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Intervention effects of puerarin on blood stasis in rats revealed by a [sup.1]H NMR-based metabonomic approach.


Puerarin possesses a wide spectrum of biological activities including ameliorating effects on blood stasis, but the definite mechanism of this effect is still not known. In this study, a [sup.1]H NMR-based plasma and urinary metabonomic approach was applied to comprehensively and holistically investigate the therapeutic effects of puerarin on blood stasis and its underlying mechanisms. Puerarin was injected intraperitoneally once daily for consecutive 7 days. The blood stasis rat model was established by placing the rats in ice-cold water during the time interval between two injections of adrenaline. With pattern recognition analysis, a clear separation of blood stasis model group and healthy control group was achieved and puerarin pretreatment group was located much closer to the control group than the model group, which was consistent with results of hemorheology studies. 15 and 10 potential biomarkers associated with blood stasis in plasma and urine, respectively, which were mainly involved in energy metabolism, lipid and membrane metabolisms, amino acid metabolism and gut microbiota metabolism, were identified. Puerarin could prevent blood stasis through partially regulating the disturbed metabolic pathways. This work highlights that metabonomics is a valuable tool for studying the essence of blood stasis as well as evaluating the efficacy of the corresponding drug treatment.



Blood stasis


Nuclear magnetic resonance


Puerarin, a daidzein-8-C-glucoside, is one of the key bioactive constituents isolated from the root of Pueraria lobata (Willd.) Ohwi, which is well known as Gegen (Chinese name) in traditional Chinese medicine (TCM). As one of three major isoflavonoid compounds of Gegen, puerarin has drawn much attention for its vasodilatory and cardioprotective activities, as well as inhibition of ischemia and reperfusion injury. Puerarin also shows antioxidative and anti-inflammatory effects. Currently, the injection form of puerarin has been approved by the State Food and Drug Administration in China for the complementary treatment of coronary heart disease, angina, myocardial infarction, etc. (Zhou et al. 2013).

Blood stasis plays an important role in the pathogenesis and development of many diseases such as angina pectoris and acute myocardial infarction (Wang et al. 2014). Described in TCM theory as a slowing or pooling of blood, it is often understood in biomedical terms in terms of hemorheological disorders (e.g., a rise in whole blood and plasma viscosity) (Li et al. 2009). However, the molecular pathogenesis of blood stasis is complicated, and thus further studies are necessary to characterize the precise underlying mechanisms.

As a new omics technique, metabonomics was defined as quantitative measurement of time-related multiparametric metabolic response of multicellular systems to pathophysiological stimuli or generic modification (Nicholson et al. 1999). Recently, using ultra-performance liquid chromatography coupled with mass spectrometry (UPLC/MS), the metabolic profiles of rats with blood stasis and the therapeutic effects of Xindi soft capsule (Zhao et al. 2008), Foshou powder (Huang et al. 2013) and Shaofu Zhuyu decoction (Su et al. 2013) were investigated. As we all know, both nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) (usually with a chromatographic separation step) are suitable techniques for metabonomic analysis but have different analytical strengths and weaknesses and give complementary information. To the best of our knowledge, metabonomic study of blood stasis in rat model based on NMR has not been reported.

Previous studies revealed that puerarin could significantly decrease the whole blood viscosity, erythrocyte aggregation index, red blood cell deformation index and maximal platelet aggregation rate which increased in rats with blood stasis (Pan et al. 2003); but little has been published on the response of rat biological systems to the intake of puerarin.

In this study, a NMR-based metabolic profiling approach was explored to characterize the metabolic signature of blood stasis in rats. The protective effects of puerarin against the metabolic alteration were also investigated with this strategy. The finding of metabolic pathways will be helpful to understand the essence of blood stasis and the underlying mechanisms of puerarin treatment.

Materials and methods

Chemicals and reagents

Water was produced by a Milli-Q, ultra-pure water system (Millipore, Bedford, MA, USA). Puerarin injection and adrenaline hydrochloride injection were purchased from Guangzhou Baiyunshan Tianxin Pharmaceutical Co., Ltd. (China) and Tianjin Jinyao Amino Acid Co., Ltd. (China), respectively. Other materials, unless otherwise stated, were obtained from Sigma-Aldrich (St. Louis, MO, USA).

Animal handling and sample collection

This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All experimental procedures were approved by the Committee on the Ethics of Animal Experiments of Guangdong Pharmaceutical University.

A total of 18 male Sprague-Dawley (SD) rats weighing 180 [+ or -] 10 g were commercially obtained from the Experimental Animal Center of Sun Yat-Sen University (Guangdong, China), kept in plastic cages at a barrier system and provided with a certified standard rat chow and tap water ad libitum. Room temperature and humidity were regulated at 22 [+ or -] 2[degrees]C and 50 [+ or -] 10%, respectively. A12/12-h light-dark cycle was set, with lights on at 8 a.m. After 7 days of acclimation, the animals were transferred to individual metabolic cages and randomly divided into the following three groups with six rats per group: (1) healthy control group, (2) blood stasis model group, (3) puerarin pretreatment group. The pretreatment group was injected intraperitoneally with puerarin at a dose of 60 mg/kg (the weight of puerarin/body weight) once daily for consecutive 7 days. The dose level in this study was set according to the literature (Pan et al. 2003). The control and model groups received once daily the same volume of vehicle as the puerarin pretreatment group for 7 days. Blood stasis was induced following established protocol: After the last dosing on day 7, rats in model and puerarin pretreatment groups were subcutaneously injected with adrenaline at a dose of 0.8 mg/kg of body weight. After 2 h, the rats were soaked in ice-cold water (0-2[degrees]C) for 5 min keeping their heads outside surface, and then re-injected with adrenaline (0.8 mg/kg) subcutaneously at 2 h after the ice-bath (Li et al. 2009). Rats in the control group only received an equal volume of saline with subcutaneous injection. Samples of 18 h urine were collected into ice-cooled vessels containing 0.5 ml of 2% sodium azide during which rats fasted and were allowed free access to water. Then, blood was collected from the retro-orbital venous sinus into heparinized (20 U/ml) tubes after rats were anaesthetized with diethyl ether inhalation and plasma was separated from blood by centrifugation at 4000 rpm for 10 min at 4[degrees]C.

Viscosity and hematocrit determination

A total of 800 [micro]l blood or plasma was used to determine the whole blood viscosity at shear rats of 1, 50, 100 and 200 [s.sup.-1] and plasma viscosity at shear rate of 50 [s.sup.-1] using a cone-plate viscometer (Model LG-R-80B, Steellex Co., China) maintained at 37[degrees]C. After centrifugation at 12,000 rpm for 10 min with a microhematocrit centrifuge (Model TGL-12B, ShangHai Anting Scientific Instrument Factory, China), hematocrit was immediately measured in a capillary tube.

All data were presented as mean [+ or -] standard deviation (SD). Statistical analysis was carried out using one-way analysis of variance (ANOVA) and Dunnett's test. The values of P < 0.05 were considered statistically significant.

Sample preparation and NMR measurements

For plasma samples, 50 [micro]l of buffer solution (0.2 M [Na.sub.2]HP[O.sub.4] and 0.2 M Na[H.sub.2]P[O.sub.4], pH 7.4) and 50 [micro]l of [D.sub.2]O were added to 400 [micro]l of each sample. For urine samples, 200 [micro]l of buffer solution (0.2 M [Na.sub.2]HP[O.sub.4] and 0.2 M Na[H.sub.2]P[O.sub.4], pH 7.4) was mixed with 400 [micro]l urine to minimize variations in the pH of the urine samples. The samples were allowed to stand for 20 min prior to centrifugation at 4000 rpm for 10 min at 4[degrees]C to remove any precipitates. Aliquots of the supernatant (500 [micro]l) from each sample were transferred into 5 mm NMR tubes followed by adding 50 [micro]l of [D.sub.2]O containing 0.05% (w/v) of sodium 3-trimethylsilyl [2,2,3,3-[d.sub.4]] propionate (TSP-[d.sub.4]). The TSP acted as a chemical shift reference ([delta]0), and the [D.sub.2]O provided a lock signal. All [sup.1]H NMR spectra were randomly measured at 298 K on a Bruker AVANCE III 500 MHz spectrometer (BrukerBiospin, Rheinstetten, Germany) operating at 500.13 MHz[sup.1]H frequency. For plasma samples, the water-suppressed Carr-Purcell-Meibom-Gill (CPMG) spin-echo pulse sequence (RD-90[degrees]-([tau]-180[degrees]-[tau])n-ACQ) with a total spin-echo delay (2n[tau]) of 100 ms was used to attenuate broad signals from proteins and lipoproteins. Sixty-four free induction decays (FIDs) were collected into 64k data points over a spectral width of 10,000 Hz with a relaxation delay of 3 s and an acquisition time of 3.28 s. For urine samples, all [sup.1]H NMR spectra were collected using a standard ID nuclear overhauser enhancement spectroscopy (NOESY)-presaturation pulse sequence. Sixty-four free induction decays (FIDs) were collected into 64k data points. Spectra were acquired with a spectral width of 10,000 Hz and an acquisition time of 3.28 s. Relaxation delay was set at 3 s.

Data processing and multivariate statistical analysis

The obtained spectra of plasma and urine samples were processed with a 0.3 Hz line-broadening factor prior to Fourier transformation and manually corrected for phase and baseline distortions using MestReNova 6.1 software package (Mestrelab Research S.L, Santiago de Compostela, Spain). The NMR spectra of plasma were referenced to methyl resonance of lactate ([delta]1.33). The integration was performed over [delta]9.0-0.5 region with the bucket width set to 0.01. The region of [delta]4.68-5.22 was removed to eliminate the effects of imperfect water saturation. The NMR spectra of urine were referenced to the chemical shift of TSP at[delta]. The spectra in the region [delta]9.5-0.5 were integrated with the bucket width set to 0.01, leaving out the region of [delta]4.48-5.98, which contained signals from residual water and urea. All remaining regions of the spectra were then normalized to the total integrated area of the spectra to reduce any significant concentration differences.

The resulting dataset was imported into SIMCA-P 12.0 software (Umetrics, Umea, Sweden) for multivariate data analysis after mean-centering and pareto scaling. The supervised partial least-squares discriminant analysis (PLS-DA) was performed to achieve the maximum separation between samples and identify differential metabolites that account for the separation between groups. To avoid overfitting of PLS-DA models, a default 7-fold cross-validation method was applied, from which values of goodness of fit ([R.sup.2]Y) and predictability ([Q.sup.2]) were computed. In addition, model validation was also performed by 200 times permutation tests. Metabolites with VIP (variable importance in the projection) values [greater than or equal to] 1.0 were considered significant in this study. Meanwhile, an independent sample t-test or Mann-Whitney U-test was further used to validate those major contributing variables from the PLS-DA models using SPSS 20.0 (SPSS Inc., Chicago, 1L, USA). A value of P < 0.05 was considered statistically significant. Only those metabolites that meet the two criteria are eventually considered as potential biomarkers.


Effects of puerarin on hemorheological parameters in rats with blood stasis

The whole blood viscosity at all shear rates and plasma viscosity significantly increased (P < 0.05) in the blood stasis model group compared to those in the control group (Table 1), and were significantly decreased in the puerarin pretreatment group relative to the model group (P < 0.05). Hematocrit was significantly higher (P < 0.05) in the model group than in the control group (Table 2). The rats pretreated with puerarin showed a decreased hematocrit (P < 0.05). Erythrocyte aggregation index (EAI) was calculated according to the equation EAI = [[eta].sub.L]/[[eta].sub.H], where [[eta].sub.L] was the value of whole blood viscosity at low shear rate of 1 [s.sup.-1] and [[eta].sub.H] was the value of whole blood viscosity at the relatively high shear rate of 100 [s.sup.-1] (Table 2). EAI in the model group increased remarkably (P < 0.05) in comparison with that in the control group. The administration of puerarin resulted in a decrease in EAI (P < 0.05), when compared with the model group.

Analysis of NMR spectroscopy

Typical [sup.1]H NMR spectra of rat plasma and urine were shown in Figs. 1 and 2, respectively. The detected signals were assigned based on matching the acquired NMR data (i.e., chemical shifts, coupling constant and multiplicity) to the reference spectra in the Human Metabolome Database version 3.0 and previous reports (Lindon et al. 1999; Nicholson et al. 1995), as well as with the aid of several two-dimensional NMR experiments including correlation spectroscopy (COSY) and total correlation spectroscopy (TOCSY).

Metabolic variation in rats induced by blood stasis

The PLS-DA scores plots constructed with NMR spectral data from rat plasma and urine samples were utilized to depict the general variation between control and model groups (Fig. 3A and B). The model group was clearly separated from the control group suggesting that plasma and urine metabolic profiles of rats with blood stasis were significantly changed compared with those of healthy controls. The model parameters were as follows: [R.sup.2]Y = 0.94, [Q.sup.2] = 0.87 for plasma; [R.sup.2]Y = 0.99, [Q.sup.2] = 0.98 for urine. In general, excellent models were obtained when values of [R.sup.2]Y and [Q.sup.2] were above 0.8 (Xuan et al. 2011). Furthermore, the robustness of these PLS-DA classification models was assessed by 200-times permutation tests. The [R.sup.2] and [Q.sup.2] values derived from the permuted data were lower than the original ones and all the blue regression lines of the [Q.sup.2]-points intersected the vertical axis below zero, indicating the validation of these PLS-DA models (Fig. 3C and D).

Identification of discriminatory metabolites associated with blood stasis

Selected according to the VIP values from the PLS-DA models (VIP [greater than or equal to] 1) and the P values from univariate statistical analysis (P < 0.05), 15 and 10 endogenous metabolites related to blood stasis were identified as potential biomarkers in plasma and urine, respectively (Fig. 4 and Table 3). As compared with the control group, VLDL/LDL -C[H.sub.3], VLDL/LDL -C[H.sub.2]-, Lipid =CHC[H.sub.2]CH=, Lipid CH=CH and trimethylamine N-oxide were significantly decreased, while isoleucine, valine, lysine, alanine, glutamate, glutamine, pyruvate, creatine, choline and phosphocholine increased in the plasma of the model group. Urine obtained from rats with blood stasis contained higher level of taurine and lower levels of N-acetyl glycoprotein, succinate, 2-oxoglutarate, citrate, dimethylamine, glycine, sarcosine, phenylacetylglycine and hippurate.

Influence of puerarin on metabolic pattern of blood stasis rat model

The PLS-DA scores plots derived from [sup.1]H NMR spectra of plasma and urine samples in the control, model and puerarin pretreatment groups were illustrated in Fig. 5. The modeling parameters were [R.sup.2]Y = 0.98, [Q.sup.2] = 0.90 (Fig. 5A); [R.sup.2]Y = 0.97, [Q.sup.2] = 0.89 (Fig. 5B); [R.sup.2]Y = 0.99, [Q.sup.2] = 0.89 (Fig. 5C) and [R.sup.2]Y = 0.99, [Q.sup.2] = 0.91 (Fig. 5D), which indicated the model had good ability of prediction and reliability. It could be clearly observed that the puerarin pretreatment group was much closer to the control group than the model group. The results suggested that puerarin could inhibit the pathological process of blood stasis and effectively normalize the metabolic perturbation in rats induced by blood stasis. Moreover, puerarin could significantly attenuate the alterations of 13 biomarkers at different degrees (except valine and glutamate) in the plasma of blood stasis rats. Meanwhile, among the 10 potential biomarkers associated with blood stasis in rat urine, changes of 8 biomarkers including N-acetyl glycoprotein, succinate, 2-oxoglutarate, citrate, glycine, sarcosine, phenylacetylglycine and hippurate were significantly reduced by puerarin (Fig. 4 and Table 3).


It is one of classical methods of establishing blood stasis to place the rats in ice-cold water during the time interval between two injections of adrenaline. As an acute-stress, injecting adrenaline could produce the hemorheological disorders in various forms, such as blood hypercoagulability and a rise of whole blood viscosity (Thrall and Lip 2005). Rapidly decreased skin blood flow could be detected when the subjects were exposed to ice-cold water (Shibahara et al. 1996). These data suggested that injection of adrenaline combined with exposure to ice-cold water might induce blood stasis, resulting in hemorheological abnormalities (Tables 1 and 2). This animal model mimicked the pathological state of blood stasis to some extent in patients and contributed greatly to important advances in the current understanding of the underlying mechanisms of blood stasis as well as treatments (Li et al. 2009; Pan et al. 2003).

Whole blood viscosity is the reflection of intrinsic resistance of blood to flow in vessels. In our experiment, whole blood viscosity, which increased significantly at all shear rates in the blood stasis model group, was decreased in the puerarin pretreatment group compared to the model group (Table 1). This indicated that blood stasis induced a hyperviscosity of blood and puerarin could reduce this induction, thereby decreasing the intrinsic resistance of blood flow and ameliorating tissue perfusion. As we know, red blood cells (RBCs), platelets and plasma influence whole blood viscosity. RBCs are one of important factors affecting whole blood viscosity, since RBCs account for almost 50% of blood volume and constitute the majority of the cellular content in blood. In the present study, hematocrit (the RBC volume in blood) was significantly decreased in puerarin pretreatment group compared to the model group, suggesting that the amelioration effect of puerarin on whole blood viscosity might be partly due to the hematocrit decrease (Table 2). The results in Table 2 also indicated that puerarin could efficiently lower the erythrocyte aggregation index. Whole blood viscosity at a low shear rate reflected RBC aggregation, since the fluid shear forces were low enough to allow red cells to form rouleaux or rouleaux-rouleaux complexes (Wen et al. 2000). The decrease in the erythrocyte aggregation index suggested that the reduction in whole blood viscosity at a low shear rate in rats pretreated with puerarin might be related to the inhibition of RBC aggregation. Plasma viscosity also plays an important role in whole blood viscosity and our results demonstrated that pretreatment with puerarin in blood stasis rats was able to prevent the increase in plasma viscosity (Table 2), which suggested that the decrease in whole blood viscosity was mediated, at least partly, via the inhibition of an increase in plasma viscosity by puerarin.

Blood is a bodily fluid in animals that delivers necessary substances such as nutrients and oxygen to the cells and transports metabolic waste products away from those same cells. Blood stasis, i.e. the decrease of blood flow velocity, disturbs the normal blood flow, and this implies that oxygen transport may be inhibited in such state, resulting in the disturbance of energy metabolism. Decreased levels of succinate, 2-oxoglutarate and citrate in urine together with increased pyruvate level in plasma were observed in the model group as compared with the control group in the present study (Fig. 4 and Table 3). Pyruvate, the product of glycolysis, represents an important junction point in carbohydrate catabolism under aerobic and anaerobic conditions. Citrate, 2-oxoglutarate and succinate are key intermediate products of tricarboxylic acid (TCA) cycle which involves not only the glucose aerobic oxidation but also the major pathways for fat and amino acid metabolisms. Therefore, our results demonstrated the inhibition of energy production through aerobic respiration induced by blood stasis (Fig. 6). Moreover, creatine/phosphocreatine (Cr/PCr) in plasma and glycine in urine were significantly up- or down-regulated in rats with blood stasis, respectively. It is well known that the transfer of the amidino group of arginine to glycine to yield L-ornithine and guanidinoacetate (GM), which is catalyzed by L-arginine:glycine amidinotransferase, represents the first of two steps in the biosynthesis of Cr. GAA is then methylated at the amidino group to give Cr by the action of S-adenosyl-L-methionine:N-guanidinoacetate methyltransferase. Cr and PCr are in rapid exchange via the reversible transphosphorylation reaction catalyzed by enzyme creatine kinase (CK) (Wyss and Kaddurah-Daouk 2000). The CK/PCr/Cr system plays an essential role in the normal energy metabolism of tissues that have high, fluctuating energy demands such as muscle and brain, because it acts as a buffer for the adenosine triphosphate (ATP) concentration. For this reason, Cr is a popular supplement among sprinters and other athletes who rely on short bursts of energy. In addition, the presence of CK in blood plasma is indicative of tissue damage and is used in the diagnosis of myocardial infarction (Schlattner et al. 2006). As mentioned above, TCA cycle activity was suppressed in rats with blood stasis, and so, on the basis of our findings, it is conceivable to suggest that synthesis of Cr was accelerated and CK/PCr/Cr system provided an alternative energy source (Fig. 6).

The up-regulation of succinate, 2-oxoglutarate, citrate and glycine as well as down-regulation of pyruvate and Cr/PCr was present in the puerarin pretreatment group compared with those in the model group, indicating that puerarin was able to efficaciously ameliorate the altered energy metabolism (Fig. 4 and Table 3).

In this study, blood stasis caused perturbation of lipid metabolism as evidenced by the significantly reduced levels of lipid signals in NMR spectra of plasma of the model group, which was consistent with a previous study (Wang et al. 2011). Puerarin could greatly inhibit the decrease of these metabolites, suggesting its protective action on lipid metabolism (Fig. 4 and Table 3).

Reduced activity of superoxide dismutase (SOD) and raised level of malondialdehyde (MDA) (Dong et al. 1996) in serum demonstrated oxidative stress might be one of the most important pathogenesis of blood stasis. Oxidative stress was able to produce cellular membrane lipid peroxidation, lipid-protein interaction alteration, enzyme inactivation and DNA breakage, and in the end, to cause cell injury, apoptosis or necrosis (Liu et al. 2010). The increased levels of choline containing metabolites, choline and phosphocholine, were detected in plasma of the blood stasis model group (Fig. 4 and Table 3). Choline is a constituent of cell membranes and lipoprotein phospholipids, and plays an important role in the integrity of cell membranes and lipid metabolism (Zeisel 1981). The increased choline levels have been associated with drug induced disruption of cellular membrane (Griffin et al. 2001). In the current study, the elevation of choline and phosphocholine was likely caused by oxidative damage to cellular membrane structure.

Meanwhile, the metabolite profiling of plasma showed that three essential amino acid including isoleucine, valine and lysine increased significantly in model rats (Fig. 4 and Table 3), indicating blood stasis induced amino acid metabolism disturbance. Protein damage by oxidation is implicated in protein misfolding. Misfolded proteins are degraded by proteasome to ensure the high quality of intracellular proteins (Goldberg 2003). The elevated levels of amino acids suggested protein degradation induced by oxidative stress. Amino acids are quantitatively the most important source of ammonia, which has a direct neurotoxic effect on the central nervous system (CNS) and must be transported to the liver for its eventual conversion to urea or to the kidney where it can be used in the excretion of protons. Although ammonia is constantly produced in the tissues, it is present at very low levels in blood. This is due both to the rapid removal of blood ammonia by the liver, and the fact that many tissues, particularly muscle, release amino acid nitrogen in the form of glutamine or alanine, rather than as free ammonia. The ATP-requiring formation of glutamine from glutamate and ammonia by glutamine synthetase occurs primarily in the muscle and liver, but is also important in the CNS where it is the major mechanism for the removal of ammonia in the brain. Glutamine, the amide of glutamate, provides a nontoxic storage and transport form of ammonia. In tissues where glycolysis is active (e.g. muscle), the glucose-alanine cycle is primarily used to remove toxic ammonia. Alanine, the [alpha]-amino acid analog of the [alpha]-keto acid pyruvate, is most commonly produced by the reductive amination of pyruvate via alanine transaminase. This reversible reaction involves the interconversion of alanine and pyruvate, coupled to the interconversion of [alpha]-ketoglutarate and glutamate (Felig 1973). Thus, the altered levels of glutamine, pyruvate, alanine and glutamate (Fig. 4 and Table 3) in the model group might reflect disorders of glucose-alanine cycle and formation of glutamine, and could be used as an index of disturbance in amino acid metabolism caused by blood stasis. Furthermore, taurine is an antioxidant and an organic osmolyte capable of protecting and stabilizing cells (Waterfield et al. 1991). The increase in urinary taurine (Fig. 4 and Table 3) might be attributed to an intrinsic self-defense against the oxidative damage triggered by blood stasis.

Puerarin could restore the increased levels of choline, phosphocholine, isoleucine, lysine, glutamine, alanine and pyruvate, manifesting its ability to rectify the disturbance of membrane and amino acid metabolisms mainly induced by oxidative stress. And this was in good agreement with the antioxidant activity of puerarin (Zhou et al. 2013).

The elevated choline level in plasma of the model group was accompanied by a decrease in trimethylamine N-oxide (TMAO) in plasma and dimethylamine (DMA) in urine (Fig. 4 and Table 3). It is well known that breakdown of choline by gut microbiota leads to the formation of trimethylamine (TMA), which is subsequently either oxidized into TMAO via the flavine monooxygenase system or decomposed to DMA prior to excretion (Smith et al. 1994). So, it was plausible to expect that blood stasis induced a disturbance to gut microbiotal colonies in rats (Fig. 6). Furthermore, the precursors of phenylacetylglycine (PAG) and hippurate are also produced by gut bacteria (Wei et al. 2009), thus, evidence for the possible damaged gut microbiota could also be supported by the observed decreased urinary PAG and hippurate (Fig. 4 and Table 3). Puerarin effectively attenuated the alterations of choline, TMAO, PAG and hippurate, reflecting its protective action on gut microbiota metabolism.

In addition, the metabolic pathway analysis (MetPA) with Metabo-Analyst ( was applied to explore the most relevant pathways involved in blood stasis. Based on the impact value greater than 0.1, seven disturbed metabolic pathways including taurine and hypotaurine metabolism, glycine, serine and threonine metabolism, valine, leucine and isoleucine biosynthesis, glyoxylate and dicarboxylate metabolism, TCA cycle, alanine, aspartate and glutamate metabolism and pyruvate metabolism were revealed (Fig. 7 and Table 4). And these pathways might denote their potential as the targeted pathways of puerarin against blood stasis.

In conclusion, a metabonomic approach based on NMR technique was developed to investigate the specific physiopathologie state of blood stasis in rats and to reveal the intervention mechanisms of puerarin. 15 and 10 potential biomarkers associated with blood stasis in plasma and urine, respectively, primarily involved in energy metabolism, lipid and membrane metabolisms, amino acid metabolism and gut microbiota metabolism, were identified. These potential metabolites appeared to have diagnostic and/or prognostic values for blood stasis, which deserved to be further investigated. Consistent with results of hemorheology studies, puerarin could reverse the pathological process of blood stasis through regulating the disturbed metabolic pathways. This proof-of-concept study indicated that the metabonomic strategy based on NMR was a promising tool to search potential biomarkers related to blood stasis and to dissect the underlying efficacy and mechanisms of drugs in treating blood stasis.


Article history: Received 25 July 2014

Revised 27 December 2014

Accepted 5 January 2015

Conflict of interest

The author(s) declare(s) that there is no conflict of interests regarding the publication of this article.


This work was supported by National Natural Science Foundation of China (No. 81173194,81274059) and Science and Technology Planning Project of Guangdong Province, China (No. 2012B060300031).


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Zhongjie Zoua (a) *, Zhong Hua Liu (b), Meng Juan Gong (a), Bin Han (a), Shu Mei Wang (a), Sheng Wang Liang (a)

(a) School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou, People's Republic of China

(b) Experimental Animal Center, South China Agricultural University, Guangzhou, People's Republic of China

* Corresponding author. Tel.: +86 20 39352177; fax: +86 20 39352174. E-mail address: (Z.J. Zou).

Table 1
Effects of puerarin on whole blood and plasma viscosity in rats with
blood stasis.

Group      Whole blood viscosity (mPa x s)

           1 [s.sup.-1]            50 [s.sup.-1]

Control    20.54 [+ or -] 1.37 *   4.66 [+ or -] 0.29 *
Model      27.19 [+ or -] 3.06     5.63 [+ or -] 0.45
Puerarin   22.26 [+ or -] 4.38 *   4.92 [+ or -] 0.52 *

Group      Whole blood viscosity (mPa x s)

           100 [s.sup.-1]         200 [s.sup.-1]

Control    4.03 [+ or -] 0.31 *   3.87 [+ or -] 0.29 *
Model      5.12 [+ or -] 0.68     4.64 [+ or -] 0.37
Puerarin   4.38 [+ or -] 0.21 *   4.15 [+ or -] 0.33 *

Group      Plasma viscosity (mPa x s)

           50 [s.sup.-1]

Control    1.23 [+ or -] 0.08 *
Model      1.59 [+ or -] 0.19
Puerarin   1.32 [+ or -] 0.11 *

Data were presented as mean [+ or -] standard deviation (n = 6).

Statistical analysis was performed by one-way ANOVA followed by
Dunnett's test.

* Compared with model group P < 0.05.

Table 2
Effects of puerarin on hematocrit and erythrocyte
aggregation index (EAI) in rats with blood stasis.

Group      Hematocrit (%)        EAI

Control    42.1 [+ or -] 1.6 *   4.96 [+ or -] 0.38 *
Model      51.7 [+ or -] 4.5     5.51 [+ or -] 0.42
Puerarin   44.9 [+ or -] 2.7 *   5.02 [+ or -] 0.51 *

Data were presented as mean [+ or -] standard
deviation (n = 6).

Statistical analysis was performed by one-way
ANOVA followed by Dunnett's test.

* Compared with model group P < 0.05.

Table 3
Potential biomarkers associated with blood stasis in rat plasma and

Metabolite               Chemical shift (ppm) (a)              VIP (b)

VLDL/LDL-C[H.sub.3]      0.87(m)                               5.07
VLDL/LDL-C[H.sub.2]-     1.28(m)                               7.83
Lipid =CHC[H.sub.2]CH=   2.75(m)                               1.55
Lipid CH=CH              5.30(m)                               2.72
Isoleucine               0.94(t), 1.01(d), 1.96(m), 3.67(d)    1.30
Valine                   0.99(d), 1.04(d)                      1.61
Lysine                   1.47(m), 1.90(m), 3.02(m)             1.21
Alanine                  1.48(d), 3.77(q)                      2.22
Glutamate                2.08(m), 2.35(m), 3.75(m)             1.09
Glutamine                2.45(m), 3.77(m)                      2.72
Pyruvate                 2.37(s)                               1.56
Creatine/phosphoc        3.04(s), 3.93(s)                      2.23
Choline                  3.20(s), 3.51(t), 4.05(t)             1.96
Phosphocholine           3.22(s), 3.61 (t), 4.21(t)            2.89
Trimethylamine N-oxide   3.27(s)                               2.21

N-acetyl glycoprotein    2.02(s)                               2.36
Succinate                2.39(s)                               3.73
2-Oxoglutarate           2.43(t), 3.00(t)                      2.78
Citrate                  2.52(d), 2.65(d)                      3.49
Dimethylamine            2-71(s)                               1.03
Taurine                  3.23(t), 3.39(t)                      2.63
Glycine                  3.56(s)                               1.22
Sarcosine                3.58(s)                               1.01
Phenylacetylglycine      3.68(s), 3.75(d), 7.35(m), 7.46(f)    1.77
Hippurate                3.97(d), 7.55(t), 7.68(t), 7.86(d)    1.92

Metabolite               FC (c)   Control (d)       Puerarin (d)

VLDL/LDL-C[H.sub.3]      0.76     [up arrow] **     [up arrow] *
VLDL/LDL-C[H.sub.2]-     0.49     [up arrow] **     [up arrow] **
Lipid =CHC[H.sub.2]CH=   0.74     [up arrow] **     [up arrow] **
Lipid CH=CH              0.65     [up arrow] **     [up arrow] **
Isoleucine               1.35     [down arrow] **   [down arrow] **
Valine                   1.21     [down arrow] *    --
Lysine                   1.39     [down arrow] **   [down arrow] *
Alanine                  1.46     [down arrow] **   [down arrow] **
Glutamate                1.20     [down arrow] **   --
Glutamine                1.39     [down arrow] **   [down arrow] *
Pyruvate                 1.96     [down arrow] **   [down arrow] *
Creatine/phosphoc        1.94     [down arrow] **   [down arrow] *
Choline                  1.27     [down arrow] **   [down arrow] **
Phosphocholine           1.37     [down arrow] **   [down arrow] *
Trimethylamine N-oxide   0.83     [up arrow] **     [up arrow] **

N-acetyl glycoprotein    0.48     [up arrow] **     [up arrow] *
Succinate                0.28     [up arrow] **     [up arrow] **
2-Oxoglutarate           0.31     [up arrow] **     [up arrow] **
Citrate                  0.27     [up arrow] **     [up arrow] *
Dimethylamine            0.71     [up arrow] *      --
Taurine                  1.85     [down arrow] **   --
Glycine                  0.79     [up arrow] *      [up arrow] *
Sarcosine                0.74     [up arrow] *      [up arrow] *
Phenylacetylglycine      0.76     [up arrow] *      [up arrow] *
Hippurate                0.35     [up arrow] **     [up arrow] **

(a) Letters in parentheses indicate the peak multiplicities: s,
singlet; d, doublet; t, triplet; q, quartet; m, multiplet.

(b) VIP was obtained from PLS-DA model (Fig. 3).

(c) Fold change (FC) was calculated as the ratio of the mean
metabolite levels between model and control groups. FC with a value
>1 indicates a relatively higher concentration while a value <1 means
a relatively lower concentration present in model group as compared
to the controls.

(d) Compared to model group: [up arrow] indicates relative increase
in signal while [down arrow] indicates relative decrease in signal.
** and * represent P < 0.01 and P < 0.05, respectively,
whereas--denotes no statistically significant difference.

Table 4
Results of ingenuity pathway analysis with MetPA.

Pathway name                                   Total   Hits   Raw P

Taurine and hypotaurine metabolism              8      1      0.114
Glycine, serine and threonine metabolism       32      5      7.05E-05
Valine, leucine and isoleucine biosynthesis    11      2      0.010839
Glyoxylate and dicarboxylate metabolism        16      i      0.21556
TCA cycle                                      20      4      0.000155
Alanine, aspartate and glutamate metabolism    24      4      0.000326
Pyruvate metabolism                            22      1      0.28434
Glycolysis or gluconeogenesis                  26      1      0.32697
Glycerophospholipid metabolism                 30      2      0.072277
Primary bile acid biosynthesis                 46      2      0.14922
Cysteine and methionine metabolism             28      1      0.34736
Arginine and proline metabolism                44      3      0.025849
Glutathione metabolism                         26      1      0.32697
D-Glutamine and D-glutamate metabolism          5      3      2.85E-05
Butanoate metabolism                           20      3      0.002806
Nitrogen metabolism                             9      2      0.007224
Aminoacyl-tRNA biosynthesis                    67      4      0.015413
Biotin metabolism                               5      1      0.072784
Cyanoamino acid metabolism                      6      1      0.086722
Methane metabolism                              9      1      0.12735
Pantothenate and CoA biosynthesis              15      i      0.2035
Propanoate metabolism                          20      1      0.26208
Porphyrin and chlorophyll metabolism           27      1      0.33724
Valine, leucine and isoleucine degradation     38      i      0.44081
Pyrimidine metabolism                          41      i      0.46626
Purine metabolism                              68      1      0.65069

Pathway name                                   -log(P)    Impact

Taurine and hypotaurine metabolism              2.1715    0.43
Glycine, serine and threonine metabolism        9.56      0.35
Valine, leucine and isoleucine biosynthesis     4.5246    0.33
Glyoxylate and dicarboxylate metabolism         1.5345    0.29
TCA cycle                                       8.7742    0.22
Alanine, aspartate and glutamate metabolism     8.0281    0.21
Pyruvate metabolism                             1.2576    0.19
Glycolysis or gluconeogenesis                   1.1179    0.10
Glycerophospholipid metabolism                  2.6273    0.07
Primary bile acid biosynthesis                  1.9023    0.06
Cysteine and methionine metabolism              1.0574    0.02
Arginine and proline metabolism                 3.6555    0.01
Glutathione metabolism                          1.1179    0.01
D-Glutamine and D-glutamate metabolism         10.467     0
Butanoate metabolism                            5.8761    0
Nitrogen metabolism                             4.9303    0
Aminoacyl-tRNA biosynthesis                     4.1726    0
Biotin metabolism                               2.6203    0
Cyanoamino acid metabolism                      2.4451    0
Methane metabolism                              2.0608    0
Pantothenate and CoA biosynthesis               1.5921    0
Propanoate metabolism                           1.3391    0
Porphyrin and chlorophyll metabolism            1.087     0
Valine, leucine and isoleucine degradation      0.81915   0
Pyrimidine metabolism                           0.76302   0
Purine metabolism                               0.42973   0

Total is the total number of compounds in the pathway; Hits is the
actually matched number from the user uploaded data; raw P is the
original P value calculated from the enrichment analysis; impact is
the pathway impact value calculated from pathway topology analysis.
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Article Details
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Title Annotation:nuclear magnetic resonance
Author:Zou, Zhong Jie; Liu, Zhong Hua; Gong, Meng Juan; Han, Bin; Wang, Shu Mei; Liang, Sheng Wang
Publication:Phytomedicine: International Journal of Phytotherapy & Phytopharmacology
Article Type:Report
Date:Mar 15, 2015
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