A metabolomic analysis of the toxicity of Aconitum sp. alkaloids in rats using gas chromatography/mass spectrometry

RAPID COMMUNICATIONS IN MASS SPECTROMETRY

Rapid Commun. Mass Spectrom. 2009; 23: 1221–1228

) DOI: 10.1002/rcm.3992
Published online in Wiley InterScience (www.interscience.wiley.com
A metabolomic analysis of the toxicity of Aconitum sp.

alkaloids in rats using gas chromatography/mass

spectrometry

Bo Sun1,2, Shengming Wu2, Ling Li2, Haijing Li2, Qi Zhang2, Hebing Chen2, Famei Li1,

Fangting Dong2* and Xianzhong Yan2*1Department of Analytical Chemistry, Shenyang Pharmaceutical University, Shenyang 110016, China2National Center of Biomedical Analysis, Beijing 100850, China

Received 3 November 2008; Revised 13 January 2009; Accepted 18 February 2009

*CorrespoBiomedicE-mails: yContract/China; coContract/China; co

A metabolomic investigation of intoxication with Aconitum sp. alkaloids was carried out. Plasma

obtained from Wistar rats administered these alkaloids was analyzed using gas chromatography/

time-of-flight mass spectrometry. Samples were analyzed following protein precipitation, liquid-

liquid extraction, and derivatization. Thirty-six metabolites were identified among the detected

compounds. Subsequent data analysis using the t test and principal component analysis revealed

metabolic differences between the control rats and treated groups as well as between the groups of

rats administered different alkaloids. Twenty-seven metabolites were significantly different

between plasma samples from the controls and treated groups. The significant decreased plasma

levels of glutamine and creatinine in all treated groups suggested impaired heart andmuscle function

caused by alkaloids. The plasma levels of 22metabolites in the hypaconitine groupwere significantly

decreased. In contrast, only 8 and 13metabolites were observed with significantly decreased levels in

the aconitine and mesaconitine groups, respectively.These results indicated that Aconitum sp.

alkaloids can cause metabolic disorders in rats. The toxicity and corresponding mechanism of

hypaconitine was different from those of aconitine and mesaconitine, based on the differences of

perturbed metabolic patterns between groups. Copyright # 2009 John Wiley & Sons, Ltd.

Metabolomics is the systematic study of the global metabolic

response of living systems to environmental stimuli. In the

post-genomic era, it has become an important tool in the

fields of biology and medicine.1–3 Metabolomic investi-

gations are usually conducted using biofluids, animal and

plant tissues, or tissue extracts as sample material. It has

recently been demonstrated that metabolomics has enor-

mous potential in diverse fields such as plant genotype

discrimination,3,4 drug discovery, toxicological mechanisms

and disease processes.5–11 One of the important applications

of metabolomics is to evaluate the biological effects of

xenobiotics, e.g., drugs and toxins, which produce distinctive

metabolic perturbations that are characteristic of the type of

tissue damage and/or the mechanism of toxicity

involved.12,13 Metabolomic techniques are widely used in

the investigation of drug-induced liver and kidney toxicity.

They have also been used in the evaluation of cardiac,

neurological, testicular, and mitochondrial toxicities.14

ndence to: X. Yan and F. Dong, National Center ofal Analysis, 27 Taiping Road, Beijing 100850, [email protected]; [email protected] sponsor: National Natural Science Foundation ofntract/grant number: 90409019.grant sponsor: Ministry of Science and Technology ofntract/grant number: 2005JG200070.

Nuclear magnetic resonance (NMR) spectroscopy and gas

chromatography/mass spectrometry (GC/MS) are the two

most commonly used techniques in the field of metabolo-

mics.15,16 Both techniques are capable of generating multi-

variate metabolic data. Moreover, GC/MS has evolved as an

indispensable technology in metabolomic analysis due to its

resolution and sensitivity.3,17–20 The application of GC/MS in

metabolomics can be traced to its use in urinary screening

procedures to determine the presence of diseases related to

organic acidemia.21 GC is being increasingly used in the

metabolomic analysis of plants,22,23 diseases,24 and toxici-

ties.25 In addition, GC/MS is being employed in microbial

and clinical metabolomics to analyze biofluids or breath

samples.26 The coupling of GC to time-of-flight mass

spectrometry (GC/TOF-MS), in particular, can achieve

accurate mass spectral deconvolution and an appreciable

linear dynamic range, which is useful in the analysis of

complex samples.19,27

The lateral root of Aconitum has been widely used in

traditional Chinese medicine known as Fu Zi. Aconitine,

mesaconitine, and hypaconitine are the main diterpenoid

alkaloids in aconite root. They have analgesic, antipyretic,

and local anesthetic activity.28 They have also been demon-

strated to be extremely effective in the treatment of

rheumatoid arthritis and other inflammations.29,30 However,

these alkaloids are also highly toxic, with a relatively narrow

Copyright # 2009 John Wiley & Sons, Ltd.

1222 B. Sun et al.

margin of safety. Themain targets of the primary toxic effects

of these alkaloids are the heart and the central nervous

system. These alkaloids can induce arrhythmias and flutter

or fibrillation of the cardiac and skeletal muscles.31 The

clinical symptoms of intoxicationwithAconitum sp. alkaloids

are nausea, vomiting, dizziness, palpitation, hypotension,

arrhythmia, shock, and coma.32

In this paper, we have explored the use of GC/MS in the

global metabolic profiling of rat plasma in toxicity studies of

Aconitum sp. alkaloids as a part of our metabolomic

investigation of the toxicity of this traditional Chinese

medicine. We aimed to investigate the mechanism of toxicity

of these alkaloids at the metabolic level to determine their

safety for use in clinical medicine.

EXPERIMENTAL

Chemicals and reagentsN-Methyl-N-(trimethylsilyl)trifluoroacetamide (MSTFA),

chlorotrimethylsilane (TMCS), methoxyamine hydrochlo-

ride, ribitol, pyridine, D-(þ)-glucose and 1,5-anhydro-D-

sorbitol (1-deoxyglucose) were purchased from Sigma-

Aldrich (St. Louis, MO, USA). Methanol and chloroform

(high performance liquid chromatography (HPLC) grade),

D-(þ)-galactose, D-fructose, andD-mannosewere purchased

from the Beijing Chemical Agent Co. (Beijing, China).

Sample collectionThirty-six male Wistar rats were allowed to acclimatize in

metabolism cages for 1week. Food and water were provided

ad libitum. The rats weighed 187–236 g at the end of the

acclimatization period. The rats were then randomly

divided into four groups of nine each. Aconitine

(0.28mg�kg�1�day�1), mesaconitine (0.30mg�kg�1�day�1),

hypaconitine (1.09mg�kg�1�day�1), or vehicle (acidified

water) was administered intragastrically to rats for 15 suc-

cessive days. The dosage for each alkaloid was about one-

third of their LD50 respectively.33,34 On the 16th day, the

animals were anesthetized with pentobarbital, and blood

samples were obtained from the femoral artery and collected

in heparinized tubes. Plasma was obtained by centrifugation

at 4000 g for 10min and then stored at �208C until analysis.

Analysis of rat plasma samples by GC/MS

Sample preparationThe plasma samples were prepared and derivatized as

reported previously.35 To each plasma sample (100mL),

methanol (500mL) was added. Then, ribitol (15mL, 0.2mg/

mL in deionized water), which was used as internal

standard, and deionized water (15mL) were added to the

mixture. The mixture was shaken (100 rpm) at 708C for

15min and subsequently centrifuged (13000 rpm for 10min).

The supernatant was separated and mixed with deionized

water (450mL) and chloroform (270mL). The mixture was

shaken (80 rpm) at 358C for 5min and centrifuged at

4000 rpm for 10min. The polar phase was separated and

evaporated under a stream of N2 gas to dryness in about

90min. The dried residue was dissolved in 40mL of


methoxamine hydrochloride (20mg/mL pyridine) and

incubated at 308C for 90min with continuous shaking. Then

40mL of MSTFA (1% TMCS) was added to exchange acidic

protons at 378C for 30min. The derivatized samples were

stored at room temperature for 120min before injection.

GC/MS conditionsGC/TOF-MS analysis was performed using an Agilent

6890N gas chromatograph and a Waters Micromass GCT

mass spectrometer. Chromatography was performed on a

DB-5 MS capillary column (30m� 0.25mm i.d.; 0.25mm

thickness). Helium carrier gas was used at a constant flow

rate of 1mL/min. The GC oven temperature was first held at

708C for 0.1min and then ramped at 58C/min to a final

temperature of 3108C that was maintained for 1min. The

injection, interface, and source temperatures were set at

2308C, 2908C, and 2208C, respectively, with an electron

energy of 70 eV and a trap current of 70mA. TheGC/TOF-MS

system was operated at a multichannel plate voltage of

2800V, a pushout voltage of 980V, and a pusher interval of

40ms. After a solvent delay of 5min, mass spectra over the

m/z range 50–800were acquired at a scan rate of 0.5 s per scan

and an interscan delay of 0.1 s in centroid mode.

Data analysisTotal ion chromatograms (TICs) were acquired using the

MassLynx software (Waters Co., USA). Peaks with intensity

higher than 10-fold of the signal-to-noise (S/N) ratio were

recorded and integrated. The electron impact (EI) GC/MS

datawere converted into CDF format files for peak extraction

by Automated Mass Spectral Deconvolution and Identifi-

cation System (AMDIS) followed by compound identifi-

cation using National Institute of Standards and Technology

(NIST02) library with EI spectra for all the recorded peaks in

the TICs. Due to the existence of isomers, the EI spectra from

sugars were very similar; therefore, the standard substances

were used for the identification of sugars. In addition, the

GC/MS data were also processed using the MarkerLynx

Applications Manager software (Waters Co., USA). The

software incorporates a peak deconvolution package,

which allows the detection and retention time alignment

of the peaks in each data file across the whole data set.

The intensities of these peaks were normalized to that of

the internal standard, which was arbitrarily set to 100.

MarkerLynx extracts components and generates a matrix of

detected peaks represented by their m/z and retention time

pairs along with their associated intensities. These data were

exported to SIMCA-P plus (Umetrics, Sweden) for principal

component analysis (PCA).

The intensities of the identified metabolites in the control

and dosed groups were compared using the two-tailed

Welch’s t test. To minimize the number of missing values,

only identified metabolites that were consistently detected in

at least 80% of the samples were included in this t test. A

value of P <0.05 was considered statistically significant.

Using the Kyoto Encyclopedia of Genes and Genomes

(KEGG) database, the metabolic pathways which were

affected by the Aconitum sp. alkaloids were identified.36


DOI: 10.1002/rcm

Analysis of the toxicity of Aconitum sp. alkaloids 1223

RESULTS AND DISCUSSION

GC/MS analysis of plasma samplesTypical GC/TOF-MS TICs of the plasma samples from the

rats in both the control and dosed groups are shown in Fig. 1.

Chromatogram analysis was restricted to 50min. Identifi-

cations of endogenousmetabolites were based on the NIST02

mass spectral database search or by comparison with the

standard compounds based on the retenton time and EI

fragments.

The identification of sugars, such as aldohexoses, was

challenging, since they are all isomers with the same

molecular weights and exhibit very similar fragment ions

in EI spectra. Therefore, they could not be identified exactly

only based on the comparison with the library. Authentic

standards should be introduced to obtain a TIC and EI

spectrum, with which the specified peak in the TICs of

samples should be consistent. An example is presented in

Fig. 2 for the identification of 1-deoxyglucose. Another

example is the identification of the compound eluting at

25.32min, the best library match to which is the glucose

2,3,4,5,6-pentakis-O-(trimethylsilyl)-, O-methyl oxime, with

only a probability of 29.0%. However, based on the

comparison with the elution sequences of possible aldo-

hexoses and the fact that the peak at 25.51min has been

identified as glucose, this peak is more likely from the

derivative of mannose. Comparison with the TIC of a

Figure 1. Comparison of GC-TOF-MS TICs of plasma from

hypaconitine group (C), and the control group (D). The figu


mannose standard and its corresponding EI-MS spectrum

confirmed this result (Fig. 3).

Thirty-six of these metabolites were identified to be amino

acids, fatty acids, sugars, and organic acids. The peaks in

TICs of the plasma samples represented the endogenous

metabolites in plasma. Therefore, each TIC could be

considered as a fingerprint of endogenous metabolites in

plasma, reflecting the metabolic changes induced by

Aconitum sp. alkaloids.

Changes in plasma metabolites of ratsadministered Aconitum sp. alkaloidsThirty-one identifed metabolites were analyzed by the two-

tailed Welch’s t test (Table 1). Among them, the concen-

trations of 27metabolites, including 12 amino acids, 8 organic

acids, and 3 sugars, in at least one dosed group were

significantly different between plasma samples from the

controls and treated groups. In particular, the concentrations

of valine, isoleucine, serine, creatinine, glutamine, ornithine,

and lysine were significantly lower in all the three dosed

groups than those in the control group. While the levels of

alanine, glycine, threonine, aspartic acid, phenylalanine, and

tyrosine were significantly lower in both the mesaconitine

and hypaconitine groups, the plasma levels of lactic acid,

phosphonic acid, erythronic acid, citrate,1-deoxyglucose,

fructose, mannose and palmitic acid were significantly

the aconitine group (A), the mesaconitine group (B), the

res above the peaks represent their retention times.


DOI: 10.1002/rcm

Figure 2. Identification of sugars in plasma samples. (A) Expanded regions of TICs for (a) a plasma sample and (b)1-

deoxyglucose standard. (B) EI-MS spectra for the peak (24.62min) identified as 1-deoxyglucose from (a) plasma sample and

(b) the authentic standard.

Figure 3. Identification of sugars in plasma samples. (A) EI-MS for the peak (25.32min) identified as

mannose and (B) EI-MS for the authentic standard.

Copyright # 2009 John Wiley & Sons, Ltd. Rapid Commun. Mass Spectrom. 2009; 23: 1221–1228

DOI: 10.1002/rcm

1224 B. Sun et al.

Table 1. Relative levels of metabolites detected by GC/TOF-MS in the plasma of rats following Aconitum sp. alkaloids treatment

No.Retentiontime (min)

Identifiedmetabolites

Control group Aconitine group Mesaconitine group Hypaconitine group

means� SD means� SD P means� SD P means� SD P

1 6.16 Lactic acid 99.91� 2.85 101.78� 33.42 2.28E-01 100.94� 2.66 4.43E-01 92.26� 7.267 1.42E-022 7.08 Alanine 29.48� 4.78 33.24� 10.49 3.48E-01 23.58� 5.78 3.17E-02 21.77� 3.77 1.71E-033 8.44 3-Hydroxybutyric acid 90.94� 12.10 68.17� 25.19 3.16E-02 81.69� 10.48 1.02E-01 72.94� 8.32 2.44E-034 9.75 Valine 26.30� 4.48 21.76� 3.66 3.21E-02 17.76� 7.39 1.09E-02 22.17� 2.64 3.32E-025 10.94 Urea 67.79� 24.52 79.67� 11.84 2.16E-01 84.94� 3.75 7.02E-02 64.81� 6.72 7.33E-016 11.24 Glycerol 49.16� 14.48 67.45� 9.85 7.27E-03 56.28� 11.83 2.71E-01 35.16� 4.71 2.08E-027 11.33 Phosphonic acid 61.42� 11.07 59.61� 10.95 7.33E-01 61.65� 7.63 9.60E-01 51.69� 7.46 4.62E-028 11.71 Isoleucine 18.54� 4.42 12.15� 3.29 3.47E-03 11.88� 3.85 3.68E-03 13.90� 1.62 1.44E-029 12.01 Glycine 47.89� 8.73 44.06� 9.58 3.88E-01 33.54� 13.96 2.09E-02 38.91� 4.56 1.80E-0210 12.66 Glyceric acid 3.85� 1.38 10.56� 4.12 9.96E-04 6.13� 3.20 7.53E-02 2.84� 0.70 7.40E-0211 13.43 Serine 43.79� 6.44 31.78� 4.18 3.67E-04 20.98� 11.61 2.10E-04 35.25� 2.75 3.87E-0312 14.05 Threonine 55.95� 7.52 48.56� 9.56 8.81E-02 33.73� 16.68 3.80E-03 45.42� 4.31 3.06E-0313 16.13 Aspartic acid 35.02� 5.65 43.90� 20.31 2.37E-01 27.91� 11.65 1.26E-01 27.53� 11.06 9.54E-0214 17.37 Proline 22.40� 4.05 38.19� 12.92 6.15E-03 35.74� 11.42 8.00E-03 20.53� 4.92 3.91E-0115 17.88 2,3,4-Erythronic acid 3.26� 0.82 4.64� 2.23 1.11E-01 3.55� 1.51 6.20E-01 2.08� 0.50 2.82E-0316 18.05 Creatinine 29.80� 8.81 8.76� 5.69 3.47E-05 3.09� 4.92 4.37E-06 20.40� 8.78 3.75E-0217 19.77 Phenylalanine 16.68� 2.73 14.61� 3.81 2.06E-01 7.94� 6.14 2.46E-03 12.37� 2.08 1.89E-0318 23.10 Glutamine 52.21� 8.14 9.58� 13.11 1.03E-06 1.24� 1.68 4.49E-08 39.94� 8.69 7.08E-0319 23.91 Ornithine 8.58� 3.31 3.52� 0.83 1.60E-03 1.49� 1.35 1.92E-04 3.69� 1.62 1.94E-0320 24.00 Citric acid 24.50� 5.84 30.70� 11.38 1.72E-01 21.55� 9.92 4.55E-01 17.51� 7.59 4.47E-0221 24.62 1-Deoxyglucose 51.11� 10.46 58.33� 24.01 4.26E-01 52.77� 16.36 8.01E-01 37.96� 4.65 5.43E-0322 25.02 Fructose 3.67� 1.45 3.25� 1.54 5.65E-01 2.51� 1.15 8.05E-02 2.36� 0.84 3.67E-0223 25.32 Mannose 32.39� 4.01 36.37� 12.15 3.73E-01 33.47� 9.41 7.57E-01 26.74� 3.55 6.08E-0324 25.54 Glucose 102.58� 2.16 103.80� 5.57 5.54E-01 107.61� 13.83 3.11E-01 106.46� 7.45 1.67E-0125 26.09 Lysine 62.34� 11.00 49.14� 9.75 1.61E-02 27.11� 19.31 4.01E-04 49.64� 9.64 1.93E-0226 26.41 Tyrosine 12.27� 3.17 13.38� 2.98 4.52E-01 6.15� 4.37 4.12E-03 8.15� 1.91 5.28E-0327 28.55 Palmitic acid 51.07� 10.68 60.96� 13.88 1.11E-01 52.77� 14.68 7.83E-01 40.80� 7.40 3.24E-0228 29.15 Inositol 20.00� 8.37 41.11� 21.16 1.86E-02 30.60� 19.18 1.57E-01 13.06� 6.20 6.44E-0229 29.50 Uric acid 1.34� 0.90 12.51� 10.18 1.17E-02 2.69� 2.36 2.02E-02 0.87� 0.39 7.66E-0230 31.44 Tryptophan 7.10� 3.05 5.01� 3.44 1.93E-01 4.07� 2.93 4.77E-02 5.33� 2.20 1.81E-0131 32.10 Stearic acid 42.74� 12.15 48.21� 18.30 4.68E-01 43.76� 18.37 8.92E-01 32.80� 10.84 8.59E-02


decreased only in the hypaconitine group. No increase in the

levels of metabolites in plasma from the hypaconitine group

was observed, whereas the concentrations of

glycerol, glyceric acid, proline, inositol, and uric acid were

significantly increased in the aconitine group or both the

aconitine and mesaconitine groups. In addition, there were

22metabolites in total whose plasma levels were significantly

decreased in the hypaconitine group. In contrast, only 8 and

13 metabolites were observed with significantly decreased

levels in the aconitine andmesaconitine groups, respectively.

These results strongly suggest that there are metabolic

differences between the dosed and control groups as well as

between the three groups of rats dosed with different

alkaloids.

PCA is a method that reduces data dimensionality by

performing a covariance analysis between factors.37 It is

suitable for the analysis of multidimensional data sets.

Principal components (PCs) are linear combinations of the

original variables. All the GC/TOF-MS data were further

analyzed by PCA. Each treated group was compared with

the control group in a PCA model.

PCA scores plots (Figs. 4(A)–4(C)) showed that the

aconitine and mesaconitine groups were well separated

from the control group along PC1, whereas the hypaconitine

and control groups were marginally separated. However,


separations between the control and each treated groupwere

all statistically significant as assessed by the t test analysis of

PCA scores (Table 2), though the P values for the aconitine

andmesaconitine groupsweremuch smaller than that for the

hypaconitine group. The much smaller P values for aconitine

and mesaconitine may indicate that they have stronger

toxicity than hypaconitine. It has been shown that the

approximate lethal dose (LD50) values (mg/kg) in mice

(intravenous, i.v.) for aconitine, mesaconitine, and hypaco-

nitine were 0.100, 0.0681, and 0.215–0.316, respectively.33,34

This indicated that the toxicity of hypaconitine is lower than

that of aconitine and mesaconitine, which is consistant with

the results of the present study. Similar changes were

observed in the metabolite levels in the aconitine and

mesaconitine groups as revealed by the corresponding

loadings, whereas these changes in the hypaconitine group

were slightly different from those in the other two groups. In

both the aconitine and mesaconitine groups, the peak

intensities of urea, proline, lactic acid, glycerol, and glucose

were increased while those of glutamine, glycine, serine,

threonine, lysine, and 3-hydroxybutyric acid were reduced

as compared to the corresponding intensities in the control

group. However, there were still some differences between

these two groups: the concentrations of alanine and aspartic

acid were increased in the aconitine group but were


DOI: 10.1002/rcm

Figure 4. PCA scores plots based on the data derived from the plasma samples of rats: (A) aconitine group compared with control

group; (B) mesaconitine group compared with control group; (C) hypaconitine group compared with control group. *, control

group; &, aconitine group; D, mesaconitine group; ^, hypaconitine group.

1226 B. Sun et al.

decreased in the mesaconitine group. On the other hand,

only the intensities of glucose were increased in the

hypaconitine group, while those of lactic acid, citric acid,

phosphonic acid, isoleucine, glycine, and serine were

decreased as compared to the corresponding values for

the control group. Similar to the results of the t test of the

peak intensities in TICs, the above-mentioned findings

indicated that the toxicity of hypaconitine was different

from that of aconitine and mesaconitine.

The above results demonstrated that the concentrations of

glutamine and creatinine were the most significantly

decreased among the metabolites. Glutamine is the most

abundant amino acid in the body38 and is involved in many

biological activities. For example, plasma glutamine plays an

important role as a carrier of nitrogen, carbon, and energy

between organs.39 It is a crucial chemical for muscles and

Table 2. The t test results of the first two PCs in each PCA

model

Aconitine group Mesaconitine group Hypaconitine group

PC1 4.685E-08 1.334E-08 1.296E-02PC2 9.744E-01 7.741E-01 3.532E-01


stimulates the growth of muscles and the synthesis of

intracellular proteins. Glutamine can protect the heart from

ischemia/reperfusion injury40 via the hexosamine biosyn-

thesis pathway and increased protein O-GlcNAc levels,41

and might be cardioprotective in patients with coronary

heart disease.42 It was also thought to be able to reduce the

toxicity caused by chemotherapy and radiation.43 Therefore,

the decreased plasma glutamine level in this study may be

indicative of the cardiac injury caused by the administration

of Aconitum sp. alkaloids, especially aconitine and mesaco-

nitine. Creatinine was formed from creatine or creatine

phosphate in muscle and was a measure of muscle mass.44

The decrease in plasma creatinine was therefore a reflection

of the reduced muscle mass or muscle atrophy, or was a

result of the reduced rate of hepatic production of creatine,

the precursor of creatinine, because of the impaired liver

function.45

The concentrations of many other metabolites, including

amino acids, fatty acids, sugars, and organic acids, were also

changed considerably by theAconitum sp. alkaloids (Table 1).

Twenty-two of these changed metabolites have been

associated with thirteen metabolic pathways related to the

tricarboxylic acid (TCA) cycle using the KEGG database36

(Fig. 5).


DOI: 10.1002/rcm

Figure 5. Effects of Aconitum sp. alkaloid toxicity on metabolic pathways. Citrate cycle, glycolysis/gluconeogenesis, fatty acid

metabolism and biosynthesis and a series of amino acids metabolism were influenced by the changes in level in plasma of

metabolites. Twenty identified metabolites were involved in these metabolic pathways.


CONCLUSIONS

In this study, we applied GC/TOF-MS to the metabolomic

analysis of plasma obtained from rats administeredAconitum

sp. alkaloids. We aimed to investigate the toxicity of these

alkaloids. Thirty-six metabolites were identified among the

detected compounds from TICs using the NIST02 mass

spectral database. The concentrations of 28 metabolites were

observed to be significantly changed in the dosed groups

when compared with the concentrations of the correspond-

ing metabolites in the control group. The patterns of the

perturbed metabolites in the rats administered aconitine and

mesaconitine were different from that in the hypaconitine

group, suggesting differences in the toxicity and the

corresponding mechanism between these alkaloids. The

TCA cycle and related pathways as well as the urea pathway

were affected by the administeredAconitum sp. alkaloids and

led to impaired metabolism.

AcknowledgementsThis work was supported by the National Natural Science

Foundation of China (No. 90409019) and the Ministry of

Science and Technology of China (No. 2005JG200070).


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DOI: 10.1002/rcm

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