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The reagents used in this study were DEHP (Tokyo Chemical Industry Co., Tokyo, Japan), SIF (93% pure, Xian Rongsheng Science and Technology, Co. Ltd., Xian, China), HPLC grade formic acid (Beijing Reagent Company, Beijing, China), HPLC grade methanol and acetonitrile (Dikma Science and Technology, Co. Ltd., Canada), leucine enkephalin (Sigma-Aldrich, St. Louis, MO, USA), and filtered distilled water (Millipore, Billerica, USA). The detection kits were aspartate aminotransferase (AST), alanine aminotransferase (ALT), creatinine (CRE), blood urea nitrogen (BUN), serum total cholesterol (TC), triglyceride (TG), high-density lipoprotein (HDL), and blood glucose (GLU) (Nanjing Jiancheng Bio-technology and Science Inc., Nanjing, China).
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Thirty-two healthy female Wistar rats aged 4−6 weeks and weighing 60−80 g from Vital River Laboratory Animal Technology Co. Ltd (Beijing, China). They were studied in accordance with guidelines from the Institute of Zoology Animal and Medical Ethics Committee of Harbin Medical University, in line with current Chinese legislation. Rats were individually housed in stainless steel, wire-mesh cages with a controlled temperature range of 20 to 24 ℃, humidity of 50%−60% and a 12 h light/dark cycle. Rats were given free access to AIN-93 M diet and drinking water.
After adaptation for 7 d, rats were randomly assigned into 4 groups (n = 8/group): the control group (H), the SIF-treated group [A, oral SIF dose of 86 mg/(kg·day), according to our previous study], the DEHP-treated group [B, oral DEHP dose of 68 mg/(kg·day) at roughly twice the no-observed-adverse-effect levels (NOAEL)[27]], the and SIF plus DEHP-treated group (D). Animal age and treatment duration were based on the recommendations of EDSTAC (https://www.epa.gov/endocrine-disruption/endocrine-disruptor-screening-and-testing-advisory-committee-edstac-final). SIF was added to AIN-93M rodent food and DEHP was given daily by gavage and both continued to be administered for 30 d between 08:00 am and 10:00 am. Drinking water was given throughout the study. The clinical parameters of rats were recorded twice daily. Rats were weighed at the end of each week and the daily diet consumption recorded during the study (Supplementary Table S1, available in www.besjournal.com).
Table S1. Effects of SI and DEHP on the fundamental variables during the experiment
Group Daily food intakes (g/d) Food utilization rate (%) Liver/weight
(g/100 g)Kidney/weight (g/100 g) Palace fat/weight (g/100 g) Perirenal fat/weight (g/100 g) H 16.28 ± 1.03 26.2 ± 4.3 2.83 ± 0.38 0.77 ± 0.08 0.85 ± 0.38 1.01 ± 0.46 A 15.29 ± 0.76 25.9 ± 2.1 2.72 ± 0.18a 0.73 ± 0.04 0.49 ± 0.15 0.48 ± 0.15 B 16.34 ± 0.68 25.9 ± 5.5 3.54 ± 0.61b 0.83 ± 0.06 0.97 ± 0.39 0.94 ± 0.42 D 15.09 ± 1.69 23.9 ± 4.0 2.94 ± 0.22b 0.78 ± 0.07b 0.62 ± 0.37 0.64 ± 0.56 Note. Values are mean ± SD; Group H [0 mg/(kg·day)]; Group A [86 mg/(kg·day)]; Group B [68 mg/(kg·day)]; Group D [86 mg/(kg·day) + 68 mg/(kg·day)]. aSignificantly different from group H at P < 0.05. bSignificantly different from group B at P < 0.05. DEHP, di-(2-ethylhexyl) phthalate; SIF, soy isoflavones; SD, standard deviation; ANOVA, analysis of variance. -
After 30 d, rats were sacrificed under sodium pentobarbital anesthesia. Before the rats were sacrificed, blood samples were obtained from the abdominal aorta, and the blood was centrifuged at 3,000 rpm for 15 min to obtain serum, which was immediately stored at −80 ℃. Serum samples were analyzed using an Autolab-PM4000 automated biochemical analyzer (AMS Co., Rome, Italy) to detect AST, ALT, BUN, CRE, TC, TG, HDL, and GLU. Adipose tissue samples obtained from each rat were frozen in liquid nitrogen and stored at −80 ℃ until use. Analysis of serum and fat samples was performed by HPLC. The liver, kidney, pararenal fat, and perirenal fat of rats were extracted and weighed to calculate organ coefficients.
For metabonomics analysis, urine samples were collected on ice packs for 24 h from rat metabolic cages for 30 d. Urine samples were collected by centrifugation (10,000 rpm, 10 min) and stored at −80 ℃ until analysis. Prior to analysis, thawed urine samples were diluted with distilled water at a ratio of 1:3 (vol/vol) and vortexed for UPLC/MS analysis.
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Chromatographic separation was performed using a Waters ACQUITY UPLC BEH-C18 Reversed-phase column (50 mm 4.6 mm i.d., 1.7 mm) (Waters Corporation, Milford, MA, USA). Analytes were eluted by a gradient method, which was performed using ten column volumes with 0.1% formic acid in water (mobile phase A) and 0.1% formic acid in acetonitrile (mobile phase B). The gradient was performed using an initial mobile phase B at 0%–2% for 0.5 min, 2%–20% B for 4.5 min, 20%–35% B for 2 min, 35%–70% B for 1 min, 70%–98% B for 2 min, 98% B for 2 min, 98%–2% for B 2 min, and finally 2% B for 10 min and the flow rate was 0.35 mL/min. Two µL aliquots of each sample were injected into a column maintained at 35 ℃. A wash cycle was performed on the autosampler to eliminate the carryover before each analysis and the eluent was introduced to the MS system in split mode.
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UPLC/Q-TOF-MS (Waters Corporation, Milford, MA, USA) analysis was performed according to our previously published method[28] with a source temperature of 100 ℃, a cone gas flow of 50 L/h, a desolvation gas temperature of 300 ℃, a desolvation gas flow of 650 L/h, a capillary voltage of 3.0 kV in the positive ion mode, and a capillary voltage of 2.8 kV in the negative ion mode with a cone voltage of 35 V. A lock mass of leucine enkephalin for accurate mass acquisition was analyzed by a lock spray interface, and a flow rate of 10 µL/min [(M+H) = 556.2771] used for positive ion modes. A centroid mode was used from m/z 50 to 1,000 and a lock spray frequency of 0.40 s with averaging over ten scans (for corrections) were used to collect MS data. Order effects in statistical analysis were avoided using a randomized crossover design. MS/MS spectra of potential biomarkers were evaluated for reproducibility using partial least squares discriminant analysis (PLS-DA) and representative pooled quality control (QC) samples.
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Peak finding, peak alignment, and for reporting the mass, retention time, and intensity of the peaks in each sample were calculated according to our previously published method[28]. UPLC/Q-TOF-MS data were processed using the MarkerLynx Application Manager 4.1 SCN 714 (Waters Corporation, Milford, MA, USA). Multivariate statistical analysis via principal components analysis (PCA) was performed using EZinfo 2.0 software. The ions with the most inter-sample variation were identified as biomarkers according to the Variable Importance in the Projection (VIP) idea and their exact masses. VIP values > 1.5 in the model were combined with the conditional differential calculation method of ANOVA to identify potential biomarkers. To reveal the net treatment effects on subjects to determine which ions have the greatest effect on sample variance, powerful multi-variate analysis via PCA and PLS-DA were performed by EZinfo software.
To analyze relative intensities of the isotopic peaks of the high-resolution MS spectra, a formula for identifying potential biomarkers was first identified based on accurate mass measurements (mass error < 30 PPM). Second, the Mass Fragment TM Application Manager (MassLynx v4.1, Waters Corp., USA) was used to analyze MS/MS fragment ions using a chemical intelligence peak matching algorithm. Standard samples with known and accurate masses were examined and MS spectra of the unknowns matched with the MS spectra of these standards obtained from various databases, including the Human Metabolome Database (HMDB, http://www.hmdb.ca), METLIN (http://metlin.scripps.edu/), and MassBank (http://www.massbank.jp/). This allowed for identification of potential biomarkers. Finally, all biomarkers identified by MS were verified by authentic chemical standards analyzed using MS/MS and retention time (RT).
The relevant pathways for identified biomarkers were determined using databases like HMDB (http://www.hmdb.ca) and the Kyoto Gene and Genomic Encyclopedia (KEGG, http://www.genome.jp/kegg/). In addition, other pathways involving identified biomarkers were identified using references.
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The following parameters were established for multivariate statistical analysis using EZinfo software: noise elimination level 10.00, Mass window 0.02 Da, RT tolerance 0.01 min, and RT window 0.2 min. The high and low mass ranges were 1,000 Da and 50 Da, respectively, and the initial and final retention times were 0.5 min and 16 min, respectively. The identified and aligned low molecular weight metabolites correspond to chromatographic peaks in base peak intensity (BPI) chromatograms. The resulting 3D matrix containing specified peak indices (RT-m/z pairs), the sample name, and normalized ion strength of each peak area was exported to EZINFO 2.0 to visualize the score map and use PLS-DA to get the maximum VIP value. Pareto-scaling was used to avoid chemical noise prior to multivariate statistical analysis.
One-way ANOVA followed by LSD or Dunnett T3 was used for statistical analysis via SPSS (version 17.0; Beijing Stats Data Mining Co. Ltd, Beijing, China). Data are presented as mean ± standard deviation (SD) and a two-tailed P value < 0.05 was considered significant.
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The daily food intake of group A and group B were not significantly different from group H after 30 d (P > 0.05) (Supplementary Table S1). The concentration of DEHP in serum and fat of group A decreased, but was not significantly different compared to group H (P > 0.05). The levels of DEHP in serum and fat of group B and D were significantly higher than those of group H (P < 0.05, Table 1). However, the levels of DEHP in serum and fat of group D were significantly lower compared to group B (P < 0.05), indicating that SIF can reduce DEHP levels in rat serum and fat.
Table 1. The influence of SIF on DEHP levels of in rat fat and serum (means ± SD, μg/g)
Groups DEHP in fat DEHP in serum H 0.16 ± 0.07 0.21 ± 0.12 A 0.14 ± 0.08 0.13 ± 0.11 B 1.09 ± 0.31a 1.13 ± 0.64a D 0.83 ± 0.28ab 0.87 ± 0.31ab Note. aP < 0.05 compared to group H. bP < 0.05 compared to group B. DEHP, di-(2-ethylhexyl) phthalate; SIF, soy isoflavones; SD, standard deviation. -
The body weight (BW) of all rats in all groups were recorded. Figure 1 showed that the BW slowly increased in the first 2 weeks, then noticeably increased in the last 2 weeks. However, there was no significant difference in BW changes between the experimental groups and the time-matched control groups at each time point (P > 0.05).
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Clinical parameters were measured in order to evaluate the toxic effects of DEHP (Table 2). Serum levels of ALT, AST, BUN, CRE, TC, TG, HDL, and GLU were significantly higher in group B than group H at 4 weeks post- treatment (P < 0.05), respectively. However, these parameters were significantly lower in group D compared to group B 4 weeks post-treatment (P < 0.05).
Table 2. Effects of SIF and DEHP on liver enzyme activities, kidney function, fatty acids levels,and energy metabolism (means ± SD, n = 8)
Groups ALT (U/L) AST (U/L) BUN (mmol/L) CRE (µmol/L) TC (mmol/L) TG (mmol/L) HDL (mmol/L) GLU (mmol/L) H 108.53 ± 7.31 35.89 ± 3.67 4.73 ± 1.02 35.15 ± 7.31 4.89 ± 1.46 1.89 ± 0.16 1.45 ± 0.26 3.89 ± 0.86 A 110.32 ± 15.28b 37.28 ± 8.15b 4.32 ± 1.18b 40.32 ± 12.46 5.32 ± 2.08 2.52 ± 0.87 1.54 ± 0.68 4.02 ± 0.69b B 140.32 ± 10.18a 55.79 ± 4.62a 7.51 ± 0.56a 60.72 ± 8.58a 6.89 ± 0.36a 3.24 ± 0.48a 2.51 ± 0.31a 7.89 ±0.33a D 125.67 ± 6.42ab 45.38 ± 3.54ab 5.24 ± 0.39ab 45.42 ± 5.96ab 3.89 ± 0.96b 2.71 ± 0.27ab 1.89 ± 0.86ab 5.89 ± 0.56ab Note. aP < 0.05 compared to group H. bP < 0.05 compared to group B. DEHP, di-(2-ethylhexyl) phthalate; ALT, alanine aminotransferase; AST, aspartic transaminase; BUN, urea nitrogen; CRE, Creatinine; TC, Serum total cholesterol; TG, Triglyceride; HDL, High-density lipoprotein; GLU, Blood glucose. -
All metabolic profiling of urine was performed in both positive ionization modes to identify as many compounds as possible (Figure 2). There were certain significant metabolic changes between the control and treatment groups as observed visually by UPLC-MS in two different positive ionization modes. Additionally, four unsupervised PCA and supervised PLS-DA models were constructed to elucidate more subtle metabolic changes and characterize their metabolite feature. PCA was performed (scores plot in Figure 3A), to identify metabolite changes of the positive-ion mode ESI data. The data plots of treatment groups showed limit overlap with the data plot of the control group. PLS-DA analysis analyzes measured variables and identifies correlations between measured data with properties of interest. Using the obtained PLS-DA score map as a positive control (for the first seven components, R2Y = 0.958 and Q2 = 0.205, Figure 3B), the three treatment groups and the control group formed four independent clusters in the PLS-DA map of data from 4 weeks post-treatment. In order to evaluate the potential errors of the current PLS-DA model, one hundred permutation tests for PLS-DA were applied. All R2Y and Q2 values on the left were lower than the original points on the right (Figure 4), suggesting that exposure results in changes in the urine metabolite composition of these rats.
Figure 2. A representative BPI chromatograms of urine in a positive mode in the control (A) and treatment group (B) at week 4 using UPLC/ESI-Q-TOF/MS. BPI, base peak intensity
Figure 3. The PCA and PLS-DA 3D score plots of data from control and treatment groups derived from UPLC/ESI-Q-TOF/MS analysis of urine in positive (A, B) ESI mode. Group A: red triangle or square; Group B: green triangle or square; Group D: blue triangle or square; Group H (control group): black triangle or square. ESI, electrospray ionization; MS, mass spectrometry; PCA, principal component analysis; UPLC/ESI-Q-TOF/MS, ultra-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry; PLS-DA, partial least squares-discriminant analysis.
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We identified eight potential biomarkers based on VIP parameters of loading plots and ANOVA. The elemental composition of each biomarker was determined by comparing their retention times and MS/MS fragmentation patterns using Q-TOF MS/MS and by searching HMDB. All biomarkers were identified using MassFragment software (Supplementary Figure S1, available in www.besjournal.com) by accurate mass, isotope distribution, and mass spectrometry patterns. Table 3 shows m/z, retention time, postulated identity, and elemental composition of biomarkers. Their chemical structure and mass fragment information were identified based on collision energies (eV) during this experiment (Supplementary Figure S1), and the intensity values of these rat urine metabolites at 4 weeks post-treatment are shown in Table 4. Compared to the control group, treated groups showed significant decreases in the intensity of peaks corresponding to p-cresol glucuronide, methyl hippuric acid, N1-methyl-2-pyridone-5-carboxamide, or lysoPC [18:2 (9Z, 12Z)], but not lysoPC (16:0). In contrast, treated groups showed higher levels of xanthosine and undecanedioic acid compared to control groups positive 30 d post-treatment (P < 0.05).
Figure S1. Chemical structure and mass fragment information of potential urine metabolites identified in this experiment with different collision energy (ev). In positive ion mode: (A) p-Cresol glucuronide (15 ev); (B) the mass spectra of p-Cresol glucuronide from the authentic standard (20 ev); (C) Xanthosine (18 ev); (D) the mass spectra of Xanthosine from the authentic standard (15 ev); (E) LysoPC [18:2 (9Z, 12Z)] (18 ev); (F) the mass spectra of LysoPC [18:2 (9Z, 12Z)] from the authentic standard (20 ev); (G) LysoPC (16:0) (18 ev); (H) the mass spectra of LysoPC (16:0) from the authentic standard (15 ev).
Table 3. Potential biomarkers identified by UPLC/Q-TOF-MS in cation mode
Retention
time (min)Measured m/z
ion (Da)Calculated m/z
ion (Da)Mass error (PPM) Elemental
compositionPostulated
identityScan mode VIP 5.87 285.0896 285.0974 27 C13H16O7 p-Cresol glucuronidea + 1.4670 7.99 285.0833 285.0835 0 C10H12N4O6 Xanthosinea + 7.8901 5.47 194.0827 194.0817 5 C10H11NO3 Methylhippuric acidb + 4.0240 10.38 496.3418 496.3403 3 C24H50NO7P LysoPC (16:0)a + 5.4966 6.57 217.1473 217.1440 15 C11H20O4 Undecanedioic acidb + 1.3758 2.26 153.0699 153.0664 22 C7H8N2O2 N1-Methyl-2-pyridone-5-carboxamideb + 2.6001 10.15 520.3411 520.3403 1 C26H50NO7P LysoPC [18:2 (9Z, 12Z)]a + 4.0616 9.15 189.1245 189.1239 3 C8H16N2O3 N6-Acetyl-L-lysineb + 4.8213 Note. aPresents ions that were identified by comparison to the standards. bBiomarkers identified by the Human Metabolome Database (HMDB) and confirmed using exact mass data and MS fragmentation. DEHP, di-(2-ethylhexyl) phthalate; UPLC/Q-TOF-MS, ultra-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry; cPA, cyclic phosphatidic acid; VIP, variable importance in projection. Table 4. Biomarkers detected in rat urine using positive ESI mode (mean ± SD, n = 8)
Groups Xanthosine p-Cresol glucuronide Undecanedioic acid N1-Methyl-2-pyridone-5-carboxamide LysoPC (16:0) LysoPC
[18:2 (9Z, 12Z)]Methylhippuric acid N6-Acetyl-L-lysine H 0.18 ± 0.15 0.17 ± 0.06 0.27 ± 0.34 18.68 ± 3.43 22.31 ± 5.51 13.04 ± 2.30 21.06 ± 3.46 0.14 ± 0.08 A 31.54 ± 10.84ac 0.79 ± 0.28a 1.62 ± 0.39bc 12.95 ± 4.42a 4.54 ± 7.19a 0.90 ± 1.49ac 7.58 ± 2.95bc 4.32 ± 2.63ac B 0.36 ± 0.14a 0.05 ± 0.11a 0.79 ± 0.13a 10.32 ± 1.15a 21.05 ± 2.64 8.17 ± 2.26a 13.21 ± 2.10a 0.26 ± 0.08 D 19.10 ± 6.56bd 2.29 ± 1.78ac 0.44 ± 0.08c 13.46 ± 1.79ac 13.92 ± 5.46c 1.23 ± 1.52ac 18.29 ± 1.91c 2.18 ± 1.40c Note. aP < 0.05 compared to group H. bP < 0.01 compared to group H. cP < 0.05 compared to group B. dP < 0.01 compared to group B. ESI: electrospray ionization.
doi: 10.3967/bes2020.012
Urine Metabonomic Analysis of Interventions Effect of Soy Isoflavones on Rats Exposed to Di-(2-ethylhexyl) Phthalate
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Abstract:
Objective Di-(2-ethylhexyl) phthalate (DEHP) is a ubiquitous environmental contaminant. As an endocrine disruptor, it seriously threatens human health and ecological environmental safety. This study examines the impact of intervention with soybean isoflavones (SIF) on DEHP-induced toxicity using a metabonomics approach. Methods Rats were randomly divided into control (H), SIF-treated (A, 86 mg/kg body weight), DEHP-treated (B, 68 mg/kg), and SIF plus DEHP-treated (D) groups. Rats were given SIF and DEHP daily through diet and gavage, respectively. After 30 d of treatment, rat urine was tested using UPLC/MS with multivariate analysis. Metabolic changes were also evaluated using biochemical assays. Results Metabolomics analyses revealed that p-cresol glucuronide, methyl hippuric acid, N1-methyl-2-pyridone-5-carboxamide, lysophosphatidycholine [18:2 (9Z, 12Z)] {lysoPC [18:2 (9Z, 12Z)]}, lysoPC (16:0), xanthosine, undecanedioic acid, and N6-acetyl-l-lysine were present at significantly different levels in control and treatment groups. Conclusion SIF supplementation partially protects rats from DEHP-induced metabolic abnormalities by regulating fatty acid metabolism, antioxidant defense system, amino acid metabolism, and is also involved in the protection of mitochondria. -
Key words:
- Di-(2-ethylhexyl) phthalate /
- Soy isoflavones /
- Metabonomics /
- UPLC-QTOF-MS/MS /
- Urine
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Figure 3. The PCA and PLS-DA 3D score plots of data from control and treatment groups derived from UPLC/ESI-Q-TOF/MS analysis of urine in positive (A, B) ESI mode. Group A: red triangle or square; Group B: green triangle or square; Group D: blue triangle or square; Group H (control group): black triangle or square. ESI, electrospray ionization; MS, mass spectrometry; PCA, principal component analysis; UPLC/ESI-Q-TOF/MS, ultra-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry; PLS-DA, partial least squares-discriminant analysis.
S1. Chemical structure and mass fragment information of potential urine metabolites identified in this experiment with different collision energy (ev). In positive ion mode: (A) p-Cresol glucuronide (15 ev); (B) the mass spectra of p-Cresol glucuronide from the authentic standard (20 ev); (C) Xanthosine (18 ev); (D) the mass spectra of Xanthosine from the authentic standard (15 ev); (E) LysoPC [18:2 (9Z, 12Z)] (18 ev); (F) the mass spectra of LysoPC [18:2 (9Z, 12Z)] from the authentic standard (20 ev); (G) LysoPC (16:0) (18 ev); (H) the mass spectra of LysoPC (16:0) from the authentic standard (15 ev).
Figure 5. The altered pathways and toxic effects in response to DEHP and/or SIF treatment. Upwards and downwards arrows represent increases or decreases, respectively, in metabolite levels in the DEHP-exposed group compared to the control group; upwards dashed arrow or downwards dashed arrows represent the significant increases or decreases in metabolite levels in the SIF plus DEHP-treated group compared to the DEHP-treated group. DEHP, di-(2-ethylhexyl) phthalate; SIF, soy isoflavones
S1. Effects of SI and DEHP on the fundamental variables during the experiment
Group Daily food intakes (g/d) Food utilization rate (%) Liver/weight
(g/100 g)Kidney/weight (g/100 g) Palace fat/weight (g/100 g) Perirenal fat/weight (g/100 g) H 16.28 ± 1.03 26.2 ± 4.3 2.83 ± 0.38 0.77 ± 0.08 0.85 ± 0.38 1.01 ± 0.46 A 15.29 ± 0.76 25.9 ± 2.1 2.72 ± 0.18a 0.73 ± 0.04 0.49 ± 0.15 0.48 ± 0.15 B 16.34 ± 0.68 25.9 ± 5.5 3.54 ± 0.61b 0.83 ± 0.06 0.97 ± 0.39 0.94 ± 0.42 D 15.09 ± 1.69 23.9 ± 4.0 2.94 ± 0.22b 0.78 ± 0.07b 0.62 ± 0.37 0.64 ± 0.56 Note. Values are mean ± SD; Group H [0 mg/(kg·day)]; Group A [86 mg/(kg·day)]; Group B [68 mg/(kg·day)]; Group D [86 mg/(kg·day) + 68 mg/(kg·day)]. aSignificantly different from group H at P < 0.05. bSignificantly different from group B at P < 0.05. DEHP, di-(2-ethylhexyl) phthalate; SIF, soy isoflavones; SD, standard deviation; ANOVA, analysis of variance. Table 1. The influence of SIF on DEHP levels of in rat fat and serum (means ± SD, μg/g)
Groups DEHP in fat DEHP in serum H 0.16 ± 0.07 0.21 ± 0.12 A 0.14 ± 0.08 0.13 ± 0.11 B 1.09 ± 0.31a 1.13 ± 0.64a D 0.83 ± 0.28ab 0.87 ± 0.31ab Note. aP < 0.05 compared to group H. bP < 0.05 compared to group B. DEHP, di-(2-ethylhexyl) phthalate; SIF, soy isoflavones; SD, standard deviation. Table 2. Effects of SIF and DEHP on liver enzyme activities, kidney function, fatty acids levels,and energy metabolism (means ± SD, n = 8)
Groups ALT (U/L) AST (U/L) BUN (mmol/L) CRE (µmol/L) TC (mmol/L) TG (mmol/L) HDL (mmol/L) GLU (mmol/L) H 108.53 ± 7.31 35.89 ± 3.67 4.73 ± 1.02 35.15 ± 7.31 4.89 ± 1.46 1.89 ± 0.16 1.45 ± 0.26 3.89 ± 0.86 A 110.32 ± 15.28b 37.28 ± 8.15b 4.32 ± 1.18b 40.32 ± 12.46 5.32 ± 2.08 2.52 ± 0.87 1.54 ± 0.68 4.02 ± 0.69b B 140.32 ± 10.18a 55.79 ± 4.62a 7.51 ± 0.56a 60.72 ± 8.58a 6.89 ± 0.36a 3.24 ± 0.48a 2.51 ± 0.31a 7.89 ±0.33a D 125.67 ± 6.42ab 45.38 ± 3.54ab 5.24 ± 0.39ab 45.42 ± 5.96ab 3.89 ± 0.96b 2.71 ± 0.27ab 1.89 ± 0.86ab 5.89 ± 0.56ab Note. aP < 0.05 compared to group H. bP < 0.05 compared to group B. DEHP, di-(2-ethylhexyl) phthalate; ALT, alanine aminotransferase; AST, aspartic transaminase; BUN, urea nitrogen; CRE, Creatinine; TC, Serum total cholesterol; TG, Triglyceride; HDL, High-density lipoprotein; GLU, Blood glucose. Table 3. Potential biomarkers identified by UPLC/Q-TOF-MS in cation mode
Retention
time (min)Measured m/z
ion (Da)Calculated m/z
ion (Da)Mass error (PPM) Elemental
compositionPostulated
identityScan mode VIP 5.87 285.0896 285.0974 27 C13H16O7 p-Cresol glucuronidea + 1.4670 7.99 285.0833 285.0835 0 C10H12N4O6 Xanthosinea + 7.8901 5.47 194.0827 194.0817 5 C10H11NO3 Methylhippuric acidb + 4.0240 10.38 496.3418 496.3403 3 C24H50NO7P LysoPC (16:0)a + 5.4966 6.57 217.1473 217.1440 15 C11H20O4 Undecanedioic acidb + 1.3758 2.26 153.0699 153.0664 22 C7H8N2O2 N1-Methyl-2-pyridone-5-carboxamideb + 2.6001 10.15 520.3411 520.3403 1 C26H50NO7P LysoPC [18:2 (9Z, 12Z)]a + 4.0616 9.15 189.1245 189.1239 3 C8H16N2O3 N6-Acetyl-L-lysineb + 4.8213 Note. aPresents ions that were identified by comparison to the standards. bBiomarkers identified by the Human Metabolome Database (HMDB) and confirmed using exact mass data and MS fragmentation. DEHP, di-(2-ethylhexyl) phthalate; UPLC/Q-TOF-MS, ultra-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry; cPA, cyclic phosphatidic acid; VIP, variable importance in projection. Table 4. Biomarkers detected in rat urine using positive ESI mode (mean ± SD, n = 8)
Groups Xanthosine p-Cresol glucuronide Undecanedioic acid N1-Methyl-2-pyridone-5-carboxamide LysoPC (16:0) LysoPC
[18:2 (9Z, 12Z)]Methylhippuric acid N6-Acetyl-L-lysine H 0.18 ± 0.15 0.17 ± 0.06 0.27 ± 0.34 18.68 ± 3.43 22.31 ± 5.51 13.04 ± 2.30 21.06 ± 3.46 0.14 ± 0.08 A 31.54 ± 10.84ac 0.79 ± 0.28a 1.62 ± 0.39bc 12.95 ± 4.42a 4.54 ± 7.19a 0.90 ± 1.49ac 7.58 ± 2.95bc 4.32 ± 2.63ac B 0.36 ± 0.14a 0.05 ± 0.11a 0.79 ± 0.13a 10.32 ± 1.15a 21.05 ± 2.64 8.17 ± 2.26a 13.21 ± 2.10a 0.26 ± 0.08 D 19.10 ± 6.56bd 2.29 ± 1.78ac 0.44 ± 0.08c 13.46 ± 1.79ac 13.92 ± 5.46c 1.23 ± 1.52ac 18.29 ± 1.91c 2.18 ± 1.40c Note. aP < 0.05 compared to group H. bP < 0.01 compared to group H. cP < 0.05 compared to group B. dP < 0.01 compared to group B. ESI: electrospray ionization. -
[1] Kato K, Silva MJ, Reidy JA, et al. Mono(2-ethyl-5-hydroxyhexyl) phthalate and mono-(2-ethyl-5-oxohexyl) phthalate as biomarkers for human exposure assessment to di-(2-ethylhexyl) phthalate. Environ Health Perspect, 2004; 112, 327−30. doi: 10.1289/ehp.6663 [2] Koch HM, Rossbach B, Drexler H, et al. Internal exposure of the general population to DEHP and other phthalates--determination of secondary and primary phthalate monoester metabolites in urine. Environ Res, 2003; 93, 177−85. doi: 10.1016/S0013-9351(03)00083-5 [3] Fay M, Donohue JM, De Rosa C. ATSDR evaluation of health effects of chemicals. VI. Di(2-ethylhexyl)phthalate. Agency for Toxic Substances and Disease Registry. Toxicol Ind Healt, 1999; 15, 651−746. doi: 10.1177/074823379901500801 [4] Petersen JH, Breindahl T. Plasticizers in total diet samples, baby food and infant formulae. Food Addit Contam, 2000; 17, 133−41. doi: 10.1080/026520300283487 [5] Fromme H, Gruber L, Schlummer M, et al. Intake of phthalates and di(2-ethylhexyl)adipate: results of the Integrated Exposure Assessment Survey based on duplicate diet samples and biomonitoring data. Environ Int, 2007; 33, 1012−20. doi: 10.1016/j.envint.2007.05.006 [6] Halden RU. Plastics and health risks. Annu Rev Public Health, 2010; 31, 179−94. doi: 10.1146/annurev.publhealth.012809.103714 [7] Hinton RH, Mitchell FE, Mann A, et al. Effects of phthalic acid esters on the liver and thyroid. Environ Health Perspect, 1986; 70, 195−210. doi: 10.1289/ehp.8670195 [8] Hsu PC, Kuo YT, Leon Guo Y, et al. The adverse effects of low-dose exposure to Di(2-ethylhexyl) phthalate during adolescence on sperm function in adult rats. Environ Toxicol, 2016; 31, 706−12. doi: 10.1002/tox.22083 [9] Silva MJ, Barr DB, Reidy JA, et al. Urinary levels of seven phthalate metabolites in the U.S. population from the National Health and Nutrition Examination Survey (NHANES) 1999-2000. Environ Health Perspect, 2004; 112, 331−8. doi: 10.1289/ehp.6723 [10] Watanabe S, Uesugi S, Kikuchi Y. Isoflavones for prevention of cancer, cardiovascular diseases, gynecological problems and possible immune potentiation. Biomed Pharmacother, 2002; 56, 302−12. doi: 10.1016/S0753-3322(02)00182-8 [11] Hsieh HM, Wu WM, Hu ML. Soy isoflavones attenuate oxidative stress and improve parameters related to aging and Alzheimer's disease in C57BL/6J mice treated with D-galactose. Food Chem Toxicol, 2009; 47, 625−32. doi: 10.1016/j.fct.2008.12.026 [12] Rimbach G, Boesch-Saadatmandi C, Frank J, et al. Dietary isoflavones in the prevention of cardiovascular disease--a molecular perspective. Food Chem Toxicol, 2008; 46, 1308−19. doi: 10.1016/j.fct.2007.06.029 [13] Malencic D, Maksimovic Z, Popovic M, et al. Polyphenol contents and antioxidant activity of soybean seed extracts. Bioresour Technol, 2008; 99, 6688−91. doi: 10.1016/j.biortech.2007.11.040 [14] Occhiuto F, Zangla G, Samperi S, et al. The phytoestrogenic isoflavones from Trifolium pratense L. (Red clover) protects human cortical neurons from glutamate toxicity. Phytomedicine, 2008; 15, 676−82. doi: 10.1016/j.phymed.2008.04.007 [15] Cederroth CR, Nef S. Soy, phytoestrogens and metabolism: A review. Mol Cell Endocrinol, 2009; 304, 30−42. doi: 10.1016/j.mce.2009.02.027 [16] Mumford SL, Kim S, Chen Z, et al. Urinary Phytoestrogens Are Associated with Subtle Indicators of Semen Quality among Male Partners of Couples Desiring Pregnancy. J Nutr, 2015; 145, 2535−41. doi: 10.3945/jn.115.214973 [17] Medigovic IM, Zivanovic JB, Ajdzanovic VZ, et al. Effects of soy phytoestrogens on pituitary-ovarian function in middle-aged female rats. Endocrine, 2015; 50, 764−76. doi: 10.1007/s12020-015-0691-x [18] Luo T, Snyder SM, Zhao B, et al. Gene Expression Patterns Are Altered in Athymic Mice and Metabolic Syndrome Factors Are Reduced in C57BL/6J Mice Fed High-Fat Diets Supplemented with Soy Isoflavones. J Agric Food Chem, 2016; 64, 7492−501. doi: 10.1021/acs.jafc.6b03401 [19] Silva P, Ribeiro TA, Tofolo LP, et al. Treatment with soy isoflavones during early adulthood improves metabolism in early postnatally overfed rats. Nutr Neurosci, 2018; 21, 25−32. doi: 10.1080/1028415X.2016.1213007 [20] Lenz EM, Wilson ID. Analytical strategies in metabonomics. J Proteome Res, 2007; 6, 443−58. doi: 10.1021/pr0605217 [21] Nicholson JK, Lindon JC, Holmes E. 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica, 1999; 29, 1181−9. doi: 10.1080/004982599238047 [22] Zhang J, Yan L, Tian M, et al. The metabonomics of combined dietary exposure to phthalates and polychlorinated biphenyls in mice. J Pharm Biomed Anal, 2012; 66, 287−97. doi: 10.1016/j.jpba.2012.03.045 [23] Want EJ, Wilson ID, Gika H, et al. Global metabolic profiling procedures for urine using UPLC-MS. Nat Protoc, 2010; 5, 1005−18. doi: 10.1038/nprot.2010.50 [24] Tepavcevic V, Atanackovic M, Miladinovic J, et al. Isoflavone composition, total polyphenolic content, and antioxidant activity in soybeans of different origin. J Med Food, 2010; 13, 657−64. doi: 10.1089/jmf.2009.0050 [25] Engel N, Lisec J, Piechulla B, et al. Metabolic profiling reveals sphingosine-1-phosphate kinase 2 and lyase as key targets of (phyto-) estrogen action in the breast cancer cell line MCF-7 and not in MCF-12A. PLoS One, 2012; 7, e47833. doi: 10.1371/journal.pone.0047833 [26] Zhang X, Choi FF, Zhou Y, et al. Metabolite profiling of plasma and urine from rats with TNBS-induced acute colitis using UPLC-ESI-QTOF-MS-based metabonomics--a pilot study. FEBS J, 2012; 279, 2322−38. doi: 10.1111/j.1742-4658.2012.08612.x [27] Carpenter CP, Weil CS, Smyth HF, et al. Chronic oral toxicity of di-(2-ethylhexyl) phthalate of rats, guinea pigs, and dogs. AMA Arch Ind Hyg Occup Med, 1953; 8, 219−26. [28] Dong X, Zhang Y, Dong J, et al. Urinary metabolomic profiling in rats exposed to dietary di(2-ethylhexyl) phthalate (DEHP) using ultra-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UPLC/Q-TOF-MS). Environ Sci Pollut Res Int, 2017; 24, 16659−72. doi: 10.1007/s11356-017-9091-5 [29] Huang C, Qiao X, Dong B. Neonatal exposure to genistein ameliorates high-fat diet-induced non-alcoholic steatohepatitis in rats. Br J Nutr, 2011; 106, 105−13. doi: 10.1017/S0007114510005799 [30] Sakai T. Studies on the evaluation of exposure to industrial chemicals. Sangyo Eiseigaku Zasshi, 1996; 38, 119−37. [31] Manini P, Andreoli R, Niessen W. Liquid chromatography-mass spectrometry in occupational toxicology: a novel approach to the study of biotransformation of industrial chemicals. J Chromatogr A, 2004; 1058, 21−37. doi: 10.1016/S0021-9673(04)01312-3 [32] Zhong H, Liu H, Jiang Z. Genistein Ameliorates Fat Accumulation Through AMPK Activation in Fatty Acid-Induced BRL Cells. J Food Sci, 2017; 82, 2719−25. doi: 10.1111/1750-3841.13856 [33] Schug TT, Janesick A, Blumberg B, et al. Endocrine disrupting chemicals and disease susceptibility. J Steroid Biochem Mol Biol, 2011; 127, 204−15. doi: 10.1016/j.jsbmb.2011.08.007 [34] Schaedlich K, Gebauer S, Hunger L, et al. DEHP deregulates adipokine levels and impairs fatty acid storage in human SGBS-adipocytes. Sci Rep, 2018; 8, 3447. doi: 10.1038/s41598-018-21800-4 [35] Jin Z, Bian F, Tomcik K, et al. Compartmentation of Metabolism of the C12-, C9-, and C5-n-dicarboxylates in Rat Liver, Investigated by Mass Isotopomer Analysis: ANAPLEROSIS FROM DODECANEDIOATE. J Biol Chem, 2015; 290, 18671−7. doi: 10.1074/jbc.M115.651737 [36] Salinari S, Bertuzzi A, Gandolfi A, et al. Dodecanedioic acid overcomes metabolic inflexibility in type 2 diabetic subjects. Am J Physiol Endocrinol Metab, 2006; 291, E1051−8. doi: 10.1152/ajpendo.00631.2005 [37] Wang SY, Wang Y, Jin XW, et al. A urinary metabolomics study of rats after the exposure to acrylamide by ultra performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry. Mol Biosyst, 2015; 11, 1146−55. doi: 10.1039/C4MB00682H [38] Mortensen PB. Formation and degradation of dicarboxylic acids in relation to alterations in fatty acid oxidation in rats. Biochim Biophys Acta, 1992; 1124, 71−9. doi: 10.1016/0005-2760(92)90128-I [39] Rusyn I, Peters JM, Cunningham ML. Modes of action and species-specific effects of di-(2-ethylhexyl)phthalate in the liver. Crit Rev Toxicol, 2006; 36, 459−79. doi: 10.1080/10408440600779065 [40] Hakkak R, Gauss CH, Bell A, et al. Short-Term Soy Protein Isolate Feeding Prevents Liver Steatosis and Reduces Serum ALT and AST Levels in Obese Female Zucker Rats. Biomedicines, 2018; 6, 55. doi: 10.3390/biomedicines6020055 [41] Lee JH, Hwang CE, Cho EJ, et al. Improvement of nutritional components and in vitro antioxidative properties of soy-powder yogurts using Lactobacillus plantarum. J Food Drug Anal, 2018; 26, 1054−65. doi: 10.1016/j.jfda.2017.12.003 [42] Crane-Robinson C, Hebbes TR, Clayton AL, et al. Chromosomal mapping of core histone acetylation by immunoselection. Methods, 1997; 12, 48−56. doi: 10.1006/meth.1997.0446 [43] Viswanathan MP, Mullainadhan V, Chinnaiyan M, et al. Effects of DEHP and its metabolite MEHP on insulin signalling and proteins involved in GLUT4 translocation in cultured L6 myotubes. Toxicology, 2017; 386, 60−71. doi: 10.1016/j.tox.2017.05.005 [44] Boulange CL, Claus SP, Chou CJ, et al. Early metabolic adaptation in C57BL/6 mice resistant to high fat diet induced weight gain involves an activation of mitochondrial oxidative pathways. J Proteome Res, 2013; 12, 1956−68. doi: 10.1021/pr400051s [45] Rutkowski B, Slominska E, Szolkiewicz M, et al. N-methyl-2-pyridone-5-carboxamide: a novel uremic toxin? Kidney Int Suppl, 2003; 63, 19−21. [46] Pelantova H, Buganova M, Holubova M, et al. Urinary metabolomic profiling in mice with diet-induced obesity and type 2 diabetes mellitus after treatment with metformin, vildagliptin and their combination. Mol Cell Endocrinol, 2016; 431, 88−100. doi: 10.1016/j.mce.2016.05.003 [47] Lesaffer G, De Smet R, Belpaire FM, et al. Urinary excretion of the uraemic toxin p-cresol in the rat: contribution of glucuronidation to its metabolization. Nephrol Dial Transplant, 2003; 18, 1299−306. doi: 10.1093/ndt/gfg107 [48] Liabeuf S, Glorieux G, Lenglet A, et al. Does p-cresylglucuronide have the same impact on mortality as other protein-bound uremic toxins? PLoS One, 2013; 8, e67168. doi: 10.1371/journal.pone.0067168 [49] Jing Z, Wei-Jie Y. Effects of soy protein containing isoflavones in patients with chronic kidney disease: A systematic review and meta-analysis. Clin Nutr, 2016; 35, 117−24. doi: 10.1016/j.clnu.2015.03.012