Gallic Acid Intake Induces Alterations to Systems Metabolism in Rats

Article pubs.acs.org/jpr

Gallic Acid Intake Induces Alterations to Systems Metabolism in Rats Xiaohuo Shi,†,§,⊥ Chaoni Xiao,‡,⊥ Yulan Wang,† and Huiru Tang†,* †

Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Centre for Biospectroscpoy and Metabonomics, Wuhan Centre for Magnetic Resonance, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, People's Republic of China ‡ College of Life Sciences, Northwest University, Xi’an 710069, People's Republic of China § University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China S Supporting Information *

ABSTRACT: Gallic acid (GA) and its metabolites are polyphenolic compounds present in daily diets and herbal medicines. To understand the GA effects on the endogenous metabolism of mammals, we systematically analyzed the metabonomic responses of rat plasma, liver, urine, and feces to a single GA dosage of 120 and 600 mg/kg, which were below the no-obvious-adverse-effectlevel of 1 g/kg for rats. Clinical chemistry and histopathological assessments were conducted to provide complementary information. Our results showed that GA intake induced significant metabonomic changes in multiple rat biological matrices. Such changes were more outstanding in liver than in the other matrices and clearly showed dose- and time-dependence. The results suggested GA-induced promotion of oxidative stress as the major effect. High-dose GA caused significant metabolic changes involving glycogenolysis, glycolysis, TCA cycle, and metabolism of amino acids, purines, and pyrimidines, together with gut microbiota functions. Low-dose GA only caused some urinary metabonomic changes and to a much less degree. The GA-induced liver metabonomic changes were not completely recoverable within a week, although such recovery completed in plasma, urine, and feces within 80 h. These findings provided new essential information on the effects of dietary polyphenols and demonstrated the great potential of this nutrimetabonomics approach. KEYWORDS: gallic acid, polyphenols, NMR, metabonomics, stress response, nutrimetabonomics protective effects due to their free radical scavenging activities19 and interactions with biomacromolecules.20 GA toxicities to mammals have been systematically studied with classical approaches. For acute oral intakes, no-obviousadverse-effect-levels (NOAEL) for GA were at least 5 g/kg for Swiss albino mice and 1 g/kg for SD rats, respectively.21,22 For subchronic oral exposure of GA, NOAEL was about 119 mg per kg (body weight) per day for F334 rats6 and 240 mg/(kg·day) for SD rats.22 With subacute exposure to a dosage as high as 1 g/(kg·day), no cumulative toxicity of GA was observable for Swiss albino mice.21 With subacute exposure at the level of 344 mg/(kg·day), no reproductive toxicity was observed for GA.23 However, oral intake of GA caused toxicity to rabbits with the LD50 of 5 g/kg.24 It was also found that intraperitoneal injection of GA led to liver injury for CD-1 mice with a dosage of 500 mg/kg.25 Furthermore, in vitro experiments showed some GA toxicities to mouse spermatogonia, spermatocytes, and sertoli cells as well as to normal rat liver epithelial cells.26,27 However, no results are available on whether the toxicities observed above are reversible or not.

1. INTRODUCTION Gallic acid (3,4,5-trihydroxybenzoic acid, GA) and its derivatives belong to a unique family of plant secondary metabolites known as polyphenolic compounds and are widely present in fruits, vegetables, and beverages such as tea and wine.1,2 Polyphenolic compounds are also widely distributed in herbs and phytomedicines,3−5 being regarded as their bioactive components. GA metabolites are inescapable components of human dietary polyphenols since average daily gallic acid intake from food sources can reach up to one gram.6 They are synthesized through shikimate pathway and mainly exist in free form, gallate esters of polyols (known as hydrolyzable tannins), and catechins such as epicatechin gallate and epigallocatechin gallate.7,8 Due to their antioxidant properties, galloyl esters such as propyl and octyl gallate are widely employed as food additives9 and are thus subjected to numerous investigations in terms of their biological functions and mode of actions. Many biological activities have been reported for GA esters, including antioxidation,10 anti-inflammatory,11 anticarcinogenic effects,12,13 and antiobesity.14,15 Both neuron-protective10 and liver-protective effects16 of GA esters were observed in cellular systems. Intake of GA esters might also be associated with the reduced risks of diabetes17 and cardiovascular diseases.18 These functions of GA esters were probably related to cell membrane © 2012 American Chemical Society

Received: November 5, 2012 Published: December 11, 2012 991

dx.doi.org/10.1021/pr301041k | J. Proteome Res. 2013, 12, 991−1006

Journal of Proteome Research

Article

Gallic acid metabolites undergo intensive metabolic biotransformations in mammals.1,2 GA esters are generally converted into free GA in gut and then into a number of metabolites via methylation, dehydroxylations, and conjugations in mammalian gut and liver.1,2,8 Previous studies indicated that GA inhibited xanthine oxidase,28,29 ribonucleotide reductase,30,31 and histamine release in mast cells.11 Furthermore, gallic acid and its derivatives affect a number of drug metabolizing enzymes.12,13 It is therefore foreseeable that GA exposure ought to affect mammalian endogenous metabolism. However, it is not clear whether intakes of gallic acid below the NOAEL affect mammalian biochemistry especially endogenous metabolism. Metabonomic analysis should be a good choice to answer these questions. Metabonomic analysis can comprehensively reveal the subtle responses of biological systems to alterations of both endogenous and environment factors. 32 Such analysis has been successfully applied to understand the molecular aspects of obesity 33 and pathogenesis of complex diseases such as diabetes,34,35 cancers,36,37 and inflammatory bowel diseases.38,39 As an excellent molecular phenotyping technique, metabonomics has been used to detect dietary exposures40,41 and the effects of biotoxins,42 drugs,43 and perfluorocarboxylic acids.44 Applications of metabonomics approaches in investigating the metabolic effects of chamomile tea ingestion,45 arginine,39,46 and dietary flavonoids47−49 have already shown the great potential of such nutrimetabonomics approaches. These studies revealed that oral intakes of polyphenolic compounds altered mammalian biochemical pathways associated with energy metabolism45,47−49 and gut microbiota functions.45,49 For example, intakes of soy isoflavones affected human osmolytic regulations and energy metabolism,47,48 whereas epicatechin intakes affected TCA cycle and gut microbiota functions49 for rats. For the time being, however, no metabonomics studies have been reported for the effects of gallic acid or its ester derivatives on mammalian metabolism. In this work, we systematically analyzed the dose-dependence of the GA-induced dynamic metabolic alterations in rat liver, plasma, urine, and feces using NMR-based metabonomics in conjunction with clinical chemistry and histopathological assessments. The objectives are (1) to detect any possible effects of gallic acid intake on mammalian biochemistry when dosages are well below NOAEL and (2) to understand the mechanistic aspects of such effects on the rat endogenous metabolism.

Experiments, Huazhong University of Science and Technology. They were housed in groups of five in cages with free access to water and a standard rodent diet in an air-conditioned room (24 °C and 45−70% humidity) with a 12 h light/dark cycle. After a week of acclimatization, rats were randomly divided into three groups, namely, control group (n = 15), low-dose group (n = 15), and high-dose group (n = 15). While gallic acid in DMSO/H2O (1:3, v/v) was administered by oral gavages for the low-dose (120 mg/kg) and high-dose (600 mg/kg) groups, the control group was treated with only carrier in the same manner. At 48 h postdose, 5 rats from each group were sacrificed following ether inhalation. The remaining animals from each group (n = 10) were euthanized at 144 h postdose. The urine and feces samples of every animal were collected once a day (between 20:00 p.m. and 08:00 a.m.) with metabolic cages during experimental period (for 13 days) and immediately snap-frozen with liquid nitrogen followed by storage in a −80 °C freezer. Blood plasma (for NMR analysis) and serum samples (for clinical chemistry) were collected into eppendorf tubes (containing sodium heparin for the former) in a standard way before animals were sacrificed at 24 and 144 h postdose, respectively. These samples were then snap-frozen in liquid nitrogen and stored at −80 °C. Liver, kidney, spleen, and brain tissues were also collected and divided into two parts with one part immediately snap-frozen in liquid nitrogen and stored at −80 °C, and the other part fixed in 4% formalin solution (for histopathological assessments).

2. MATERIALS AND METHODS

2.4. Sample Preparation for NMR Measurements

2.3. Histopathological and Clinical Chemistry Assessments

Histopathological assessments were performed by a qualified pathologist with standard procedures of tissue staining with hematoxylin and eosin. Clinical chemistry measurements were conducted with classical methods including total protein (TP), albumin (ALB), albumin-to-globulin ratio (A/G), glucose (Glc), total cholesterol (CHOL), triglycerides (TG), alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (AKP), glutamate transpeptidase (GGT), direct bilirubin (DBIL), total bile acid (TBA), blood urea nitrogen (BUN), creatinine (CRN), and uric acid (UA). Clinical chemistry data were expressed as mean ± standard deviation (SD). Differences between control and treated groups were determined using Student’s t-test with p < 0.05 being considered as statistically significant. Each plasma sample (200 μL) was mixed with 400 μL of saline solution (containing 75% D2O, 0.9% NaCl, 0.04% NaN3) and transferred into 5 mm NMR tubes for NMR analysis. A previously optimized method was adopted for preparation of urine samples.50 In brief, 550 μL urine was blended with 55 μL of phosphate buffer (K2HPO4/NaH2PO4, 1.5 M, pH 7.4, 100% D2O) containing 0.01% TSP for chemical shift reference (δ0.00). After centrifugation (11 180 ×g, 4 °C) for 10 min, 550 μL supernatant was transferred into 5 mm NMR tubes for NMR analysis. Each liver tissue sample (55 ± 5 mg) was extracted three times with 600 μL of methanol/H2O (2:1) using a Qiagen tissuelyzer (Retsch GmBH, Germany) as previously reported.51 Three resultant supernatants were combined and subjected to centrifugation (16 099 ×g, 4 °C, 10 min). After removing methanol in vacuo, the resultant supernatant was lyophilized, weighed, and reconstituted into 600 μL phosphate buffer (0.1 M, K2HPO4/NaH2PO4, pH 7.4) containing 0.005%

2.1. Chemicals

Sodium chloride, methanol, ether, gallic acid (99.8%), dimethylsulfoxide (99.8%), K2HPO4·3H2O and NaH2PO4·2H2O (all in analytical grade) were obtained from Sinopharm Chemical Reagent Co. Ltd. (Shanghai, P.R. China). Analytical grade sodium azide was purchased from Tianjin Fuchen Chemical Reagent Factory (Tianjin, P.R. China). Sodium 3-trimethylsilyl [2,2,3,3-d4] propionate (TSP) and D2O (99.9% in D) were bought from Cambridge Isotope Laboratories, Inc. (MA, U.S.). Phosphate buffer was prepared with K2HPO4 and NaH2PO4 as previously reported50 with good solubility and low-temperature storage stability. 2.2. Animal Experiments and Sample Collection

All animal experiments were carried out at the Center for Animal Experiments at Wuhan University according to the National Guidelines for Experimental Animal Welfare (MOST of People’s Republic of China, 2006). 45 male SPF Wistar rats (200 ± 10 g) were purchased from the Center for Animal 992



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Figure 1. Typical 600 MHz 1H NMR spectra of plasma (P) and liver extract (L) from Wistar rats 48 h after dose with 0 mg/kg (control group, C) and 600 mg/kg gallic acid (high dose group, H). The region of δ6.3−9.0 in the plasma spectra was vertically expanded 20 times compared with the region of δ0.5−5.5. The region of δ5.5−9.7 in the liver extract spectra were vertically expanded 10 times compared with the region of δ0.5−5.5. Metabolite keys are shown in Table 1.

TSP, 50% D2O and 0.01% NaN3. After final centrifugation (16 099 ×g, 4 °C, 10 min), each supernatant (550 μL) was transferred into a 5 mm NMR tube for NMR analysis. Each fecal sample was extracted as previously optimized52,53 with slight modifications. In brief, samples (55 ± 5 mg) were extracted twice with 500 μL phosphate buffer (0.1 M, K2HPO4/ NaH2PO4, pH 7.4) containing 0.005% TSP, 30% D2O and 0.01% NaN3. After centrifugation (16 099 ×g, 4 °C, 10 min), 600 μL combined supernatants were transferred into 5 mm NMR tubes for NMR analysis.

For NMR signal assignment purposes, a set of two-dimensional (2D) NMR spectra were acquired on 600 MHz spectrometers for selected samples including 1H J-resolved, 1H−1H COSY and TOCSY, 1H−13C HSQC, and HMBC spectra and processed as previously reported.4,54 2.6. NMR Data Processing and Multivariate Data Analysis

All 1H NMR spectra of urine, liver, and feces were phase- and baseline-corrected manually using TOPSPIN (V2.0, Bruker Biospin, Germany) and referenced to TSP (δ0.00). The plasma spectra were referenced to the anomeric proton signal of α-glucose (δ5.23). All of these spectra were then integrated into regions with the bucket-width of 0.004 ppm using the AMIX package (V3.8, Bruker Biospin, Germany). Chemical shifts for urinary citrate and histidine were corrected manually since their signals had large intersample variations. In order to obtain only the endogenous metabolite changes induced by GA intakes, signals from the exogenous compounds including ether, methanol, DMSO, and GA metabolites were carefully discarded together with regions containing urea and H2O signals. This treatment will avoid any contributions of GA and DMSO metabolites to intergroup differentiations. In liver extract spectra, these discarded regions include δ3.340−3.382 (for methanol) and δ4.460−5.180 (for H2O); in plasma spectra, these regions include δ1.142−1.222 and δ3.542−3.602 (for ether), δ3.130− 3.180 (DMSO2), δ4.454−5.222 (for H2O) and δ5.942−6.750 (for urea). In urine spectra, these regions included δ2.710− 2.842 (for DMSO), δ2.585−2.602 and δ2.866−2.874 (for DMSO satellites), δ3.082−3.222 (for DMSO2), δ3.970−3.990 and δ5.342−7.446 (for GA metabolites), and δ4.460−5.240 (for H2O) and δ5.502−6.202 (for urea). For fecal spectra, the discarded regions included δ2.702−2.790 (for DMSO), δ3.140−3.170 (for DMSO2), δ4.460−5.176 (for H2O) and δ5.340−7.450 (for GA metabolites). Although some losses of endogenous metabolite signals may occur during this process, such losses are limited since most of metabolites having signals in these discarded regions also have signals elsewhere.

2.5. NMR Measurements 1

H NMR spectra of plasma were acquired at 298 K on a Varian INOVA 600 MHz NMR spectrometer (599.904 MHz for proton frequency) with an inverse detection probe. Spectra for urine and feces were acquired on a Bruker AVII 500 MHz NMR spectrometer (500.13 MHz for proton frequency) with a TXI probe whereas those for liver extracts were recorded on a Bruker AVIII 600 MHz NMR spectrometer (600.13 MHz for proton frequency) with an inverse cryogenic probe. Only one spectrum was acquired for urine and fecal samples using the first increment of the NOESY pulse sequence (RD-90°-t1-90°tm-90°-acquisition; t1 = 4 μs, tm = 100 ms). For plasma samples, two more spectra were acquired, respectively, using the Carr− Purcell−Meiboom−Gill sequence (RD-90°-(τ-180°-τ)n-acquisition; τ = 500 μs, n = 100) and the diffusion-edited sequence (RD-90°-G1-180°-G1-90°-G2-Δ-90°-G1-180°-G1-90°-G3-Te-90°acquisition; Te = 5 ms, Δ = 200 ms). For liver extracts, only one spectrum was acquired for each sample using the standard noesygppr1d pulse sequence (RD-G1-τ-90°-t1-90°-tm-G2-τ-90°acquisition; τ = 200 μs, tm = 80 ms, t1 = 4 μs). For all experiments, 64 transients were collected with 32k data points and a spectral width of 20 ppm. 90° pulse length was adjusted to about 10 μs and recycle delay (RD) was set to 2 blanks . For all spectra, an exponential window function was applied with a line-broadening factor of 1 Hz prior to Fourier Transformation (FT). 993



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Figure 2. Typical 500 MHz 1H NMR spectra of urine (U) and feces (F) from Wistar rats 24 h after dose with 0 mg/kg (control group, C) and 600 mg/kg gallic acid (high dose group, H). The region of δ5.0−9.5 in the urine spectra was vertically expanded 5 times compared with the region of δ0.7−4.7. The region of δ5.5−9.0 in the feces spectra were vertically expanded 10 times compared with the region of δ 0.7−5.5. Metabolite keys are shown in Table 1.

in the high-dose group were lower than controls (Table S2 of the SI). Histopathological assessments showed no abnormality in liver, kidney, spleen, and brain tissues in both low-dose and high-dose groups (Figure S1 of the SI). This is consistent with literature results for GA treatment with dosages below NOAEL.6,21

The obtained results would be conserved thus safe from overclaiming. Multivariate data analysis was conducted with the SIMCA-P+ package (V.12, Umetrics, Sweden). Principal Component Analysis (PCA) was first performed on all of the mean-centered data to obtain an overview of data distributions and to identify outliers. Projection to Latent Structure with Discriminant Analysis (PLS-DA) or Orthogonal Projection to Latent Structure with Discriminant Analysis (OPLS-DA) was then carried out using the NMR data as X-matrix and class information as Y-matrix with Pareto scaling. To avoid overinterpreting, only two components were calculated for PLS-DA and OPLS-DA models with obtained R2 and Q2 values as initial indicators for model quality. All OPLS-DA model qualities were evaluated by 7-fold cross-validation and further ensured with CV-ANOVA with p < 0.05 considered as valid.55 Due to limited sample numbers for plasma (n = 5) at 48 h post dose, only PLS-DA was performed with the model quality ensured by a 5-fold cross-validation and further rigorous permutation test (200 permutations).55 The loadings plots from these models were generated using an in-house developed MATLAB script following back-transformation,44 where signals were color-coded with correlation coefficients to reveal significantly altered metabolites. A cutoff value for the correlation coefficient was chosen (according to the sample number) to select metabolites with statistical significance between different groups (p < 0.05) according to the discriminating significance of the Pearson’s productmoment correlation coefficient.44

3.2. Metabolite Assignment with 1H NMR Spectroscopy

Figure 1 shows some typical 1H NMR spectra of rat plasma (P) and liver extract (L) from the control (C) and high-dose groups (H) at 48 h postdose, whereas Figure 2 shows some spectra of rat urine (U) and feces (F) from the control (C) and high-dose group (H) at 24 h postdose. NMR signals were assigned to specific metabolites for both 1H and 13C resonances (Table 1) based on the literature data and further confirmed individually with 2D NMR data from J-resolved, COSY, TOCSY, HSQC, and HMBC spectra. It was found that plasma and liver extract samples mainly contained glucose, lactate, choline, lipids, TCA intermediate metabolites, and a series of amino acids. Nucleotide metabolites, such as inosine and uracil, were observable in liver extract spectra (Figure 1). Some TCA cycle intermediates were clearly visible in urine spectra such as 2-ketoglutarate (2-KG) and succinate (Figure 2) whereas short chain fatty acids (SCFA) such as butyrate and propionate were detectable in feces (Figure 2) together with some keto acids (e.g., 2ketoisovalerate and 2-ketoisocaproate) and hemicellulosic sugars (arabinose and xylose) as previously reported.52,53 Some metabolites of gallic acid (e.g., pyrogallol and resorcinol) and DMSO (carrier) were detectable in urine and fecal samples (Figure 2 and Table 1). Dihydroxyacetone was also detected in plasma here. Visual inspection of 1H NMR spectra indicated that GA-induced metabolic variations were evident. For example, rat liver extracts from the high-dose GA group (Figure 1) contained higher level of NAD and NADP but lower level of glycogen than controls. To obtain more detailed information about the GA-induced metabonomic changes, we further performed multivariate analysis of these data.

3. RESULTS 3.1. Clinical Chemistry and Histopathology

Body and organ weights showed no significant dose-related changes (Table S1 of the Supporting Information, SI). Clinical chemistry data for serum showed that low-dose (120 mg/kg) group had slightly higher BUN level than controls at 48 h postdose. At 144 h postdose, the levels of Glc and BUN in both GA groups together with TP and ALB in the low-dose group were higher than controls whereas the levels of AST and urate 994



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Table 1. 1H and 13C NMR Data for Metabolites in Rat Plasma, Liver Extract, Urine and Fecesa keys

metabolites

moieties

1

Valine

2

Leucine

3

Isoleucine

4

Lactate

5

Alanine

6

Acetate

7 8 9

N-acetyl-glycoprotein O-acetyl-glycoprotein Lipid

10

Glutamate

11

Glutamine

12

GSSG

13 14

Pyruvate Succinate

15

Aspartate

16

Citrate

17

Methionine

18

2-Ketoglutarate

αCH βCH γCH3 γ′CH3 αCH βCH2 γCH δCH3 δ′CH3 αCH βCH β′CH3 γCH2 γCH2′ δCH3 αCH βCH3 COOH αCH βCH3 COOH CH3 COOH CH3 CH3 CH3(CH2)n CH3CH2CH2C (CH2)n CH2CH2CO CH2CC CH2CO CCCH2CC −CHCH− αCH βCH2 γCH2 CHCOOH CH2COOH αCH βCH2 γCH2 2-CH 3-CH2 4-CH2 7-CH 12-CH2 12-CH2′ CH3 CH2 COOH αCH βCH2 βCH2′ CH2 CH2′ αCH βCH2 γCH2 S-CH3 βCH2 γCH2 995

δ 1H (multiplicityb)

δ 13C

samplesc

3.62(d) 2.28(m) 1.00(d) 1.05(d) 3.73(t) 1.72(m) 1.72(m) 0.96(d) 0.97(d) 3.68(d) 1.99(m) 1.02(d) 1.26(m) 1.47(m) 0.94(t) 4.12(q) 1.33(d)

63.2 31.9 19.6 20.9 # 26.9 42.7 24.2 24.7 62.4 39.1 17.8 27.6 27.6 14.2 71.3 23.0 185.5 53.3 19.2 179.0 26.1 184.3 24.9 23.2 16.7 16.7 32.4 27.6 29.5 35.9 28.3 131.0 57.2 29.9 35.9 177.6 184.3 57.1 29.8 33.9 57.1 29.4 34.3 55.7 41.9 41.9 29.2 37.3 186.3 54.5 39.3 39.3 # # # # # # 33.4 38.2

P, L, F

3.77(q) 1.49(d) 1.93(s) 2.04(s) 2.13(s) 0.86(t) 0.88(t) 1.27(m) 1.57(m) 2.01(m) 2.23(m) 2.76(m) 5.30(m) 3.75(m) 2.07(m) 2.35(m)

3.78(m) 2.17(m) 2.46(m) 3.79(t) 2.17(m) 2.54(m) 4.74(dd) 2.98(dd) 3.32(dd) 2.37(s) 2.41(s) 3.91(m) 2.68(dd) 2.82(dd) 2.69(d) 2.81(d) 3.87(t) 2.16(m) 2.65(t) 2.14(s) 2.43(t) 3.00(t)

P, L, F

P, L, F

P, L, U, F

P, L, U, F

P, L, U, F P, F P P

L

L, F

L

P, L, F L, U, F L, F

P, U P, L

P, U, F



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Table 1. continued metabolites

moieties


δ 13C

samplesc

19

Dimethylglycine Creatine

21

Asparagine

22

Acetone

2.92(s) 3.71(s) 3.04(s) 3.93(s) 3.95(m) 2.84(dd) 2.94(dd) 2.23(s)

23 24

Acetoacetate Lysine

25 26

DMSO2 Choline

27

GPC

28

Betaine

29

Glycine

30

Glycerol

2.28(s) 3.76(t) 1.92(m) 1.49(m) 1.74(m) 3.03(t) 3.15(s) 4.07(t) 3.53(t) 3.21(s) 4.33(t) 3.69(t) 3.23(s) 3.27(s) 3.91(s) 3.57(s) 3.56(dd)

46.6 62.4 39.2 56.2 # # # 28.1 218.2 32.3 # 33.0 24.5 29.5 42.6 44.2 58.5 70.3 56.9 62.5 68.9 56.9 56.4 69.2 44.5 175.5 65.4

P, U, F

20

CH3 CH2 CH3 CH2 αCH βCH2 βCH2′ CH3 C(O) CH3 αCH βCH2 γCH2 δCH2 εCH2 CH3 αCH2 βCH2 N−CH3 αCH2 βCH2 CH3 CH3 CH2 CH2 COOH CH2 CH2′ CH 1-CH 2-CH 3-CH 4-CH 5-CH 6-CH 6-CH′ 1-CH 2-CH 3-CH 4-CH 5-CH 6-CH CH(OH) 2-CH 5-CH 6-CH 2-CH 2′-CH 3′-CH 4′-CH 2-CH 7-CH 2′-CH 3′-CH 4′-CH 5′-CH CH2 CH 2,6-CH 3,5-CH

3.65(dd) 3.77(m) 4.66(d) 3.26(dd) 3.50(t) 3.41(dd) 3.47(m) 3.73(dd) 3.90(dd) 5.24(d) 3.54(dd) 3.71(dd) 3.42(dd) 3.84(m) 3.78(m) 5.43(d) 3.6(m) 5.81(d) 7.55(d) 7.89(s) 5.86(d) 4.73(m) 4.41(m) 8.36(s) 8.25(s) 6.10(d) 4.78(t) 4.48(t) 4.29(m) 3.85(m) 6.52(s) 7.20(d) 6.91(d)

65.4 74.8 98.9 77.2 78.8 72.6 78.9 63.7 63.7 95.1 74.5 75.7 72.6 74.6 75 102.6 75.5 104.1 146.7 140.5 90.5 # # 143.4 149.5 91.4 77.0 73.4 88.6 65.6 138.0 133.7 118.9

keys

31

β-Glucose

32

α-Glucose

33

Glycogen

34

Uracil

35

Xanthosine

36

Inosine

37 38

Fumarate Tyrosine

996

P P

P, F P P, L, F

P, L, U, F P, L, F

L

P, L, U P, L, U, F P, L

P, L, U, F

P, L, U, F

L L, F L

L

P, L, F P, L, F



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Table 1. continued keys

metabolites

moieties

39

Histidine

40

Phenylalanine

41

Nicotinamide

42 43

Xanthine Cytidine

44

Hypoxanthine

45 46

Formate NAD

47

NADP

48

NMN

49 50

Allantoin Threonine

51 52

Bile acids 3-Hydroxybutyrate

53

Ether

54 55 56 57

Hydroquinone Dimethylamine Dihydroxyacetone Malate

58

α-Mannose

59

UDP

60

IMP

61 62

Guanosine Triglyceride

2-CH 4-CH 2,6-CH 3,5-CH 4-CH 2-CH 4-CH 5-CH 6-CH CH 2-CH 3-CH 2-CH 7-CH CH 2-CH 4-CH 5-CH 6-CH 2″-CH 7″-CH 2-CH 4-CH 5-CH 6-CH 3-CH 4-CH 5-CH 6-CH CH αCH βCH γCH3 CH3 αCH2 αCH2′ βCH γCH3 CH3 CH2 CH CH3 CH3 CH2 CH2′ CH 2-CH 3-CH 2-CH 3-CH 2′-CH 3′-CH 2-CH 7-CH 2′-CH 3′-CH 4′-CH 2-CH CH2OCOR CH2OCOR′ CHOCOR 997


δ 13C

samplesc

7.10(s) 7.91(s) 7.33(m) 7.43(m) 7.38(m) 8.95(t) 8.26(dd) 7.60(dd) 8.72(dd) 7.90(s) 7.85(d) 6.07(d) 8.22(s) 8.20(s) 8.46(s) 9.30(s) 9.10(d) 8.18(m) 8.82(d) 8.42(s) 8.14(s) 9.35(s) 9.16(d) 8.19(m) 8.84(d) 9.37(d) 9.01(d) 8.33(m) 9.60(s) 5.39(s) 3.58(d) 4.24(m) 1.32(d) 0.73(s) 2.32(dd) 2.42(dd) 4.16(dt) 1.20(d) 1.18(t) 3.55(q) 6.81(s) 2.74(s) 4.46(s) 2.38(dd) 2.67(dd) 4.30(dd) 5.19(d) 3.94(m) 5.97(d) 7.97(d) 5.96(d) 4.42(m) 8.58(s) 8.24(s) 6.15(d) 4.75(m) 4.52(m) 8.01(s) 4.06(m) 4.26(m) 5.20(m)

120.2 139.1 132.3 131.9 130.3 150.9 139.6 127.6 155.2 143.7 # # 145.2 148.3 # # # # # # # # # # # # # # # # 63.1 68.2 22.3 21.6 49.6 49.6 68.6 24.4 # # 118.3 # 67.6 45.5 45.8 73.5 97.1 75.3 # # # # # # # # # # # # #

P, L P, L, F

P, L

L, F L L, F P, L, U, F L

L

L

U P, F

L P, L

P L L P L, U

L L

L

L P



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metabolites

moieties

63

Ethylmalonate

64

Isovalerylglycine

65

Propionate

66

β-Mannose

67

Methylmalonate

68

3-Hydroxyisovalerate

69

Phosphorylcholine

70

N-Acetylglutamate

71

Creatinine

72 73 74

TMAO Trimethylamine Trigonelline

75

2-Ketoisocaproate

76

Benzoate

CH3 CH2 CH CH3 CH CH2 CH2COOH CH3 CH2 COOH 2-CH 3-CH CH3 CH COOH CH3 CH C(CH3)2 αCH2 βCH2 CH3 αCH βCH2 βCH2′ γCH2 CH3 CH3 CH2 C(NH) C(O) CH3 CH3 2-CH 4-CH 6-CH 5-CH CH3 CH3 CH CH2 1-C 2,6-CH 3,5-CH 4-CH COOH 4-CH 5-CH 6-CH 7-CH CH3 αCH βCH2 γCH3 CH3 CH2 βCH γCH γCH′ γ′CH3 δCH3 CH3

77

Indoxyl sulfate

78

Butyrate

79

Ethanol

80

3-Methyl-2-oxovalerate

81

2-Ketoisovalerate


δ 13C

samplesc

0.89(t) 1.72(m) 3.00(m) 0.94(d) 2.01(m) 2.17(d) 3.76(d) 1.06(t) 2.18(q)

16.1 24.9 # 24.1 28.8 47.6 46.5 13.2 19.1 173.2 96.8 76.7 18.0 54.9 183.6 30.7 52.1 72.8 61.1 69.5 56.9 # # # # # 33.1 59.6 172.4 191.7 62.2 # 148.8 147.5 148.4 130.6 51.2 # # # 138.9 131.2 130.9 134.2 178.8 115.2 122.8 125.6 120.3 119.0 42.3 22.0 16.2 # # # # # # # #

U

4.91(d) 3.95(m) 1.25(d) 3.18(q) 1.27(s) # 4.17(t) 3.60(t) 3.23(s) 4.13(m) 1.84(m) 2.02(m) 2.23(m) 1.99(s) 3.05(s) 4.05(s)

3.27(s) 2.88(s) 9.13(s) 8.85(m) 8.84(m) 8.09(m) 4.44(s) 0.94(d) 2.10(m) 2.61(d) 7.88(d) 7.49(dd) 7.56(t) 7.51(m) 7.21(m) 7.28(m) 7.71(m) 7.37(s) 2.16(t) 1.56(m) 0.90(t) 1.19(t) 3.66(q) 2.93(m) 1.47(m) 1.69(m) 1.10(d) 0.88(t) 1.13(d) 998

U

U, F

L U

U

L

U

U

U U U

F

U

U

F

U F

F



Article


metabolites

82 83

Methanol α-Arabinose

84

β-Arabinose

85

β-Xylose

86

α-Xylose

87

Urocanate

88 89

DMSO UMP

90

CDP

91

CMP

92

AMP

93

ADP

94

ATP

95

ITP

96 97

GTP 2-Aminoisobutyrate

98

Gallic acid

99

Pyrogallol

100

Resorcinal

101

U1

102 103

U2 U3

moieties CH CH3 2-CH 3-CH 2-CH 3-CH 2-CH 3-CH 2-CH 3-CH 3-CH 5-CH CHCOOH CHCHCOOH CH3 2-CH 3-CH 2′-CH 3′-CH 2-CH 3-CH 2′-CH 3′-CH 2-CH 3-CH 2′-CH 3′-CH 2-CH 7-CH 2-CH 7-CH 2-CH 7-CH 2-CH 7-CH 7-CH CH3 CCH3 COOH 2,6-CH 4-C−OH −COOH 4,6-CH 5-CH 2-C−OH 1,3-C−OH 2-CH 4,6-CH 5-CH 1,3-C−OH 2-CH 3-CH 4-CH 5-CH


δ 13C

3.03(m) 3.37(s) 5.24(d) 3.82(dd) 4.52(d) 3.53(dd) 4.57(d) 3.24(dd) 5.20(d) 3.52(dd) 7.90(s) 7.38(s) 6.40(d) 7.34(d) 2.73(s) 5.99(d) 8.12(d) 5.99(d) 4.38(m) 6.13(d) 7.97(d) 6.00(d) 4.33(m) 6.14(d) 8.10(d) 6.01(d) 4.33(m) 8.61(s) 8.27(s) 8.54(s) 8.27(s) 8.55(s) 8.28(s) 8.51(s) 8.24(s) 8.15(s) 1.47(s)

# 51.3 94.8 # 99.2 # 99.4 # 95.1 # # # # # 41.4 # # # # # # # # # # # # # # # # # # # # # 17.3 53.3 178.6 112.1 140.9 177.5 111.4 # 138.4 152.7 105.4 110.1 # 159.5 100.4 72.7 74.9 86.8 # #

7.01(s)

6.53(d) 6.92 (t)

6.42 (t) 6.47 (dd) 7.17 (t) 5.62(dd) 4.10(m) 4.05(m) 4.21(m) 3.13(s) 6.26(s)

samplesc F F F F F F

U, F L

L

L

L L L

L P

U, F

U, F

U, F

L, F

P L

a

Keys: GSSG, glutathione disulfide; DMSO, dimethylsulfoxide; DMSO2, dimethylsulfone; GPC, glycerophosphocholine; TMAO, trimethylamine oxide; NAD, nicotinamide adenine dinucleotide; NADP, nicotinamide adenine dinucleotide phosphate; NMN, Nicotinamide mononucleotide; UMP, uridine monophosphate; UDP, uridine diphosphate; IMP, inosine monophosphate; ITP, inosine triphosphate; CMP, cytidine monophosphate; CDP, cytidine diphosphate; AMP, adenosine monophosphate; ADP, adenosine diphosphate; ATP, adenosine triphosphate; GTP, guanosine triphosphate. b s, singlet; d, doublet; t, triplet; q, quartet; m, multiplet; dd, doublet of doublets. cP, plasma; L, liver extract; U, urine; F, feces; #, not determined. 999



Article

Figure 3. Scores and loadings plots from a PLS-DA model for Wistar rat plasma (n = 5) 48 h after dose with 0 mg/kg (black) and 600 mg/kg GA (blue). Metabolite keys are shown in Table 1.

3.3. GA-Induced Plasma and Liver Metabonomic Changes

Table 2. Gallic Acid Induced Significant Changes of Metabolites in Rat Plasma and Liver Extracta

Two sets of plasma samples collected at 48 h post dose and 144 h post dose were analyzed here. PLS-DA results indicated that high-dose (600 mg/kg) GA treatment induced metabolic changes were clearly visible at 48 h post dose including level elevations for Glc, 2-KG, Phe, and dihydroxyacetone together with level decrease for lactate compared with controls (Figure 3, Table 2). In contrast, no significant metabonomic differences were observable between the low-dose GA (120 mg/kg) group and controls at 48 h postdose. At 144 h postdose, no metabonomic differences were observable between GA groups and controls. As for liver extracts, OPLS-DA results showed that at 48 h postdose (Figure 4A, Table 2) high dose (600 mg/kg) GA group had significant elevation of various nucleotide metabolites (inosine, guanosine, ADP, UDP, UMP, CDP, and CMP), NMN, NAD, NADP, GSSG, hydroquinone, GPC, betaine, bile acids, and formate compared with controls. Furthermore, GA induced obvious level decreases for glycogen, mannose, glycerol, a number of amino acids (Val, Leu, Ile, Lys, Tyr, His, and Phe), IMP, xanthine, uracil, and cytidine (Figure 4A, Table 2). In contrast, at 144 h postdose, such differences between the GA group and controls became much less indicating recovery to large extent (Figure 4B, Table 2).

metabolites

correlation coefficient (control vs high dose) plasma (48 h p.d.)

Valine (1) Leucine (2) Isoleucine (3) Alanine (5) Lysine (24) Glutamate (10) Phenylalanine (40) Tyrosine (38) Histidine (39) Glycogen (33) Glucose (32) Mannose (58) Glycerol (30) Dihydroxyacetone (56) Citrate (16) 2-Ketoglutarate (18) Lactate (4) Formate (45) GSSG (12) Hydroquinone (54) GPC (27) Betaine (28) Bile acid (51) Xanthine (42) Inosine (36) Guanosine (61) Uracil (34) Cytidine (43) IMP (60) ADP (93) UDP (59) UMP (89) CDP (90) CMP (91) NMN (48) NAD (46) NADP (47) 2-Aminoisobutyrate (97) U1(101)

3.4. GA-Induced Urinary and Fecal Metabonomic Changes

We further analyzed the GA-induced urinary and fecal metabonomic changes at six time points (12−24 h, 36−48 h, 60−72 h, 84−96 h, 108−120 h, and 132−144 h postdosage) to explore the time-dependence and recoverability of the GA effects. PCA results showed that one sample in urine at 36−48 h postdose contained outstandingly higher lactate level compared to others in the same group; three fecal samples at 12−24 h postdose were suspected to be contaminated with some spurious signals present in their spectra. Therefore, these four samples were excluded in the subsequent analysis. OPLS-DA models were found to be valid with CV-ANOVA (p < 0.05) for urine samples at 12−24 h (Figure 5A,C) and 36− 48 h postdose (Figure 5B,D). At the first time point, high-dose GA induced significant elevations for succinate and alanine but marked level decreases for 2-KG, citrate, malate, 3-hydroxyisovalerate, isovalerylglycine, N-acetylglutamate, propionate, and creatinine (Figure 5C, Table 3). In contrast, low-dose GA treatment only induced elevations for citrate and succinate together with decreases for creatinine, 2-KG, pyruvate, and N-acetylglutamate (Figure 5A, Table 3); such changes for N-acetylglutamate, creatinine and 2-KG were all much less (Figure 5A,C, Table 3). At 36−48 h after GA treatment, both treated groups had some recovery by showing less changes compared with controls in terms of the number of changed

liver extract (48 h p.d.)

liver extract (144 h p.d.)

−0.93b −0.96 −0.88 −0.80 −0.83 −0.71 0.92

−0.97 −0.93 −0.84 −0.86

0.95 −0.84 −0.96

−0.66

0.87 −0.65 0.84 −0.94 0.90 0.95 0.85 0.93 0.88 0.94 −0.95 0.92 0.94 −0.86 −0.92 −0.89 0.91 0.97 0.96 0.95 0.86 0.87 0.98 0.98

0.70

0.75 0.66

0.72 0.75

0.81 −0.84

a

High dose: 600 mg gallic acid/kg body weight; p.d. represents post-dose. Positive and negative values indicate level elevations and decreases compared with control, respectively.

b

metabolites and degree of changes (Figure 5). Only the level decrease of 2-ketoglutarate remained for both GA-treated groups. Nevertheless, valine showed some elevation for both 1000



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Figure 4. Scores and loadings plots from OPLS-DA models for liver extracts from Wistar rats dosed with 0 mg/kg (black) and 600 mg/kg GA (blue). (A) 48 h postdose; (B) 144 h postdose. Metabolite keys are shown in Table 1.

Figure 5. Scores and loadings plots from OPLS-DA models for urinary data from Wistar rats dosed with 0 mg/kg (black), 120 mg/kg (red), and 600 mg/kg GA (blue). (A) control vs low dose group at 12−24 h postdose; (B) 36−48 h postdose; (C) control vs high dose group at 12−24 h postdose; and (D) 36−48 h postdose. Metabolite keys are shown in Table 1.

4. DISCUSSION Gallic acid (GA) and its derivatives are inescapable exposure components in our daily life. Both antioxidation and prooxidation activities have been proposed in literature,8,14−17 leading to contradictory conclusions for the effects of GA on human health.7,9,17−19 Histopathology and clinical chemistry results indicated that a single dose below its NOAEL (1 g/kg) caused no obvious adverse effects on rats.22 Many studies showed that gallic acid underwent extensive biotransformation including phase I and phase II metabolism by mammalian liver and gut microbiota.1,2,56 It is therefore conceivable that gallic acid intakes ought to affect endogenous metabolism of mammals even when dosage is below NOAEL. This work has indicated the effects of a single intake of gallic acid on rat

groups which seemed to be a reverse from the changes at 12−24 h postdose (Figure 5, Table 3). For the high-dose group, furthermore, elevation of lactate, creatinine, and propionate together with the decline of pyruvate also appeared at this time point (Figure 5, Table 3). At 12−24 h postdose, only the high-dose GA treatment induced significant changes in fecal metabonome judged from the rigorously tested OPLS-DA models. No significant differences were observed at all the rest time points. Compared with controls, high-dose GA treatment induced significant elevation of propionate, butyrate, aspartate, and methanol together with the level decreases for pyruvate, 2-ketoglutarate, choline, creatinine, lysine, alanine, 2-ketoisovalerte, 2-ketoisocaproate, hypoxanthine, and uracil (Figure 6, Table 3). 1001



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findings that GA is mainly metabolized in liver2 and only present in blood for 6 h.56

Table 3. Gallic Acid Induced Significant Changes of Metabolites in Rat Urine and Fecesa metabolites

urine control vs low dose 12−24h p.d.

Valine (1) Alanine (5) N-Acetylglutamate (70) 3-Hydroxyisovalerate (68) Isovalerylglycine (64) Pyruvate (13) Lactate (4) Citrate (16) 2-Ketoglutarate (18) Succinate (14) Malate (57) Ethylmalonate (63) Ethanol (79) Creatinine (71) Propionate (65) Butyrate (78) Lysine (24) Aspartate (15) 2-Ketoisocaproate (75) 2-Ketoisovalerate (81) Methanol (82) Choline (26) Hypoxanthine (44) Uracil (34)

36−48h p.d. 0.74b

−0.69

Oral Intake of Gallic Acid Induces Oxidative Stress and Affects Energy Metabolism

feces control vs high dose

12−24h p.d. −0.70 0.80 −0.97

36−48h p.d.

control vs high dose

Significant depletion of hepatic glycogen, mannose, and glycerol together with the increase of ADP in the high-dose (600 mg/kg) group suggest the GA-induced increases in energy expenditure (Figure 7). Similar observations were also made with consumption of green tea extracts.57 This together with the level increase in plasma glucose58 indicated that gallic acid consumption might induce oxidative-stress responses in Wistar rats. Supportive evidence is available that GA can facilitate oxygen uptake. 59 Such stress often promotes glycogenolysis, glycolysis, and the TCA cycle to produce more ATP. The elevation of hematic 2-ketoglurate and level decrease of lactate in the high-dose group further supported the notion of enhanced aerobic glycolysis. The level increases for NAD, NADP, and NMN are probably related to the demand for more ATP generation as well with NADH converted into NAD+ through electron-transport chain (or oxidative phosphorylation). To overcome the reactive oxygen species produced by oxidative phosphorylation,60 the glutathione-dependent antioxidant enzymes, such as glutathione peroxidase (GPX), would be activated.60 It is therefore reasonable to observe the elevation of oxidized form of glutathione (GSSG) in hepatic tissue. Marked elevation of hematic dihydroxyacetone indicates that GA treatment also promotes the glycerol-to-dihydroxyacetone conversion in liver.61 The concurrent level rise of succinate and decreases of malate and 2-ketoglutarate seen in urine further suggest that gallic acid may have effects on specific enzymes in TCA cycle such as succinate dehydrogenase and 2-ketoglutarate dehydrogenase, which warrants further investigation.

12−24h p.d.

0.71 −0.68

−0.73 −0.72 −0.59 0.57 −0.82 0.63

−0.57

−0.67

−0.58 −0.96 0.91 −0.72 −0.63 −0.79 −0.84 −0.69

−0.62 0.75

−0.68

−0.61

−0.59

0.52 0.53

−0.57 0.60 0.69 −0.80 0.57 −0.63 −0.74 0.67 −0.77 −0.74 −0.62

Gallic Acid Intakes Induce Alterations to Amino Acid Metabolism

a

Low dose: 120 mg/kg; high dose: 600 mg/kg; p.d. represents postdose. bCorrelation coefficients from OPLS-DA. Positive and negative values indicate level elevations and decreases compared with controls, respectively.

The GA-induced level decreases for amino acids (Ala, Val, Leu, Ile, Lys, Tyr, Phe, and His) in rat liver (Figure 4) are probably associated with the GA-caused enhancement of protein synthesis and increase in energy expenditure. Clinical chemistry results confirmed that GA-treatments induced rises for total protein and albumin (Table S2 of the SI). Elevation of hematic phenylalanine observed here was also reported previously for rats under both acute and chronic stresses62 which was probably related to demands for catecholamine biosynthesis upon stress. Nevertheless, stress-induced increases in energy expenditure can also cause elevated consumptions of amino acids as an energy source. These suggest that single intake of gallic acid at 120−600 mg/kg induces stresses to rats to some extent.

biochemistry by systematically analyzing the time course of metabolic alterations in multiple biological matrices (i.e., plasma, liver extract, urine, and feces) with the dosages well below NOAEL. Intake of gallic acid affects glycogenolysis, glycolysis, TCA cycle, and metabolism of nucleotides, choline, amino acids, bile acids together with gut microbial activities (Figure 7). Such GA effects were dynamic and dose-dependent. GA effects were greater on liver than on the other matrices and not completely recoverable within 144 h. GA-induced metabolic changes in plasma were minimal with the GA dosage of 120 mg/kg. Such observations are consistent with the

Figure 6. Scores and loadings plots from an OPLS-DA model for the fecal extracts collected from Wistar rats at 12−24 h after dosed with 0 mg/kg (black) and 600 mg/kg GA (blue). Metabolite keys are shown in Table 1. 1002



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Figure 7. A summary of the metabolic pathways altered by gallic acid intakes. Metabolites in red or blue had significant level increase or decrease in the GA-treated group compared with controls. Metabolites in black had no significant changes or were not detected.

Gallic Acid Intakes Cause Changes of Nucleotide Metabolism in Liver

Gallic Acid Intakes Result in Changes in Gut Microbiota Functions

GA induced significant rises in inosine and guanosine levels accompanied with reduction of xanthine in rat liver (Figure 7). Inosine and guanosine are normally converted to hypoxanthine and guanine, respectively, by purine nucleoside phosphorylase. Hypoxanthine and guanine can further be converted into xanthine by xanthine oxidase catalysis and deamination, respectively. Our observation is consistent with some findings that GA inhibits both xanthine oxidase28,29 and the conversion of guanine to xanthine.63 The GA-induced decrease of uric acid in our clinical chemistry data (Table S2 of the SI) provides further support to this notion since xanthine-to-urate conversion relies on xanthine oxidase as well. This study observed the GA-induced elevations of UDP, UMP, CDP, and CMP accompanied with decreases of uracil and cytidine. This is also in accord to the inhibitory effects of gallic acid on nucleotidase and ribonucleotide reductase30,31 since nucleotidase is involved in CMP-to-cytidine and UMP-to-uracil conversions while ribonucleotide reductase catalyzes UDP-to-dUDP and CDP-to-dCDP conversions.

The GA-induced level rises for a number of fecal metabolites indicated that intakes of gallic acid caused alterations to metabolism related to gut microbiota. Fecal propionate and butyrate are fermentation products of insoluble polysaccharides and proteins.64 Significant level increases of propionate and butyrate in the feces (Figure 7) and decreases of pyruvate, 2ketoglutarate, lysine, alanine, and keto-acids (2-ketoisovalerate and 2-ketoisocaproate) suggest that GA promoted the gut microbiota fermentation of both proteins and polysaccharides. A parallel study also revealed that a single GA intake at the level lower than NOAEL for mice caused no metabolic changes for Kunmin mice.65 This implies that metabolic responses to gallic acid intakes are species-dependent. It is worth-noting that GA dosages employed in this study are much higher than the average human daily intakes (about 20 mg/kg) from food sources though well below NOAEL (1 g/kg) for rats. In order to understand the effects of daily GA intakes on human metabolism, further studies are ongoing to understand the effects of chronic GA exposure on mammalian metabolism especially at the levels equivalent to human daily intakes.

GA-Treatment Alters Hepatic Functions and Choline Metabolism

5. CONCLUSIONS This study has shown that a single oral intake of gallic acid can induce rat metabolic alterations in multiple biological matrices including plasma, liver extract, urine, and feces even when GA dosage is well below NOAEL (Figure 7). These GA-induced changes are probably associated with alterations to liver functions resulting from some mild oxidative stresses, thus pro-oxidative activities (Figure 7). Metabolic changes associated with GA intake include promotion of glycogenolysis, glycolysis, and TCA cycle, some effects on metabolism of nucleotides, choline, bile acids, amino acids, and gut microbiota (Figure 7). Such effects are both dose- and time-dependent. GA effects on liver metabolism were more severe than on the other

The GA-induced significant increases in hepatic GPC, betaine, and bile acids observed in the high dose-group here indicate that a single dose of GA at the level of 600 mg/kg induced stresses to hepatic functions. This is because GPC is an essential component of cell membrane, while the rise of betaine implies the demand for osmotic homeostasis regulation (Figure 7). The rises of these two metabolites were commonly observed in the case of chemical induced liver injury42 and further support the notion of GA-induced oxidative stress. 1003



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Bunge induced by water depletion. J. Proteome Res. 2010, 9 (3), 1460− 1475. (5) Dai, H.; Xiao, C.; Liu, H.; Hao, F.; Tang, H. Combined NMR and LC-DAD-MS analysis reveals comprehensive metabonomic variations for three phenotypic cultivars of Salvia miltiorrhiza Bunge. J. Proteome Res. 2010, 9 (3), 1565−1578. (6) Niho, N.; Shibutani, M.; Tamura, T.; Toyoda, K.; Uneyama, C.; Takahashi, N.; Hirose, M. Subchronic toxicity study of gallic acid by oral administration in F344 rats. Food Chem. Toxicol. 2001, 39 (11), 1063−1070. (7) Singleton, V. L. Naturally occurring food toxicants: Phenolic substances of plant origin common in foods. Adv. Food Res. 1981, 27, 149−242. (8) Haslam, E. Natural polyphenols (vegetable tannins) as drugs: Possible modes of action. J. Nat. Prod. 1996, 59 (2), 205−215. (9) Vanderheijden, C. A.; Janssen, P. J. C. M.; Strik, J. J. T. W. A. Toxicology of gallatesA review and evaluation. Food Chem. Toxicol. 1986, 24 (10−11), 1067−1070. (10) Lu, Z.; Nie, G.; Belton, P. S.; Tang, H.; Zhao, B. Structureactivity relationship analysis of antioxidant ability and neuroprotective effect of gallic acid derivatives. Neurochem. Int. 2006, 48 (4), 263−274. (11) Kim, S. H.; Jun, C. D.; Suk, K.; Choi, B. J.; Lim, H.; Park, S.; Lee, S. H.; Shin, H. Y.; Kim, D. K.; Shin, T. Y. Gallic acid inhibits histamine release and pro-inflammatory cytokine production in mast cells. Toxicol. Sci. 2006, 91 (1), 123−131. (12) Soleas, G. J.; Grass, L.; Josephy, P. D.; Goldberg, D. M.; Diamandis, E. P. A comparison of the anticarcinogenic properties of four red wine polyphenols. Clin. Biochem. 2002, 35 (2), 119−124. (13) Ow, Y. Y.; Stupans, I. Gallic acid and gallic acid derivatives: Effects on drug metabolizing enzymes. Curr. Drug Metab. 2003, 4 (3), 241−248. (14) Jang, A.; Srinivasan, P.; Lee, N. Y.; Song, H. P.; Lee, J. W.; Lee, M.; Jo, C. Comparison of hypolipidemic activity of synthetic gallic acid-linoleic acid ester with mixture of gallic acid and linoleic acid, gallic acid, and linoleic acid on high-fat diet induced obesity in C57BL/6 Cr Slc mice. Chem. Biol. Interact. 2008, 174 (2), 109−117. (15) Moon, H. S.; Lee, H. G.; Choi, Y. J.; Kim, T. G.; Cho, C. S. Proposed mechanisms of (−)-epigallocatechin-3-gallate for antiobesity. Chem. Biol. Interact. 2007, 167 (2), 85−98. (16) Kanai, S.; Okano, H. Mechanism of the protective effects of sumac gall extract and gallic acid on the progression of CCl4-induced acute liver injury in rats. Am. J. Chin. Med. 1998, 26 (3−4), 333−341. (17) Punithavathi, V. R.; Prince, P. S.; Kumar, R.; Selvakumari, J. Antihyperglycaemic, antilipid peroxidative and antioxidant effects of gallic acid on streptozotocin induced diabetic Wistar rats. Eur. J. Pharmacol. 2011, 650 (1), 465−471. (18) Morton, L. W.; Abu-Amsha Caccetta, R.; Puddey, I. B.; Croft, K. D. Chemistry and biological effects of dietary phenolic compounds: Relevance to cardiovascular disease. Clin. Exp. Pharmacol. Physiol. 2000, 27 (3), 152−159. (19) Chen, C.; Tang, H. R.; Sutcliffe, L. H.; Belton, P. S. Green tea polyphenols react with 1,1-diphenyl-2-picrylhydrazyl free radicals in the bilayer of liposomes: Direct evidence from electron spin resonance studies. J. Agric. Food Chem. 2000, 48 (11), 5710−5714. (20) Tang, H. R.; Covington, A. D.; Hancock, R. A. Structure-activity relationships in the hydrophobic interactions of polyphenols with cellulose and collagen. Biopolymers 2003, 70 (3), 403−413. (21) Rajalakshmi, K.; Devaraj, H.; Niranjali Devaraj, S. Assessment of the no-observed-adverse-effect level (NOAEL) of gallic acid in mice. Food Chem. Toxicol. 2001, 39 (9), 919−922. (22) Jaijoy, K.; Soonthornchareonnon, N.; Lertprasertsuke, N.; Panthong, A. S. S. Acute and chronic oral toxicity of standardized water extract from the fruit of Phyllanthus emblica Linn. Int. J. Appl. Res. Nat. Prod. 2010, 3 (1), 48−58. (23) Booth, A.; Amen, R. J.; Scott, M.; Greenway, F. L. Oral doseranging developmental toxicity study of an herbal supplement (NT) and gallic acid in rats. Adv. Ther. 2010, 27 (4), 250−255.

matrices and metabolic changes in plasma were only minor at 120 mg/kg GA. Within 80 h after a single oral dose of both 120 and 600 mg/kg, such metabolic effects were completely recoverable for urinary, plasma, and fecal metabonomes. In contrast, GA-induced metabolic changes in liver were not completely recoverable within 144 h. These results provided essential information for understanding the effects of polyphenolic acids on mammalian biochemistry and demonstrated that the metabonomics approach has an important role to play in understanding the effects of nutrients on mammalian health.

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ASSOCIATED CONTENT

* Supporting Information S

Figure S1, Histopathological results for liver, kidney, spleen, and brain of Wistar rats dosed with 0 mg/kg (control), 120 mg/kg (low dose), and 600 mg/kg (high dose) GA at 48 h postdose. Figure S2, Permutation test results (200 permutations) for PLS-DA model from plasma data for Wistar rats dosed with 0 mg/kg (control) and 600 mg/kg (high dose) at 48 h postdose. Table S1. Body and organ weight data from Wistar rats dosed with 0 mg/kg (control), 120 mg/kg (low dose) and 600 mg/kg GA (high dose) at 48h postdose and 144 h postdose. Table S2, clinical chemistry data for Wistar rats dosed with 0 mg/kg (control), 120 mg/kg (low dose), and 600 mg/kg GA (high dose) at 48 and 144 h postdose. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Tel: +86-27-87198430; fax: +86-27-87199291; e-mail: Huiru. [email protected]. Author Contributions ⊥

These authors contributed equally to this work

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We acknowledge the financial supports from the Ministry of Science and Technology of China (2009CB118804 and 2010CB912501), National Natural Science Foundation of China (20825520, 20775086, 20775087, 21221064, 21005062 and 21175149) and Chinese Academy of Sciences (KJCX2-YW-W13 and KSCX1-YW-02). The authors thank Dr. Hang Zhu from Wuhan Institute of Physics and Mathematics for developing the MATLAB scripts used for color-coded coefficient plots.

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REFERENCES

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Gallic Acid Intake Induces Alterations to Systems Metabolism in Rats

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