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Antagonist of Prostaglandin E Receptor 4 Induces Metabolic Alterations in Liver of Mice Ning Li, Limin Zhang, Yanpeng An, Lulu Zhang, Yipeng Song, Yulan Wang, and Huiru Tang J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/pr501236y • Publication Date (Web): 11 Feb 2015 Downloaded from http://pubs.acs.org on February 18, 2015

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Journal of Proteome Research

Antagonist of Prostaglandin E2 Receptor 4 Induces Metabolic Alterations in Liver of Mice Ning Li,† Limin Zhang†, Yanpeng AnЖ, Lulu Zhang†, Yipeng Song†, Yulan Wang,*†Φ and Huiru Tang*†Ж †

Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory

of Magnetic Resonance and Atomic and Molecular Physics, National Centre for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, University of Chinese Academy of Sciences, Wuhan, 430071, P. R. China. Ж

State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for

Genetics and Development, Metabolomics and Systems Biology Laboratory, School of Life Sciences, Fudan University, Shanghai 200433, China. Φ

Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases,

Hangzhou 310058, P. R. China.

*

Corresponding Author: Prof. Huiru Tang, Tel: +86-(0)27-87198430; Fax:

+86-(0)27-87199291. E-mail: [email protected]. Prof. Yulan Wang, Tel: +86-(0)27-87197104; Fax: +86-(0)27-87199291. E-mail:[email protected]

Keywords: Prostaglandin E2 receptor 4; metabonomics; transcriptomics; NMR; L-161982

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Abstract Prostaglandin E2 receptor 4 (EP4) is one of the receptors for Prostaglandin E2 and plays important roles in various biological functions. EP4 antagonists have been used as an anti-inflammatory drug. To investigate the effects of an EP4 antagonist on the endogenous metabolism in a holistic manner, we employed a mouse model, and obtained metabolic and transcriptomic profiles of multiple biological matrices, including serum, liver and urine of mice with and without EP4 antagonist (L-161982) exposure. We showed that the EP4 antagonist caused significant changes in fatty acid metabolism, choline metabolism and nucleotide metabolism. EP4 antagonist exposure also induced oxidative stress to mice. Our research is the first of its kind to report the information on the alteration of metabolism associated with an EP4 antagonist. This information could further our understanding of current and new biological functions of EP4.


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Introduction Prostaglandin E2 (PGE2) is produced from arachidonic acid under the enzymatic action of cyclooxygenases (COX).1 It has four receptor subtypes, EP1-4. PGE2 receptors belong to G protein–coupled receptors and play important roles in the biological functions within a living organism. EP4 is the most recently discovered PGE2 receptor and originates from the piglet saphenous vein.2 The coupling of EP4 to PGE2 can increase cAMP levels through the activation of adenylyl cyclase via Gsa.2,3 In recent years, EP4 has also been found to couple with many other proteins.4 EP4 is expressed in many parts of the body, in areas such as the kidneys, hearts, lungs, intestinal tract, blood vessels and skin, and plays important physiologic and pathologic roles within these different parts.5 A large number of studies using either EP4

-/-

(Ptger4

-/-

) mice or EP4 antagonists and agonists have suggested that EP4

receptor is associated with many chronic diseases, such as cancer,6,7 hypertrophic cardiomyopathy,8 cardiovascular disease,9 bone diseases.10 EP4 receptor also affects many biological functions, such as, gastrointestinal homeostasis,11,12 renal function,13 immunoregulation14,15 and hematopoietic function.16 EP4 deficient can cause improper development of heart in neonate, such as patent ductus arteriosus.17 This is because that EP4 can promote the accumulation of hyaluronic acid, an important metabolite in the formation of an intimal cushion that leads to ductus arteriosus closure.18 Therefore, EP4 has cardioprotective functions.19 PGE2 can protect against chemotoxicity induced liver injury20 and bacterial infection21 through EP4. EP4 agonists have been shown to inhibit the release of proinflammatory cytokines, and down regulate the expressions of genes encoding for E-selectin and ICAM-1 adhesion molecules.22 EP4 can promote mucin secretion and maintain gastrointestinal integrity.23 Previous studies have mainly focused on studying the biological functions of EP4 on disease models using antagonists and agonists of EP4, however there has been less investigation on the effects of systematic biochemical composition associated with EP4 functions. L-161982 has been proven to be an effective and selective EP4 antagonist.24 The L-161982 also suppresses bone formation24 and blocks cancer cell



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proliferation, via the EP4/extracellular regulated kinases (ERK) /response element binding protein (CREB) pathway.25 Studying the impact of EP4 antagonist on endogenous metabolism can offer important knowledge on the mechanisms of action of EP4. Metabonomic analysis is a good choice for studying such effects since metabonomic approaches can reveal holistic metabolic alterations related to the effects of drug or pathological, physiological events. On a technical basis, the metabonomic approach employs statistical methods to analysis the global metabolic profiles of biofluids and tissue extracts using 1H NMR spectroscopy or mass spectrometry. This approach has been widely applied in studies on disease etiology (such as obesity,26,27 diabetes,28,29 cancers,30,31 and inflammatory diseases32-35) as well as observing the metabolic effects of biotoxins36,37 and drugs38,39 in transgenic animal models. These studies reveal that oral intake of such xenobiotics result in hepatotoxicity and an alteration of mammalian biochemical pathways such as energy metabolism, nucleic acid metabolism and gut microbial functions. Global metabonomics analysis combined with a molecular biology strategy has been successfully demonstrated to confirm altered metabolic network generated by metabonomics investigations.40 In the current study, we employed a combinational approach of metabonomics and transcriptomics to explore the holistic metabolic responses to EP4 antagonist, L-161982. To the best of our knowledge it is the first investigation of its kind to utilize this combinational approach. We first utilized NMR spectroscopy to obtain global metabolic profiles of biofluids and liver of L-161982-treated mice. We then studied the transcriptomics changes in liver, to validate the metabonomics data. In addition, lipid compositions of liver were also analyzed. Our research has provided detailed novel information on the alteration of metabolism associated with L-161982, which can deepen our understanding of the biological functions of EP4 and provide clues for the discovery of new functions associated with EP4.

Materials and Methods Chemicals


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A range of chemicals and reagents, such as, sodium chloride, methanol, DMSO (99.8%), K2HPO4·3H2O, NaH2PO4·2H2O and hexane (analytical grade) were purchased from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China). Deuterium oxide (D2O, 99.9% D) and sodium 3-trimethylsilyl [2,2,3,3-2H4]- propionate (TSP) were bought from Cambridge Isotope Laboratories, Inc. (MA, U.S.A.). Sodium azide (NaN3) and L-161982 were obtained from Tianjin Fu Chen chemicals reagent factory (Tianjin, China) and Cayman Chemical Company (Michigan, USA) respectively. 3,5-Di-tertbutyl-4-hydroxytoluene (BHT) and mixture of methylesters of fatty acids were bought from Supelco (Bellefonte,PA). For profiles of urine analysis, the buffer (1.5 M, pH 7.41) was made in D2O containing 0.1% NaN3 (w/v) and 0.1% TSP (w/v),41 whereas for NMR profiles of serum, the buffer (45 mM, pH 7.41) was made with 50% D2O (v/v) and 0.9% NaCl (w/v). For tissue extracts, the buffer was prepared in 50% D2O (0.15 M, pH 7.43) containing 0.001% TSP, 0.1% NaN3.

Animal Experimental Procedure Male BALB/c mice (48) aged 6 weeks old (25 ± 3 g) were obtained from the Center for Disease Control (Hubei, P. R. China) and accommodated in the animal house (SPF) located at Wuhan Institute of Virology (Hubei, P. R. China). The animal experiments were performed in line with the national guidelines for animal research (MOST of P. R. China, 2006). Mice were given free access to a basal diet and water. After two weeks of acclimatization, the mice were allocated into four groups with each group having 12 mice at random: control group (C), low-dose group [L, 3 mg/kg.bw], moderate-dose group [M, 6 mg/kg.bw], and high-dose group [H, 20 mg/kg.bw]. These dosages are based on previous report24 that 10 mg/kg/day of L-161982 can suppress bone formation. The L-161982 were dissolved in a mixture of DMSO and saline (0.2 mL, 3:7, v/v) and administrated into mice daily via intraperitoneal injection for 4 consecutive weeks. Drops of urine were obtained at 1 day before L-161982 treatment and then on a weekly basis till 4 weeks post dose. After 2 weeks of treatment, serum of each mouse was obtained from orbital venous plexus. At the end of 4 weeks dosage, all mice were killed under isoflurane anesthesia after fasting for 12 h. Serum and liver



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samples were obtained. All samples were immediately put in liquid nitrogen and kept at −80 °C.

Clinical Biochemistry Measurements Clinical biochemistry measurements were performed with an AEROSET multitask system (Abbott Laboratories, Abbott Park, IL, U.S.A.). The parameters measured were expressed as “mean ± standard deviation” in Table S1. Student’s t-test with P < 0.05 was used for the statistical significant analysis for the discrepancies between control and treated groups.

Sample Preparation for NMR Spectroscopy Serum sample for NMR analysis was made by adding 30 µL of phosphate buffer (45 mM) into 30 µL of serum and then transfer 50 µL of supernatant into a 1.7 mm NMR tube after centrifugation. Urine sample (100 µL) was added into 400 µL of 50% D2O and 50 µL of phosphate buffer (1.5 M, pH 7.4).41 After mixing with a vortex and centrifugation (11 180 ×g, 4°C) for 10 min, 500 µL supernatant of each mouse urine sample was pipetted into 5 mm NMR tube. The liver tissues (about 50 mg) were homogenized in cold methanol and water (2:1) using Qiagen Tissue-Lyser (Retsch GmBH, Germany). The supernatant was collected after centrifugation (16 099 ×g, 4 °C, 10 min). The remaining pellets were extracted further twice using the same procedure. The supernatants collected from the above procedure were mixed together and freeze-dried after removal of methanol in vacuo. Then the residue was dissolved in 600 µL phosphate buffer (0.1 M). The supernatant (550 µL) was transferred into a 5 mm NMR tube ready for NMR analysis.

NMR Spectroscopy NMR spectra were recorded on a Bruker AVANCE III 600 MHz NMR spectrometer (600.13 MHz for 1H frequency) at 298 K with cryogenic probe (Bruker Biospin, Germany). A standard one dimensional NMR spectrum was obtained for urine and liver extract samples using the first increment of NOESY pulse sequence


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(RD-90°-t1-90°-tm-90°-acquisition; t1 = 4 µs, tm = 100 ms). A Carr-PurcellMeiboom-Gill sequence ((RD−90°−(τ−180°−τ)n− acquisition); τ = 350 µs, n = 100) was used for acquiring NMR spectrum of serum. The 90° pulse length was set to about 10 µs. Each NMR spectrum was consisted 64 scans with 32 k data points and a 20 ppm spectral width. For identification of NMR signals, a range of two dimensional (2D) NMR spectra were acquired for selective samples.42

NMR Data Analysis Exponential window function with 1 Hz line-broadening factor was applied to the free induction decays prior to Fourier transformation. All the Fourier transformed NMR spectra were manually corrected for phase and baseline distortion using Topspin (V3.0, Bruker Biospin, Germany). The spectra of urine and liver extracts were calibrated with TSP (δ 0.00) and the serum spectra were calibrated to the anomeric proton of α-glucose at δ 5.233. The spectra were then integrated into bins with the width of 0.002 ppm using AMIX software package (V3.8.3, Bruker Biospin). Some unwanted signals, such as water signals (δ 4.5−5.18) and urea resonance (δ 5.45−6.15 for serum and δ 5.45−6.19 for urine samples) were removed. The remaining spectral data were then normalized: for urinary spectral data, normalization to the total sum of the integrals was used and for the spectra of liver extracts and serum, normalization to the wet weight of tissues and sample volumes were respectively applied. Multivariate data analysis was subsequently carried out using SIMCA-P+ (V11.0 and 12.0, Umetrics AB, Umea, Sweden). Principal component analysis (PCA) was performed with the data scaled to mean-center to provide an overview for group separations and to detect potential outliers. Orthogonal Projection to Latent Discriminant Analysis (OPLS-DA) models were also constructed with the NMR data scaled to Pareto-scaling. Here Y-matrix needed for modelling are the classification information. All OPLS-DA models were validated with a standard 7-fold cross-validation method and further validated by CV-ANOVA and P < 0.05 was regarded as valid.43 A coefficient plot was generated by plotting the back-transformed loadings44 integrated with color coded correlation coefficient of each loading with



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MATLAB script (The Mathworks Inc., version 7.1). The red-colored metabolites are more important in the differentiation between the compared groups than the blue-colored ones. The cutoff value for the correlation coefficients was chosen based on the discrimination significance (P < 0.05).44 In addition, multiple univariate data analysis (MUDA) was employed for the data analysis.27

Fatty Acids Analysis Fatty acids composition were analyzed using a previously reported method45 with some improvement. In short, the samples were homogenized with methanol. Internal standards (20 µL of methyl heptadecanoate and methyl tricosanate) and BHT were mixed with 100 µL of liver homogenate and methanol-hexane mixture (1 mL, 4:l v/v). A total of 100 µL of precooled acetyl chloride was gently added into the above mixture. The mixture was kept at 25 oC in dark for 24 h and followed by neutralization with K2CO3 solution. The mixture was extracted with 600 µL of hexane and the resultant supernatants were evaporated to dryness followed by re-dissolved in 100 µL of hexane for GC−FID/MS analysis. The above samples were analyzed on a Shimadzu GC 2010 Plus GC-MS spectrometer (Shimadzu Scientific Instruments, USA) equipped with a flame ionization detector (FID) and a mass spectrometer. A DB-225 capillary GC column (Agilent Technology) was employed. A total of 1 µL of sample was injected into the injection port (230 °C). Fatty acids were assigned and quantified with standards and further confirmed by their m/z values. The results were calculated as micromole fatty acids per gram of liver. Different fatty acids, such as saturated fatty acids (SFAs), unsaturated fatty acids (UFAs), monounsaturated fatty acids (MUFAs), and polyunsaturated fatty acids (PUFAs) were also calculated. The activities of a range of desaturases were derived based on the ratios of unsaturated fatty acids, and these are D5D (C20:4n6/C20:3n6), D6D (C18:3n6/C18:2n6), SCD16 (C16:1n7/C16:0) and SCD18 (C18:1n9/C18:0).

Transcriptomic Analysis


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Transcriptomic analysis of liver was completed by Capital Bio (Beijing, China). In brief, total RNA was extracted from mice livers using Trizol Reagent (Invitrogen Corp., Carlsbad, CA). The Affymetrix Mouse Genome 430 2.0 array, with a total of 34,000 gene probes, was used for gene expression detection. The gene expression signals were log-transformed and normalized array-wise using Affymetrix Dchip 2006. A permutation test was employed for the detection of the false-discovery rate (n = 500, < 5%). The expression fold changes greater than 2 or less than 0.5 were regarded as significant alterations between control and treated groups.

Results Clinical Chemistry Mice continuously treated with high levels of L-161982 for 4 weeks displayed a decrease in the levels of HDL-C, while no changes in clinical chemistry were observed in mice treated with the other two levels of L-161982 (Table S1 in the Supporting Information).

Metabolite Assignments for 1H NMR Spectroscopy More than 50 metabolites were assigned in the 1H NMR spectra of serum, urine and liver extracts (Figure 1). The NMR assignments were based on publically available literature data46,47 and then verified by a series of 2D NMR spectra such as COSY, TOCSY, JRES, HSQC and HMBC (Table S2 in the Supporting Information). Serum spectra contained signals from lipoproteins, glycoproteins (OAG, NAG), glucose, a range of amino acids, and choline metabolites (choline, PC, GPC). The spectra of liver extracts included signals from amino acids, carbohydrates, glycolysis products (lactate, pyruvate), choline metabolites, and a range of nucleotides (uracil, hypoxanthine, xanthine). Urine spectra displayed signals from metabolites in the glycolysis pathway, TCA cycle intermediates, organic bases (dimethylamine and trimethylamine), as well as co-metabolites with microbial metabolism (hippurate).

EP4 Antagonist Induced Metabolic Changes



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OPLS-DA is carried out for 1H NMR spectral data sets from liver extracts. The cross validation of the models suggested that metabonomes of livers from mice dosed with moderate- and high-levels of EP4 antagonist differed markedly from those of control mice. The moderate-dose levels of EP4 antagonist caused marked elevations in the levels of choline, glycogen and hypoxanthine and depletions in the levels of lactate, succinate, glutamine, GSSG, tyrosine, and nucleotides in liver extracts (Figure 2 and Table S3 in the Supporting Information). High-dose levels of EP4 antagonist induced elevated levels of cholesterol, amino acids, choline metabolites, uracil and purine metabolites (hypoxanthine and xanthine) and resulted in depleted levels of succinate, glutamine and a range of nucleotides (AMP, ADP and UMP) in the liver extracts. Generally, EP4 antagonist with our dosages only induced some weak metabolic alterations in urine and serum reflected by the quality of OPLS-DA models, whose p-values from CV-ANOVA were greater than 0.05. Further univariate data analysis of NMR peaks27 confirmed that there were only limited metabolic changes for NAG, choline metabolites (e.g., TMAO, TMA, DMA) and TCA cycle intermediates (e.g., citrate, 2-ketoglutarate, fumarate) in urine whilst lactate, OAG and some amino acids in serum; hence no further discussion will be considered for the biofluids (Table S3 and S4 in the Supporting Information).

EP4 Antagonist Induced Fatty Acid Changes in Liver We also investigated fatty acid compositions of livers being exposed to L-161982. The results show that high levels of L-161982 exposure resulted in a decline of all fatty acids, which include SFAs, MUFAs and PUFAs. In addition, an increased level of D6D presents in the liver tissues of mice in the high-dose group (Figure 3 and Table S5 in the Supporting Information).

EP4 Antagonist Induced Transcriptomics Changes in Liver Expression levels of 18 genes were up-regulated and that of 72 genes were down-regulated in high-dose exposure group. Genes related to the changes in metabonome in their expression levels are plotted in Figure 4.


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Discussion EP4 is the most recently discovered PGE2 receptor and is known to possess various biological functions, such as stimulating bone formation and within the development of the circulatory system. Here we employed a EP4 antagonist, L-161982, in a murine model, to investigate the metabolic effects of EP4 at a systemic level. The most prominent metabolic effect of L-161982 is the reduction of total fatty acids recovered in the liver (Figure 3). mRNA analysis showed that expressions of genes involved in lipid metabolism are altered after treatment of L-161982 (Figure 4). Firstly, a reduction in total fatty acids biosynthesis was associated with down regulated gene expressions of insulin-induced gene 2 (INSIG2). INSIG2 is an endoplasmic reticulum protein that promotes the biosynthesis of cholesterol and fatty acids in the liver via blocking the proteolytic activation of sterol regulatory element-binding proteins (SREBPs).48 In particular, we found a reduction of unsaturated fatty in the liver of exposed mice (Table S5), which could be directly related to the gene expressions of genes encoding for SCD1 (Stearoyl-CoA desaturase-1), an rate-limiting desaturase for the synthesis of unsaturated fatty acids, which catalyzes oleic acid (C18:1n9) from stearic acid (C18:0). This is also consistent with the increased level of D6D, which contributes to the conversion of linoleic acid (C18:2n6) and α-linolenic acid (C18:3n3) into PUFAs, such as n6 and n3 fatty acids. Besides the decreased fatty acid biosynthesis, fatty acid transportation from serum to liver is also down regulated in L-161982 exposed mice, leading to reduced total fatty acids in the liver. This is evident by the reduced expression of CD36, which are macrophage surface receptors that facilitate the transportation of long-chain fatty acid to hepatocytes.49 We also observed a decreased expression of gene encoding for CYP39A1, which can convert 24-hydroxycholesterol into 7-hydroxylated bile acids.50 The decreased expression of CYP39A1 is coherent with the increased levels of cholesterol in liver (Figure 2). Disturbed fatty acid metabolism associated with L-161982 exposure also manifested in increased fatty acid oxidation. We found a downregulated expression of



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genes encoding for ACACB, a protein known to catalyze malonyl-CoA from acetyl-CoA.51 Malonyl-CoA can regulate the process of fatty acid oxidation by inhibiting carnitine palmitoyltransferase I, the rate-limiting enzyme affecting fatty acid uptake and oxidation in mitochondria.52 In addition, RGS16 (Regulator of G Protein Signaling) is known to downregulate the expression of genes encoding for fatty acid oxidation in liver, resulting in high levels of ketone bodies when knocking out RGS16.53 Hence, the reduction in the expression levels of RGS16 indicates promoted fatty acid oxidation being associated with exposure to L-161982. The stimulated lipid β-oxidation by L-161982 exposure inevitably generates large amounts reactive oxygen species (ROS), thus causing oxidative stress. ROS can be scavenged by conversion glutathione into its oxidized form of glutathione disulfide (GSSG).54 The observed changes in the levels of GSSG in liver from L-161982 treated mice might be associated with the oxidative stress induced by L-161982. Supportive evidence can be found in the expressions of several genes, such as the down-regulation of GSTM3 (glutathione S-transferase, mu 3) in the liver of L-161982 exposed mice. The GSTM3 can catalyze the conjugation of GSH with a variety of electrophilic compounds for the purpose of detoxification. The activity of GSTM3 is dependent upon a supply of GSH from the synthetic enzymes and capability to remove the conjugated GSH.55 Oxidative stress associated with L-161982 exposure was also manifested in changes in expression of genes within metabolic pathway involved in vitamin A, vitamin B3 and folate; these genes include RETSAT, NAMPT, NNMT, and MTHFR. Exposure to L-161982 also affects the biosynthesis of phosphatidylcholine (PC). The expression levels of choline kinase alpha (CHKA) decreased by more than three folds

(Figure

4).

CHKA catalyzes

the

phosphorylation

of

choline

into

phosphocholine.56 The down regulation of CHKA is consistent with elevated levels of choline found in liver. Finally, exposure to L-161982 affected nucleic acid metabolism. UPP2 (uridine phosphorylase 2) is known to catalyze the conversion of uridine to uracil and ribose-1-phosphate.57,58 We observed up-regulated expression of UPP2 with parallel


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increase of uracil levels and decrease of UMP levels, indicating elevated nucleotide metabolism. In addition, the expression levels of ADK and CTPS2 are down-regulated, together with the decreases in the levels of AMP and ADP. CTPS2 (cytidine 5'-triphosphate synthase 2) and ADK (adenosine kinase) convert UTP into CTP and adenosine into AMP respectively.59,60 These observations indicated that exposure to EP4 inhibited nucleotide biosynthesis. In conclusion, we have utilized a systems approach of combining metabonomics and transcriptomics to holistically investigate the responses of L-161982 exposed mice. In general, EP4 antagonist (L-161982) caused a mild disturbance to endogenous metabolisms of mice, which is manifested in the weak disruption noted in the metabolic profiles of urine and serum. High levels of L-161982 caused changes in fatty acid metabolism, choline metabolism and nucleotide metabolism and induced oxidative stress in mice. Targeted analysis on the changes of arachidonic acids metabolism will provide more functional information regarding L-161982 and how it can affect arachidonic acid metabolism.

Supporting Information Available: Supplementary Table S1, Clinic biochemistry obtained from serum of control and L-161982-treated mice. Supplementary Table S2, Table of NMR assignments of the metabolites in urine, serum and liver. Supplementary Table S3, Correlation coefficients of important metabolites discriminating the liver from L-161982-treated mice and those from control mice. P-values of metabolites are generated from multiple univariate data analysis of metabolic profiles of serum from control mice and those from L-161982-treated mice. Supplementary Table S4, Important alterations of urinary metabolites with P values in discrimination control group and L-161982-treated groups. Supplementary Table S5, Fatty acid compositions in liver tissues from mice. This material is available free



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of charge via the Internet at http://pubs.acs.org.

Acknowledgements We acknowledge financial support from the National Natural Science Foundation of China (21175149, 91439102 and 21375144) and the Ministry of Science and Technology of China (2012CB934004 and 2010CB912500).


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References (1) Smith, W. L.; DeWitt, D. L.; Garavito, R. M., Cyclooxygenases: structural, cellular, and molecular biology. Annu. Rev. Biochem. 2000, 69, 145-82. (2) Coleman, R. A.; Smith, W. L.; Narumiya, S., International union of pharmacology classfication of prostanoid receptors-properties, distribution, and structure of the receptors and their subtypes. Pharmacol. Rev. 1994, 46, (2), 205-229. (3) An, S. Z.; Yang, J. H.; Xia, M. H., et al., Cloning and expression of the EP2 subtype of human receptors for prostaglandin-E2. Biochem. Biophys. Res. Commun.. 1993, 197, (1), 263-270. (4) Yokoyama, U.; Iwatsubo, K.; Umemura, M., et al., The prostanoid EP4 receptor and its signaling pathway. Pharmacol. Rev. 2013, 65, (3), 1010-1052. (5) Konya, V.; Marsche, G.; Schuligoi, R., et al., E-type prostanoid receptor 4 (EP4) in disease and therapy. Pharmacol. Ther. 2013, 138, (3), 485-502. (6) Kundu, N.; Ma, X.; Kochel, T., et al., Prostaglandin E receptor EP4 is a therapeutic target in breast cancer cells with stem-like properties. Breast Cancer Res. Treat. 2014, 143, (1), 19-31. (7) Xu, S.; Zhang, Z. Y.; Ogawa, O., et al., An EP4 antagonist ONO-AE3-208 suppresses cell invasion, migration, and metastasis of prostate cancer. Cell Biochem. Biophys. 2014, 70, (1), 521-527. (8) Qian, J. Y.; Harding, P.; Liu, Y. H., et al., Reduced cardiac remodeling and function in cardiac-specific EP4 receptor knockout mice with myocardial infarction. Hypertension 2008, 51, (2), 560-566. (9) Walch, L.; Labat, C.; Gascard, J. P., et al., Prostanoid receptors involved in the relaxation of human pulmonary vessels. Br. J. Pharmacol. 1999, 126, (4), 859-866. (10) McCoy, J. M.; Wicks, J. R.; Audoly, L. P., The role of prostaglandin E2 receptors in the pathogenesis of rheumatoid arthritis. J. Clin. Invest. 2002, 110, (5), 651-658. (11) Araki, H.; Ukawa, H.; Sugawa, Y., et al., The roles of prostaglandin E receptor subtypes in the cytoprotective action of prostaglandin E2 in rat stomach. Aliment. Pharmacol. Ther. 2000, 14, 116-124. (12) Takeuchi, K.; Yagi, K.; Kato, S., et al., Roles of prostaglandin E-receptor subtypes in gastric and duodenal bicarbonate secretion in rats. Gastroenterology 1997, 113, (5), 1553-1559. (13) Leduc, M.; Hou, X.; Hamel, D., et al., Restoration of renal function by a novel prostaglandin EP4 receptor-derived peptide in models of acute renal failure. Am. J. Physiol. Regul. Integr. Comp. Physiol. 2013, 304, (1), R10-R22. (14) Yao, C.; Sakata, D.; Esaki, Y., et al., Prostaglandin E2-EP4 signaling promotes immune inflammation through TH1 cell differentiation and TH17 cell expansion. Nat. Med. 2009, 15, (6), 633-640. (15) Soontrapa, K.; Honda, T.; Sakata, D., et al., Prostaglandin E2-prostoglandin E receptor subtype 4 (EP4) signaling mediates UV irradiation-induced systemic immunosuppression. Proc. Natl. Acad. Sci. USA 2011, 108, (16), 6668-6673. (16) Hoggatt, J.; Mohammad, K. S.; Singh, P., et al., Differential stem- and progenitor-cell trafficking by prostaglandin E2. Nature 2013, 495, (7441), 365-369. (17) Segi, E.; Sugimoto, Y.; Yamasaki, A., et al., Patent ductus arteriosus and neonatal death in prostaglandin receptor EP4-deficient mice. Biochem. Biophys. Res. Commun.. 1998, 246, (1), 7-12. (18) Yokoyama, U.; Minamisawa, S.; Quan, H., et al., Chronic activation of the prostaglandin recepor EP4 promotes hyaluronan-mediated neointimal formation in the ductus arteriosus. J. Clin. Invest. 2006, 116, (11), 3026-3034.



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

(19) Xiao, C. Y.; Yuhki, K.; Hara, A., et al., Prostaglandin E2 protects the heart from ischemia-reperfusion injury via its receptor subtype EP4. Circulation 2004, 109, (20), 2462-2468. (20) Stachura, J.; Tarnawski, A.; Ivey, K. J., et al., Prostaglandin protection of carbon tetrachloride-induced liver cell necrosis in the rat. Gastroenterology 1981, 81, (2), 211-7. (21) Takano, M.; Nishimura, H.; Kimura, Y., et al., Prostaglandin E-2 protects against liver injury after Escherichia coli infection but hampers the resolution of the infection in mice. J. Immunol. 1998, 161, (6), 3019-3025. (22) Kuzumoto, Y.; Sho, M.; Ikeda, N., et al., Significance and therapeutic potential of prostaglandin E2 receptor in hepatic ischemia/reperfusion injury in mice. Hepatology 2005, 42, (3), 608-617. (23) Takahashi, S.; Takeuchi, K.; Okabe, S., EP4 receptor mediation of prostaglandin E2-stimulated mucus secretion by rabbit gastric epithelial cells. Bioche. Pharmacol. 1999, 58, (12), 1997-2002. (24) Machwate, M.; Harada, S.; Leu, C. T., et al., Prostaglandin receptor EP4 mediates the bone anabolic effects of PGE(2). Mol. Pharmacol. 2001, 60, (1), 36-41. (25) Cherukuri, D. P.; Chen, X. B. O.; Goulet, A. C., et al., The EP4 receptor antagonist, L-161,982, blocks prostaglandin E2-induced signal transduction and cell proliferation in HCA-7 colon cancer cells. Exp. Cell. Res. 2007, 313, (14), 2969-2979. (26) An, Y. P.; Xu, W. X.; Li, H. H., et al., High-fat diet induces dynamic metabolic alterations in multiple biological matrices of rats. J. Proteome Res. 2013, 12, (8), 3755-3768. (27) Duan, Y. X.; An, Y. P.; Li, N., et al., Multiple univariate data analysis reveals the inulin effects on the high-fat-diet induced metabolic alterations in rat myocardium and testicles in the preobesity state. J. Proteome Res. 2013, 12, (7), 3480-3495. (28) Xu, W. X.; Wu, J. F.; An, Y. P., et al., Streptozotocin-induced dynamic metabonomic changes in rat biofluids. J. Proteome Res. 2012, 11, (6), 3423-3435. (29) Zhang, X. Y.; Wang, Y. L.; Hao, F. H., et al., Human serum metabonomic analysis reveals progression axes for glucose intolerance and insulin resistance statuses. J. Proteome Res. 2009, 8, (11), 5188-5195. (30) Chen, W. X.; Lou, H. Y.; Zhang, H. P., et al., Metabonomic characterization of the low-grade human astrocytomas and meningiomas using magic-angle spinning 1H nuclear magnetic resonance spectroscopy and principal component analysis. Prog. Biochem. Biophys. 2008, 35, (10), 1142-1153. (31) Yang, Y. X.; Li, C. L.; Nie, X., et al., Metabonomic studies of human hepatocellular carcinoma using high-resolution magic-angle spinning 1H NMR spectroscopy in conjunction with multivariate data analysis. J. Proteome Res. 2007, 6, (7), 2605-2614. (32) Dong, F. C.; Zhang, L. L.; Hao, F. H., et al., Systemic responses of mice to dextran sulfate sodium-induced acute ulcerative colitis using 1H NMR spectroscopy. J. Proteome Res. 2013, 12, (6), 2958-2966. (33) Dong, F. C.; Wang, B.; Zhang, L. L., et al., Metabolic response to Klebsiella pneumoniae infection in an experimental rat model. Plos One 2012, 7, (11). (34) Li, H. H.; An, Y. P.; Zhang, L. L., et al., Combined NMR and GC-MS analyses revealed dynamic metabolic changes associated with the carrageenan-induced rat pleurisy. J. Proteome Res. 2013, 12, (12), 5520-5534. (35) Zhu, X.; Lei, H.; Wu, J., et al., Systemic responses of BALB/c mice to salmonella typhimurium infection. J. Proteome Res. 2014, 13, (10), 4436-4445. (36) Jiang, L. M.; Huang, J.; Wang, Y. L., et al., Metabonomic analysis reveals the CCl4-Induced systems alterations for multiple rat organs. J. Proteome Res. 2012, 11, (7), 3848-3859.


Page 16 of 26

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(37) Zhang, L.; Ye, Y.; An, Y., et al., Systems responses of rats to aflatoxin B1 exposure revealed with metabonomic changes in multiple biological matrices. J. Proteome Res. 2010, 10, (2), 614-623. (38) Huang, C. Y.; Lei, H. H.; Zhao, X. J., et al., Metabolic influence of acute cyadox exposure on Kunming mice. J. Proteome Res. 2013, 12, (1), 537-545. (39) Shi, X. H.; Xiao, C. N.; Wang, Y. L., et al., Gallic acid intake induces alterations to systems metabolism in rats. J. Proteome Res. 2013, 12, (2), 991-1006. (40) Li, H.; Wei, H.; Wang, Y., et al., Enhanced green fluorescent protein transgenic expression in vivo is not biologically inert. J. Proteome Res. 2013, 12, (8), 3801-3808. (41) Xiao, C. N.; Hao, F. H.; Qin, X. R., et al., An optimized buffer system for NMR-based urinary metabonomics with effective pH control, chemical shift consistency and dilution minimization. Analyst 2009, 134, (5), 916-925. (42) Ding, L. N.; Hao, F. H.; Shi, Z. M., et al., Systems biological responses to chronic perfluorododecanoic acid exposure by integrated metabonomic and transcriptomic studies. J. Proteome Res. 2009, 8, (6), 2882-2891. (43) Eriksson, L.; Trygg, J.; Wold, S., CV-ANOVA for significance testing of PLS and OPLS (R) models. J. Chemom. 2008, 22, (11-12), 594-600. (44) Cloarec, O.; Dumas, M. E.; Trygg, J., et al., Evaluation of the orthogonal projection on latent structure model limitations caused by chemical shift variability and improved visualization of biomarker changes in 1H NMR spectroscopic metabonomic studies. Anal. Chem. 2005, 77, (2), 517-526. (45) Xu, Z. D.; Harvey, K.; Pavlina, T., et al., An improved method for determining medium- and long-chain FAMEs using gas chromatography. Lipids 2010, 45, (2), 199-208. (46) Nicholson, J. K.; Foxall, P. J. D.; Spraul, M., et al., 750 MHz 1H and 1H-13C NMR spectroscopy of human blood plasma. Anal. Chem. 1995, 67, (5), 793-811. (47) Fan, T. W. M., Metabolite profiling by one-and two-dimensional NMR analysis of complex mixtures. Magn. Reson. Chem. 1996, 28, (2), 161-219. (48) Boes, E.; Kollerits, B.; Heid, I. M., et al., INSIG2 polymorphism is neither associated with BMI nor with phenotypes of lipoprotein metabolism. Obesity 2008, 16, (4), 827-833. (49) Han, J. H.; Hajjar, D. P.; Febbraio, M., et al., Native and modified low density lipoproteins increase the functional expression of the macrophage class B scavenger receptor, CD36. J. Biol. Chem. 1997, 272, (34), 21654-21659. (50) Ikeda, H.; Ueda, M.; Ikeda, M., et al., Oxysterol 7 alpha-hydroxylase (CYP39A1) in the ciliary nonpigmented epithelium of bovine eye. Lab. Invest. 2003, 83, (3), 349-355. (51) Riancho, J. A.; Vázquez, L.; García-Pérez, M. A., et al., Association of ACACB polymorphisms with obesity and diabetes. Mol. Genet. Metab. 2011, 104, (4), 670-676. (52) McGarry, J. D.; Brown, N. F., The mitochondrial carnitine palmitoyltransferase system - from concept to molecular analysis. Eur. J. Biochem. 1997, 244, (1), 1-14. (53) Pashkov, V.; Huang, J.; Parameswara, V. K., et al., Regulator of G protein signaling (RGS16) inhibits hepatic fatty acid oxidation in a carbohydrate response element-binding protein (ChREBP)-dependent manner. J. Biol. Chem. 2011, 286, (17), 15116-15125. (54) Anderson, M. E., Glutathione: an overview of biosynthesis and modulation. Chem. Biol. Interact. 1998, 112, 1-14. (55) Cho, S.-G.; Lee, Y. H.; Park, H.-S., et al., Glutathione s-transferase mu modulates the stress-activated signals by suppressing apoptosis signal-regulating kinase 1. J. Biol. Chem. 2001, 276,



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

(16), 12749-12755. (56) Weinhold, P. A.; Rethy, V. B., Separation, purification, and characterization of ethanolamine kinase and choline kinase from rat liver. Biochemistry 1974, 13, (25), 5135-5141. (57) Cappiello, M.; Mascia, L.; Scolozzi, C., et al., In vitro assessment of salvage pathways for pyrimidine bases in rat liver and brain. Biochim. Biophys. Acta. 1998, 1425, (2), 273-281. (58) Eckel-Mahan, K. L.; Patel, V. R.; Mohney, R. P., et al., Coordination of the transcriptome and metabolome by the circadian clock. Proc. Natl. Acad. Sci. USA 2012, 109, (14), 5541-5546. (59) Bjursell, Magnus K.; Blom, Henk J.; Cayuela, Jordi A., et al., Adenosine kinase deficiency disrupts the methionine cycle and causes hypermethioninemia, encephalopathy, and abnormal liver function. Am. J. Hum. Genet. 2011, 89, (4), 507-515. (60) Kizaki, H.; Williams, J. C.; Morris, H. P., et al., Increased cytidine 5′-triphosphate synthetase activity in rat and human tumors. Cancer Res. 1980, 40, (11), 3921-3927.


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Figure captions: Figure 1 Typical 1H NMR spectra (600 MHz) obtained from serum (S), urine (U) and aqueous extracts of liver (L) from control (C) and high-dose group (H) after 4 weeks dosage. The keys for the metabolites are given in Table S2. Regions at δ5.1-5.5 and 5.5−8.5 in SC and SH were vertically expanded 4 and 32 times respectively. Regions at δ0.5−4.6, 4.9-5.6 and 6.2-9.2 in UC and UH were vertically expanded 2, 2 and 16 times, respectively. Regions at δ0.5−3.1, 5-5.6 and 5.6-9.1 in LC and LH were expanded 4, 4 and 32 times, respectively. Keys: 1, cholesterol; 2, O-acetyl glycoprotein; 3, N-acetyl glycoprotein; 4, isoleucine; 5, leucine; 6, valine; 7, lactate; 8, alanine; 9, lysine; 10, 3-hydroxybutyrate; 11, acetate; 12, glutamate; 13, glutamine; 14, GSSG; 15, pyruvate; 16, succinate; 17, dimethylglycine; 18, dimethylamine; 19, trimethylamine; 20, aspartate; 21, choline; 22, phosphorylcholine; 23, taurine; 24, creatine; 25, trigonelline; 26, glucose; 27, methanol; 28, glycogen; 29, uridine; 30, uracil; 31, fumarate; 32, tyrosine; 33, phenylalanine; 34, histidine; 35, formate; 36, hypoxanthine; 37, inosine; 38, xanthine; 39, nicotinamide; 40, AMP; 41, ADP; 42, UMP; 43, UDP; 44, citrate; 45, α-ketoglutaric acid; 46, trimethylamine N-oxide; 47, allantoin; 48, hippurate; 49, creatinine; 50, indoxyl sulfate; 51, butyrate; 52, 3-ureidopropionic acid; 53, malonate; 54, putrescine; 55, methylamine; 56, guanidoacetic acid; 57, unsaturated fatty acids. For details of chemical shifts and multiplies, refer to Supporting Information Table S2. Figure 2 Cross-validated OPLS-DA scores plots (left) and the corresponding loadings plots (right) of liver extracts data from control mice (black dots) and those from mice dosed with moderate (green boxes) and high (red boxes) levels of EP4 antagonist after 4 weeks dosage. (OPLS-DA: Q2 = 0.61 and 0.43 for moderate and high-dose group, respectively. CV-ANOVA: P = 1.41×10-3 and 4.71×10-2 for moderate and high-dose group, respectively). Keys: 1, cholesterol; 7, lactate; 9, lysine; 13, glutamine; 14, GSSG; 16, succinate; 17, DMG; 20, aspartate; 21, choline; 28, glycogen; 30, uracil; 32, tyrosine; 33, phenylalanine; 36, hypoxanthine; 38, xanthine; 40, AMP; 41, ADP; 42, UMP; 46, TMAO. Figure 3 Fatty acid compositions obtained from GC-FID/MS analysis of liver of mice



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(A) and calculated desaturases (B) from liver of mice. *P < 0.05, **P < 0.01. ToFA: total fatty acids; SFAs: saturated fatty acids; UFAs: unsaturated fatty acids; MUFAs: monounsaturated fatty acids; PUFAs: polyunsaturated fatty acids. D6D: the ratio of C18:3n6 to C18:2n6. Figure 4 Transcriptomic Analysis of mRNA expression levels in liver of control and high-dose groups. Significant difference level is fold change >2 (up-regulated genes) or

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