sampling cone voltage, 35.0 V; extraction cone voltage, 3.0 V. Data were centroided and reference mass correction on each sample was performed with a continuous infusion of a 0.2 ng mL⁻¹ solution of leucine enkephalin at a flow rate of 100 μl min⁻¹, generating a reference ion for positive ion mode ([M + H]⁺ = 556.2771) and negative ion mode ([M − H]⁻ = 554.2614) to ensure accuracy during the MS analysis. The mass range was acquired using Masslynx™ (V4.1) software for each sample from 50 to 1000 Da with a 0.3 s scan time and a 0.1 s interscan delay over a 12 min run time in both positive modes. Analytes were identified by accurate mass detection.

2.5 Pattern recognition analysis

The mass data acquired were imported to Markerlynx (version 4.1, Waters Corporation, MA, USA) within Masslynx software (version 4.1) for peak detection and alignment. All data were normalized to the summed total ion intensity per chromatogram, and the resultant data matrices were introduced to EZinfo 2.0 software (which is included in MarkerLynx Application Manager and can be applied directly) for principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and orthogonal projection to latent structures (OPLS) analysis.

3. Results and discussion

3.1 Acquisition and processing of metabolic profile data

To compare the Fuzi metabolome and its processed preparations, global profiling of its positive ion mode was analyzed by UPLC-HDMS. Using the optimal UPLC-HDMS condition described above, the representative Based Peak Intensity (BPI) chromatograms for their samples in positive ion modes are presented in Fig. 2, respectively. As can be seen from Fig. 2, the low molecular mass metabolites could be separated well in 3.5 min. In order to better visualize the subtle similarities and differences among these complex data sets, multiple pattern recognition methods were employed to phenotype their metabonome. Here, PCA, PLS and OPLS-DA were used to classify the metabolic phenotypes and identify the differentiating metabolites. In the PCA scores, each point represents an individual sample. The PCA results are displayed as score plots indicating the scatter of the samples, which indicate similar metabolomic compositions when clustered together and compositionally different metabolomes when dispersed. The PCA scores plot could divide the different samples into different blocks, suggesting that the metabolic profiles have distinguish Fuzi and its processed products (Fig. 3A). The unsupervised PCA analysis reveals a much greater metabolic signature difference from the pre- and post-treatment. It's worth noting that this change is in a diversification manner. PLS-DA was employed for classification or discrimination analyses because PLS-DA calculation yielded a significantly distinct degree of biological variation. A loadings plot predicates the list of metabolites helping in the positioning of the distance from different groups. The metabolic markers of Fuzi and its processed products were plotted by the PLS-DA, depicting the variable metabolic patterns at the phenotype (Fig. 3B). A variable importance parameters (VIP) plot was used to identify the metabolites according to the orders of their contributions to the separation of clustering. The mass spectrometry signals responsible for this differentiation are characterized by the VIP plot from PLS-DA analysis (Fig. 4, 5 and 6). The farther away from the origin, the higher value of the ions in VIP score plot. S-plot analysis represented the highest contributing signals for the pre- and post-treatment. Potential markers were extracted from S-plots constructed following the OPLS analysis, and markers were chosen based on their contribution to the variation and correlation within the data set. Based on VIP and the corresponding loadings plot, 19 metabolites were identified and were selected as the biomarkers.

Fig. 2 Typical total ion chromatogram of Fuzi and its processed products by UPLC-HDMS in positive ESI mode. a, Fuzi; b, Yanfuzi; c, Heishunpian; d, Baifupian.

Fig. 3 PCA score plots of Fuzi and its processed products by UPLC-HDMS in positive ESI mode (A) and corresponding PLS-DA analysis (B) for biomarker recognition. SFP, Fuzi; YFP, Yanfuzi; HSP, Heishunpian; BFP, Baifupian.

Fig. 4 Potential biomarkers in the loading S- and variable important (VIP)-plot of OPLS-DA model between Fuzi and Yanfuzi resulting from the UPLC/MS spectra in positive ESI mode. S-plot analysis representing the highest contributing signals for the pre- and post-treatment. SFP, Fuzi; YFP, Yanfuzi.

Fig. 5 Potential biomarkers in the loading S- and VIP-plot of OPLS-DA model between Fuzi and Heishunpian resulting from the UPLC/MS spectra in positive ESI mode. SFP, Fuzi; HSP, Heishunpian.

Fig. 6 Potential biomarkers in the loading S- and variable important (VIP)-plot of OPLS-DA model between Fuzi and Baifupian resulting from the UPLC/MS spectra in positive ESI mode. SFP, Fuzi; BFP, Baifupian.

3.2 Biomarker characterization

The robust UPLC-HDMS analysis that was performed in positive mode using the aforementioned protocol indicated the retention time and precise molecular mass and provided the MS/MS data that was necessary for the structural identification of the biomarkers. PCA is a data visualization method useful for overviewing relationships or ‘groupings’ within multivariatedata.²⁵ The results of PCA displayed as score plots enabled us to compare the metabolite profiles of different groups. PLS-DA analysis showed distinct metabolites clustered according to the characteristic change of control or processed samples profiles.²⁶ The precise molecular mass was determined within a reasonable degree of measurement error (<5 ppm) using Q-TOF, and the potential element composition, degree of unsaturation and fractional isotope abundance of the compounds were also obtained. We searched for the presumed molecular formula ChemSpider (http://www.chemspider.com), MassBank (http://www.massbank.jp/), SMPD (http://www.smpdb.ca/) and other databases to confirm possible chemical compositions, and the MS/MS data were screened to determine the potential structures of the ions. All the detected ions were arranged according to VIP value (>2), and there were 19 ions indentified in positive mode. What follows is a description of the identification procedure taking hypaconitine ion as an example. Compound (VIP 7.2912, Table 1) exhibited [M + H]⁺ ions at m/z 616. It gave product ions of 584 [M + H–CH₄O]⁺, 556 [M + H–C₂H₄O₂]⁺, 524 [M + H–C₃H₈O₃]⁺, 494 [M + H–C₄H₁₀O₄]⁺, 105 [M + H–C₂₆H₄₁NO₉]⁺ (Table 4). The molecular formula of the aconitine compound was determined as C₃₃H₄₅NO₁₀ on the basis of UPLC-MS data and the analysis of its elemental composition and fractional isotope abundance. These data indicated that it was plausibly characterized as hypaconitine based on a database search (Fig. 7). According to the protocol above, 11 potential biomarkers between Fuzi and YFZ were identified (Table 1). They are hypaconitine, neoline, songorine, talatisamine, aconitine, 14-acetylkarakoline, 14-acetyl-talatisamine, fuziline, isotalatizidine, benzoylmesaconine, mesaconitine. 19 potential biomarkers were identified between Fuzi and HSP (Table 2), including mesaconitine, benzoylmesaconine, neoline, fuziline, benzolhypacone, 10-OH-mesaconitine, deoxyaconitine, isotalatizidine, hypaconitine, 14-acetyl-talatisamine, songorine, 8-acetyl-15-hydroxyneoline, benzoylaconine, aconitine, talatisamine, chasmanine, karacoline, karacolidine, 14-acetylkarakoline. 17 potential biomarkers were identified between Fuzi and BFP (Table 3), such as benzoylmesaconine, mesaconitine, benzolhypacone, hypaconitine, neoline, 10-OH-mesaconitine, fuziline, deoxyaconitine, benzoylaconine, 14-acetyl-talatisamine, isotalatizidine, aconitine, talatisamine, songorine, 14-acetylkarakoline, chasmanine, 8-acetyl-15-hydroxyneoline.

Table 1 Potential biomarkers identified between Fuzi and Yanfuzi in positive ESI mode^a

VIP	Rt	m/z [M + H]^+		Identified compounds	Elemental composition	Structure type	Content variance
		Measured m/z	Theoretical m/z
a Note: ↑, content increased; ↓, content decreased. MDAs, monoester diterpene alkaloids; ADAs, amine diterpenoid alkaloids; DDAs, diester diterpene alkaloids.
7.2912	2.38	616.3051	616.3043	Hypaconitine	C33H45NO10	DDAs	↑
4.5883	1.57	438.2761	438.2777	Neoline	C24H39NO6	ADAs	↑
3.8287	1.43	358.2317	358.2304	Songorine	C22H31NO3	Veatchine	↑
3.7463	1.67	422.2839	422.2828	Talatisamine	C24H39NO5	ADAs	↑
3.3732	2.38	646.3151	646.3149	Aconitine	C34H47NO11	DDAs	↓
3.3519	1.60	420.2645	420.2672	14-Acetylkarakoline	C24H37NO5	MDAs	↓
3.2856	1.90	464.2936	464.2934	14-Acetyl-talatisamine	C26H41NO6	MDAs	↓
2.9356	1.52	454.2723	454.2727	Fuziline	C24H39NO7	ADAs	↑
2.8417	1.36	408.2673	408.2672	Isotalatizidine	C23H37NO5	ADAs	↑
2.8241	2.12	590.2895	590.2883	Benzoylmesaconine	C31H43NO10	MDAs	↓
2.3077	2.32	632.2998	632.2993	Mesaconitine	C33H45NO11	DDAs	↓

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Fig. 7 Mass spectra of hypaconitine and its fragmentation pathway in positive mode. Product ion spectrum of biomarker hypaconitine in positive ion mode (A) and proposed MS fragmentation mechanism (B).

Table 2 Potential biomarkers identified between Fuzi and Heishunpian in positive ESI mode^a

VIP	Rt	m/z [M + H]^+		Identified compounds	Elemental composition	Structure type	Content variance
		Measured m/z	Theoretical m/z
a Note: ↑, content increased; ↓, content decreased. MDAs, monoester diterpene alkaloids; ADAs, amine diterpenoid alkaloids; DDAs, diester diterpene alkaloids.
6.6783	2.32	632.2998	632.2993	Mesaconitine	C33H45NO11	DDAs	↓
6.4895	2.12	590.2885	590.2883	Benzoylmesaconine	C31H43NO10	MDAs	↑
5.0967	1.57	438.2761	438.2777	Neoline	C24H39NO6	ADAs	↓
4.7443	1.52	454.2723	454.2727	Fuziline	C24H39NO7	ADAs	↓
4.6270	2.23	574.2953	574.2938	Benzolhypacone	C31H43NO9	MDAs	↑
3.6771	2.26	648.2942	648.2942	10-OH-mesaconitine	C33H45NO12	DDAs	↓
3.2174	2.44	630.3209	630.3200	Deoxyaconitine	C34H47NO10	DDAs	↓
3.1976	1.36	408.2673	408.2672	Isotalatizidine	C23H37NO5	ADAs	↓
3.1282	1.9	464.2936	464.2934	14-Acetyl-talatisamine	C26H41NO6	MDAs	↓
2.9466	1.43	358.2314	358.2304	Songorine	C22H31NO3	Veatchine	↓
2.8202	2.21	604.3033	604.3043	Benzoylaconine	C32H45NO10	MDAs	↑
2.7664	1.67	422.2819	422.2828	Talatisamine	C24H39NO5	ADAs	↓
2.6718	2.38	646.3151	646.3149	Aconitine	C34H47NO11	DDAs	↓
2.3515	3.28	496.2889	496.2832	8-acetyl-15-hydroxyneoline	C26H41NO8	MDAs	↑
2.3400	2.38	616.3051	616.3043	Hypaconitine	C33H45NO10	DDAs	↓
2.2912	1.82	452.2936	452.2934	Chasmanine	C25H41NO6	ADAs	↓
2.2846	1.36	378.2569	378.2566	Karacoline	C22H35NO4	ADAs	↓
2.2828	1.13	394.2514	394.2515	Karacolidine	C22H35NO5	ADAs	↓
2.2245	1.6	420.2645	420.2672	14-acetylkarakoline	C24H37NO5	MDAs	↓

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Table 3 Potential biomarkers identified between Fuzi and Baifupian in positive ESI mode^a

VIP	Rt	m/z [M + H]^+		Identified compounds	Elemental composition	Structure type	Content variance
		Measured m/z	Theoretical m/z
a Note: ↑, content increased; ↓, content decreased. MDAs, monoester diterpene alkaloids; ADAs, amine diterpenoid alkaloids; DDAs, diester diterpene alkaloids.
7.6957	2.12	590.2899	590.2883	Benzoylmesaconine	C31H43NO10	MDAs	↑
7.0069	2.32	632.2998	632.2993	Mesaconitine	C33H45NO11	DDAs	↓
5.4621	2.23	574.2943	574.2938	Benzolhypacone	C31H43NO9	MDAs	↑
4.4166	2.38	616.3051	616.3043	Hypaconitine	C33H45NO10	DDAs	↓
3.9260	1.57	438.2761	438.2777	Neoline	C24H39NO6	ADAs	↓
3.8299	2.26	648.2942	648.2942	10-OH-mesaconitine	C33H45NO12	DDAs	↓
3.7518	1.52	454.2723	454.2727	Fuziline	C24H39NO7	ADAs	↓
3.7263	2.44	630.3209	630.3200	Deoxyaconitine	C34H47NO10	DDAs	↓
3.4060	2.21	604.3066	604.3043	Benzoylaconine	C32H45NO10	MDAs	↑
3.1107	1.9	464.2936	464.2934	14-Acetyl-talatisamine	C26H41NO6	MDAs	↓
2.9309	2.38	646.3151	646.3149	Aconitine	C34H47NO11	DDAs	↓
2.6283	1.67	422.2873	422.2828	Talatisamine	C24H39NO5	ADAs	↓
2.6138	1.36	408.2373	408.2672	Isotalatizidine	C23H37NO5	ADAs	↓
2.4717	1.43	358.2314	358.2304	Songorine	C22H31NO3	Veatchine	↓
2.1994	1.6	420.2645	420.2672	14-acetylkarakoline	C24H37NO5	MDAs	↓
2.1964	1.82	452.2936	452.2934	Chasmanine	C25H41NO6	ADAs	↓
2.1082	3.28	496.2899	496.2832	8-acetyl-15-hydroxyneoline	C26H41NO8	MDAs	↑

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3.3 Changes of relative intensity of metabolic biomarkers

PCA based on the metabolite accumulation patterns clearly showed differences among Fuzi and its processed products by UPLC-HDMS analysis. In examining the multivariate data in more detail, clear clustering into four parts from PCA was observed, indicating that Paozhi makes its components change. A comparison of metabolite profiles was conducted to determine the variation extent between Fuzi and its processed products. In order to more clearly characterize the action of Paozhi, the expression level of markers identified in the different processed groups was analyzed to illustrate how Paozhi exhibits detoxification (Fig. 8, 9 and 10). One advantage of the metabolomics approach is that it can give marker metabolites that contribute to the differentiation. At present, a comparison of the key metabolites in Fuzi revealed that relative concentrations of DDAs, i.e., aconitine, mesaconitine, hypaconitine, deoxyaconitine, 10-OH-mesaconitine, in HSP and BFP, were significantly decreased, while that of YFZ was increased (Fig. 8). It is already known that the detoxification of its roots is bound to a hydrolysis procedure. HSP and BFP is thoroughly boiled with salt solution or steamed over a few days. In general, the DDAs content of unprocessed roots is higher than that of processed roots. YFZ had lower concentrations of MDAs, i.e., benzolhypacone, benzoylmesaconine, benzolhypacone, and HSP and BFP had higher concentrations than did the Fuzi group. However, YFZ had higher concentrations of MDAs, i.e., 14-acetyl-talatisamine, 8-acetyl-15-hydroxyneolin, chasmanine, 14-acetylkarakolin, and HSP and BFP had lower concentrations than did the Fuzi group (Fig. 9). Compared to Fuzi and its processed products, the relative level of ADAs (except fuziline) in the processed HSP and BFP was increased (Fig. 10). These results showed that Paozhi could play a key role in detoxification. Metabolomic analysis represents the highest contributing signals for Paozhi that could decompose the poisonous DDAs into less or non-toxic derivatives.

Fig. 8 Graphical representation of relative intensity of biomarkers diester diterpene alkaloids (DDAs) between Fuzi and its processed products. The graph displays significant changes in the metabolite level (relative changes; data are standardized).

Fig. 9 The relative mean height intensity of biomarkers MDAs between Fuzi and its processed products. The graph displays significant changes in the metabolite level (relative changes; data are standardized).

Fig. 10 Graphical representation of relative changes of biomarkers ADAs between Fuzi and its processed products. The graph displays significant changes in the metabolite level (relative changes; data are standardized).

In the post genomics era, metabolomics is fast emerging as a vital source of information to aid in solving systems biology puzzles with an emphasis on the metabolome of a biological sample.^27–29 Coverage of the entire plant metabolome is a daunting task as it is estimated that there are over 200 000 different metabolites within the plant kingdom.³⁰ Metabolomics is a whole metabolite profiling approach for plant samples. It is often combined with multivariate statistical analysis to efficiently assess principal factors contributing to the phenotypic changes and to monitor the concentration changes of metabolite and profiles in response to environmental challenges.^31,32 At present, metabolomics has provide information about the basic toxicity mechanism of Fuzi and its processed products, as well as potential metabolic biomarkers which can be used for safety evaluation processes.

Conclusion

In this study, the comprehensive metabolomic characters of Fuzi and its processed products by UPLC-Q-TOF-HDMS combined with pattern recognition methods was investigated and highly desirable in order to guarantee the clinical safety and its low toxicity in decoctions. The present study proves that metabolomics based UPLC-Q-TOF-HDMS coupled with multivariate analyses successfully identify specific changes of metabolites between Fuzi and its processed preparations, and 19 key biomarkers which are responsible for the detoxifying measure of Paozhi were discovered. The underlying regulations of Paozhi-perturbed metabolic pathways were discussed according to the identified metabolites. The UPLC-Q-TOF-HDMS analysis revealed that the levels of these metabolites as biomarkers were significantly changed after Paozhi. Traditional Chinese processing approach, Paozhi can decompose the poisonous DDAs into less or non-toxic derivatives. Metabolomic analysis represents the highest contributing signals for the pre- and post-treatment. This method is fast and efficient, with more accurate characterization in the detoxification by traditional Paozhi. It will be helpful to investigate Fuzi in toxicology and pharmacology. Further, it can be expected that the Paozhi approach provides an effective and essential tool for detoxification of Chinese toxicity medicines.

Table 4 Main MS/MS fragmentations of potential biomarkers of Fuzi and its processed products in positive mode^a

No	Compound	Proposed structure	MS/MS	Type
a Note: MDAs, monoester diterpene alkaloids; ADAs, amine diterpenoid alkaloids; DDAs, diester diterpene alkaloids.
1	Benzoylmesaconine		590[M+H]^+	MDAs
			572[M+H–H2O]^+
			558[M+H–CH4O]^+
			540[M+H–CH6O2]^+
			508[M+H–C2H10O3]^+
			386[M+H–C9H16O5]^+
			105[M+H–C24H39NO9]^+
2	Mesaconitine		632[M+H]^+	DDAs
			602[M+H–CH2O]^+
			572[M+H–C2H4O2]^+
			540[M+H–C3H8O3]^+
			512[M+H–C4H8O4]^+
			496[M+H–C5H12O4]^+
			390[M+H–C11H14O6]^+
			105[M+H–C26H41NO10]^+
3	Benzolhypacone		574[M+H]^+	MDAs
			542[M+H–CH4O]^+
			510[M+H–C2H8O2]^+
			386[M+H–C8H12O5]^+
			105[M+H–C24H39NO8]^+
4	Hypaconitine		616[M+H]^+	DDAs
			584[M+H–CH4O]^+
			556[M+H–C2H4O2]^+
			524[M+H–C3H8O3]^+
			494[M+H–C4H10O4]^+
			105[M+H–C26H41NO9]^+
5	Neoline		438[M+H]^+	ADAs
			420[M+H–H2O]^+
			402[M+H–H4O2]^+
			388[M+H–CH6O2]^+
			370[M+H–CH8O3]^+
			342[M+H–C3H12O3]^+
6	10-OH-Mesaconitine		648[M+H]^+	DDAs
			616[M+H–CH4O]^+
			598[M+H–CH6O2]^+
			590[M+H–C2H2O2]^+
			556[M+H–C3H8O3]^+
			538[M+H–C3H10O4]^+
			528[M+H–C4H8O4]^+
			506[M+H–C4H14O5]^+
			105[M+H–C26H41NO11]^+
7	Fuziline		454[M+H]^+	ADAs
			436[M+H–H2O]^+
			422[M+H–CH4O]^+
			408[M+H–C2H6O]^+
			404[M+ H–CH6O2]^+
			374[M+H–C2H8O3]^+
			344[M+H–C4H14O3]^+
8	Deoxyaconitine		630[M+H]^+	DDAs
			570[M+H–C2H4O2]^+
			538[M+H–C3H8O3]^+
			510[M+H–C4H8O4]^+
			478[M+H–C5H12O5]^+
			416[M+H–C10H14O5]^+
			388[M+H–C11H14O6]^+
			105[M+H–C27H43NO9]^+
9	Benzoylaconine		604[M+H]^+	MDAs
			586[M+H–H2O]^+
			572[M+H–CH4O]^+
			496[M+H–C4H12O3]^+
			105[M+H–C25H41NO9]^+
10	14-Acetyl-talatisamine		464[M+H]^+	MDAs
			432[M+H–CH4O]^+
			372[M+H–C4H12O2]^+
			314[M+H–C6H14O4]^+
11	Aconitine		646[M+H]^+	DDAs
			586[M+H–C2H4O2]^+
			556[M+H–C3H6O3]^+
			522[M+H–C4H12O4]^+
			404[M+H–C11H14O6]^+
			105[M+H–C27H43NO10]^+
12	Talatisamine		422[M+H]^+	ADAs
			390[M+H–CH4O]^+
			358[M+H–C2H8O2]^+
			344[M+H–C3H10O2]^+
			326[M+H–C3H12O3]^+
13	Isotalatizidine		408[M+H]^+	ADAs
			390[M+H–H2O]^+
			358[M+H–CH6O2]^+
			340[M+H–CH8O3]^+
			328[M+H–C2H8O3]^+
14	Songorine		358[M+H]^+	Veatchine
			340[M+H–H2O]^+
			330[M+H–C2H4]^+
			322[M+H–H4O2]^+
			298[M+H–C3H8O]^+
15	14-Acetylkarakoline		420[M+H]^+	MDAs
			402[M+H–H2O]^+
			388[M+H–CH4O]^+
			370[M+H–CH6O2]^+
			356[M+H–C2H8O2]^+
			340[M+H–C2H8O3]^+
			328[M+H–C4H12O2]^+
			398[M+H–C4H10O4]^+
16	Chasmanine		452[M+H]^+	ADAs
			420[M+H–CH4O]^+
			388[M+H–C2H8O2]^+
			370[M+H–C2H10O3]^+
			328[M+H–C5H16O3]^+
			312[M+H–C5H16O4]^+
17	8-Acetyl-15-hydroxyneoline		496[M+H]^+	MDAs
			436[M+H–C2H4O2]^+
			404[M+H–C3H8O3]^+
			376[M+H–C4H8O4]^+
			360[M+H–C4H8O5]^+
			358[M+H–C4H10O5]^+
18	Karacoline		378[M+H]^+	ADAs
			360[M+H–H2O]^+
			342[M+H–H4O2]^+
			328[M+H–C2H10O]^+
19	Karacolidine		376[M+H]^+	ADAs
			358[M+H–H4O2]^+
			328[M+H–CH6O3]^+
			322[M+H–C4H8O]^+

---

Abbreviations

UPLC-Q-TOF-HDMS	ultra-performance liquid-chromatography/electrospray-ionization synapt high-definition mass spectrometry
TCM	traditional Chinese medicine
Fuzi	lateral root of Aconitum carmichaelii Debx
CW	Chuanwu
DDAs	diester diterpene alkaloids
MDAs	monoester diterpene alkaloids
ADAs	Amine diterpenoid alkaloids
YFZ	Yanfuzi
HSP	Heishunpian
BFP	Baifupian
CP	Chinese Pharmacopoeia
PCA	the principal component analysis
PLS-DA	partial least squares-discriminant analysis
OPLS	orthogonal projection to latent structures analysis
BPI	based peak intensity
VIP	variable importance plot.

Acknowledgements

This work was supported by grants from the Key Program of the Natural Science Foundation of the State (Grant No. 90709019), the National Key Program on the Subject of Drug Innovation (Grant No. 2009ZX09502-005), the National Specific Program on the Subject of Public Welfare (Grant No. 200807014), and the National Program for Key Basic Research Projects in China (Grant No. 2005CB523406).

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