Effects of Diet Quality and Psychosocial Stress on the Metabolic

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Effects of diet quality and psychosocial stress on the metabolic profiles of mice Tatsuhiko Goto, Shozo Tomonaga, and Atsushi Toyoda J. Proteome Res., Just Accepted Manuscript • Publication Date (Web): 23 Mar 2017 Downloaded from http://pubs.acs.org on March 25, 2017

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Journal of Proteome Research

Title:

Effects of diet quality and psychosocial stress on the metabolic profiles of mice

Authors: Tatsuhiko Goto,†,‡,⊥, Shozo Tomonaga§, 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 §

Graduate School of Agriculture, Kyoto University, Kyoto, 606-8502, Japan

∥

United Graduate School of Agricultural Science, Tokyo University of Agriculture

and Technology, Fuchu-city, Tokyo 183-8509, Japan ⊥Present

address; Obihiro University of Agriculture and Veterinary Medicine,

Obihiro, Hokkaido 080-8555, Japan

Short Title: Diet and stress impact on metabolites in mice

Address for correspondence*

A. Toyoda, College of Agriculture, Ibaraki University, 3-21-1 Chuo Ami, Ibaraki 300-0393,

Japan,

E-mail:

[email protected]

+81-29-888-8584 Fax: +81-29-888-8584 1

ACS Paragon Plus Environment

Tel:


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Abstract There has been an increasing interest in relationship between stress and diet. To address this relationship, we evaluated an animal model of depression: male C57BL/6J mice subjected to subchronic mild social defeat stress (sCSDS) for 10 consecutive days using male ICR mice under two different calorie-adjusted diets conditions—non-purified (MF) and semi-purified (AIN) diets made from natural and chemical ingredients mainly, respectively. Our previous study indicates diet quality and purity affect stress susceptibility in sCSDS mice. We therefore hypothesized there are some key peripheral metabolites to change stress susceptible behavior. GC/MS metabolomics of plasma, liver, and cecal content were performed on four test groups: sCSDS + AIN diet (n = 7), sCSDS + MF diet (n = 6), control (no sCSDS) + AIN diet (n = 8), and control + MF diet (n = 8). Metabolome analyses revealed the number of metabolites changed by food was larger than the number changed by stress in all tissues. Enrichment analysis of the liver metabolite set altered by food implies that stress susceptible mice show increased glycolysis-related substrates in the liver. We found metabolites that were affected by stress (e.g., plasma and liver 4-hydroxyproline and plasma beta-alanine are higher in sCSDS than in control) and a stress × food interaction (e.g., plasma GABA is lower in sCSDS + AIN than in sCSDS + MF). Because functional compounds were altered by both stress and food, diet may be able to attenuate various stress-induced symptoms by changing metabolites in peripheral tissues. Keywords: chronic social stress, diet, metabolome, mouse, plasma, peripheral tissue 2


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Introduction In many countries, general interest in the relationship between diet and health is increasing. Eating habits may be important in determining reactions to stresses in humans and animal models. Under stressful conditions, both animals and humans change their feeding behaviors, with some individuals increasing and others decreasing their caloric intake.1,2 There is some evidence that diet composition—including carbohydrates, proteins, and fats—could attenuate stress-induced symptoms.3 As stress-related biomarkers under various diets are discovered, they will become increasingly useful in preventing stress-induced health problems such as depression. Rodents subjected to chronic social defeat stress4,5 are used as animal models of depression due to their higher construct, face, and predictive validities compared to other models of depression.6,7 Toyoda and his colleagues8-11 studied several aspects of nutrition and metabolism in chronically socially defeated rats, including appetite regulation and nutrient absorption in the digestive tract, to understand psychological stress-induced disorders and find some clinically useful insights. We previously reported that mice exposed to subchronic and mild social defeat stress (sCSDS) show increased body weight gain, food and water intake, body water content, social avoidance behavior, and four liver metabolites, and delays in nest-building behavior, when fed a semi-purified diet (AIN-93G).12-16 To understand the effect of diet quality on the sCSDS-induced symptoms, we established sCSDS mice using two kinds of diet—standard laboratory diet (non-purified diet; MF) and AIN-93G—and evaluated body weight gain, food and 3



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water intake, and social avoidance behavior. Although there is no difference in diet composition and calorie content, semi-purified and non-purified diets are produced using chemically pure sources and natural ingredient-based cereal products, respectively.17 Although non-purified diet is often used for behavioral neuroscience research, semi-purified diet is mainly used for nutritional research because non-purified diet has some inter-lot differences of raw materials (e.g., plants and fish) with ingredients that cannot be precisely followed.12 To assess the relationship between stress (behavior) and diet (nutrition), we have to accumulate the behavioral data about the differences among the two diets. We found that sCSDS mice fed the semi-purified diet showed increased social avoidance behavior (rate of stress susceptibility is 73.9%) compared to sCSDS mice fed the non-purified diet (rate of stress susceptibility is 34.8%), which suggests that diet quality and purity affect susceptibility and resilience to stress in the sCSDS mouse model.17 In this study, we used gas chromatography-mass spectrometry (GC/MS) metabolomics with sCSDS mice to analyze plasma, liver, and cecal content metabolites and to determine which metabolites significantly influence susceptibility and resilience to psychological stress in mice fed semi-purified and non-purified diets. We previously conducted capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS) metabolomic analyses for sCSDS mice.16 The CE-TOFMS revealed that only four liver metabolites and no plasma metabolites were significantly changed compared with the control non-stressed mice under semi-purified diet condition.16 Both identification and quantification of metabolites are affected by the analytical method used; methods include GC/MS 4


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and CE-TOFMS. GC/MS is a widely used method for finding biomarkers in humans by both “non-targeted” global approach and targeted method.18 Therefore, there are extensive databases of fragmentation patterns and the data are highly reproducible.19 Metabolomics studies approaching the relationship between psychosocial stress and diet quality have been reported so far. Using stress models of mice, there are some reports that lipids and amino acids in plasma20 and glutamatergic circuitry in the prefrontal cortex21 are changed by stressful events. There is an evidence that amino acid and energy metabolisms, adenosine receptors, and neurotransmitters in the hippocampus are perturbed by the drug treatment in mouse model of chronic mild stress.22 Daniel et al.23 analyzed the effect of diet quality with mice fed two different feeding conditions. They showed that a high-fat diet has an impact on metabolites and the gut bacterial ecosystem. Although these studies have produced fruitful evidences, there are few researches focusing on the relationship between stress and diet. Therefore, we tried to carry out metabolomics using GC/MS for analyzing plasma, liver, and cecal contents in sCSDS mice fed two kinds of diets (MF and AIN-93G). Plasma is a clinically important sample to know the status of animal health. Liver plays an important role in the various metabolism. Cecal contents will give us some information about nutritional status including substrates and final products related to gut microbiota, which have major impact on the health and behavior of the host animal.24 The metabolome analyses in these three parts will give us peripheral dynamics of the sCSDS mice. In this study, we hypothesized that there will be some key peripheral 5



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metabolites to change stress susceptibility of sCSDS mice under the two kinds of diet conditions. We searched for psychological stress-induced and food-induced metabolic changes in sCSDS mice by “non-targeted” global approach with GC/MS in order to assess the relationship between stress and diet. We report metabolite changes caused by both stress and diet quality and discuss the relationship between diet and health.

Methods Animals Seven-week-old male C57BL/6JJmsSlc (B6) mice and adult (more than 5 months old) male Slc:ICR (ICR) mice were purchased from SLC Japan (Shizuoka, Japan) and habituated to the Ibaraki University animal facility for one week. Because male B6 and ICR mice are used to establish social defeat stress model well (e.g., a standardized protocol7), we selected these two strains and previously established our protocol with them.13 They were housed in individual cages (143 mm × 293 mm × 148 mm; Charles River Laboratories Japan, Kanagawa, Japan) with wood-shaving bedding and a 12 h light and dark cycle (light on 07:00–19:00) during habituation period. Food and water were available ad libitum. This study was approved by the Animal Care and Use Committee of Ibaraki University (Authorization No. 162) following the guidelines published by the Ministry of Education Culture, Sports, Science, and Technology (MEXT), Japan (Notification No. 71).

Experimental design 6


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To evaluate both diet and stress effects, B6 mice were divided into four body weight-matched groups. As in our previous study,17 these became four test groups: semi-purified pellet diet (AIN-93G diet, Oriental Yeast, Tokyo, Japan) (n = 7) and non-purified pellet diet (MF diet, Oriental Yeast) (n = 6) under sCSDS conditions and semi-purified diet (n = 8) and non-purified diet (n = 8) under control conditions. Mice were fed their specified diet throughout the habituation and experimental periods: from the time of their arrival in the animal facility (Day -6) to the time of tissue sampling (Day 12), as shown in Figure 1.

Body weight gain, food intake, and water intake During the habituation and experimental periods, body weight, food weight, and water weight were measured daily in the afternoon; body weight gain, food intake, and water intake were calculated as previously described.12 Briefly, we measured the weights in the afternoon (13:00–14:00) and calculated daily intakes of food and water. The daily body weight gain was calculated with body weight of the initial day. Changes from Day -6 to Day 0 and from Day 0 to Day 10 were calculated for the periods of habituation and stress, respectively, for all three measures.17

Subchronic mild social defeat stress (sCSDS) The subchronic mild social defeat stress (sCSDS) experiment was performed in the afternoon according to our previous protocols.12,13 We screened ICR mice for aggressiveness with a three-minute resident-intruder test (with B6 mice as intruders) three times a day for five consecutive days (15 trials 7



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in total). Aggressive ICR mice were those that showed a high ratio of trials with an attack latency of less than 30 s. From Day -2, aggressive ICR mice were kept in a social defeat (SD) cage (220 mm × 320 mm × 135 mm; Natsume Seisakusho, Tokyo, Japan) to establish their territories. The SD cages were divided into two compartments by a 5 mm thick acrylic divider with several holes; aggressive ICR mice were reared in one compartment with no mice in the other. The sCSDS experiment was carried out in the afternoon on 10 consecutive days (Day 1 to Day 10) according to a slightly modified method12,13 of a standard protocol.7 Briefly, the intruder mouse was placed in the same compartment of the SD cage as the ICR mouse (the established territory of the ICR mouse); the duration of physical contact was set as 5 min from the first attack bite on Day 1, and then reduced by 0.5 min per day from Days 2 to 10. To reduce the level of physical stress, we adopted the method in a stepwise manner (decreasing contact time from 5 min on Day 1 to 0.5 min on Day 10). The combinations of B6 and ICR mice were rotated daily to face different counterparts. Behavior was recorded by a video camera (Everio, JVC KENWOOD, Japan). The number of attack bites was manually counted according to Takahashi et al.25 The average number of attack bites received was 29 ± 2.0 bites per day over 10 days, which is the same as our previous report (30 ± 1.6 bites).12

Social interaction test The social interaction test was performed in the morning (09:00–12:00) on Day 11. As in our previous reports,12,13 an open-field arena (40 cm × 40 cm × 40 cm) with an interaction box was set under 20 lux lighting. After 30 min 8


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habituation in the testing room, each B6 mouse was subjected to two 2.5-min trials without or with a social target. The social target was an unfamiliar male ICR mouse. The interaction zone was a 6–7 cm wide area surrounding the interaction box.12,13 The time (sec) spent in the interaction zone and total distance traveled (cm) were measured by Image SI software (O’Hara & Co., Tokyo, Japan). The social interaction score was calculated as 100 × (time spent in the interaction zone with target) / (time spent in the interaction zone without target).26 Stress-susceptible mice were defined as those with a social interaction score of less than 100—the others were defined as resilient mice—following the method described by Krishnan et al.26

Tissue sampling On Day 12, food was removed at the beginning of the light phase (07:00). Mice were fasted for 3 h before euthanasia and tissue sampling. Blood, liver, and cecum sampling was performed 10:00–14:00. Mice were killed by decapitation. Trunk blood was collected in a tube at a final concentration of 0.13% EDTA-2K. The blood tube was centrifuged at 1200 × g for 10 min. The supernatant of the tube (blood plasma) was collected and stored at −80°C until analysis. Liver and cecum were collected, frozen with liquid nitrogen, and stored at -80 °C until analysis. Since there was evidence that diets have a major impact on the mouse cecal microbiota that extends to major alterations in bacterial physiology and metabolite landscape,23 we focused on the cecal contents as a target of metabolome analysis.

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Metabolomic analysis of the plasma, liver, and cecum Prior to metabolomics, the frozen liver was pulverized into powder in liquid nitrogen using a frozen cell crusher (Cryo-Press; Microtec Co. Ltd., Chiba, Japan) and a mini compressor (E8005, Kiso Power Tool Mfg. Co. Ltd., Osaka, Japan). Cecal contents (20 mg) were diluted with H2O in two stages—first a 5-fold dilution, then a 4-fold dilution—for a total 20-fold dilution. In each stage, samples were centrifuged at 20,000 × g at 4 °C for 5 min and supernatants were collected. Supernatants (20 µL) were mixed with 250 µL of pretreatment liquid (methanol:H2O:chloroform = 2.5:1:1) and 5 µL of internal standard solution (2-isopropylmalic acid, 1 mg/mL). Plasma samples (50 µL) and liver samples (20 mg) were each similarly mixed with the pretreatment liquid (250 µL) and the internal standard solution (5 µL). Liver solutions were homogenized with a Physcotron (Microtec Co. Ltd., Takidai, Japan). For all samples, after vortex mixing the sample tube was shaken at 1,200 rpm at 37°C for 30 min in the dark. The tubes were centrifuged at 16,000 × g at 4°C for 5 min. Supernatants (225 µL) were placed in new tubes with 200 µL H2O. The tubes were vortex mixed, centrifuged at 16,000 × g at 4°C for 5 min, then supernatants were collected into new tubes and centrifuged at room temperature in a vacuum for 20 min. After cooling in -80°C for 10 min, the tubes were centrifuged at room temperature in a vacuum for 6–8 h. Methoxyamine hydrochloride in pyridine (20 mg/mL, 40 µL) was added into the tubes and vortex mixed. The tubes were shaken at 1,200 rpm at 30°C for 90 min in the dark for oximation. Then, MSTFA (N-methyl-N-trimethylsilyltrifluoroacetamine) (20 µL) was added to each tube and the contents were vortex mixed. To prepare 10


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trimethylsilyl (TMS) derivatives, the tubes were shaken at 1,200 rpm at 37°C for 45 min in the dark. GC/MS analysis was carried out using a GCMS-QP2010 Ultra (Shimadzu Inc., Kyoto, Japan) on the 37-min mode as per the instruction manual. Data processing was performed with GCMSsolution (Shimadzu), and Smart Metabolites Database (Shimadzu). Peaks of 87 samples (29 mice × 3 samples) were recorded over the mass range 45–600 m/z and were manually investigated using Smart Metabolites Database, which includes 568 metabolites (data in Table S1). A data quality check was conducted by comparing reference samples with known profiles similar to those of the experimental samples (with a threshold set as a similarity ratio > 50%). Finally, 95, 107, and 71 metabolites for plasma, liver, and cecum were identified, respectively (data in Tables S2-S4). Metabolite levels were semi-quantified using the peak area of each metabolite relative to an internal standard (2-isopropylmalic acid).

Statistical analysis Body weight gain, food intake, water intake, and scores in social interaction test were analyzed with two-way analysis of variance (ANOVA) to test effects of ‘stress’, ‘food’, and ‘stress × food’. Post-hoc tests were performed with Tukey’s HSD test. Data were reported as mean ± SEM and analyzed with StatView ver. 5 (SAS Institute Inc., Cary, NC, USA). To detect differences of susceptibility rates between groups of mice fed different diets, a 2 × 2 contingency table with Fisher’s exact test was generated using R software (www.R-project.org).17 The significant threshold was set to p < 0.05. 11



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For metabolomic analyses, two-way ANOVA were used to compare the factors ‘stress’, ‘food’, and ‘stress × food’. To control the p-value for multiple comparisons, the false discovery rate was determined using the methods of Benjamini and Hochberg.27 The Q-value was defined as the p value adjusted after Benjamini-Hochberg correction. 27 The metabolome-wide significance threshold was set at Q < 0.1 according to the previous studies.16,28,29 For the stress effect, a multivariate analysis method, orthogonal partial least squares-discriminant analysis (OPLS-DA) was performed by the SIMCA-P 12.0 software (Umetrics, Umea, Sweden), in order to discriminate the difference of the effect of stress under two different feeding conditions between sCSDS + AIN diet and control + AIN diet and between sCSDS + MF diet and control + MF diet. Threshold of variable importance in the projection (VIP) was set at VIP > 1 to know metabolites responsible for the difference among two sets of groups. In addition, OPLS-DA scatter plots were also drawn for four different groups together according to the metabolic profiles of plasma, liver, and cecum in Figures S1-S3. For the food effect, metabolites that were significantly changed were used in an over-representation analysis (ORA) of a metabolite set enrichment analysis,30 because a relatively high number of metabolites were significantly altered by food. The ORA was performed on a web-based system, MetaboAnalyst 3.0,31 in order to determine related metabolism pathways.

Results Body weight gain, food intake, and water intake 12


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To check for baseline changes, body weight gain, food intake, and water intake of the mice were analyzed for the seven days (from Days -6 to 0) of the habituation period (data in Table S5). A two-way ANOVA for body weight gain revealed no significant effects of stress (F1, 25 = 1.959, p = 0.17), food (F1, 25 = 2.320, p = 0.14), or stress × food (F1, 25 = 0.956, p = 0.34). A two-way ANOVA for food intake indicated a significant main effect of diet type (F1, 25 = 38.080, p < 0.0001), whereas there were no significant effects of stress (F1, 25 = 1.890, p = 0.18) or the stress × food interaction (F1, 25 = 0.463, p = 0.50). Similarly, a two-way ANOVA for water intake found a significant main effect of food (F1, 25 = 130.772, p < 0.0001), but no significant effects of stress (F1, 25 = 0.257, p = 0.62) or the stress × food interaction (F1, 25 = 0.167, p = 0.69). Baseline consumption of food and water in the mice fed MF (sCSDS + MF and control + MF) were significantly higher than in the mice fed AIN-93G (sCSDS + AIN and control + AIN) in the habituation period. To evaluate changes during the stress period (Days 0–10), body weight gain, food intake, and water intake were monitored daily (Table 1). A two-way ANOVA of body weight gain revealed two significant main effects of stress (F1, 25 = 30.942, p < 0.0001) and food (F1, 25 = 12.307, p = 0.0017), and a trend toward an effect of the stress × food interaction (F1, 25 = 3.786, p = 0.06). The sCSDS mice experienced greater body weight gain than the control mice, whereas AIN-93G-fed mice had higher body weight gain values than MF-fed mice. For food intake, there were significant main effects of food (F1, 25 = 189.847, p < 0.0001) and the stress × food interaction (F1, 25 = 21.332, p = 0.0001), but the stress effect was not significant (F1, 25 = 2.158, p = 0.15). MF-fed mice showed 13



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higher values of food intake under both sCSDS and control conditions than AIN-93G-fed mice. In control mice, MF-fed mice showed the highest value, although AIN-93G-fed mice had the lowest value. For water intake, two-way ANOVA revealed significant main effects of stress (F1, 25 = 70.148, p < 0.0001) and food (F1, 25 = 4.758, p = 0.0388). The stress × food interaction was not significant (F1, 25 = 0.062, p = 0.81). There was a clear result that the sCSDS induced significant polydipsia-like behavior, although MF-fed mice showed higher values of water intake than AIN-93G-fed mice whether with or without sCSDS.

Social interaction test (Day 11) For the social interaction test, total distance traveled and time spent in the interaction zone were measured (Table 2). In the target-absent condition, two-way ANOVA indicated a significant stress effect on total distance traveled (F1, 25 = 5.603, p = 0.0260) but no significant effect of food (F1, 25 = 0.081, p = 0.78) and stress × food interaction (F1, 25 = 0.152, p = 0.70). The sCSDS mice showed significantly decreased locomotor activity than the control mice in the novel environment. There were no effects of stress (F1, 25 = 0.050, p = 0.82), food (F1, 25 = 0.431, p = 0.52), or stress × food interaction (F1, 25 = 1.084, p = 0.31) on time spent in the interaction zone without a target. In the target-present condition, a significant main effect of stress (F1, 25 = 8.292, p = 0.0080) was revealed for total distance, although no effects of food (F1, 25 = 1.399, p = 0.25) or stress × food interaction (F1, 25 = 0.088, p = 0.77) were found. Whereas sCSDS induced decreased locomotor activity, the sCSDS mice with a target showed 14


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lower values of locomotor activity than those under the target-absent condition. This implied that locomotor activity of the sCSDS mice was more inhibited by the presence of social target. Two-way ANOVA of time spent in the interaction zone with the target showed a trend toward an effect of stress (F1, 25 = 3.141, p = 0.09) but no effects of food (F1, 25 = 0.745, p = 0.40) or stress × food interaction (F1, 25 = 1.728, p = 0.20). The sCSDS mice fed either diet indicated lower values of time spent in the interaction zone than those of the control mice. Social interaction scores and their distribution are plotted in Figure 2. Although two-way ANOVA revealed no effects of stress (F1, 25 = 0.620, p = 0.44), food (F1, 25 = 0.080, p = 0.78), or their interaction (F1, 25 = 0.030, p = 0.86), sCSDS mice fed AIN-93G showed the lowest social interaction scores. Under sCSDS conditions, the group fed AIN-93G consisted of five stress-susceptible and two stress-resilient mice (susceptible rate = 71.4%; Table 3). However, the MF-fed group had two susceptible and four resilient mice (susceptible rate = 33.3%; Table 3). Although there was no statistically difference in the rate of susceptible mice between the two groups (p = 0.29) because of relatively low sample size only for the Fisher’s exact test, the rates of susceptible mice were similar to our previous results,17 which were 73.9% and 34.8% of AIN-93G- and MF-fed mice, respectively. Since both the present and previous experiments were performed on the same environmental condition, we merged the data and confirmed that susceptibility rates of sCSDS mice were 73.3% (22 susceptible mice in 30 individuals) for AIN-93G and 34.5% (10 susceptible mice in 29 individuals) for MF, which showed statistically significant difference (p < 0.05). In the non-stressed control condition, both AIN-93G- and MF-fed mice showed 15



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normal social interactions (Table 3). There was no difference in the rate of susceptible-like phenotypes between the two group (p = 1.00).

Metabolomic analysis (Day 12) Because this and previous studies indicates diet quality and purity affect stress susceptibility in sCSDS mice, we therefore hypothesized that there are some key peripheral metabolites to change stress susceptible behavior. To address this hypothesis, we undertook a “Non-targeted” approach of GC/MS metabolomics and investigated effects of stress, food, and their interaction below. Metabolites that had social stress-induced changes are listed in Table 4. Eight plasma and five liver metabolites were significantly changed by stress. Three plasma metabolites (4-hydroxyproline, 3-aminopropanoic acid, and orotic acid) were higher and five (urea, dihydroxyacetone, ribose, lactic acid, and glycerol) were lower in sCSDS mice than in control mice. In liver, four metabolites (4-hydroxyproline, malic acid, asparagine, and fumaric acid) were higher and one (niacinamide) was lower in sCSDS mice than in control mice. Table 5 shows metabolites with food-induced changes. Compared with stress-induced changes, a relatively large number of metabolites were significantly altered—22, 27, and 31 metabolites for plasma, liver, and cecum respectively. In plasma, 13 metabolites (including inositol and urea) were up-regulated and nine (including 2-hydroxyisovaleric acid and 2-hydroxyisobutyric acid) were down-regulated in MF-fed mice compared to AIN-93G-fed mice. Nineteen liver metabolites (including nicotinic acid and 16


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inositol) were higher and eight (including 2-hydroxy-3-methylbutyric acid and N-acetylglutamine) were lower in MF-fed mice than in AIN-93G-fed mice, whereas 24 cecal metabolites (including fructose and xylulose) were up-regulated and seven (including p-hydroxyphenylacetic acid and 5-aminopentanoic acid) were down-regulated in MF-fed mice relative to those in AIN-93G-fed mice. Finally, Table 6 shows the metabolites significantly changed by the social stress × food interaction effect. Five (succinic acid, fumaric acid, malic acid, 4-aminobutyric acid, and 2-ketoisocaproic acid) plasma metabolites and one (pantothenic acid) cecum metabolite were lower in sCSDS mice than in control mice fed the AIN-93G diet, whereas the opposite was seen in those fed the MF diet, where all six of these metabolites were higher in sCSDS mice than in control mice. As an impressive result, the sCSDS mice fed the AIN-93G diet showed lower levels of plasma gamma-aminobutyric acid (GABA) than those of control mice, and the sCSDS mice fed the MF diet indicated higher levels than those of control mice. Taken together, plasma, liver, and cecum metabolites in sCSDS mice were affected more by food quality than chronic stress in this study. Since we found the relatively large number of metabolites significantly altered by diet, over-representation analysis (ORA) of metabolite set enrichment analysis was conducted with each metabolite set for plasma (22), liver (27), and cecum (31) that were significantly altered by food, to know which metabolic pathways are significantly influenced by food. These results are listed in data in Tables S6-S8. Most notably, ORA with the liver metabolite set revealed that gluconeogenesis and glycolysis are significantly enriched at the threshold of Q < 17



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0.1 (Figure 3 and data in Table S7). Especially, three metabolites, which are glucose-6-phosphate, phosphoenolpyruvic acid, and pyruvic acid, contributed to both gluconeogenesis and glycolysis (Figure 3), and the levels of those metabolites were different in the MF and AIN-93G groups. To confirm the difference of the effect of stress under two different feeding conditions by two-way ANOVA, we performed OPLS-DA to discriminate between sCSDS + AIN diet and control + AIN diet and between sCSDS + MF diet and control + MF diet. Under the AIN-93G feeding condition, R2Y and Q2Y values of the OPLS-DA models were 0.998 and 0.869, 0.960 and 0.506, and 0.951 and 0.533 for the plasma, liver, and cecum, respectively. Under the MF feeding condition, R2Y and Q2Y values of the OPLS-DA models for the plasma, liver, and cecum were 0.999 and 0.657, 0.898 and 0.395, and 0.526 and 0.137, respectively. The lists of metabolites responsible for the difference (VIP > 1) for the plasma, liver, and cecum were shown in Tables S9-S11. we confirmed that many of metabolites detected by two-way ANOVA are commonly found in our OPLS-DA.

Discussion This study was conducted with a hypothesis that there will be some key peripheral metabolites to change stress susceptibility of sCSDS mice under the two kinds of diet conditions. A “non-targeted” global approach with GC/MS metabolome analyses was performed to search for psychological stress- and food-induced metabolic changes. The analyses revealed that the number of metabolites changed by food was larger than the number changed by stress in 18


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plasma, liver, and cecal content and that some functional compounds were altered by stress, food, and their interaction. These metabolites may be key to change stress susceptibility of sCSDS mice, suggesting that some dietary components may be able to attenuate various stress-induced symptoms by changing metabolites in peripheral tissues.

Effects of diet/stress on responses to social stress In this study, we performed GC/MS metabolomics using sCSDS mice fed with two kinds of food—AIN-93G and MF diets—to find novel insights induced by both social stress and food. As in our previous studies,12,13 sCSDS mice showed increased body weight gain and water intake following 10 days of stress exposure. Social interaction tests divide chronic social defeat stress mice into stress-susceptible (55.4% of mice) and resilient (44.6%) phenotypes.26 Our previous study revealed that diet quality affects stress vulnerability even though the levels of body weight gain and water intake of sCSDS mice fed AIN-93G are similar to those fed MF.17 In this study, we got the same tendency described above (but not statistically significant difference with samples of this study only), suggesting that susceptibility to stress will be altered by diet: the rate of susceptible mice of sCSDS mice fed the AIN-93G diet (rate of stress susceptibility is 71.4%) is higher than that of mice fed the MF diet (rate of stress susceptibility is 33.3%) in this study, similar to our previous results (rates of stress susceptibility are 73.9% and 34.8% for AIN-93G and MF diet, respectively).17 In addition, low locomotor activity in social interaction test without a social target was seen in the sCSDS mice. Moreover, the locomotor activity of 19



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the sCSDS mice was even more inhibited by the presence of social target. This behavioral phenotype under novel environments (conspecies and object) will be a useful indicator in identifying foods and farm products that attenuate social stress-induced deficits.15

Metabolite changes with diet/stress and their interactions GC/MS metabolome analyses with sCSDS mice fed one of two kinds of food revealed that the number of metabolites changed by food was larger than the number changed by stress in plasma, liver, and cecum. These results suggest that food quality can greatly affect the nutritional materials available to gut microbiota. We confirmed that intestinal flora is changed by sCSDS when mice were fed AIN-93G.32 Therefore, diversity of the intestinal flora may be altered by sCSDS with an MF diet but in a different manner than with an AIN-93G diet. Recently, it has been shown that differences in gut microbiota can alter behavior in animal models.24 Our previous study clearly indicated that diet quality changes stress vulnerability in sCSDS mice fed either AIN-93G or MF.17 Since this study indicated the rates of stress-susceptible mice are similar to those of the previous report,17 we speculate that the metabolomic profiles found in this study can be commonly seen in our sCSDS mice reported previously.17 In addition, Daniel et al.23 have reported that diets have a major impact on the mouse cecal microbiota that extends to major alterations in bacterial physiology and metabolite landscape using carbohydrate and high-fat diet. Therefore, diet quality and purity may influence intestinal environments for gut microbiota and affect stress-vulnerable behavior in mice. Further studies, such as metagenome 20


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analysis, are necessary to confirm the differences in gut microbiota between sCSDS mice fed AIN-93G and MF diets. In addition, gluconeogenesis and glycolysis metabolisms were significantly enriched in the liver metabolite set that was altered by food. Specifically, the levels of glucose-6-phosphate, phosphoenolpyruvic acid, and pyruvic acid were different in the MF and AIN-93G groups. According to our previous report,16 these liver metabolites were higher in the sCSDS mice than in the control mice, whereas ATP, ADP, and CoA-related metabolites in the sCSDS mice were lower levels than those in control mice under AIN-93G feeding condition. Moreover, we confirmed that CoA-related metabolites such as acetyl-CoA and malonyl-CoA in the livers of the sCSDS mice were significantly lower levels than those of control mice fed an MF diet in a different set of experiments.33 These results imply that sCSDS increases glycolysis-related substrates and decreases energy-related substrates in the liver. Velagapudi et al.34 investigated how the gut microbiota affects host energy and lipid metabolism by comparing between conventionally raised and germ-free mice. Their metabolome analyses showed increased energy metabolites such as pyruvic acid in conventional mice and the microbiota modified a number of lipid species. Their study demonstrate that the gut microbiota plays a role in modulating host metabolism. In this study, higher levels of energy metabolites such as pyruvic acid were found in MF groups than in AIN-93G groups. This result may support that MF diet (than AIN-93G) will lead to more natural gut micro-environment, which is similar to those of conventional mice than germ free mice.29 Since sCSDS mice fed MF diet were less vulnerable to stress than those fed AIN-93G,17 it may be useful to evaluate anti-stress 21



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supplements using AIN-93G diet-fed sCSDS mice. Further studies of nutritional supplementation using this type of mice are needed.15 Plasma and liver 4-hydroxyproline were significantly higher in sCSDS mice than in control mice under both AIN-93G and MF feeding conditions. Carbon tetrachloride-induced hepatic fibrosis rat and mouse models show increased hepatic and serum hydroxyproline as an indicator of increased connective tissue.35-37 Given that the sCSDS mice show hepatic damage, the other hepatic markers such as ALT and AST should be increased. However, our previous study showed that there is no difference in serum ALT and AST between sCSDS and control mice.12 Therefore, increased hydroxyproline in plasma and liver may indicate an anti-stress response rather than hepatic damage. In fact, increased hydroxyproline, arginine, and glycine, which are functional amino acids, induced by proline supplementation improve anti-oxidative, immune response, and stress tolerance in white shrimp.38 Recently, hydroxyproline synthesized from proline has been recognized as a substrate for glycine, pyruvate, and glucose,39 and hydroxyproline can be recycled via a redox shuttle and may lead to reductive oxidants.40 In future studies, hydroxyproline supplementation for sCSDS mice should be investigated. Plasma urea concentration of the sCSDS mice was significantly lower than that of the control mice in both AIN-93G and MF diets. Since this result was consistent with our previous findings using an enzymatic method,12 we conclude that this is normally seen in sCSDS mice. Because the sCSDS mice showed a striking polydipsia-like phenotype and had much more water in their body than non-sCSDS mice,12 it might be that plasma urea remains low due to 22


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overhydration. This is supported by human studies that show that overhydration leads to low levels of blood urea nitrogen.41 In addition, plasma levels of 3-aminopropanoic acid (beta-alanine) was higher and that of glycerol was lower in the sCSDS mice than in control mice in this study, respectively. In depression models of chronic unpredictable mild stress rats, the level of beta-alanine was higher and glycerol was lower in the prefrontal cortex, compared to healthy control.42 Central and peripheral changes in beta-alanine are related to a depression-like phenotype in mice,42-45 and beta-alanine is also known to act as a neurotransmitter.46 Until now, we cannot discuss the relationships among these metabolites because it seems that each metabolite is in different metabolic pathways. In any case, these metabolites may be good indicators for the symptoms induced by chronic stress. Plasma 4-aminobutyric acid (gamma-aminobutyric acid: GABA) was found to have a stress × food interaction in this study. GABA is well known as an inhibitory neurotransmitter.47 Plasma GABA levels have been proposed as an index of brain GABA activity,48 and low GABA levels were found in the brain, cerebrospinal fluid, and plasma of depressed patients.49 In addition, a study with post-traumatic stress disorder (PTSD) patients suggested that a plasma GABA level above 0.20 mmol/mL may protect against chronic PTSD and may represent a marker of recovery from trauma.50 A study of chronic mild stress model mice supported that GABA levels may be decreased in an animal model of depression and can be recovered its GABA levels and depression-like behavior by drug.51 Interestingly, the sCSDS mice fed the AIN-93G diet (high rate of stress-vulnerable mice) showed lower levels of plasma GABA, and the sCSDS 23



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mice fed the MF diet (low rate of stress-vulnerable mice) indicated higher levels than those of control mice. We cannot explain whether this phenomenon is a cause or a result. Furthermore, since how the diet might alter plasma GABA levels remain uncertain, further studies are strongly required. In any case, the plasma GABA level may be a good indicator of the stress vulnerability that is altered by diet in sCSDS. This study conducted metabolome analyses with a weight gain model of chronic social defeat stress.12 Until now, several stress models with body weight gain52-56 and with body weight loss7,57,58 have been established. Gautam et al.59 reported metabolomic profiles with a social defeat model with weight gain.56 Because features of body weight gain will influence changes of many metabolites in the body, further studies such as metabolomics of several stress models with both weight gain and weight loss described above will lead to understanding common important insights of stress-induced alterations. Our studies revealed that dietary components may influence both stress-related behaviors and metabolites in peripheral tissues. Until now, we created and evaluated a “bare” sCSDS mice to know what indicators are changeable in “natural” rearing environments. Since quantities of food and water consumptions may affect metabolites and behaviors in mice, it will be difficult to find which dietary components attenuate stress-induced features using only the experimental condition. Therefore, further experiments, including food and water matched conditions in both stress and control mice, should be needed to understand deeper in nutrition.

24


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Meta-analyses have shown some evidence of a relationship between diet type and depression in humans.60,61 They suggested that consumption of a diet high in fruit, vegetables, whole grains, and fish may reduce depression risk and pointed out the importance of dietary interventions as a strategy in the prevention of depression.61 The present results will suggest shared mechanisms between mice and humans and provide some direction for further research. This study is the first step to understand the mechanism of how stress-induced symptoms are altered by diet in animal models and humans.

Acknowledgments We thank all the members of Laboratory of Feed Science of Ibaraki University, especially Ms. H. Shimonishi for her assistance of the manuscript preparation. This research was supported in part by an Ibaraki University Cooperation between Agriculture and Medical Science (IUCAM) (The MEXT, Japan), the Research Project on the Development of Agricultural Products and Foods with Health-promoting benefits (NARO) (The MAFF, Japan), and the Council for Science, Technology and Innovation (CSTI) under the Cross-ministerial Strategic Innovation Promotion Program (SIP) ‘‘Technologies for creating next-generation agriculture, forestry and fisheries’’ (Bio-oriented Technology Research Advancement Institution, NARO) (The Cabinet Office, Japan) to AT.

Supplementary Table 1. All 568 metabolites in the Smart Database. Supplementary Table 2. All plasma metabolites. Supplementary Table 3. All liver metabolites. 25



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Supplementary Table 4. All cecum metabolites. Supplementary Table 5. Summary of total body weight gain, food intake, and water intake from Day -6 to Day 0. Supplementary Table 6. Over-representation analysis of plasma metabolite set significantly altered by food via MetaboAnalyst. Supplementary Table 7. Over-representation analysis of liver metabolite set significantly altered by food via MetaboAnalyst. Supplementary Table 8. Over-representation analysis of cecal metabolite set significantly altered by food via MetaboAnalyst. Supplementary Table 9. List of plasma metabolites responsible for the stress effect by the OPLS-DA under AIN-93G and MF conditions. Supplementary Table 10. List of liver metabolites responsible for the stress effect by the OPLS-DA under AIN-93G and MF conditions. Supplementary Table 11. List of cecal metabolites responsible for the stress effect by the OPLS-DA under AIN-93G and MF conditions.

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(57) 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 systhesis. J Lipid Res 2010, 51, 1344–1353. doi: 10.1194/jlr.M002196 (58) 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. doi: 10.1016/j.biopsych.2012.06.006 (59) Gautam, A.; D’Arpa, P.; Donohue, D. E.; Muhie, S.; Chakraborty, N.; Luke, B. T.; Grapov, D.; Carroll, E. E.; Meyerhoff, J. L.; Hammamieh, R.; Jett, M. Acute and chronic plasma metabolomic and liver transcriptomic stress effects in a mouse model with features of post-traumatic stress dieorder. PLoS ONE 2015, 10, e0117092. doi: 10.1371/journal.pone.0117092 (60) Lai, J. S.; Hiles, S.; Bisquera, A.; Hure, A. J.; McEvoy, M.; Attia, J. A systematic review and meta-analysis of dietary patterns and depression in community-dwelling adults. Am J Clin Nutr 2014, 99, 181–197. doi:10.3945/ajcn.113.069880 (61) O’Neil, A.; Quirk, S. E.; Housden, S.; Brennan, S. L.; Williams, L. J.; Pasco, J. A.; Berk, M.; Jacka, F. N. Relationship between diet and mental health in children and adolescents: a systematic review. Am J Public Health 2014, 104, e31–42. doi:10.2105/AJPH.2014.302110

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Table 1. Summary of total body weight gain, food intake, and water intake from Day 0 to Day 10. Control (n) sCSDS (n) AIN-93G MF AIN-93G MF Stress Body weight gain 1.1 ± 0.3 (8) -0.4 ± 0.3 (8) 2.2 ± 0.3 (7) 1.7 ± 0.1 (6) *** (g) Food intake (g) 28.0 ± 0.6 (8) 39.4 ± 0.6 (8) 31.7 ± 0.6 (7) 37.5 ± 0.8 (6) ns Water intake (g) 47.3 ± 3.0 (8) 57.2 ± 2.4 (8) 82.5 ± 6.5 (7) 90.4 ± 3.4 (6) *** 1 ns Two-way ANOVA. p > 0.05, *p < 0.05, **p < 0.01, ***p < 0.001.

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ANOVA1 Food Stress × Food **

ns

*** *

*** ns

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Table 2. Social interaction test. Behavior Control (n) AIN-93G (8) MF (8) Total distance traveled 1012 ± 101 1000 ± 100 (cm) with no target

1

sCSDS (n) AIN-93G (7) MF (6)

Stress

ANOVA1 Food Stress × Food

697 ± 129

774 ± 130

*

ns

ns

Time spent in interaction zone (s) with no target

57 ± 5

54 ± 6

50 ± 12

65 ± 12

ns

ns

ns

Total distance traveled (cm) with target

600 ± 79

659 ± 25

391 ± 91

488 ± 42

**

ns

ns

Time spent in interaction zone (s) with target

78 ± 9

73 ± 6

48 ± 14

69 ± 8

ns

ns

ns

Two-way ANOVA. nsp > 0.05, *p < 0.05, **p < 0.01.

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Table 3. Summary of No. of susceptible and resilient mice and susceptible rate in each group Diet Control condition1 AIN-93G MF sCSDS condition AIN-93G MF

Total No. of mice

No. of susceptible

No. of resilient

Susceptible rate (%)

Fisher's exact test2 p = 1.00 (1.00)

8 (24) 8 (12)

2 (3) 1 (1)

6 (21) 7 (11)

25.0 (12.5) 12.5 (8.3) p = 0.29 (0.02)

7 (23) 6 (23)

5 (17) 2 (8)

2 (6) 4 (15)

71.4 (73.9) 33.3 (34.8)

1

Under control condition, the control mice which showed susceptible-like and resilient-like phenotypes were counted as susceptible-mice and resilient-mice, respectively. 2

A 2 × 2 contingency table with Fisher's exact test was generated for each condition.

Our previous data (Goto et al., 2016) were shown in the parenthesis to understand the similarity between present and previous studies.

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Table 4. Metabolites with social stress-induced changes (Q < 0.1). Metabolite

Ratio1 AIN MF (vs Ct) (vs Ct)

Plasma (8) Urea 0.7 0.8 Dihydroxyacetone 0.5 0.5 4-Hydroxyproline 1.5 1.3 Ribose 0.6 0.4 3-Aminopropanoic acid 1.1 1.6 Orotic acid 1.7 1.3 Lactic acid 0.8 0.9 Glycerol 0.8 0.9 Liver (5) 4-Hydroxyproline 1.5 1.6 Niacinamide 0.9 0.8 Malic acid 1.6 1.3 Asparagine 1.4 1.8 Fumaric acid 1.6 1.2 1 Ratio was shown as social defeat/control in each diet.

Two-way ANOVA Stress p-value Q-value < 0.0001 < 0.0001 0.0008 0.0011 0.0021 0.0022 0.0039 0.0055

< 0.0095 < 0.0048 0.0253 0.0261 0.0399 0.0348 0.0529 0.0653

< 0.0001 0.0001 0.0016 0.0020 0.0033

< 0.0108 0.0054 0.0576 0.0540 0.0713

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Table 5. Metabolites with food-induced changes (Q < 0.1). 1

Ratio Metabolite

Ct (vs AIN)

St (vs AIN)

1

Two-way ANOVA

Ratio

Food p-value

Metabolite

Q-value

Plasma (22)

Two-way ANOVA

Ct (vs AIN)

St (vs AIN)

Food p-value

Q-value

Liver (Continued)

2-Hydroxyisovaleric acid

0.7

0.7

< 0.0001

< 0.0095

O-Phosphoethanolamine

0.9

0.7

0.0128

0.0728

2-Hydroxyisobutyric acid N-Acetylglutamine

0.4 0.6

0.3 0.7

< 0.0001 < 0.0001

< 0.0048 < 0.0032

Pyruvic acid Glutamine

1.1 0.4

1.3 0.8

0.0136 0.0173

0.0734 0.0890

Inositol-(2)

1.6

2.3

< 0.0001

< 0.0024

Glucose 6-phosphate-(1)

1.4

3.0

0.0176

0.0864

Urea

1.2

1.3

< 0.0001

< 0.0019

Pantothenic acid

1.5

1.2

0.0200

0.0939

1,5-Anhydro-glucitol Tartaric acid Citric acid

0.5 0.0 1.1

0.7 0.0 1.4

0.0003 0.0007 0.0010

0.0048 0.0095 0.0119

N-Acetylserine Malic acid Sorbitol

1.5 1.5 1.9

1.1 1.1 1.4

0.0221 0.0223 0.0238

0.0995 0.0963 0.0989

Glycolic acid Glycine

1.5 1.2

1.2 1.5

0.0014 0.0050

0.0148 0.0475

Inosine monophosphate Cecum (31)

0.5

0.8

0.0241

0.0964

Lysine

0.5

0.6

0.0056

0.0484

3.6

7.4

< 0.0001

< 0.0071

Boric acid Threonine Xylulose Succinic acid Lactic acid 4-Hydroxyproline Fucose-(1)

0.9 0.6 0.8 0.9 0.8 0.9 1.2

0.8 0.9 0.9 1.5 0.9 0.8 1.3

0.0060 0.0108 0.0109 0.0146 0.0147 0.0157 0.0162

0.0475 0.0789 0.0740 0.0925 0.0873 0.0877 0.0855

Fructose-(1) 4-Hydroxyphenylacetic acid Xylulose Nicotinic acid Citric acid Uracil Glycolic acid Inosine

0.2 4.1 1.5 2.8 2.1 2.2 5.3

0.2 8.6 2.1 3.6 3.8 2.3 2.9

< 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001

< 0.0036 < 0.0024 < 0.0018 < 0.0014 < 0.0012 < 0.0010 < 0.0009

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Table 5. (Continued) Guanosine Pantothenic acid

0.8 1.2

0.5 1.4

0.0173 0.0188

0.0865 0.0893

Phosphoric acid Thymine

3.4 1.8

3.9 2.7

< 0.0001 < 0.0001

< 0.0008 < 0.0007

Phosphoric acid Cystine

0.9 1.5

0.9 1.5

0.0200 0.0206

0.0905 0.0890

5-Aminovaleric acid Glutamic acid

0.2 1.3

0.1 2.0

0.0001 0.0002

0.0006 0.0012

2-Ketoisocaproic acid-(2)

3.1

2.7

0.0003

0.0016

Pantothenic acid Hypoxanthine Xanthine Inositol(2)

1.1 1.1 1.4 1.2

2.7 2.0 2.4 1.9

0.0004 0.0005 0.0014 0.0022

0.0020 0.0024 0.0062 0.0092

Liver (27)

1

2-Hydroxyisovaleric acid Nicotinic acid Inositol-(2) N-Acetylglutamine Phosphoenolpyruvic acid

0.7 1.8 1.6 0.6

0.6 1.6 1.4 0.7

< 0.0001 < 0.0001 < 0.0001 < 0.0001

< 0.0108 < 0.0054 < 0.0036 < 0.0027

1.8

1.3

0.0003

0.0065

Cysteine

0.6

0.6

0.0033

0.0130

Inosine

1.6

1.3

0.0004

0.0072

N-Acetylglutamine

1.7

2.4

0.0034

0.0127

Galacturonic acid-(1) Urea Xanthosine Cysteine

1.5 1.4 1.5 0.7

1.7 1.5 1.2 0.6

0.0005 0.0009 0.0011 0.0025

0.0077 0.0122 0.0132 0.0270

Galactose-(2) Adenine Ornithine Malic acid

1.8 0.5 1.2 1.2

2.5 0.9 1.8 2.8

0.0036 0.0042 0.0049 0.0087

0.0128 0.0142 0.0158 0.0269

1,5-Anhydro-glucitol

0.6

0.6

0.0027

0.0265

Threonic acid

0.9

0.6

0.0094

0.0278

Niacinamide

1.2

1.1

0.0031

0.0279

Pyruvic acid

1.5

1.7

0.0100

0.0284

Ribose 5-phosphate-(1) Glutamic acid Taurine

1.8 1.5 0.8

1.4 1.5 0.7

0.0035 0.0039 0.0044

0.0291 0.0301 0.0317

Cytosine Aspartic acid 5-Oxoproline

1.5 0.6 1.2

1.4 0.9 1.6

0.0103 0.0126 0.0134

0.0281 0.0331 0.0340

4-Aminobutyric acid

1.4

1.2

0.0082

0.0554

3-Hydroxybutyric acid

1.5

2.5

0.0140

0.0343

3-Phosphoglyceric acid Xylitol

1.5 1.3

1.1 1.2

0.0093 0.0107

0.0591 0.0642

Ribose Lactic acid

1.0 0.7

1.9 1.0

0.0166 0.0266

0.0393 0.0609

Ratio was shown as MF/AIN-93G in each condition. 41



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Table 6. Metabolites with social stress × food-induced changes (Q < 0.1). Metabolite

Ratio1 AIN MF (vs Ct) (vs Ct)

Plasma (5) Succinic acid 0.7 1.1 Fumaric acid 0.5 1.1 Malic acid 0.5 1.1 4-Aminobutyric acid 0.8 1.3 2-Ketoisocaproic acid-(2) 0.5 1.1 Cecum (1) Pantothenic acid 0.6 1.4 1 Ratio was shown as social defeat/control in each diet.

Two-way ANOVA Stress*Food p-value Q-value 0.0004 0.0015 0.0022 0.0023 0.0050

0.0380 0.0713 0.0697 0.0546 0.0950

0.0013

0.0923

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Figure Legends

Figure 1. Experimental design.

Figure 2. Social interaction scores in four groups, which are sCSDS + AIN diet (n = 7), sCSDS + MF diet (n = 6), control (no sCSDS) + AIN diet (n = 8), and control + MF diet (n = 8). Bar shows mean ± SEM. Individual values are represented by triangle on the bar. Whereas no effects of stress (p = 0.44), food (p = 0.78), and their interaction (p = 0.86) were found by two-way analysis of variance, sCSDS mice fed AIN-93G showed the lowest social interaction scores. The sCSDS mice fed AIN-93G consisted of five stress-susceptible and two stress-resilient mice (susceptible rate = 71.4%), although the MF-fed sCSDS mice had two susceptible and four resilient mice (susceptible rate = 33.3%). A 2 × 2 contingency table with Fisher’s exact test revealed no difference in the rate of susceptible mice between the two groups (p = 0.29). For only comparison, our previous data are shown at right neighbor of the bar (Goto et al., 2016). The rates of susceptible mice in this study were similar to our previous results, which were 73.9% and 34.8% of AIN-93G- and MF-fed mice, respectively. Both the present and previous experiments were performed under the same environmental condition. 43



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Figure 3. Metabolite set enrichment analysis with significant food-induced 27 liver metabolites via the MetaboAnalyst. Over-representation analysis (ORA) of the metabolites was conducted to determine which metabolic pathways are significantly influenced by food. The ORA revealed that gluconeogenesis and glycolysis are significantly enriched (Q < 0.1).

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Figure 1

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Figure 2 46


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Figure 3 47



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Abstract Graphic

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Effects of Diet Quality and Psychosocial Stress on the Metabolic

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