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Metabonomics Approach to Assessing the Metabolism Variation and Endoexogenous Metabolic Interaction of Ginsenosides in Cold Stress Rats Zhihao Zhang, Xiaoyan Wang, Jingcheng Wang, Zhiying Jia, Yumin Liu, Xie Xie, Chongchong Wang, and Wei Jia J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00015 • Publication Date (Web): 06 May 2016 Downloaded from http://pubs.acs.org on May 11, 2016

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Journal of Proteome Research

Metabonomics Approach to Assessing the Metabolism Variation and Endo-exogenous Metabolic Interaction of Ginsenosides in Cold Stress Rats

Zhihao Zhang

1†

, Xiaoyan Wang1*†, Jingcheng Wang†, Zhiying Jia†, Yumin Liu¡, Xie Xie†, Chongchong Wang † and Wei Jia*†

† ¡

Ministry of Education Key Laboratory of Systems Biomedicine, Shanghai Center for Systems Biomedicine and

Instrumental Analysis Center of SJTU , Shanghai Jiao Tong University, Shanghai, 200240, P. R. China

*

To whom correspondence should be addressed:

Xiaoyan Wang at Ministry of Education Key Laboratory of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China. Phone: 86-21-34207343;Fax: 86-21-34206059; E-mail: [email protected] Wei Jia at Ministry of Education Key Laboratory of Systems Biomedicine, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China. Phone: 86-21-34207343;Fax: 86-21-34206059; E-mail: [email protected] 1

These authors contributed equally to this work.

1

ACS Paragon Plus Environment


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ABSTRACT Metabolic profiling technology, a massive information provider, has promoted the understanding of the metabolism of multi-component medicines and its interactions with endogenous metabolites, which was previously a challenge in clarification. In this study, an untargeted GC/MS-based approach was employed to investigate the urinary metabolite profile in rats with oral administration of ginsenosides and the control group. Significant changes of urinary metabolites contents were observed in total ginsenosides group, revealing the impact of ginsenosides as indicated by the up- or down-regulated several pathways involving neurotransmitters related metabolites, tricarboxylic acid (TCA) cycle, fatty acids β-oxidation and intestinal microflora metabolites. Meanwhile, a targeted UPLC-QQQ/MS based metabonomic approach was developed to investigate the changes of urinary ginsenoside metabolites during the process of acute cold stress. Metabolic analysis indicated that upstream ginsenosides (rg1, re and rf) increased significantly whereas downstream ginsenosides (ck, ppd and ppt) decreased correspondingly after cold exposure. Finally, the relationships between ginsenosides and significantly changed metabolites were investigated by correlation analysis.

Keywords: Metabonomics, ginsenosides, acute cold stress, GC/MS, UPLC-QQQ/MS

2


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1. INTRODUCTION Multicomponent herbal medicines and botanical-based nutraceuticals have gained a growing application as complementary interventions against many illnesses, including metabolic diseases and cancer.1,

2

Unlike the

pharmacology of mono-component drugs, the pharmacology of multi-component drugs is based on the synergic effect of complex components and entails a “network” approach, in which multiple compounds interact with multiple targets in vivo with interdependent activities to achieve an optimal effect.3, 4 Due to the large number of primary and secondary metabolites in natural products and their wide dynamic range, it is difficult to evaluate the whole pharmacology and develop health products/drugs. Moreover, the coexistence of multiple compounds may result in metabolic and pharmacokinetic interactions, with such a complicated network including a large number of variables. The metabolism of exogenous (xenobiotic) drugs may affect the endogenous metabolites and even directly impact its pharmacological effects. It is also probable that the metabolism of exogenous drugs would vary with the pathological and physiological state of the body. For complex components however, especially natural active ingredients, it is difficult to clarify the relationship between the complex components and endogenous metabolites from a systems biological level. Metabonomics technology is able to simultaneously analyze thousands of metabolic variables, which make it well suited for simultaneously measuring multiple components of drugs in vivo, as well as identifying the metabolic profiling in humans exposed to medicines.5-7 In addition, metabonomics on efficacy and toxicity of herbal medicine has been a key point of recent herbal and drug research.8-10 . Although development of analytical technologies had promoted our understanding of the endogenous metabolites and exogenous drug’s metabolites, assessing the living organism (endogenous) biochemical responses to exogenous compounds remained a technical challenge. Our previous studies reported

that acute stress and chronic stress led to significant variation of metabolic

profiling that was time-dependently reversible in rat urine.11 Moreover, ginsenosides possessed the protective effect against chronic unpredictable mild stress (CUMS)-induced depression symptoms and metabolic disturbances, as well as improving the metabolic disturbances in response to acute cold stress.12, 13 Therefore, this study was designed to explore the change of endogenous and exogenous metabolites and the correlation relationships between the two groups of metabolites. We conducted an untargeted metabonomic study by means of gas chromatography/mass spectrometry (GC/MS)-based urinary metabolite analysis. A targeted UPLC-QQQ/MS based metabonomic approach was developed to detect the metabolic profiles of ginsenosides. Thus we hope to make an exploratory investigation on the mutual relations between ginsenosides and significantly changed metabolites, which might be helpful to enhance the understanding of pharmacological mechanism of such important natural drugs as Ginseng. The study timeline and experimental scheme were illustrated in Figure 1. 3



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2. MATERIALS AND METHODS 2.1. Chemicals and Reagents Ethyl

chloroformate

and

heptadecanoic

acid

were

purchased

from

Sigma-Aldrich

(St.

Louis,

MO).

L-2-chlorophenylalanine was purchased from Intechem Tech. Co. Ltd. (Shanghai, China). Total ginsenosides was obtained from Hangzhou Greensky Biological Tech. Co., Ltd., China. The reference compounds including Ginsenoside rb1, rb2, rc, rd, re, rg1, rf, rg2, f2, rg3, rh1, f1, ck, ppt, ppd, rh2, panaxadiol and panaxtriol were from the National Institute for Food and Drug Control (Beijing, China). 2.2. Animal Handling and Sampling The present study conformed to the Chinese national legislation and local guidelines. Fourteen eight-week-old male SD rats (200 ± 20 g) were obtained from the Shanghai Laboratory Animal (SLAC, Shanghai, China). The rats were fed normal chow and water ad libitum (room temperature 24 ± 1 °C, humidity 45 ± 15%). After 2 weeks of acclimatization, rats were randomly divided into the following two groups: control group (C), total ginsenosides group (TG). Total ginsenosides, containing 18 ginsenosides including rb1, rb2, rc, rd, re, rg1, rf, rg2, f2, rg3, rh1, f1, ck, ppt, ppd, rh2, panaxadiol and panaxtriol, were resuspended in saline solution. TG group rats received TG (100 mg/kg), while the control group rats received the same volume of saline solution. On the fourteenth day, all the rats were exposed to cold stress (-10 °C) for 2 h. Then they were transferred back to metabolic cages under room temperature. Urine samples were collected and centrifuged at 6000 × g for 10 min at room temperature to remove particle contaminants, and then the supernatants were stored at -80 °C before analysis, at the 4 time points: day 7, 13, 14 and 17.

2.3. GC/MS Sample Preparation and Analysis. Urine samples were pretreated and the relevant spectral acquisition was obtained according to the published methods.12 Briefly, a urine sample (600µL) diluted at a ratio of 1:1 with distilled water was derivatized with ethyl chloroformate, using L-2-chlorophenylalanine (0.1 mg mL−1) as an internal standard. Metabonomic analysis was performed using a GC/MS (Perkin-Elmer gas chromatograph and Turbo Mass coupled to an Autosystem XL mass spectrometer). The column used was DB-5MS capillary column coated with 5% diphenyl cross-linked 95% dimethylpolysiloxane (30 m × 250 µm i.d., 0.25-µm film thickness (Agilent J&W Scientific, Folsom, U.S.A.). 2.4. LC/MS Sample Preparation and analysis Waters Acquity Ultra Performance LC system equipped with a Triple Quadrupole Mass Spectrometer (UPLC-3QMS, ACQUITY UPLC & SCIEX SelexION Triple Quad™ 5500 System) was used for the determination of 18 ginsenosides in the urine samples of TG group. Chromatographic separation was carried out on an BEH C18 column 4


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(50mm×2.1mm, 1.7 µm particle size, Waters, UK). The analytical column was maintained at 45 ºC; the flow rate was 0.45 mLmin−1; and every 5 µL sample solution was injected for each cycle. The UPLC mobile phase consisted of 0.1% acetonitrile in water (A) and acetonitrile (B). The initial composition was 95% A and 5% B. The gradient program was set as following: 0-0.5 min, 5% B; 0.5-1.0 min, 5-19% B; 1.0-3.5 min, 19-31% B; 3.5-6.0 min, 31-33% B; 6.0-11.0 min, 33-90% B; 11.0-12.0 min, 90-95% B; 12.0-14.0 min, 95% B; 14.0-15.0 min, 95-5% B; 15.0-16.0 min, 5% B. Mass spectrometry was operated in the positive ESI mode. Capillary voltage was set at 3.5 kV. Desolvation gas ﬂow and cone gas ﬂow were set at 700 L/h and 50 L/h respectively. Other parameters were set as following: desolvation temperature (350 ◦C), source temperature (120 ◦C), LM resolution (15.1), HM resolution (14.5), and multiplier voltage (650 V). Detection by multiple reaction monitoring (MRM), the parameters were listed in Figure 2. We prepared stock solutions of the 18 ginsenosides separately in DMSO. Pyridine was used to prepare Digoxin stock solution as an internal standard. The preparation of working solutions was conducted by mixing known amount of all the compounds together in methanol. Urine samples were prepared as following: 700 µL of urine supernatant with 50 µL of internal standard solution (Digoxin 1 µg/mL−1) was concentrated to dry under centrifugal vacuum for 9 hours. Then 400 µL methanol was added to extract the residue under ultrasonic for 10 min and then the solution were centrifuged at 13,200 × g for 5 min at 4 ºC to remove solid materials. The supernatant was concentrated to dry under centrifugal vacuum and added 190 µL methanol to extract the residue under ultrasonic for 10 min. After centrifugation at 13,200 × g for 5 min at 4 ºC, 5µL of the supernatant was injected into the UPLC-QQQ MS. 2.5. Data processing and statistical analysis. Before the multivariate analysis, the conversion of peak information from the GC/MS data was conducted according previous data preprocessing methods.11 The mass data acquired from LC/MS was imported to Analyst®1.5.2 for peak detection and alignment. Relative contents of the ginsenosides in percentage were calculated with Areas of Peak Normalization Method which was used to eliminate the influence of variations in urine volumes on the level of ginsegosides. The resulting data set, including retention time, sample names and peak areas were introduced into the SIMCA-P 13.0 Software package (Umetrics, Umeå, Sweden) for multivariate statistical analysis. The principal component analysis (PCA) was used to visualize differences, trends, or outliers among the observations. Partial least-square-discriminant analysis (PLS-DA) was utilized to verify the PCA model and identify the differential metabolites. Additionally, a majority of the metabolites detected were identified using the reference compounds available and the commercial compound libraries: NIST and Wiley. Fold changes (TG group/control group or post-cold exposure group/pre-cold exposure group) was calculated by 5



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Metaboanalysis 3.0. Student's T test and Mann-Whitney test were used to judge the statistical significance of the results by SPSS 19.0. Kendall tau rank correlation analysis was applied to measure the correlation between ginsenosides and

differentially expressed metabolites. Kendall tau rank, a non-parametric test for statistical dependence based on the ranks of the data, has often been used in correlation analysis.14, 15 VIP values (Variable importance in the projection) represents the whole contribution of each variable to the PLS-DA model. When VIP values of variables are greater than 1.0, they are considered to be responsible for the group discrimination. The critical p-value of Student's T and Mann-Whitney tests was set to 0.05 in this study. 3. RESULTS 3.1 Metabolic variation induced by TG. PCA scores plot was derived from the GC/MS data of the urine metabolites between the control and TG groups at week 2, as depicted in Figure 3A. A clear separation in metabolic states was observed between the control and TG groups at week 2 (Figure 3A), and a supervised method, PLS-DA, was also built to exhibit the metabolic distinction between the two groups (Figure S1A). The PCA and PLS-DA results indicated the marked alteration of urinary metabolites in the rats received TG. Differentially expressed metabolites contributing to the deviated metabolic profile in TG group were identified and summarized in Table 1, which were selected by using the VIP values (> 1.0) from PLS-DA combined with results from Student’s t test (p < 0.05) and Mann-Whitney U test (p < 0.05). To further understand the metabolic

changes between control and TG groups, a clustering heatmap was used to visualize the change of metabolites. Figure 4A showed that the control and TG groups could be clearly separated based on concentration of 25 metabolites.

3.2 Variation of 18 ginsenosides concentrations induced by cold stress

UPLC-3Q/MS chromatograms of typical urine sample was acquired by using the above developed method to compare the 18 ginsenosides concentrations between pre- and post-cold stress exposure in the urine samples of TG group. The PCA analysis was performed on the urine ginsenosides concentrations between pre- and post-cold stress exposure (Figure 3C), which showed a trend of intergroup separation on the scores plot. Another PCA analysis was performed on the urine ginsenosides concentrations between day 7 and day 14 (pre-cold stress exposure) (Figure 3D), which showed no separation on the scores plot, implying that ginsenosides concentrations are relatively stable

before cold stress exposure. PLS-DA was applied to exhibit the metabolic distinction between the pre- and post-cold stress exposure, and a separation in metabolic states was observed, suggesting the alteration of urine ginsenosides induced by cold stress (Figure S1B). A number of differentially expressed ginsenosides selected by VIP values (> 1.0) 6


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from PLS-DA combined with results from Student’s t test (p < 0.05) and Mann-Whitney U test (p < 0.05), were

identified and displayed in Table 2. In addition, heatmap showed directly the variation of each differential ginsenosides. Figure 4B presented differential ginsenosides (VIP>1) between the pre- and post-cold stress exposure. Red color indicated the relative up-regulated concentration and blue color indicated down-regulated concentration. As shown in Figure 4B, the pre- and post-cold stress exposure could be clearly separated based on 8 ginsenosides. Hierarchical cluster analysis was performed to understand the potential relationships among the ginsenosides. These 8 ginsenosides were clustered according to their Pearson correlation coefficients, which were shown on the plot at different colors (Figure S2A). The closely related metabolites were clustered, three major clusters were observed (Figure. 4C, I–III). Cluster I consisted of panaxtriol, panaxadiol and ppd. Cluster II included ck and ppt. Cluster III

was rg1 and re. This result indicated that upstream metabolites (re and rg1) in ginsenoside metabolic pathway distributed in the same cluster and had similar changing trends. Downstream metabolites (ck and ppt) also distributed in the same cluster and had similar changing trends. As shown in Table 2, rg1, re and rf increased in TG group after exposing to the cold stress. However, ck, ppd, ppt, panaxtriol and panaxadiol decreased after exposing to the cold stress. Among them, rg1, re and ppd changed significantly (p < 0.05). Altered abundances of 18 ginsenosides between pre- and post-cold stress exposure were displayed in Figure 5A. Significantly altered abundances of rg1, re and rf were displayed in Figure 5B. 3.3 Metabolic variation induced by cold stress in TG group As shown in Figure 3B, the PCA plot was derived from the GC/MS data. The PCA analyses were performed on the metabolites between pre- and post-cold stress exposure in the urine samples of TG group. The two clusters, pre- and post-cold stress exposure, can't be separated from each other completely. This result was consistent with our previous study, which indicated ginsenosides reduced the impacts of acute cold stress on endogenous metabolites.12

Differentially expressed metabolites were identified and displayed in Table S1. They were selected by using the VIP values (> 1.0) from PLS-DA combined with results from Student’s t test (p < 0.05) and Mann-Whitney U test (p < 0.05). 3.4 Correlation of 18 ginsenosides with the differentially expressed metabolites We used the Kendall tau rank correlation coefficient to directly measure the correlation between the 18

ginsenosides with the differentially expressed metabolites (between control and TG group, n=7) at 4 different time points (described in 2.2. Animal Handling and Sampling) (Figure 5D). We found that rb1 significantly positively correlated with phenylacetic acid at 4 time points; rc significantly positively correlated with octadecanoic acid, hexadecanoic acid and proline at 4 time points; rd significantly positively correlated with alanine, octadecanoic acid and 7



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hexadecanoic acid at 4 time points; rg2 significantly positively correlated with phenylalanine at 4 time points; f2 significantly negatively correlated with proline and benzoic acid at 4 time points; rh1 significantly negatively correlated with octadecanoic acid, quinolinic acid and hexadecanoic acid at 4 time points; f1 significantly negatively correlated with propanedioic acid and quinolinic acid at 4 time points; ck significantly negatively correlated with quinolinic acid and phenylacetic acid at 4 time points; ppt significantly positively correlated with p-hydroxycinnamic acid at 4 time points; ppd significantly negatively correlated with trans-ferulic acid at 4 time points; rh2 significantly negatively correlated with alanine, phthalic acid and phenylacetic acid at 4 time points; panaxadiol significantly negatively correlated with 2-propenoic acid and isoleucine and significantly positively correlated with phenylacetic acid at 4 time points; panaxtriol significantly positively correlated with phenylacetic acid at 4 time points; rb2 significantly positively correlated with glutaric acid and butanoic acid at 4 time points. In addition, it is interesting that ppd, panaxadiol and panaxtriol significantly positively correlated with methylsuccinate, octadecanoic acid, quinolinic acid, hexadecanoic acid, proline and suberate at time point 2.

We next used the Kendall tau rank correlation coefficient to measure the correlation between alterations of 3 significantly altered ginsenosides (between pre- and postexposure to cold, n=7) with the alterations of differentially expressed metabolites (between pre- and postexposure to cold, n=7). In Figure S2B, rg1 and re showed the similar correlation coefficient while ppd showed the relatively different correlation coefficient. Alterations of rg1 and re significantly positively correlated with alterations of benzaldehyde, isopentanol, hexanedioic acid, butanedioic acid and ethanedioic acid and significantly negatively correlated with alterations of picolinoylglycine 3-hydroxyisovaleric acid. Alterations of ppd significantly positively correlated with alterations of quinolinic acid and significantly negatively correlated with alterations of 3-hydroxyisovaleric acid. 4. DISCUSSION Ginsenosides, a group of active ingredients of the herbal drug named Ginseng, have been considered to be the principal ingredients responsible for the pharmacological activities as diminishing inflammation, reducing oxidative stress, glycolipid metabolic and anti-tumor effects.16,17 Ginsenosides might be the most suitable natural effective constituents for the aforementioned endo-exogenous metabolites study because more than 30 different ginsenosides with various efficacies have been isolated, identified and profiled as "exogenous metabolome" that was easy to be distinguished from endogenous metabolome.18, 19 Systemically study and understand the effects of ginsenosides on normal organism will undoubtedly enrich our current knowledge of the beneficial effects of Ginseng and provide insights into the mechanism of ginsenosides activities. Stress affects multiple biochemical regulatory systems, and triggers many conditions and disorders. It is regarded as one of the root of various diseases including hypertension, 8


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gastric ulcer and depression.20 Our previous studies have demonstrated that ginsenosides possess potent protective effect against acute and chronic unpredictable mild stress in rats11, 12. As a follow-up investigation, we select acute cold stress rat model in present study. The primary objective of this study was threefold: (1) to investigate the effect of ginsenosides on metabolic profiles in healthy rats; (2) to investigate the effect of acute cold stress on ginsenosides metabolic profiles; (3) to investigate the correlation of ginsenosides with endogenous metabolites under normal and acute cold stress condition. Untargeted GC-TOF/MS-based urinary metabonomics revealed variation of the

pathways of alanine, aspartate and glutamate metabolism, citrate cycle, phenylalanine metabolism, glutamine and glutamate metabolism and arginine and proline metabolism (Figure 6). Targeted UPLC-QQQ/MS-based urinary metabonomics indicated the alteration of ginsenosides metabolism in vivo (Figure 5C). Metabolic changes in response to ginsenosides

Phenylalanine, an essential amino acid, is the precursor for tyrosine and the catecholamines (epinephrine, tyramine, dopamine and norepinephrine) which are major neurotransmitters. Tyramine is produced by tyrosine via decarboxylation. Phenylalanine was significantly increased whereas tyramine, a downstream metabolite of catecholamines, was decreased in the urine samples of TG group. These alterations indicated that under normal circumstances, ginsenosides inhibited the sympathetic nervous system (SNS) activity, leading to a down-regulated catecholamine metabolic pathway. It is of interest that ginsenosides could enhance SNS activity by regulating catecholamine metabolic pathway to exert a protective effect when animals were exposed to cold stress.12 Glutamic acid is a major excitatory neurotransmitter in the mammalian central nervous system (CNS).21 It is reported that ginsenosides are capable of regulating excitatory and inhibitory neurotransmitters (glutamate and GABA), and block injury induced by glutamate in brain tissue,22 which is in agreement with this result of decreased level of glutamic acid (an excitatory amino acid) and elevated level of glycine and alanine (inhibitory amino acids) in TG group of this study. Significantly increased alanine, phenylalanine, proline, glycine and isoleucine were observed also in TG group. Furthermore, trans-Ferulic acid and p-Hydroxycinnamic acid are associated with intestinal flora metabolism of dietary fiber, indicating that ginsenosides may affect the metabolism of intestinal flora.23 Our results showed that a number of important members of the tricarboxylic acid (TCA) cycle were decreased in urine from TG group, such as citrate, succinic acid, and fumaric acid. This result indicated decreased energy consumption in rats received with ginsenosides. Octadecanoic acid and hexadecanoic acid are two saturated fatty acids, both of which increased in urine samples of TG group. High urinary excretion of fatty acids may reduce fatty acids β-oxidation, leading to a decrease of energy production. In addition, some organic acids changed significantly in TG group, such as glutaric acid and acrylic acid. 9



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Ginsenosides changes induced by cold stress As shown in Figure 3E, minor variance was obtained between the first two time-points (day 7 and day 14, before exposure to cold stress) whereas obvious change of metabolic profiling was occurred between pre- and postexposure to cold stress (Figure 3C, 2D). As shown in Table 2, rg1, re and rf increased after exposing to cold stress, while Ck,

ppd, ppt, panaxtriol and panaxadiol decreased. Particularly, the contents of some ginsenosides e.g. rg1, re and ppd changed significantly (p < 0.05), suggesting cold stress indeed impacted in vivo metabolic process of the ginsenosides. Intestinal microflora and enzyme play an important role in absorption and metabolism of ginsenosides.24, 25 Through deglycosylation in varying degrees, ginsenosides with large molecular weight could be degraded into small molecular weight ginsenosides with (Figure 5C), which can be further absorbed.26, 27 In addition, it has been reported that some ginsenosides, such as Rb1, Re, Rg1, has low bioavailability due to their low permeability of the gastrointestinal mucosa.28, 29 The major functions of the gastrointestinal tract have conventionally been considered to be limited to the digestion and absorption of nutrients and electrolytes and to water homeostasis.30,

31

However, another vital

important function of gastrointestinal tract is its ability to prevent the passage of harmful substances including foreign antigens, microorganisms and their toxins.30,

32

Due to its intercellular tight junctions, the intestinal

epithelial barrier regulates the equilibrium between tolerance and immunity to nonself-antigens.33 It has been reported that mesenteric organs produced large amount of norepinephrine and its metabolites, representing 45-50% of total body production.34, 35 Catecholamine, especially norepinephrine could significantly affect the intestinal flora growth.36, 37 The study demonstrated that psychological and physical stress can significantly alter the intestinal microflora, such as decreased concentration of Lactobacilli and increased Shigella spp. and Campylobacter spp.37 Our previous study also reported that gut microbiota metabolites including 4-methyphenol, 4-hydroxyphenylacetate and hippurate showed significant changes in response to cold exposure.12 This indicated that there is a significant involvement of gut microbiota in the response to cold stress. Increased norepinephrine causing Gram-negative bacteria increases might be one reason. Changes of gastrointestinal secretion and motility induced by enhanced sympathetic activity might be another reason for the alteration of intestinal microflora. Actually, both psychological and physical stress can be the incentive of changes on normal gastrointestinal function, involving gut motility and permeability as well as alterations in ion, fluid and mucus secretion and absorption.38 Animal models of acute, cold-restraint stress induced an increase in transcellular and paracellular intestinal permeability.39 In our study, some upstream metabolites, such as rg1, re and rf, increased significantly whereas downstream metabolites (ck, ppd and ppt) decreased correspondingly after cold exposure (Figure 5C), 10


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implying that these upstream ginsenosides were absorbed and then excreted into urine without sufficient degradation. Therefore, we believe that two reasons might be responsible for ginsenosides alterations: (a). cold stress caused changes of intestinal microflora which has a directly impact on degradation of ginsenosides. (b). cold stress increased the intestinal permeability so that ginsenosides with high molecular weight can permeate the intestinal mucosa easily. Then these ginsenosides were absorbed and excreted more into urine.

Relationship of ginsenosides and differentially expressed metabolites Based on above results and our previous studies, we knew that metabolic states changed after two weeks oral administration of TG in rats, implying that ginsenosides could cause variation of endogenous metabolites. In addition, cold stress also leads to a variation of metabolic profile, namely systemic biochemical and physiological changes in rats. Moreover, cold stress was able to change the metabolic states of ginsenosides including up-regulation of rg1, re and rf and down-regulation of ppd, panaxtriol, ck, ppt, panaxadiol. Both endogenous metabolites and ginsenosides were influenced by cold stress. In Figure S2B, variations of up-stream ginsenosides rg1 and re showed strong positive correlation with variations of benzaldehyde, isopentanol, hexanedioic acid, butanedioic acid and ethanedioic acid

and strong negative correlation with variations of picolinoylglycine and 3-hydroxyisovaleric acid. Isopentanol is a by-product of gut microbial fermentation.40 Butanedioic acid is an intermediate in the TCA cycle and is capable of donating electrons to the electron transport chain. Ethanedioic acid degrades by gastrointestinal bacteria in human colon.41 This result indicated that changes of rg1 and re were related to changes of TCA cycle and gut microflora. Variation of down-stream ginsenoside ppd showed strong positive correlation with variation of quinolinic acid and strong negative correlation with variations of 3-hydroxyisovaleric acid. Quinolinic acid is a metabolite of tryptophan

with a possible role in neurodegenerative disorders. This result indicated that altered ppd may have a relationship with altered tryptophan metabolism. We also interested in mutual relations between 18 ginsenosides and significantly changed metabolites induced by oral administration of ginsenosides with the passage of time. The results were shown in result section, suggesting that certain ginsenoside showed the same changing trend with certain metabolites at 4 time points. For instance, rg2 significantly positively correlated with phenylalanine at 4 time points while f2 significantly negatively correlated with proline and benzoic acid at 4 time points. This method might give us a general idea which individual component from multi-component drug associated with which metabolites or metabolic pathways, which might be helpful to enhance the understanding of mechanism of multi-component drug action.

Due to the limitations of conditions and previous experiences, the contrast of before-after time points was conducted to replace a single group receiving ginsenosides without cold exposure and the absolute values of TGs , 11



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which may lose some information of the parallel metabolic state during the whole experimental time. In addition, there is no sufficient data to support speculation that enhancing intestinal permeability would result in significant changes in the genisenoside metabolism. In future, we plan to validate the speculation by determining the intestinal permeability using lactulose/mannitol—used for the past 50 years to gauge intestinal permeability.42

5. CONCLUSION A GC/MS-based untargeted metabonomic approach revealed several major metabolic pathways including TCA cycle, amino acids, fatty acids β-oxidation and intestinal microflora were regulated by ginsenosides. A targeted UPLC-QQQ/MS based metabonomic approach was developed to find up-regulation of upstream ginsenosides (rg1,

re and rf) and down-regulation of downstream ginsenosides (ck, ppd and ppt) after cold exposure, demonstrating cold stress can significantly alter the ginsenosides metabolism. The mutual relations between ginsenosides and significantly changed metabolites was then shown that changes of Rg1 and Re were related to changes of TCA cycle and gut microflora and altered PPD may have a relationship with altered tryptophan metabolism.

ASSOCIATED CONTENT Method validation of LC-MS Table S1. Differentially expressed urine metabolites between pre-exposure and post-exposure to cold stress in TG group in positive ion mode. Figure S1. Effects of total ginsenosides on urinary metabolic variation. PLS-DA score plot of GC/MS spectral data from control group and TG group at week 2 (A). Effects of cold stress on variation of 18 ginsenosides concentration in urine. PLS-DA score plot of LC/MS spectral data from urine samples in TG group at these two states (pre-exposure to cold and postexposure to cold) (B). Figure S2. (A) Hierarchical clustering of differential ginsenosides. Correlation analysis of the 8 differential ginsenosides in the TG group between the pre- and post-cold stress exposure. (B) Correlation of alterations of 3 significantly altered ginsenosides (between pre- and postexposure to cold, n=7) with the alterations of differentially expressed metabolites (between pre- and postexposure to cold, n=7).

ACKNOWLEDGMENT This work was financially supported by National Nature Science Foundation of China (30901997) and the National 12


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Basic Research Program of China (2012CB910102, 2007CB914700). We are grateful to Mr. Weng Leong Choy for his aid in the English polish.

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Table and figure legends Table 1. Differentially expressed urine metabolites between control and TG group based on metabonomic profiling in positive ion mode Table 2. Differentially expressed urine ginsenosides induced by cold stress based on metabonomic profiling in positive ion mode

Figure 1. The study timeline (A) and experimental scheme (B) for this article. Figure 2. Chemical structures and main MS parameters of ginsenosides. Figure 3. Effects of total ginsenosides on urinary metabolic variation. PCA scores plot of GC/MS spectral data from control group and TG group at week 2 (A). Effects of cold stress on urinary metabolic variation in TG group. PCA scores plot of GC/MS spectral data from urine samples in TG group at these two states (pre-exposure to cold and post-exposure to cold) (B). Effects of cold stress on variation of 18 ginsenosides concentration in urine. PCA scores plot of LC/MS spectral data from urine samples in TG group at these two states (pre-exposure to cold and postexposure to cold) (C). PCA scores plot of LC/MS spectral data from urine in TG group samples at day 7 and day 14 (pre-exposure to cold) (D). Figure 4. A. Heat maps of 25 significant metabolites between control and TG group at 4 time points. The color of each section corresponds to a concentration value of each metabolite calculated by peak area normalization method. B. Heat maps of 8 ginsenosides (VIP>1) in TG group from the pre- and post-cold stress exposure (red, upregulated; blue, downregulated). Figure 5. (A) Comparison of 24-hour accumulative excretion content of 18 major metabolites of ginsenosides in the animal pre- and postexposure to cold stress. *, statistical significance (p

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