Metabonomic analysis of toxic action of long-term low-level exposure to acrylamide in rat serum

Abstract

This study assessed the effects of long-term, low-dose acrylamide (AA) administration in rats using ultra-performance liquid chromatography–mass spectrometry. Forty male Wistar rats were randomly divided into the following four groups: control, low-dose AA (0.2 mg/kg BW), middle-dose AA (1 mg/kg BW), and high-dose AA (5 mg/kg BW). AA was administered to rats via drinking water ad libitum. After 16-week treatment, rat serum was collected for metabonomic analysis. Biochemical tests were further conducted to verify metabolic alterations. Eleven metabolites were identified with significant changes in intensities (increased or reduced) as a result of treatment. These metabolites included citric acid, pantothenic acid, isobutyryl-l-carnitine, eicosapentaenoic acid, docosahexaenoic acid, sphingosine 1-phosphate, LysoPC(20:4), LysoPC(22:6), LysoPE(20:3), undecanedioic acid, and dodecanedioic acid. Results indicate that chronic exposure to AA at no observed adverse effect level does not exert a toxic effect on rats at the body metabolism level. AA disturbed the metabolism of lipids and energy, affected the nervous system of rats, and induced oxidative stress and liver dysfunction.

Keywords

Acrylamide rat serum metabonomics NOAEL toxicity

Introduction

Acrylamide (AA), a water-soluble chemical, has been universally used in different chemical and industrial processes and has received considerable attention as an occupational hazard for decades.^1,2 In addition to industrial and laboratory uses, AA is formed when carbohydrate-rich foods are cooked in high temperature, including French fries, potato chips, and bread.^3,4 In daily life, exposure to AA can originate from the intake of contaminated food and water. However, limited data are available regarding the consequences of long-term, low-level AA exposure on health at relevant doses. Therefore, AA health risk has become an increasing concern.

The toxicity of AA has been extensively investigated and has received considerable attention. AA is shown to be a reproductive toxicant, germ-cell mutagen, and carcinogen in rodents.^5

–9 The neurotoxic properties of AA have been studied in humans from occupational exposure and in laboratory animals.^10
–12 Despite AA-induced toxicity has been thoroughly studied, the mechanism underlying the toxicity is the subject of debate. Thus, modern technique has raised considerable attention for studying the toxic effects and mechanism of long-term, low-dose AA exposure.

Metabonomics is defined as “the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification.”¹³ Owing to its high resolution, high sensitivity, and high efficiency, ultra-performance liquid chromatography coupled with mass spectrometry (UPLC-MS) is used widely in metabonomics to investigate subtle metabolite alterations. Metabonomics has shown considerable potential in identifying of biomarkers, diagnosis, and toxicological mechanisms.^14
–16 It focuses on the analysis of low-molecular weight metabolites in biofluids, such as urine or blood, which represent the functional phenotype in a system under a specified set of conditions emphasizing the whole system rather than individual parts. According to the recent literature, only a few studies have reported the toxic effects of AA in rat urine.^17,18 However, AA-induced alterations of serum metabolites in rats are currently not reported. The combined study on urine and serum is conducive to the systematic understanding of the toxic action of AA.

In recent years, the strategy of metabonomic integration has become an important development trend. The integration includes not only various techniques and methods but also biological samples (blood, urine, etc.) from different sources. Metabonomic analysis, data comparison, and comprehensive evaluation of biological samples (urine, blood, etc.) from diverse sources in the body can make the metabonomic results more complete and accurate by integrated metabonomics.¹⁹

No observed adverse effect level (NOAEL) is defined as “the highest exposure level at which there are no statistically or biologically significant increases in the frequency or severity of adverse effect between exposed and control groups.”²⁰ In our previous study, the toxic effects of long-term exposure to low-level AA on rat urine were investigated using UPLC-MS. The results indicate that chronic exposure to AA at NOAEL does not exert a toxic effect on rats at the body metabolism level. Furthermore, AA can disrupt lipid and amino acid metabolisms and induce oxidative stress, neurotoxicity, and liver and kidney dysfunction in rats.²¹ In the present study, the metabolic profile of rat serum was analyzed to investigate if chronic exposure to AA at NOAEL could induce toxicity at the body metabolism level. Furthermore, the potential exposure biomarkers, metabolic pathways, and mechanisms of AA toxicity were investigated. The combination of data from urine and serum can give a comprehensive overview of the effect of AA in vivo.

Materials and methods

Chemicals and reagents

AA (99.8% purity) and leucine enkephalin were supplied by Sigma-Aldrich (St. Louis, Missouri, USA). High Performance Liquid Chromatography (HPLC)-grade methanol and acetonitrile were obtained from Dikma Science and Technology, Co. Ltd (Los Angeles, California, USA). Standards of isobutyryl-l-carnitine (97% purity), citric acid (99.5% purity), undecanedioic acid (97% purity), and dodecanedioic acid (99% purity) were purchased from Sigma-Aldrich (Germany). Standard of pantothenic acid (PA; 99% purity) was obtained from ABBLIS Chemicals LLC (Houston, Texas, USA). Standards of sphingosine 1-phosphate (S1P; 98% purity), eicosapentaenoic acid (EPA; 98% purity), and docosahexaenoic acid (DHA; 98% purity) were purchased from Cayman Chemical (Ann Arbor, Michigan, USA). Distilled water was filtered using a Milli-Q Ultrapure water system (Millipore, Billerica, Massachusetts, USA). The assay kits for aspartate aminotransferase (AST), alanine aminotransferase (ALT), high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglyceride (TG), and total cholesterol (TCHO) were purchased from Wako Pure Chemical Industries, Ltd. (Nagoya, Japan), and kit for alkaline phosphatase (ALP) was from Sichuan Maker Biotechnology Co., Ltd. (Chengdu, China).

Animal treatment

All rats were handled in accordance with the standard guide for the care and use of laboratory animals. The study protocol was approved by the Animal Ethical Committee of Harbin Medical University (HMUPHIRB2015006).

The experiments were performed on male Wistar rats weighing 180–220 g. A total of forty 8-week old rats were supplied by Vital River Laboratory Animal Technology Co. Ltd. (Beijing, China). The rats were housed individually in metabolic cages and acclimatized to new surroundings for a week under controlled humidity (50–60%) and temperature (22 ± 2°C) with a 12-h light–dark cycle. AIN-93M diets and drinking water were available ad libitum.

After acclimatization, the rats were randomly distributed according to body weight basis to four groups (10 animals per group): low-dose group (A1, 0.2 mg/kg BW, NOAEL), middle-dose group (A2, 1 mg/kg BW, 5 times the NOAEL), high-dose group (A3, 5 mg/kg BW, 25 times the NOAEL), and control group (C). The NOAEL of AA for a noncarcinogenic end point was 0.2mg/kg BW per day was noted by the Joint FAO/WHO Expert Committee on Food Additives (JECFA) at its 64th meeting. This end point was based on the induction of morphological nerve changes in rats following administration of AA in drinking water. AA was administered to rats continually for 16 weeks via drinking water ad libitum. For the body weight of rats that grew fast in the preceding period, the amount of AA in the water was adjusted twice a week for the first 8 weeks, and then once a week after 8 weeks (body weight of rat were measured weekly.) until the end of the experiment. Daily food and water consumption of each rat was recorded during the study. At each time point, no significant difference was observed for water consumption among all groups (p > 0.05).

Sample collection

All of the animals survived until the end of the treatment period. Rats were anesthetized with chloral hydrate via intraperitoneal injection. Blood samples were collected from the abdominal aorta with pro-coagulation tubes. Serum was obtained by centrifugation at 936 × g for 15 min after the blood samples clotted and then divided into two aliquots. The serum samples from one aliquot were immediately analyzed using a Hitachi 7100 automated biochemical analyzer (Hitachi Co. Japan) to test for ALP, AST, ALT, HDL, LDL, TG, and TCHO. Another aliquot of the serum samples was stored at −80°C until use for metabonomic analysis.

Sample preparation

Before UPLC-MS analysis, the protein of serum precipitated by adding 450 µL methanol to 150 µL of serum in a tube followed by vortex shaking for 2 min at room temperature and centrifuged at 13,201 × g for 10 min at 4°C to remove any precipitate. The obtained supernatant was then transferred to a clean tube and was dried using a Bath Nitrogen Blow Instrument (TTL-DCI, Beijing, China), reconstituted in a 600 µL acetonitrile and water mixture (2:1, v/v). The mixture was vortexed for 2 min and then centrifuged at 13,201 × g for 10 min. The obtained supernatant was used for the UPLC-MS analysis. In parallel, a quality control (QC) sample was prepared by mixing equal volumes of different individual serum samples (a total of 40).

Chromatography

Chromatographic separation was conducted by a UPLC BEH C18 column (100 mm × 2.1 mm, i.d. 1.8 μm, Waters Corporation, Milford, Massachusetts, USA) using a Waters ACQUITY UPLC System (Waters Corporation). The temperatures of the autosampler and column were preserved at 4°C and 35°C, respectively. A 2-μL aliquot of each sample was injected onto the column. The pooled QC samples were injected every 10 samples to further monitor the stability of the analysis. The UPLC mobile phase consisted of 0.1% formic acid in water (solvent A) and acetonitrile (solvent B). The gradient duration was 16 min at a constant flow rate of 0.35 mL/min. The optimized UPLC elution conditions were: 0–0.5 min, 2% B; 0.5–1.5 min, 2–20% B; 1.5–6 min, 20–70% B; 6–10.0 min, 70–98% B; 10–12 min, 98% B; 12–14 min, 98–70% B; and 14–16 min, 70–2% B.

Mass spectrometry

MS analysis was performed using a Waters Micromass Q-TOF (Quatropde Time-of-Flight) micro Mass Spectrometer (Manchester, UK) with electrospray ionization in positive and negative modes and a full-scan mode from m/z 50–1000. The capillary voltage was set to 3.0 kV in positive-ion mode and at 2.8 kV in negative-ion mode, and the cone voltage was set to 35 V. Nitrogen served as both the desolvation gas (650 L/h) and cone gas (50 L/h). The desolvation and source temperatures were set at 300°C and 100°C, respectively. A lock spray was used to ensure the accuracy and reproducibility of all analyses. A lock mass of leucine enkephalin was used via a lock spray interface at a flow rate of 10 μL/min for monitoring in the positive ([M+H]⁺ = 556.2771) and negative ion modes ([M−H]⁻ = 554.2615). The lock spray frequency was set at 0.48 s and the lock mass data were averaged over 10 scans for correction.

Data processing and metabolite identification

The raw data were imported into MarkerLynx software (which is incorporated in the MassLynx software; version 4.1; SCN 714; Waters Corporation). The MarkerLynx ApexTrack peak integration was used for peak detection and alignment. The parameters of the data processing method were as follows: Retention Time (RT) window, 0.2 min; mass window, 0.05 Da; and mass tolerance, 10 mDa. The high and low mass ranges were set at 1000 Da and 50 Da, respectively, and the initial and final RTs to 0.5 min and 14 min, respectively, minimum intensity, 80; noise elimination level, 6.0; deisotope data, Yes.

The three-dimensional data including peak number (RT-m/z pair), sample name, and normalized peak areas were exported to the EZinfo statistical analysis software (version 2.0; Umetrics AB, Umeå, Sweden) for multivariate statistical analysis. The data were mean centered and Pareto scaled before multivariate statistical analysis. Principal component analysis (PCA) was first used for all samples to evaluate..... its quality and reveals similarities between observations, tendencies, and segregative samples. After an initial overview of the data, the partial least-squares discriminant analysis (PLS-DA) was conducted. The PLS method was used for the analysis of time changes. To avoid the overfitting of supervised PLS-DA models, a default sevenfold cross-validation procedure and testing with 100 random permutations were performed using SIMCA-P software (version 12.0; Umetrics AB, Umeå, Sweden). In order to make its contribution to the classification of samples clear, the Variable Importance in Projection (VIP) values of each variable in the model have been calculated. The metabolites with a VIP value of >1.0 would be selected between different groups. Based on the relative intensities of the metabolites from the normalized spectrum, analysis of variance (ANOVA) was used to reveal the significant differences (p < 0.05) of identified metabolites between the control group and treated group. The structure of metabolite was elucidated by tandem MS/MS fragmentation with collision energy ranging from 15 eV to 35 eV.

For identifying the potential biomarkers, the ion spectrum of potential biomarkers was matched with the structure message of metabolites acquired from the Human Metabolome Database or ChemSpider with the Mass Fragment software (MassLynx v4.1, Waters Corporation) by way of chemically intelligent peak-matching algorithms. The identification of potential biomarkers was achieved by comparison with free online databases, such as ChemSpider (http://www.chemspider.com), the Human Metabolome Database (HMDB) (http://www.hmdb.ca), Scripps Center for Metabonomic (http://metlin.scripps.edu), and LIPID MAPS (http://www.lipidmaps.org), using exact mass and MS/MS spectra. Finally, the biomarkers were further confirmed by standard compounds based on both retention times and MS/MS spectra.

The implicated pathways of biomarkers were interpreted using databases, including HMDB (http://www.hmdb.ca) and KEGG (http://www.kegg.com).

Statistical analysis

Statistical analysis was performed by analysis of covariance and one-way ANOVA using SPSS (version 21.0; Beijing Stats Data Mining Co. Ltd., China) and p < 0.05 was considered to be statistically significant. Receiver operator characteristic (ROC) curve analysis, based on a logistic regression model, was performed using SPSS to determine the area under the curve (AUC) as a measure for comparing the predictive ability of important metabolites.

Results

Body weight

The body weight (BW) of rats at each time point is shown in Online Supplementary Table S1. Except for the significantly decreased BW in group A3 on the 16th week (p < 0.05), no significant changes were observed in the three AA-treated groups at any time point compared with the time-matched control group (p > 0.05).

Biochemical parameters in serum

Diverse biochemical indices were measured in the serum of the control and treatment rats at the end of the experiment (Table 1). The activity of ALP, AST, and ALT and the contents of TCHO, TG, and LDL were significantly increased in groups A2 and A3 compared with those in group C (p < 0.05 or p < 0.01). By contrast, HDL was significantly decreased in groups A2 (p < 0.05) and A3 (p < 0.01) compared with those of the control group. No significant changes were observed in the low-dose group compared with the control group (p > 0.05).

Table 1.

Effect of AA on selected clinical chemistry parameters.^a

Groups	ALP (U/L)	AST (U/L)	ALT (U/L)	TCHO (mmol/L)	TG (mmol/L)	LDL-C (mmol/L)	HDL-C (mmol/L)
C	55.50 ± 10.51	109.1 ± 20.21	45.00 ± 8.45	1.86 ± 0.26	0.61 ± 0.22	0.46 ± 0.08	1.53 ± 0.36
A1	60.30 ± 11.12	118.0 ± 19.77	52.70 ± 9.26	2.15 ± 0.34	0.82 ± 0.35	0.59 ± 0.16	1.36 ± 0.18
A2	66.80 ± 10.12^b	133.10 ± 25.58^b	58.60 ± 12.26^c	2.32 ± 0.55^b	0.99 ± 0.38^b	0.67 ± 0.19^c	1.21 ± 0.27^b
A3	71.70 ± 11.80^c	139.0 ± 31.39^c	69.70 ± 11.86^c	2.72 ± 0.61^c	1.12 ± 0.43^c	0.76 ± 0.19^c	1.07 ± 0.31^c

C: control group; A1: low-dose group; A2: middle-dose group; A3: high-dose group; AA: acrylamide. ALP: alkaline phosphatase; AST: aspartate aminotransferase; ALT: alanine aminotransferase; TCHO: total cholesterol; TG: triglyceride; LDL-C: low-density lipoprotein cholesterol; HDL-C: high-density lipoprotein cholesterol; ANOVA: analysis of variance.

^aValues expressed as mean ± SD (n = 10).

^bSignificantly different from control group at p < 0.05 (ANOVA).

^cSignificantly different from control group at p < 0.01 (ANOVA).

Metabolic profiling

Serum samples were analyzed by UPLC-QTOF-MS/MS in positive and negative modes. An original overview of the analytical run quality was gained by unsupervised PCA of the sample data set including the QC samples (Figure 1). The reproducibility and reliability of the UPLC-MS system was assessed by QC samples. The QC samples were clustered tightly in the middle of the score plot. Six ions extracted from the chromatographic peaks (m/z 212.3110, 240.1493, 311.0998, 361.1183, 446.6763, and 517.1604 in the negative ion mode) were selected for method reliability. The relative standard deviations (CV) of peak intensity, retention times, and m/z were calculated as 1.12–1.28%, 0.42–0.54% and 0.1–0.29%, respectively.

Figure 1.

PCA score plot of urine in the positive and negative modes. PCA score t [1] versus t [2] of data obtained from mean variables of serum collected from the control and treated groups. (a) The positive mode and (b) the negative mode. *C: control group; ▪A1: low-dose group; ♦A2: middle-dose group; ▴ A3: high-dose group; •QC: quality control. PCA: principal component analysis.

PLS-DA was further performed for more clear separation (Figure 2). The parameters of the PLS-DA model including the values of R2Y and Q2 (0.933, 0.904 in positive mode and 0.895, 0.852 in negative mode) were all more than 0.5, indicating that these models were appropriate for recognition analysis. Permutation test for PLS-DA in the negative and positive modes was performed to assess the risk that the current PLS-DA models would become spurious. All R2X and Q2 values to the left were lower than the original points to the right (Online Supplementary Figure S1). Furthermore, the results of CV-ANOVA suggest that the PLS-DA models are highly significant (Online Supplementary Tables S2 and S3). In the negative and positive modes, the data points groups A2 and A3 were clearly separated from those of the control group, and the cluster of each group was extremely close (Figure 2). However, overlaps between the low-dose and control groups were observed.

Figure 2.

PLS-DA score plot of serum in the positive and negative modes. PCA score t [1] versus t [2] of data obtained from mean variables of serum collected from the control and treated groups. (a) The positive mode and (b) the negative mode. *C: control group; ▪A1: low-dose group; ♦A2: middle-dose group; ▴A3: high-dose group; PLS-DA: partial least-squares discriminant analysis.

In total, 11 metabolites (four in the positive and seven in the negative mode) were identified based on the aforementioned methods (data processing and metabolite identification; Table 2). These metabolites included citric acid, PA, isobutyryl-l-carnitine, EPA, DHA, S1P, LysoPC(20:4), LysoPC(22:6), LysoPE(20:3), undecanedioic acid, and dodecanedioic acid. The metabolite intensity values are shown in Online Supplementary Tables S4 and S5. The intensities of nine biomarkers decreased significantly in groups A2 and A3 relative to those in group C (p < 0.05 or 0.01), which include PA, isobutyryl-l-carnitine, LysoPC(20:4), LysoPC(22:6), LysoPE(20:3), citric acid, EPA, DHA, and S1P. Meanwhile, the intensities of undecanedioic acid and dodecanedioic acid were significantly enhanced in groups A2 and A3 relative to those in group C (p < 0.01).

Table 2.

Potential biomarkers in positive and negative modes.

Retention time (min)	Measured mass (Da)	Calculated mass (Da)	Mass error (Da)	Elemental composition	Scan mode	Metabolite	Trend
2.12	220.124	220.1179	0.0061	C₉H₁₇NO₅	+	Pantothenic acid^a,b	↓
2.25	232.1659	232.1543	0.0116	C₁₁H₂₁NO₄	+	Isobutyryl-l-carnitine^a,b	↓
7.43	544.3396	544.3398	0.0002	C₂₈H₅₀NO₇P	+	LysoPC(20:4)^a	↓
6.76	568.3376	568.3398	0.0022	C₃₀H₅₀NO₇P	+	LysoPC(22:6)^a	↓
0.92	191.0202	191.0197	0.0005	C₆H₈O₇	−	Citric acid^a,b	↓
4.72	215.126	215.1289	0.0029	C₁₁H₂₀O₄	−	Undecanedioic acid^a,b	↑
4.79	229.1423	229.1445	0.0022	C₁₂H₂₂O₄	−	Dodecanedioic acid^a,b	↑
7.96	301.2192	301.2173	0.0019	C₂₀H₃₀O₂	−	Eicosapentaenoic acid^a,b	↓
8.80	327.232	327.233	0.001	C₂₂H₃₂O₂	−	Docosahexaenoic acid^a,b	↓
5.23	378.2442	378.2415	0.0027	C₁₈H₃₈NO₅P	−	Sphingosine 1-phosphate^a,b	↓
5.29	502.2923	502.2939	0.0016	C₂₅H₄₆NO₇P	−	LysoPE(20:3)^a	↓

↑ The intensity of the metabolite was significantly increased in the treated groups compared with the control group. ↓ The intensity of the metabolite was significantly decreased in the treated groups compared with the control group.

^aThe ions were confirmed by comparison to the metabolites databases.

^bThe ions were identified by comparison to standard substance.

ROC curve analysis was performed to evaluate the sensitivity and specificity of the metabolites. All AUC values for the 11 metabolites were above 0.7 at 95% confidence interval (Figure 3). These results increased sensitivity and specificity for all identified potential biomarkers.

Figure 3.

ROC analysis for discrimination of the AA-dosed and control groups for the 11 metabolites. ROC: receiver operator characteristic; AA: acrylamide.

Discussion

PCA and PLS-DA score plots (Figures 1 and 2) suggest that toxic effects of AA can be detected at the metabolic level. The 11 metabolites that significantly contributed to this discrimination were identified through metabonomic analysis. These metabolites were considered as potential biomarkers of AA-induced toxicity. Ten of the identified biomarkers were involved in lipid metabolism including fatty acid β-oxidation pathway, sphingolipid metabolism pathway, and glycerophospholipid metabolism pathway. Another pathway concerns the metabolism of energy as shown in Figure 4.

Figure 4.

The disturbed metabolism pathways in response to AA exposure. AA: acrylamide.

First, PA is essential for the synthesis of coenzyme A (CoA). Cellular CoA level and the ratio of free CoA to total CoA in the cell are controlled by PA.²² To date, PA deficiency may lead to a decline in CoA levels and then induces the selective inactivation and/or decreased synthesis of enzymes catalyzing major steps in fatty acid β-oxidation in peroxisomes.²³ In addition, PA is a necessary coenzyme for the synthesis of Glutataione (GSH) and has been shown to protect cells from oxidative damage.²⁴ Thus, the decrease in PA levels in this study led to a decline in GSH levels as confirmed in our previous study.²¹ Therefore, decrease in PA intensities indicated that exposure to AA can affect the fatty acid β-oxidation pathway and induce oxidative stress.

Isobutyryl-l-carnitine is a product of the acyl-CoA dehydrogenases which are a group of mitochondrial enzymes involved in the metabolism of fatty acid β-oxidation. The main function of l-carnitine in cellular energy metabolism is as a requisite in the transport of long-chain fatty acids to the inner mitochondrial membrane and the site of β-oxidation.²⁵ Its other functions include buffering mitochondrial acyl CoA/CoA ratio, oxidizing branched amino acids, and exerting protective effects on the endogenous antioxidant defense system.²⁶ Accordingly, isobutyryl-l-carnitine was significantly decreased in treated groups, indicating peroxisomal dysfunction and reduced β-oxidation.

Undecanedioic acid and dodecanedioic acid are dicarboxylic acids. Normally, these acids can be rapidly oxidized in the peroxisomes and then transferred to the mitochondria for further degradation, so they are rarely detected in the serum and urine. Dicarboxylic acids have been found in the urine of patients with ketosis,²⁷ uremia,²⁸ and carnitine deficiency.²⁹ Passi et al.³⁰ considered that dicarboxylic acids might be derived from long monocarboxylic acids through an initial ω-oxidation followed by β-oxidation. Thus, in the present study, these two metabolites were markedly increased in treated groups, showing that dicarboxylic acids are at least partly metabolized via β-oxidation.

Another main pathway is related to sphingolipid metabolism. S1P, carried mainly on HDL,³¹ is a lipid mediator with various biological effects.^32,33 As a large amount of S1P is carried on HDL in the circulation and HDL is produced mainly from the liver, a possibility remains that the liver has several roles for S1P metabolism. A previous study had found that plasma S1P concentration was reduced in chronic hepatitis C patients and that plasma S1P concentration decreased upon the development of carbon tetrachloride-induced liver fibrosis in rats.³² Therefore, the reduced S1P in the present study indicates hepatic dysfunction. A study on S1P-deficient mice has shown that S1P not only regulated the level of key sphingolipids but also revealed functional linkages between sphingolipid metabolism and metabolic pathways of other diverse lipids.³³ In the current study, AA reduced the S1P level, which may be correlated with sphingolipid metabolism. In addition, significant changes in routine liver function parameters (ALT, AST, and ALP; Table 1) in rat serum further supported the aforementioned metabonomic results.

Disturbance in glycerophospholipid metabolism leads to changes in intensity values of LysoPC(20:4), LysoPC(22:6), LysoPE(20:3), DHA, and EPA. LysoPC and LysoPE belong to lysophospholipids which are structural components of animal cell membranes. LysoPC and LysoPE were decreased after massive destruction of membranes, as indicated by cell death observed during the histological examination of the livers.³⁴ In the current study, AA reduced the LysoPC and LysoPE levels, which may be correlated with liver dysfunction and abnormal lipid metabolism. Additionally, routine lipid parameters (TG, TCHO, LDL, and HDL) and clinical parameters in serum such as ALT, AST, and ALP (the markers of liver damage) in the middle and high groups were statistically significantly changed compared with those in the control group. These changes in clinical parameters provided further support to the abovementioned results. DHA and EPA are two main omega-3 essential polyunsaturated fatty acids (PUFAs), which serve critical roles in cell function and response. The membrane phospholipids of the brain contain high levels of PUFAs, particularly DHA and EPA. The rapid accumulation of long-chain n-3 PUFA in the brain during prenatal and preweaning development suggests that the provision of n-3 fatty acids to the developing brain may be necessary for normal growth and functional development.^35,36 A double-blind randomized placebo-controlled study found that DHA or EPA supplementation can reduce behavioral problems in children with autism.³⁷ Therefore, decreased DHA and EPA may be a cause of AA-related neurotoxicity. This result shows that AA is a neurotoxicant, which is in agreement with LoPachin’s study.³⁸

Citric acid is derived from the tricarboxylic acid cycle (TCA) and an integral component of energy production. Citric acid metabolism contributes to energy production by providing a major alternative pathway for nicotinamide adenine dinucleotide (NAD⁺) regeneration and allowing acetyl phosphate to yield acetate/adenosine triphosphate.³⁹ Acetyl-CoA is the starting point for the TCA cycle, and obtained from various sources.⁴⁰ Citrate is converted from acetyl-CoA by citrate synthase. In this study, acetyl-CoA was impaired by glycerophospholipid metabolism, reduction in PUFAs, and fatty acid β-oxidation pathway. Disturbance in acetyl-CoA formation may lead to decreased citric acid levels. Thus, the metabolism of energy may be disturbed by AA. Moreover, it is reported that citric acid has been shown to protect the liver from lipid peroxidation by reducing polymorphonuclear cell degranulation and attenuating the release of myeloperoxidase.^41,42 Thus, the diminished citric acid in treated groups may have partially aggravated oxidative stress and liver damage caused by AA.

This study has one limitation that AA was administered to the rats via drinking water according to the actual situation of human exposure. This method is not as accurate as gavage, and the actual dose of AA may be affected due to the poor palatability. However, gavage also has some shortcomings, such as the ease to damage the esophagus and so on. In this study, there was no significant difference in water consumption between experimental groups and control group during the experiment (p > 0.05), indicating that the experimental results were not affected by the way of exposure.

Conclusions

Chronic exposure to AA at NOAEL does not exert a toxic effect on rats at the body metabolism level. Furthermore, AA can disrupt the metabolism of lipids and energy and induces oxidative stress, liver dysfunction, and neurotoxicity. Further studies are needed to measure the metabonomic profiles of tissues for a comprehensive understanding of the toxic effects of AA.

Supplemental material

Supplementary - Metabonomic analysis of toxic action of long-term low-level exposure to acrylamide in rat serum

Supplementary for Metabonomic analysis of toxic action of long-term low-level exposure to acrylamide in rat serum by C Cao, H Shi, M Zhang, L Bo, L Hu, S Li, S Chen, S Jia, YJ Liu, YL Liu, X Zhao, and L Zhang in Human & Experimental Toxicology

Footnotes

Acknowledgment

The authors gratefully acknowledge the support from the National Natural Science Foundation of China. They also acknowledge the support from the Laboratory of Nutrition and Food Hygiene in Harbin Medical University which is the key laboratory of Heilongjiang Province and Heilongjiang Higher Education Institutions.

Author’s contribution

Can Cao and Haidan Shi contributed equally to this work and should be considered as co-first authors.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is financially supported by the National Natural Science Foundation of China (81573157).

Supplemental material

Supplemental material for this article is available online.

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