Plasma and Liver Metabolic Profiles in Mice Subjected to Subchronic

Dec 1, 2014 - Previously, we established a subchronic and mild social defeat stress (sCSDS) model in mice, which showed significantly higher body weig...
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Plasma and Liver Metabolic Profiles in Mice Subjected to Subchronic and Mild Social Defeat Stress Tatsuhiko Goto,†,‡ Yoshifumi Kubota,† and Atsushi Toyoda*,†,‡,§ †

College of Agriculture, Ibaraki University, Ami, Ibaraki 300-0393, Japan Ibaraki University Cooperation between Agriculture and Medical Science (IUCAM), Ami, Ibaraki 300-0393, Japan § United Graduate School of Agricultural Science, Tokyo University of Agriculture and Technology, Fuchu-city, Tokyo 183-8509, Japan ‡

S Supporting Information *

ABSTRACT: To improve the quality of life of animals, understanding of stress-induced changes is necessary. Previously, we established a subchronic and mild social defeat stress (sCSDS) model in mice, which showed significantly higher body weight gain, food intake, and water intake compared to control mice. In this study, we elucidated metabolic profiles of plasma, liver, and urine in sCSDS mice by using metabolome and biochemical analyses. There was no significant difference between defeated and control mice in the plasma metabolites. In the liver of sCSDS mice, levels of taurocyamine (GES), phosphorylcholine, D-alanyl-D-alanine (D-ala-D-ala), and 1-methylnicotinamide (MNA) were elevated compared to controls. Taurine plays a role in osmotic regulation, and GES is a potential inhibitor of the taurine transporter. The polydipsia and increased body water content in sCSDS mice may disrupt body fluid balance following GES elevation. Furthermore, sCSDS increased heart and spleen weight significantly. Because MNA and D-ala-D-ala have anti-inflammatory and hepatoprotective effects, they may reduce inflammation in the liver of sCSDS mice. Finally, suppressed excretion of urine sodium was observed in sCSDS mice. Therefore, sCSDS induces various changes in metabolite concentrations, especially related to osmoregulation and inflammation, that may be used as biomarkers for stress-induced changes in animals. KEYWORDS: liver, metabolome, mouse, plasma, social defeat stress, urine

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social stress.7,8 Some reports show decreased body weight gain in defeated mice subjected to 10 days of stress,9−11 whereas others report that CSDS increased body weight gain.12,13 Irrespective of these conflicting results, metabolic changes can be expected to occur in several tissues of CSDS mice, given the unequivocal influence of CSDS on body weight gain. We have previously established the subchronic and mild social defeat stress (sCSDS) model in mice, and found significantly higher body weight gain, food intake, water intake subsequent to increased body water content, and social avoidance behavior after the 10-day stress period.14 Our sCSDS paradigm consists of a half-scale physical stress condition compared with a standard CSDS method as published by Golden et al.15 Thus, our sCSDS conditions would likely induce milder phenotypes in defeated mice compared to the standard CSDS mice.14 In this study, we compared metabolic features of sCSDS mice with those of control mice to investigate the consequence of stress-induced weight gain and mild abnormalities by using comprehensive

INTRODUCTION There is evidence that excessive stress leads to harmful physiological consequences in animals. Stressful events also invoke several prominent physiological responses in mice, for example, hyperactivity of the hypothalamus-pituitary-adrenal (HPA) axis.1 Mouse models of stress are generated using several types of social stress, e.g., chronic unpredictable mild stress and chronic social defeat stress (CSDS).2−4 Various types of animal models of stress-induced changes are necessary to explain the broad range of pathophysiology. Through further studies using these animal models, potential biomarkers and effective therapeutics in humans may be discovered.5 Neuroscientific research has been central in the establishment and characterization of the CSDS model in mice. Neuroscientists focused their studies on the central nervous system of the CSDS mice and found strong evidence for several physiological consequences in the subjects, e.g., downregulation of hippocampal brain derived-neurotrophic factor (BDNF) and disrupted reward system in CSDS mice.6 However, information on the effect of such stress on peripheral physiological organs and processes is limited, although some reports have described diet-induced obesity under chronic © XXXX American Chemical Society

Received: October 7, 2014

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with high scores were selected in order starting from the highest-scoring individual. sCSDS was performed in the afternoon (14:00−16:00 pm) and consisted of approximately half-scale physical contact times, compared with 5 min physical contact for 10 days in the standard CSDS.15 Briefly, physical contact was set to 5 min after the first attack bites on Day 1, after which the duration was reduced in a stepwise manner by 0.5 min per day. Finally, 0.5 min of physical contact was facilitated on Day 10, as described by Goto et al.14 After physical contact, the subjects (B6 mice) were moved into the compartment neighboring that of the ICR mice, separated by a divider for 24-h using a SD cage. Aggressive behavior was monitored using a video camera (JVC KENWOOD, Kanagawa, Japan) for 10 days, and then the number of attack bites received by the subject (B6) mice was counted as reported by Takahashi et al.26 The strength of physical stress was confirmed to be comparable to those in a previous report.14

metabolomics techniques. Metabolome analyses have revealed many biomarkers for diseases such as cancer, cardiovascular disease, dyslipidemia, and schizophrenia.16−20 In addition, metabolomic analyses of blood and liver have been conducted for diabetes, shedding light on mechanisms underlying insulin resistance.21−23 Because blood and liver are important for evaluating metabolic changes in the animal body, we decided to conduct metabolomic profiles of blood plasma and liver in the sCSDS model. In addition, we also obtained urinary samples from our subjects, as they are relatively easy to obtain in a noninvasive manner, and are routinely used for determining the health status of animals.24,25 Thus, we attempted to search for biomarkers in sCSDS mice for stress-induced mild behavioral abnormalities. In this study, we describe changes in the metabolic profiles of the plasma, liver, and urine in sCSDS mice. This study provides insights into metabolic changes induced by social stress in mice.

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MATERIALS AND METHODS

Social Interaction Test

On Day 11 in the morning (9:00−12:00 am), social behaviors were tested by the previously described methods.14 A plastic interaction box with 3 wire-mesh windows was placed in openfield arena (40 × 40 × 40 cm3) and a 6−7 cm-wide area surrounding the interaction box was set as an interaction zone. Behaviors of the B6 mice were monitored for 2.5 min with the box empty (target absent), and after a 1 min interval, the behaviors were monitored for 2.5 min in the target condition (with unfamiliar male ICR mice). Social interaction scores (% of target absent) were estimated as 100 × (interaction time with target present/interaction time with target absent), as described by Krishnan et al.9

Animals

This study was approved by the Animal Care and Use Committee of Ibaraki University and conformed to the guidelines of Ministry of Education, Culture, Sports, Science, and Technology (MEXT), Japan (Notification, No. 71). Male C57BL/6JJmsSlc (B6) mice (7 weeks of age) and Slc:ICR (ICR) mice (retired, older than 5 months of age) were introduced in the animal facility of College of Agriculture, Ibaraki University from SLC Japan (Shizuoka, Japan) under a 12 h light−dark cycle (light on at 8:00 am). The mice were treated in the same manner as done in the study by Goto et al.14 Prior to experiments, mice were individually housed in a single cage (143 mm × 293 mm × 148 mm; Charles River Laboratories Japan, Kanagawa, Japan) with wood chips. Semipurified diet (AIN-93G, Oriental Yeast, Tokyo, Japan) was fed to B6 mice, and standard laboratory diet (MF) was fed to ICR mice (Oriental Yeast, Tokyo, Japan). Dietary ingredients and food composition of semipurified diet were shown in Supplementary Table 1 in the Supporting Information. Food and water were under ad libitum conditions, and weighed every day to monitor daily consumption of food and water in B6 mice. Body weight was also weighed every day to calculate body weight gain.

Biochemical Assay for Urine Components

On Day 12 in the afternoon (15:00−17:00 pm), subject mice were moved into empty cages individually. For an hour, the experimenter observed their behavior and collected the urine sample in a tube on ice by using a micropipette. Then, the samples were frozen at −80 °C until use. Concentrations of urine components, including total protein (TP), urea nitrogen (UN), creatinine (CRE), uric acid (UA), sodium (Na), potassium (K), chlorine (Cl), calcium (Ca), inorganic phosphorus (IP), and glucose (GLU) were measured using the Hitachi 7180 auto analyzer (Hitachi, Tokyo, Japan) in the Nagahama Life Science Laboratory (Shiga, Japan) by the previously described methods.14 TP and UA were separately analyzed by the pyrogallol red method and the enzymatic method, respectively. Because occasional urine was sampled in this study, urinary components are shown as creatininecorrected values. The urine osmolality (mOsm/kg) was estimated by the formula 2Na + GLU/18 + UN/2.8.27

Experimental Design

After habituation to the environments in Ibaraki University for 1 week (from Day −7 to Day 0), the B6 mice were divided into two groups: sCSDS (n = 5) and nonstressed control (n = 6) groups (Supplementary Figure 1 in the Supporting Information). The sCSDS conditions were applied for 10 days on Days 1−10 in a social defeat (SD) cage (220 mm × 320 mm × 135 mm; Natsume Seisakusho, Tokyo, Japan). After physical contact on the last day of this period, the mice were housed individually in single cages. On Day 11, the social behaviors of the mice were examined. Urine samples were collected on Day 12. Tissue sampling was carried out on Day 13.

Tissue Sampling

After a 3-h fast in the morning (9:00−12:00 am) on Day 13, trunk blood was collected in a tube on ice. Final concentration was set at 0.13% EDTA-2K. The tube was centrifuged at 1200g at room temperature for 10 min. The supernatant of the tube was collected as blood plasma, and stored in another tube at −80 °C until use. Approximately 50 mg from the left lobe of the liver was removed after being collected and weighed. The sample was immediately frozen by liquid nitrogen, and then stored at −80 °C until use. Heart, lung, spleen, kidney, epididymal fat, and muscle (Gastrocnemius and Soleus) tissues

Subchronic and Mild Social Defeat Stress (sCSDS)

Five aggressive ICR mice were selected from 12 ICR mice following the method of Goto et al.14 Through 5 days of screening (three 3 min trials per day), a score of aggressive behaviors was evaluated as percentage of trials in which the attack latency was less than 30 s, and then aggressive ICR mice B

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were collected and weighed. Tissue volume was corrected by body weight (BW) on Day 13. Metabolomic Analysis of the Plasma and Liver

Capillary electrophoresis time-of-flight mass spectrometry (CETOFMS) metabolome measurements were performed using an Agilent CE Capillary Electrophoresis System equipped with an Agilent 6210 Time of Flight mass spectrometer (Agilent Technologies, Waldbronn, Germany) at Human Metabolome Technology Inc. (Tsuruoka, Japan) following the methods described previously.16,28−30 In short, approximately 50 mg of frozen liver sample was immersed into 1800 μL of 50% acetonitrile/Milli-Q water (Millipore-Japan, Tokyo, Japan) containing internal standards (H3304-1002, Human Metabolome Technology Inc., Tsuruoka, Japan). The tissue was homogenized using the homogenizer BMS-M10N21 (BMS Co., Ltd., Tokyo, Japan) and then was centrifuged at 2300g at 4 °C for 5 min. After that, 800 μL of upper layer was centrifugally filtered using a Millipore 5-kDa cutoff filter (Millipore-Japan, Tokyo, Japan) at 9100g at 4 °C for 120 min. The filtrate was resuspended in 50 μL of Milli-Q water for CE-MS analysis. For plasma, 50 μL of sample was added in 450 μL of methanol containing internal standards (H3304-1002, Human Metabolome Technologies, Tsuruoka, Japan). The extract solution was mixed with 500 μL of chloroform and 200 μL of Milli-Q water and centrifuged at 2300g at 4 °C for 5 min. From the upper layer, 400 μL was filtered through the Millipore 5-kDa cutoff filter, as described above. Finally, the filtrate was resuspended in 25 μL of Milli-Q water for CE-MS analysis. The metabolites identified from the metabolome library were assigned to the Kyoto Encyclopedia of Genes and Genomes (KEGG), facilitating search for the metabolic pathways involved.31 Statistical Analysis

Body weight gain, food intake, and water intake were analyzed by two-way analysis of variance (ANOVA) for repeated measures to test factor of “stress”, “time”, and “stress × time”. Multiple comparison test was performed in each time point by unpaired two-tailed Student’s t-test. Social interaction scores and urine components were tested by unpaired twotailed Student’s t-tests and Welch’s t-tests, respectively. Data were shown in mean ± SEM and analyzed by StatView ver. 5 (SAS Institute Inc., Cary, NC). For metabolomic analyses, Welch’s t-tests were used to compare the factor of ‘stress’. To control p-value for multiple comparisons, false discovery rate was determined by methods of Benjamini and Hochberg32 and Storey and Tibshirani.33 The significant threshold was set to Q < 0.1.

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Figure 1. Effects of subchronic and mild social defeat stress (sCSDS) in mice. Temporal changes were monitored in body weight gain, food intake, and water intake (n = 5−6 in each group). (A) A significant stress effect on body weight gain was found by two-way repeated measure ANOVA (F1,90 = 7.465, p = 0.0231). (B) For food intake, a significant stress effect was discovered by two-way repeated measure ANOVA (F1,90 = 6.740, p = 0.0289). (C) Two-way repeated measure ANOVA revealed a significant stress effect on water intake (F1,90 = 37.239, p = 0.0002). Data are expressed as the mean ± SEM. †p < 0.10, *p < 0.05, **p < 0.01 versus control in each time point.

RESULTS

Body Weight Gain, Food Intake, and Water Intake

not significant stress × time effect (F10,90 = 0.682, p = 0.7383) on FI were detected. There was a significant stress effect on WI by two-way repeated measure ANOVA (F1,90 = 37.239, p = 0.0002). As shown in Figure 1C, water intake of the defeated mice was also more than twice that of control mice. Significant time (F10,90 = 5.272, p < 0.0001) and stress × time (F10,90 = 5.016, p < 0.0001) effects on WI were discovered.

We monitored body weight gain (BWG), food intake (FI), and water intake (WI) daily to evaluate the effect of sCSDS. Twoway ANOVA for repeated measures revealed a significant stress effect on BWG for 10-days stress periods (F1,90 = 7.465, p = 0.0231). The defeated mice showed higher BWG than control mice (Figure 1A). Significant time (F10,90 = 23.399, p < 0.0001) and stress × time (F10,90 = 2.270, p = 0.0203) effects on BWG were found. Two-way repeated measure ANOVA showed a significant stress effect on FI (F1,90 = 6.740, p = 0.0289). The FI of the defeated mice was higher than that of control (Figure 1B). A significant time effect (F10,90 = 10.341, p < 0.0001) and

Social Interaction Test (Day 11)

Social interaction scores (% target absent) were calculated to evaluate the effect of sCSDS. As shown in Figure 2, there was a significant difference (p = 0.0030) between the stress group and C

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Metabolomic Analysis (Day 13)

In the liver, 218 metabolites were detected by CE-TOFMS. As shown in Table 2, we detected 4 metabolites in the liver showing significant differences between defeated and control mice at the threshold of Q < 0.1. The relative quantities of taurocyamine (guanidinoethanesulfonic acid; GES), phosphorylcholine, D-alanyl-D-alanine (D-ala-D-ala), and 1-methylnicotinamide (MNA) in defeated mice were significantly higher than those in control mice. Additionally, 19 metabolites were found in the liver with substantial differences in quantity (p < 0.05) between defeated and control mice (Supplementary Table 3 in the Supporting Information). Among them, the relative quantity of 10 metabolites was lower in the defeated mice, while that of 9 metabolites was higher than those of control mice. In the plasma, CE-TOFMS revealed 171 metabolites. Although there was no significant difference between defeated and control mice at the threshold of Q < 0.1, we discovered 3 metabolites in the plasma with substantial differences at p < 0.05 (Supplementary Table 4 in the Supporting Information). The quantities of 2-deoxycytidine and ethanolamine of defeated mice were higher, while their diethanolamine levels were lower than those of control mice.

Figure 2. Social interaction scores of defeated and control mice. Social interaction (% of target absent) were estimated as 100 × (interaction time with target present/interaction time with target absent). Defeated mice (n = 5) showed significantly lower value than control mice (n = 6) (**p < 0.01 versus control). Data are expressed as the mean ± SEM.

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the control group. Although the social interaction score of the control mice was more than 100%, the scores of the defeated mice were less than 100%. These features implied that the defeated mice showed lower level of social behavior than control mice.

DISCUSSION As in our previous study, sCSDS mice showed increased body weight gain and food and water intake but decreased social interaction behavior.14 Identification of initial metabolic biomarkers is important for understanding stress-induced changes; therefore, we evaluated the effects of sCSDS on the metabolism of mice by using comprehensive biochemical analyses. In this study, we focused on the metabolic profiles in the sCSDS mice, because our sCSDS mice interestingly showed increased body weight gain under the stressful condition. Plasma and liver metabolites of sCSDS and control mice were analyzed and compared comprehensively using capillary electrophoresis-time-of-flight mass spectrophotometer (CETOFMS). The metabolomics approach has had much success in the discovery of biomarkers for diseases such as cancer, dyslipidemia, and schizophrenia.16,18−20 In this study, we found 4 metabolites (out of 218 identifiable metabolites in the liver) showing significant differences in quantity between defeated and control mice. Levels of taurocyamine (GES), phosphorylcholine, D-alanyl-D-alanine (D-ala-D-ala), and 1-methylnicotinamide (MNA) were observed to be elevated in sCSDS mice

Biochemical Assay of Urine Components (Day 12)

To evaluate the effects of sCSDS on urine, 10 components as well as the osmolality of urine were compared between defeated and control mice (Table 1). There was a significant difference in Na (p = 0.0187), and substantial differences in UN and CRE (p = 0.0560 and 0.0846, respectively). Na of the defeated mice was lower than that of control mice. UN and CRE of the defeated mice were lower and higher than those of control mice, respectively. In the remaining 8 parameters, there was no significant difference between both groups. Tissue Weight (Day 13)

As shown in Supplementary Table 2 in the Supporting Information, there were significant differences between defeated and control mice in the heart and the spleen (p = 0.0265 and 0.0351, respectively). Tissue weights (% of BW) of both tissues of the defeated mice were higher than those of control mice. In the other tissues, there was no significant difference between the two groups. Table 1. Biochemical Properties of Urine on Day 12 parameter TP (g/g CRE) UN (g/g CRE) CRE (mg/dL) UA (g/g CRE) Na (g/g CRE) K (g/g CRE) Cl (g/g CRE) Ca (g/g CRE) IP (g/g CRE) GLU (g/g CRE) Urine Osmolality (mOsm/kg) a

control (n = 6) 15.9 111.3 42.1 0.3 343.2 1350.1 424.8 0.3 3.6 0.9 1763.2

± ± ± ± ± ± ± ± ± ± ±

social defeat (n = 4)

2.5 11.4 4.5 0.0 32.7 197.6 79.7 0.0 0.4 0.1 202.4

16.8 80.0 54.7 0.8 190.1 1025.2 283.3 0.3 3.0 1.0 1645.5

± ± ± ± ± ± ± ± ± ± ±

2.5 4.0 3.6 0.2 31.6 93.1 46.3 0.1 0.2 0.0 98.6

t-testa

p-value

ns † † ns * ns ns ns ns ns ns

0.8230 0.0560 0.0846 0.1717 0.0187 0.2204 0.2506 0.9427 0.1963 0.1635 0.6508

Data are expressed as mean ± SEM. *p < 0.05 versus control; †p < 0.10 versus control. D

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Table 2. Liver Metabolites with Social Stress-Induced Changes (Q < 0.1) ratio (defe/cont)a

p

Q

KEGG pathway

Taurocyamine (GES) Phosphorylcholine

1.6 2.6

8.4 × 10−05 0.0006

0.0179 0.0639

Ala-Ala(D-ala-D-ala)

1.2

0.0007

0.0469

1-Methylnicotinamide (MNA)

1.5

0.0027

0.0955

Taurine and hypotaurine metabolism Glycerophospholipid metabolism Metabolic pathways D-Alanine metabolism Peptidoglycan biosynthesis Metabolic pathways Nicotinate and nicotinamide metabolism Metabolic pathways Bile secretion

metabolite

a

Defe/cont, social defeat/control.

(Table 2). We also used CE-TOFMS to analyze the metabolites in blood plasma, however, no significant difference was observed in the 171 metabolites between the two groups. Therefore, sCSDS mice may provide a preliminary model for the initial metabolic symptoms associated with social stress. Similar success has been had through the metabolomic elucidation of biomarkers for identifying early abnormalities of diabetic nephropathy.34 Taurocyamine (GES) is an intermediate of taurine and hypotaurine metabolism and an inhibitor of the taurine transporter.35 It is well-known that taurine influences osmotic regulation.36,37 In this study, elevation of GES quantity was observed in the liver of the sCSDS mice (Table 2), but there are no changes in taurine and taurocholic acid which is one of the metabolites of taurine. Therefore, synthesis of taurocholic acid in the liver might be intact in sCSDS mice. Given that polydipsia induces hypotonicity, it can be expected that liver cells and other tissues in sCSDS mice may show increased cell volume and then regulatory volume decrease (RVD) as a consequence. In fact, it has been shown that the cell volume of isolated rat hepatocytes increases after exposure to hypotonic solution, after which the cell exhibits RVD.38 In this manner, taurine plays an important role in regulating the cell volume.39 After exposure to hypotonicity liver mass increases, taurine is released from the cells, reducing the increased cell volume.40 Because GES is an inhibitor of the taurine transporter, GES elevation in sCSDS mice may result in the dysfunction of taurine-induced osmoregulation following increased body water composition. In the future, we should elucidate GES function in the liver of sCSDS mice. Moreover, taurine has anxiolytic and antidepressive effects in mice and rats.41−44 Previously, we characterized some features of CSDS rats, 45 and our preliminary metabolomic data show elevation in taurine levels in the liver of CSDS rats (Iio et al., unpublished data). Therefore, taurine metabolism in the liver may be initially influenced by the psychological stress. Additionally, elevated levels of phosphorylcholine were observed in the liver of sCSDS mice (Table 2). Phosphorylcholine and glycerophosphocholine work as a cytosol choline storage in various tissues.46,47 Choline and glycerol-3-phosphate are made from glycerophosphocholine and H2O. Phosphorylcholine is synthesized from choline and ATP by the enzymatic action of choline phosphotransferase. Then, CDPcholine and diphosphate are created from phosphorylcholine and CTP. From this pathway for phospholipid biosynthesis (Figure 3), liver metabolites such as phosphorylcholine and CDP-choline showed elevated levels in sCSDS mice compared to those of control mice (Table 2 and Supplementary Table 3 in the Supporting Information). Therefore, choline phospho-

Figure 3. Choline-related metabolites in liver. The pathway of cholinerelated metabolites in liver is shown. Welch’s t-tests were used to compare “stress” using both defeated mice (n = 4) and control mice (n = 4).

transferase, the enzyme converting choline to phosphorylcholine, may be upregulated in the liver of sCSDS mice. In the future, we will analyze the activity of this enzyme in sCSDS mice. In addition, choline-related metabolites act as osmolytes.47 Since the heart of the sCSDS mice was heavier than that of control mice (Supplementary Table 2 in the Supporting Information), our sCSDS mice may show anticardiovascular disease. In concordance with this hypothesis, Wideman et al.48 have reported CSDS-induced cardiomyopathy in rats. Furthermore, because patients with psychiatric disorders show psychogenic polydipsia,49,50 our sCSDS mice may be useful for understanding the mechanisms of stress-induced polydipsia. The level of 1-methylnicotinamide (MNA) was increased in the liver of sCSDS mice (Table 2). There is some evidence that endogenous MNA was produced in the liver of rats.51 Pumpo E

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et al.,52 Bryniarski et al.,53 and Sternak and Larsen54 have reported that MNA has an anti-inflammatory action and a hepatoprotective role in the liver of mice. Moreover, a previous report described that CSDS induces inflammatory response and monocyte trafficking from the spleen to the brain, leading to anxiety-like behavior in mice.55 In addition, D-Ala-D-Ala was increased in the liver of the sCSDS mice (Table 2). D-Ala-D-Ala is known as an important player in cross-linking step peptidoglycan synthesis in bacteria.56,57 Antibiotics such as vancomycin and penicillin interact with the terminal D-Ala-DAla of the pentapeptide side chains of peptidoglycan precursors, inhibiting cell wall biosynthesis.58,59 Because there is little information about D-Ala-D-Ala in the liver, we propose some possibilities about the functions of liver D-Ala-D-Ala in the context of the sCSDS model. D-Ala-D-Ala in the liver may be from bacteria that inhabit the gut lumen. In fact, Yoshimoto et al.19 reported that vancomycin-sensitive Gram-positive gut bacteria may promote hepatocellular carcinoma through the enterohepatic circulation of gut bacterial metabolites or toxins. Suppose that our sCSDS mice may have a leaky intestinal barrier, D-ala-D-ala, a normal part of gut microbiota, may translocate from the intestinal tract to the liver through enterohepatic circulation. Stress is well-known to induce intestinal barrier dysfunctions.60 Stress increases intestinal permeability via both the transcellular and paracellular pathways in rats, although there is no change in mucosal structures of the intestine.61−63 These results suggest that our sCSDS mice show an anti-inflammatory-like response in the liver. Because we observed spleen hypertrophy of the sCSDS mice (Supplementary Table 2 in the Supporting Information), a general inflammatory response in the body likely occurred in sCSDS mice, as was also corroborated by a previous report.64 Although infections from some physical injuries may be one of the possibilities in enlarged spleen in sCSDS mice, previous reports described that inflammatory responses in the body may contribute to the onset of psychiatric disorders such as depression.65−67 Thus, metabolites related to inflammation, such as those mentioned above, may serve as potential biomarkers that are upregulated in response to social stress. Gut microbiota and the intestinal barrier should also be studied in sCSDS mice in the future. To discover noninvasive biomarkers for social stress, we also analyzed the occasional urine from the sCSDS mice (Table 1). After creatinine correction, decreased Na were observed in the urine from mice under the stress condition (Table 1). In our previous report,14 suppression of BUN was found in serum, whereas the serum Na level was not changed in the sCSDS model. If psychogenic polydipsia increased the blood volume, it would result in a relative reduction of serum Na level. However, Na level in serum of sCSDS mice was sustained in physiological level, as was the case in our previous study.14 In the sCSDS mice, Na-reuptake from the renal tubule may be upregulated and consequently, the Na level of urine decreased. In the future, pooled urine samples from sCSDS mice will be analyzed in detail. In summary, we established the sCSDS mice, which showed psychogenic-like polydipsia following some significant metabolic changes. In the future, we will also try to focus on the intestinal metabolites that may help to identify functional foods and ingredients that may reduce the risk of stress-induced symptoms in light of metabolic data relevant to the phenotypes.

Article

ASSOCIATED CONTENT

S Supporting Information *

Table S1, food composition list of semipurified diet. Table S2, tissue volumes in subchronic and mild social defeat stress model on Day 13. Table S3, liver metabolites with social stressinduced changes (p < 0.05). Table S4, plasma metabolites with social stress-induced changes (p < 0.05). Table S5, all liver metabolites. Table S6, all plasma metabolites. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Address: College of Agriculture, Ibaraki University, Ami, Ibaraki 300-0393, Japan. E-mail: [email protected]. Tel: +81-29-888-8584. Fax: +81-29-888-8584. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank Dr. Teruo Miyazaki, Dr. Yasunori Miyamoto, Dr. Akira Honda, Dr. Shigeru Chohnan, Dr. Tsuyoshi Koide, Dr. Takamitsu Tsukahara, and Dr. Masaya Katsumata for fruitful comments regarding the manuscript. We also appreciate Dr. Naoko Moriya, Dr. Ayako Aoki-Yoshida, Dr. Reiji Aoki, Dr. Yoshiharu Takayama, and Dr. Chise Suzuki for their helpful comments and technical supports for tissue sampling. We would like to acknowledge the efforts of staffs of Human Metabolome Technologies for metabolite extraction, CETOFMS analysis, and data analysis of metabolomics profiling. This research was supported in part by Ibaraki University Cooperation between Agriculture and Medical Science (IUCAM) (The MEXT, Japan) and the Research Project on Development of Agricultural Products and Foods with Healthpromoting benefits (NARO) (The MAFF, Japan).

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REFERENCES

(1) Kolber, B. J.; Wieczorek, L.; Muglia, L. J. HPA axis dysregulation and behavioral analysis of mouse mutants with altered GR or MR function. Stress 2008, 11, 321−38. (2) Miczek, K. A.; Yap, J. J.; Covington, H. E., III. Social stress, therapeutics and drug abuse: Preclinical models of escalated and depressed intake. Pharmacol. Ther. 2008, 120, 102−28. (3) Duman, C. H. Models of depression. Vitam. Horm. 2010, 82, 1− 21. (4) Krishnan, V.; Nestler, E. J. Animal models of depression: Molecular perspectives. Curr. Top. Behav. Neurosci. 2011, 7, 121−47. (5) Nestler, E. J.; Hyman, S. E. Animal models of neuropsychiatric disorders. Nat. Neurosci. 2010, 13, 1161−9. (6) Berton, O.; McClung, C. A.; DiLeone, R. J.; Krishnan, V.; Renthal, W.; Russo, S. J.; Graham, D.; Tsankova, N. M.; Bolanos, C. A.; Rios, M.; Monteggia, L. M.; Self, D. W.; Nestler, E. J. Essential role of BDNF in the mesolimbic dopamine pathway in social defeat stress. Science 2006, 311, 864−8. (7) Bartolomucci, A.; Cabassi, A.; Govoni, P.; Ceresini, G.; Cero, C.; Berra, D.; Dadomo, H.; Franceschini, P.; Dell’Omo, G.; Parmigiani, S.; Palanza, P. Metabolic consequences and vulnerability to diet-induced obesity in male mice under chronic social stress. PLoS One 2009, 4, e4331. (8) Patterson, Z. R.; Abizaid, A. Stress induced obesity: lessons from rodent models of stress. Front. Neurosci. 2013, 24, 130. (9) Krishnan, V.; Han, M.-H.; Graham, D. L.; Berton, O.; Renthal, W.; Russo, S. J.; Laplant, Q.; Graham, A.; Lutter, M.; Lagace, D. C.; Ghose, S.; Reister, R.; Tannous, P.; Green, T. A.; Neve, R. L.; Chakravarty, S.; Kumar, A.; Eisch, A. J.; Self, D. W.; Lee, F. S.; F

dx.doi.org/10.1021/pr501044k | J. Proteome Res. XXXX, XXX, XXX−XXX

Journal of Proteome Research

Article

Tamminga, C. A.; Cooper, D. C.; Gershenfeld, H. K.; Nestler, E. J. Molecular adaptations underlying susceptibility and resistance to social defeat in brain reward regions. Cell 2007, 131, 391−404. (10) Chuang, J.-C.; Cui, H.; Mason, B. L.; Mahgoub, M.; Bookout, A. L.; Yu, H. G.; Perello, M.; Elmquist, J. K.; Repa, J. J.; Zigman, J. M.; Lutter, M. Chronic social defeat stress disrupts regulation of lipid synthesis. J. Lipid Res. 2010, 51, 1344−53. (11) Warren, B. L.; Vialou, V. F.; Iniguez, S. D.; Alcantara, L. F.; Wright, K. N.; Feng, J.; Kennedy, P. J.; Laplant, Q.; Shen, L.; Nestler, E. J.; Bolanos-Guzman, C. A. Neurobiological sequelae of witnessing stressful events in adult mice. Biol. Psychiatry 2013, 73, 7−14. (12) Patterson, Z. R.; Khazall, R.; MacKay, H.; Anisman, H.; Abizaid, A. Central ghrelin signaling mediates the metabolic response of C57BL/6 male mice to chronic social defeat stress. Endocrinology 2013, 154, 1080−91. (13) Savignac, H. M.; Finger, B. C.; Pizzo, R. C.; O’leary, O. F.; Dinan, T. G.; Cryan, J. F. Increased sensitivity to the effects of chronic social defeat stress in an innately anxious mouse strain. Neuroscience 2011, 192, 524−36. (14) Goto, T.; Kubota, Y.; Tanaka, Y.; Iio, W.; Moriya, N.; Toyoda, A. Subchronic and mild social defeat stress accelerates food intake and body weight gain with polydipsia-like features in mice. Behav. Brain Res. 2014, 270, 339−48. (15) Golden, S. A.; Covington, H. E., III; Berton, O.; Russo, S. J. A standardized protocol for repeated social defeat stress in mice. Nat. Protoc. 2011, 6, 1183−91. (16) Sugimoto, M.; Wong, D. T.; Hirayama, A.; Soga, T.; Tomita, M. Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles. Metabolomics 2009, 6, 78−95. (17) Wang, Z.; Klipfell, E.; Bennett, B. J.; Koeth, R.; Levison, B. S.; Dugar, B.; Feldstein, A. E.; Britt, E. B.; Fu, X.; Chung, Y. M.; Wu, Y.; Schauer, P.; Smith, J. D.; Allayee, H.; Tang, W. H.; DiDonato, J. A.; Lusis, A. J.; Hazen, S. L. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 2010, 472, 57−63. (18) Ooga, T.; Sato, H.; Nagashima, A.; Sasaki, K.; Tomita, M.; Soga, T.; Ohashi, Y. Metabolomic anatomy of an animal model revealing homeostatic imbalances in dyslipidaemia. Mol. BioSyst. 2011, 7, 1217− 23. (19) Yoshimoto, S.; Loo, T. M.; Atarashi, K.; Kanda, H.; Sato, S.; Oyadomari, S.; Iwakura, Y.; Oshima, K.; Morita, H.; Hattori, M.; Honda, K.; Ishikawa, Y.; Hara, E.; Ohtani, M. Obesity-induced gut microbial metabolite promotes liver cancer through senescence secretome. Nature 2013, 499, 97−101. (20) Koike, S.; Bundo, M.; Iwamoto, K.; Suga, M.; Kuwabara, H.; Ohashi, Y.; Shinoda, K.; Takano, Y.; Iwashiro, N.; Satomura, Y.; Nagai, T.; Natsubori, T.; Tada, M.; Yamasue, H.; Kasai, K. A snapshot of plasma metabolites in first-episode schizophrenia: A capillary electrophoresis time-of-flight mass spectrometry study. Transl. Psychiatry 2014, 4, e379. (21) Li, L. O.; Hu, Y. F.; Wang, L.; Mitchell, M.; Berger, A.; Coleman, R. A. Early hepatic insulin resistance in mice: A metabolomics analysis. Mol. Endocrinol. 2010, 24, 657−66. (22) Kleemann, R.; van Erk, M.; Verschuren, L.; van den Hoek, A. M.; Koek, M.; Wielinga, P. Y.; Jie, A.; Pellis, L.; Bobeldijk-Pastorova, I.; Kelder, T.; Toet, K.; Wopereis, S.; Cnubben, N.; Evelo, C.; van Ommen, B.; Kooistra, T. Time-resolved and tissue-specific systems analysis of the pathogenesis of insulin resistance. PLoS One 2010, 5, e8817. (23) Sysi-Aho, M.; Ermolov, A.; Gopalacharyulu, P. V.; Tripathi, A.; Seppanen-Laakso, T.; Maukonen, J.; Mattila, I.; Ruohonen, S. T.; Vahatalo, L.; Yetukuri, L.; Harkonen, T.; Lindfors, E.; Nikkila, J.; Ilonen, J.; Simell, O.; Saarela, M.; Knip, M.; Kaski, S.; Savontaus, E.; Oresic, M. Metabolic regulation in progression to autoimmune diabetes. PLoS Comput. Biol. 2011, 7, e1002257. (24) Zheng, S.; Yu, M.; Lu, X.; Huo, T.; Ge, L.; Yang, J.; Wu, C.; Li, F. Urinary metabonomic study on biochemical changes in chronic unpredictable mild stress model of depression. Clin. Chim. Acta 2010, 411, 204−9.

(25) Zhao, Y. Y.; Liu, J.; Cheng, X. L.; Bai, X.; Lin, R. C. Urinary metabonomics study on biochemical changes in an experimental model of chronic renal failure by adenine based on UPLC Q-TOF/ MS. Clin. Chim. Acta 2012, 413, 642−9. (26) Takahashi, A.; Nagayasu, K.; Nishitani, N.; Kaneko, S.; Koide, T. Control of intermale aggression by medial prefrontal cortex activation in the mouse. PLoS One 2014, 9, e94657. (27) Mahon, W. A.; Holland, J.; Urowitz, M. B. Hyperosmolar, nonketotic diabetic coma. Canad. Med. Assoc. J. 1968, 99, 1090−2. (28) Soga, T.; Heiger, D. H. Amino acid analysis by capillary electrophoresis electrospray ionization mass spectrometry. Anal. Chem. 2000, 72, 1236−41. (29) Soga, T.; Ueno, Y.; Naraoka, H.; Ohashi, Y.; Tomita, M.; Nishioka, T. Simultaneous determination of anionic intermediates for Bacillus subtilis metabolic pathways by capillary electrophoresis electrospray ionization mass spectrometry. Anal. Chem. 2002, 74, 2233−9. (30) Soga, T.; Ohashi, Y.; Ueno, Y.; Naraoka, H.; Tomita, M.; Nishioka, T. Quantitative metabolome analysis using capillary electrophoresis mass spectrometry. J. Proteome Res. 2003, 2, 488−94. (31) Kanehisa, M.; Goto, S.; Sato, Y.; Kawashima, M.; Furumichi, M.; Tanabe, M. Data, information, knowledge and principle: Back to metabolism in KEGG. Nucleic Acids Res. 2014, 42, D199−205. (32) Benjamini, Y.; Hochberg, Y. Controling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Statist. Soc. Ser. B 1995, 57, 298−300. (33) Storey, J. D.; Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. U.S.A. 2003, 100, 9440−5. (34) Hirayama, A.; Nakashima, E.; Sugimoto, M.; Akiyama, S.; Sato, W.; Maruyama, S.; Matsuo, S.; Yomita, M.; Yuzawa, Y.; Soga, T. Metabolic profiling reveals new serum biomarkers for differentiating diabetic nephropathy. Anal. Bioanal. Chem. 2012, 404, 3101−9. (35) Sergeeva, O. A.; Chepkova, A. N.; Doreulee, N.; Eriksson, K. S.; Poelchen, W.; Monnighoff, I.; Heller-Stilb, B.; Warskulat, U.; Haussinger, D.; Haas, H. L. Taurine-induced long-lasting enhancement of synaptic transmission in mice: role of transporters. J. Physiol. 2003, 550, 911−9. (36) Voisin, D. L.; Bourque, C. W. Integration of sodium and osmosensory signals in vasopressin neurons. Trends Neurosci. 2002, 25, 199−205. (37) Antunes-Rodrigues, J.; de Castro, M.; Elias, L. L.; Valenca, M. M.; McCann, S. M. Neuroendocrine control of body fluid metabolism. Physiol. Rev. 2004, 84, 169−208. (38) Pfaller, W.; Willinger, C.; Stoll, B.; Hallbrucker, C.; Lang, F.; Haussinger, D. Structural reaction pattern of hepatocytes following exposure to hypotonicity. J. Cell Physiol. 1993, 154, 248−53. (39) Hussy, N.; Deleuze, C.; Desarmenien, M. G.; Moos, F. C. Osmotic regulation of neuronal activity: a new role for taurine and glial cells in a hypothalamic neuroendocrine structure. Prog. Neurobiol. 2000, 62, 113−34. (40) Brand, H. S.; Meijer, A. J.; Gustafson, L. A.; Jorning, G. G.; Leegwater, A. C.; Maas, M. A.; Chamuleau, R. A. Cell-swelling-induced taurine release from isolated perfused rat liver. Biochem. Cell Biol. 1994, 72, 8−11. (41) Chen, S. W.; Kong, W. X.; Zhang, Y. J.; Li, Y. L.; Mi, X. J.; Mu, X. S. Possible anxiolytic effects of taurine in the mouse elevated plusmaze. Life Sci. 2004, 75, 1503−11. (42) Murakami, T.; Furuse, M. The impact of taurine- and betaalanine-supplemented diets on behavioral and neurochemical parameters in mice: antidepressant versus anxiolytic-like effects. Amino Acids 2010, 39, 427−34. (43) Celetti, G.; Olguins, D. B.; Pedrollo, E. F.; Barros, H. M.; Gomez, R. Antidepressant effect of taurine in diabetic rats. Amino Acids 2012, 43, 1525−33. (44) Iio, W.; Matsukawa, N.; Tsukahara, T.; Toyoda, A. The effects of oral taurine administration on behavior and hippocampal signal transduction in rats. Amino Acids 2012, 43, 2037−46. G

dx.doi.org/10.1021/pr501044k | J. Proteome Res. XXXX, XXX, XXX−XXX

Journal of Proteome Research

Article

(45) Iio, W.; Takagi, H.; Ogawa, Y.; Tsukahara, T.; Chohnan, S.; Toyoda, A. Effects of chronic social defeat stress on peripheral leptin and its hypothalamic actions. BMC Neurosci. 2014, 15, 72. (46) Zeisel, S. H. Choline and human nutrition. Annu. Rev. Nutr. 1994, 14, 269−96. (47) Hollenbeck, C. B. An introduction to the nutrition and metabolism of choline. Cent. Nerv. Syst. Agents Med. Chem. 2012, 12, 100−13. (48) Wideman, C. H.; Cierniak, K. H.; Sweet, W. E.; Moravec, C. S.; Murphy, H. M. An animal model of stress-induced cardiomyopathy utilizing the social defeat paradigm. Physiol. Behav. 2013, 120, 220−7. (49) de Leon, J.; Verghese, C.; Tracy, J. I.; Josiassen, R. C.; Simpson, G. M. Polydipsia and water intoxication in psychiatric patients: A review of the epidemiological literature. Biol. Psychiatry 1994, 35, 408− 19. (50) Duraiswamy, K.; Rao, N. P.; Venkatasubramanian, G.; Behere, R. V.; Varambally, S. S.; Gangadhar, B. N. Psychogenic polydipsia in bipolar affective disorderA case report. Gen. Hosp. Psychiatry 2011, 33, 84.e9−10. (51) Chlopicki, S.; Swies, J.; Mogielnicki, A.; Buczko, W.; Bartus, M.; Lomnicka, M.; Adamus, J.; Gebicki, J. 1-Methylnicotinamide (MNA), a primary metabolite of nicotinamide, exerts anti-thrombotic activity mediated by a cyclooxygenase-2/prostacyclin pathway. Br. J. Pharmacol. 2007, 152, 230−9. (52) Pumpo, R.; Sarnelli, G.; Spinella, A.; Budillon, G.; Cuomo, R. The metabolism of nicotinamide in human liver cirrhosis: A study on N-methylnicotinamide and 2-pyridone-5-carboxamide production. Am. J. Gastroenterol. 2001, 96, 1183−7. (53) Bryniarski, K.; Biedron, R.; Jakubowski, A.; Chlopicki, S.; Marcinkiewicz, J. Anti-inflammatory effect of 1-methylnicotinamide in contact hypersensitivity to oxazolone in mice; involvement of prostacyclin. Eur. J. Pharmacol. 2008, 578, 332−8. (54) Sternak, R. T.; Larsen, F. S. Therapeutic hypothermia for acute liver failure. Crit. Care Med. 2009, 37, S258−64. (55) Wohleb, E. S.; McKim, D. B.; Shea, D. T.; Powell, N. D.; Tarr, A. J.; Sheridan, J. F.; Godbout, J. P. Re-establishment of anxiety in stress-sensitized mice is caused by monocyte trafficking from the spleen to the brain. Biol. Psychiatry 2014, 75, 970−81. (56) Wright, G. D.; Walsh, C. T. D-Alanyl-D-alanine ligases and the molecular mechanism of vancomycin resistance. Acc. Chem. Res. 1992, 25, 468−73. (57) Kumar, I.; Josephine, H. R.; Pratt, R. F. Reactions of peptidoglycan-mimetic beta-lactams with penicillin-binding proteins in vivo and in membranes. ACS Chem. Biol. 2007, 21, 620−4. (58) Yocum, R. B.; Rasmussen, J. R.; Strominger, J. L. The mechanism of action of penicillin. Penicillin acylates the active site of bacillus stearothermophilus D-alanine carboxypeptidase. J. Biol. Chem. 1980, 255, 3977−86. (59) Woodford, N.; Johnson, A. P.; Morrison, D.; Speller, D. C. Current perspectives on glycopeptide resistance. Clin. Microbiol. Rev. 1995, 8, 585−615. (60) Söderholm, J. D.; Perdue, M. H. Stress and gastrointestinal tract. II. Stress and intestinal barrier function. Am. J. Physiol. Gastrointest. Liver Physiol. 2001, 280, G7−13. (61) Kiliaan, A. J.; Saunders, P. R.; Bijlsma, P. B.; Berin, M. C.; Taminiau, J. A.; Groot, J. A.; Perdue, M. H. Stress stimulates transepithelial macromolecular uptake in rat jejunum. Am. J. Physiol. 1998, 275, G1037−44. (62) Santos, J.; Saunders, P. R.; Hanssen, N. P.; Yang, P. C.; Yates, D.; Groot, J. A.; Perdue, M. H. Corticotropin-releasing hormone mimics stress-induced colonic epithelial pathophysiology in the rat. Am. J. Physiol. 1999, 277, G391−9. (63) Saunders, P. R.; Kosecka, U.; McKay, D. M.; Perdue, M. H. Acute stressors stimulate ion secretion and increase epithelial permeability in rat intestine. Am. J. Physiol. 1994, 267, G794−9. (64) Allen, R. G.; Lafuse, W. P.; Powell, N. D.; Webster Marketon, J. I.; Stiner-Jones, L. M.; Sheridan, J. F.; Bailey, M. T. Stressor-induced increase in microbicidal activity of splenic macrophages is dependent upon peroxynitrite production. Infect. Immun. 2012, 80, 3429−37.

(65) Raison, C. L.; Capuron, L.; Miller, A. H. Cytokines sing the blues: inflammation and the pathogenesis of depression. Trends Immunol. 2006, 27, 24−31. (66) Fagundes, C. P.; Glaser, R.; Hwang, B. S.; Malarkey, W. B.; Kiecolt-Glaser, J. K. Depressive symptoms enhance stress-induced inflammatory responses. Brain Behav. Immun. 2013, 31, 172−6. (67) D’Mello, C.; Swain, M. G. Liver-brain interactions in inflammatory liver diseases: Implications for fatigue and mood disorders. Brain Behav. Immun. 2014, 35, 9−20.

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