Association of CYP2E1, GST and mEH genetic polymorphisms with urinary acrylamide metabolites in workers exposed to acrylamide

Toxicology Letters 203 (2011) 118–126

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Association of CYP2E1, GST and mEH genetic polymorphisms with urinary acrylamide metabolites in workers exposed to acrylamide Yu-Fang Huang a,b , Mei-Lien Chen a,∗∗ , Saou-Hsing Liou b , Ming-Feng Chen b , Shi-Nian Uang c , Kuen-Yuh Wu d,∗ a

Institute of Environmental and Occupational Health Sciences, National Yang Ming University, Taipei, Taiwan Division of Environmental Health and Occupational Medicine, National Health Research Institutes, Miaoli County, Taiwan Division of Analytical Chemistry, Institute of Occupational Safety and Health, Council of Labor Affairs, Executive Yuan, Taiwan d Department of Public Health and Institute of Occupational Medicine and Industrial Hygiene, National Taiwan University, College of Public Health, Taipei, Taiwan b c

a r t i c l e

i n f o

Article history: Received 11 August 2010 Received in revised form 7 March 2011 Accepted 7 March 2011 Available online 12 March 2011 Keywords: Acrylamide Genetic polymorphisms Cytochrome P450 2E1 Microsomal epoxide hydrolase Glutathione transferases

a b s t r a c t This study elucidates the association of acrylamide metabolites, N-acetyl-S-(2-carbamoylethyl)-cysteine (AAMA), N-acetyl-S-(1-carbamoyl-2-hydroxyethyl)-cysteine (GAMA2), and N-acetyl-S-(2-carbamoyl2-hydroxyethyl)-cysteine (GAMA3) in urine with genetic polymorphisms of the metabolic enzymes cytochrome P450 2E1 (CYP2E1), microsomal epoxide hydrolase (mEH) in exon 3 and exon 4, glutathione transferase theta (GSTT1) and mu (GSTM1), involved in the activation and detoxification of acrylamide (AA) in humans. Eighty-five workers were recruited, including 51 AA-exposed workers and 34 administrative staffs serve as controls. Personal air sampling was performed for the exposed workers. Each subject provided pre- and post-shift urine samples and blood samples. Urinary AAMA, GAMA2 and GAMA3 levels were simultaneously quantified using liquid chromatography-electronspray ionization/tandem mass spectrometry (LC-ESI–MS/MS). CYP2E1, mEH (in exon 3 and exon 4), GSTT1, and GSTM1 were analyzed using polymerase chain reaction (PCR). Our results reveal that AA personal exposures ranged from 4.37 × 10−3 to 113.61 ␮g/m3 with a mean at 15.36 ␮g/m3 . The AAMA, GAMA2, and GAMA3 levels in the exposed group significantly exceeded those in controls. The GAMAs (the sum of GAMA2 and GAMA3)/AAMA ratios, potentially reflecting the proportion of AA metabolized to glycidamide (GA), varied from 0.003 to 0.456, and indicate high inter-individual variability in the metabolism of AA to GA in this study population. Multivariate regression analysis demonstrates that GSTM1 genotypes significantly modify the excretion of urinary AAMA and the GAMAs/AAMA ratio, exon 4 of mEH was significantly associated with the urinary GAMAs levels after adjustment for AA exposures. These results suggest that mEH and/or GSTM1 may be associated with the formation of urinary AAMA and GAMAs. Further study may be needed to shed light on the role of both enzymes in AA metabolism. © 2011 Elsevier Ireland Ltd. All rights reserved.

1. Introduction Acrylamide (AA) is widely used in industries to manufacture numerous products, including adhesives, mining chemicals, fibers, pharmaceuticals, animal feed, paper sizing, molded parts, textiles, and coagulant aids (Smith and Oehme, 1991; NSC, 2006) and to prepare polyacrylamide gels for electrophoresis in biolog-

∗ Corresponding author at: Institute of Occupational Medicine and Industrial Hygiene, National Taiwan University, Rm 721, No. 17 Shiujou Rd., Taipei 100, Taiwan. Tel.: +886 2 33668091; fax: +886 2 33668267. ∗∗ Corresponding author at: Institute of Environmental and Occupational Health Sciences, National Yang Ming University, No. 155, Section 2, Li-Nong St., Beitou District, Taipei 112, Taiwan. Tel.: +886 2 28267239; fax: +886 2 28278254. E-mail addresses: [email protected] (M.-L. Chen), [email protected], [email protected] (K.-Y. Wu). 0378-4274/$ – see front matter © 2011 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.toxlet.2011.03.008

ical laboratories (IARC, 1994). The number of laboratory workers exposed to AA in preparing polyacrylamide gels was estimated at 100,000–200,000 (EPA, 1988). AA is also present in tobacco smoke (Smith et al., 2000) and reported present in foods processed at high temperatures (Tareke et al., 2002). AA caused neurotoxic, genotoxic, and reproductive effects to animals (Smith and Oehme, 1991; IARC, 1994), and neurotoxic effects to the exposed workers (Calleman et al., 1994; Hagmar et al., 2001; Kjuus et al., 2004). AA induced significantly increased incidences of thyroid follicular cell tumors, scrotal sac mesotheliomas, mammary gland fibroadenomas, and lung adenomas to rodents, depending on routes of treatment (Bull et al., 1984; Johnson et al., 1986; Friedman et al., 1995). With limited evidence in epidemiology studies, AA has been classified as a probable human carcinogen by International Agency for Research on Cancer (IARC) (IARC, 1994) and characterized as “likely to be carcinogenic to humans” (US EPA, 2010).

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Exposures to AA in workplaces and daily ingestion of AA from consumption of high-temperature processed foods have been of great concerns. After AA is absorbed, it can be metabolized by cytochrome P450 2E1 (CYP2E1) to glycidamide (GA) in animals and humans (Sumner et al., 1999; Ghanayem et al., 2005; Settels et al., 2008). GA is considered as the active metabolite of AA and may be responsible for its carcinogenicity (Doerge et al., 2005; Tareke et al., 2006). Both AA and GA can be detoxified by glutathione transferases (GSTs) to form glutathione conjugates and further metabolized to mercapturic acids (MAs), N-acetylS-(2-carbamoylethyl)-cysteine (AAMA), N-acetyl-S-(1-carbamoyl2-hydroxyethyl)-cysteine (GAMA2), and N-acetyl-S-(2-carbamoyl2-hydroxyethyl)-cysteine (GAMA3) (Sumner et al., 1992, 1997). AAMA can be oxidized to N-acetyl-S-(2-carbamoylethyl)-cysteineS-oxide (AAMA-sulfoxide) (Fennell et al., 2005, 2006; Kopp and Dekant, 2009). GA can also be detoxified by microsomal epoxide hydrolase (mEH) to glyceramide (Kirman et al., 2003). AA and GA can also interact with valine of hemoglobin (Hb) to form N-(2-carbamoylethyl)valine (AAVal) and N-(2-carbamoyl-2hydroxyethyl)valine (GAVal). AAVal and GAVal were measured in the blood of animals and humans and proposed to serve as biomarkers for longterm AA exposures (Fennell et al., 2005; Ghanayem et al., 2005; Bjellaas et al., 2007b; Chevolleau et al., 2007; Doroshyenko et al., 2009; Duale et al., 2009). Alternatively, urine samples are noninvasive and easy to collect in a field study. These urinary AA metabolites have been analyzed by using isotope-dilution liquid-chromatography coupled with tandem mass spectrometry (LC–MS/MS) methods and recommended as biomarkers for current AA exposures. For examples, urinary AAMA alone or with GAMA2 and GAMA3 were analyzed to serve as biomarkers of AA exposures for the general populations from tobacco smoke or the consumption of high-temperature processed foods (Bjellaas et al., 2005, 2007a; Boettcher and Angerer, 2005; Boettcher et al., 2005; Li et al., 2005; Fennell et al., 2006; Urban et al., 2006; Huang et al., 2007; Kopp et al., 2008). The ratios of GAMA3 to AAMA also served an important indicator for the proportion of absorbed AA metabolized to GA (Besaratinia and Pfeifer, 2005). In a well-control voluntary human study, urinary AAMA, GAMA3, and GAMA2 accounted for 51.7%, 4.6%, and 0.8% of the total ingested 1 mg deuterium-labeled AA within 46 h (Hartmann et al., 2009). The half-lives of AAMA, GAMA2, and GAMA3 were reported to be 11, 19, and 19 h, respectively (Hartmann et al., 2009). Since CYP2E1, GSTs and mEH are involved in AA metabolism, genetic polymorphisms of these enzymes may predetermine their metabolic activity, modify the formation of these MAs, and affect the susceptibility of individuals to AA exposure. A recent study reported that the GAVal/AAVal ratios for 43 subjects were related to polymorphic differences in exon 4 (His139Arg) of mEH, GSTM1, and GSTT1 (Duale et al., 2009). Another study on the effects of CYP2E1 inhibition or induction on the AA, AAMA, and GAMA3 toxicokinetics revealed that CYP2E1 probably accounts for one fourth of primary AA metabolism in humans. Furthermore, genetic polymorphisms of CYP2E1 had no significant effect on AA toxicokinetics; GSTP1, GSTM1, and GSTT1 enzymes were concluded to play no major role in the conjugation of AA and GA with glutathione (Doroshyenko et al., 2009). However, the association between urinary AA metabolites and the polymorphisms of AA metabolic genes, including CYP2E1, GSTT1, GSTM1, and mEH (in exon 3 of codons 113 and in exon 4 of codons 139) in humans has not been exploited. Therefore, the objective of this study was to investigate whether excretion of AAMA, GAMA2, and GAMA3 can be modified by genetic polymorphisms of CYP2E1, mEH (in exon 3 and exon 4), GSTT1, and GSTM1 among AA-exposed workers. With good quatitation of personal exposures, the interactions between environmental exposures and

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genetic polymorphisms of host genes in exposed workers can be better characterized. 2. Materials and methods 2.1. Chemicals Acrylamide (>99.9%) was purchased from Sigma (St. Louis, MO, USA); 13 C3 labeled AA (>99.0%) was obtained from Cambridge Isotopes (Cambridge, MA, USA); both GA (>98.0%) and 13 C3 -labeled GA (>98.0%) from Toronto Research Chemicals Inc. (North York, Ontario, Canada). All other solvents were HPLC grade and Milli-Q water was used throughout. The AAMA and 13 C3 -AAMA were previously synthesized and characterized in our laboratory (Li et al., 2005). The GAMA2, 13 C3 -GAMA2, GAMA3, and 13 C3 -GAMA3 were also synthesized according to the published procedures (Fennell et al., 2006). Characterization and quantification of our purified standards and 13 C3 -labeled standards were described in our previous publication (Huang et al., 2010). 2.2. Subjects and sample collection Fifty-one exposed workers involved in production and uses of AA and 34 controls consisting of administrative workers with no AA exposure history were recruited from 4 plants for this study. Each subject completed a self-report questionnaire and provided pre- and post-shift urine samples and blood samples in EDTA pre-treated tubes. Information regarding to age, gender, weight, height, smoking status, alcohol consumption, dietary status at one day before urine collection and history of occupational exposure was collected using questionnaires. This study was approved by the Ethics Committee of the National Health Research Institutes, Taiwan. All subjects provided written informed consent prior to their participation. 2.3. Personal monitoring of AA exposure and analysis Personal samples were collected to assess AA exposures for the 51 workers in a full work shift between October 2006 and August 2007. Details of the AA sampling and analysis were described in our previous report (Wu et al., 2010). Briefly, airborne AA was collected using a sampling train that consisted of a 13 mm glass fiber and a standard (75/150) silica gel tube and was connected to a personal sampling pump. AA was analyzed using isotope-dilution gas chromatography–mass spectrometry under selected ion monitoring (SIM) mode. 2.4. Urine collection and analysis of AAMA, GAMA2, and GAMA3 Post-shift urine samples were collected from each exposed worker on the same shift that personal samples and pre-shift urine samples were collected. The preand post-shift urine samples from the control group were collected on the same day that samples of exposed workers were collected. In total, 98 urine samples were collected from the exposed group, and 68 samples were collected from the control group. These urine samples were immediately aliquoted, sent back to our lab under 4 ◦ C, and stored at −20 ◦ C until used for analysis. Urinary AAMA, GAMA2, and GAMA3 were analyzed using our newly established LC-electron spray ionization (ESI)–MS/MS method (Huang et al., 2010) with minor modifications from a previous method (Fennell et al., 2006). Briefly, 70 ␮L of each urine sample was mixed with 70 ␮L of 1.6 ␮g/mL 13 C3 -GAMA2, 1.2 ␮g/mL 13 C3 GAMA3 and 4.8 ␮g/mL 13 C3 -AAMA as internal standards (ISs), rigorously vortexed, and centrifuged at 14,000 rpm for 5 min; supernatant was then transferred into an Eppendorf tube. After 70 ␮L water was added, the samples were vortexed again and centrifuged at 14,000 rpm for 5 min. The supernatant was used for analysis by using LC-ESI–MS/MS operated under positive ion and multiple reaction mode (MRM). The ion pairs monitored for quantitation were m/z 251 → 162 for GAMA2, m/z 254 → 162 for 13 C3 -GAMA2, m/z 251 → 123 for GAMA3, and m/z 254 → 123 for 13 C3 -GAMA3. The negative ion mode was operated for quantitation of AAMA by monitoring the ion pairs m/z 233 → 104 for AAMA and m/z 236 → 107 for 13 C3 AAMA. Detection limits were 10 ␮g/L, 20 ␮g/L, and 5 ␮g/L for GAMA2, GAMA3, and AAMA in urine, respectively. The accuracy was determined by repeated analysis of three calibration standard solutions. The accuracy range was 93.7–103.9% and the precision representing with relative standard deviation (RSD) was <8.9%. Creatinine levels were determined as the creatinine–picrate complex according to the method of Jaffe (1886) using an automated method. Urine samples with a creatinine level less than 0.3 g/L or greater than 3.0 g/L were excluded from further data analysis. Urinary GAMA2, GAMA3, and AAMA concentrations were adjusted with creatinine level and expressed as ␮g/g creatinine. 2.5. DNA extraction and genotyping Whole blood was collected from EDTA-coated tubes and centrifuged for 10 min to isolate the buffy coat. Genomic DNA was extracted from 100 ␮L of the buffy coat using the PuregeneTM DNA purification kit (Gentra Systems, Minneapolis, USA) following the manufacturer’s instructions. All polymerase chain reaction (PCR) amplications were performed with a MultiCycler programmable thermal cycler-200 (MJ Research Laboratories).

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Table 1 Characteristics and genotype distributions of the study population. AA-exposed (n = 51) Personal characteristics, mean ± SD Age (yrs) BMI (kg/m2 ) Working yeara (yrs), mean ± SD Gender,b n (%) Male Female Habitual status, n (%) Smokingb Yes Alcohol consumption b Yes Dietary status, n (%) Preferred fried-foodb Yes Intake of fried-food on the preceding dayb Yes Genotype distributions, n (%) CYP2E1c c1/c1 c1/c2 c2/c2 GSTM1b Positive Deficient GSTT1b Positive Deficient mEH, exon3b Tyr/Tyr Tyr/His His/His mEH, exon4b His/His His/Arg Arg/Arg mEH activityb Low Medium High

Non-exposed (n = 34)

p-Value

a

a b c *

40.1 ± 7.7 23.6 ± 2.9 13.7 ± 8.9

41.3 ± 8.5 23.1 ± 2.8 14.3 ± 7.6

0.500 0.432 0.479

50 (98.0) 1 (2.0)

19 (55.9) 15 (44.1)

<0.001*

27 (52.9)

5 (14.7)

<0.001*

15 (30.6)

8 (23.5)

0.408

22 (43.1)

22 (64.7)

0.076

29 (56.9)

19 (55.9)

0.552

27 (52.9) 22 (43.1) 2 (3.9)

17 (50.0) 15 (44.1) 2 (5.9)

0.902

27 (52.9) 24 (47.1)

17 (50) 17 (50)

23 (45.1) 28 (54.9)

15 (44.1) 19 (55.9)

43(84.3) 5(9.8) 3(5.9)

27(79.4) 0(0) 7(20.6)

44 (86.3) 7 (13.7) 0 (0)

28 (82.4) 4 (11.8) 2 (5.9)

5 (9.8) 42 (82.4) 4 (7.8)

7 (20.6) 23 (67.6) 4 (11.8)

0.827 0.929

0.02*

0.212

0.259

t-Test. Fisher’s exact test. 2 test. p < 0.05.

CYP2E1 genotyping was performed using a PCR-restriction fragment length polymorphism (RFLP) method, which was described by Stephens et al. (1994). Briefly, the primers were 5 -CCAGTCGAGTCTACATTGTCA-3 and 5 AGACCTCCACATTGACTAGC-3 . The PCR condition was initialized at 95 ◦ C for 5 min for denaturation, followed by 35 cycles of 32 s at 95 ◦ C and 35 s at 61 ◦ C for annealing, and 20 s at 72 ◦ C for extension, and a final elongation at 72 ◦ C for 5 min. The PCR product was separated electrophoretically on a 2.5% agarose gel (110 mV, 30 min) to generate a 552 base pair (bp) fragment. Each PCR product (12.2 ␮L) was then digested with10 units of Rsa I restriction enzymes in a 10× restriction buffer at 37 ◦ C for 16 h. The incubation product was separated electrophoretically on a 2.5% agarose gel (110 mV, 30 min). The presence of the Rsa I restriction site yielded two fragments (200 bp and 350 bp) of the 552 bp PCR product. Genotyping for mEH was performed using a PCR-RFLP method, as described by Smith and Harrison (1997). The mEH gene allele-specific method was used to detect the Tyr-His polymorphism at residue 113 in exon 3 and the HisArg polymorphism at residue 139 in exon 4. The primers for mEH in exon 3 were 5 -GATCGATAAGTTCCGTTTCACC-3 and 5 -ATCCTTAGTCTTGAAGTGAGGAT-3 . The primers for mEH in exon 4 were 5 -ACATCCACTTCATCCACGT-3 and 5 ATGCCTCTGAGAAGCCAT-3 . The exon 3 of mEH gene was amplified at 95 ◦ C for 5 min, followed by 35 cycles of 95 ◦ C for 10 s, 54.2 ◦ C for 50 s, 72 ◦ C for 40 s, and 72 ◦ C for 5 min. The PCR products (13.6 ␮L) were digested with 10 units of EcoR V restriction enzymes to generate fragments of 140 bp and 23 bp in the case of wildtype, and 163 bp in the case of mutant. The exon 4 of mEH gene was amplified at 95 ◦ C for 5 min and followed by 35 cycles of 95 ◦ C for 32 s, 61 ◦ C for 35 s, 72 ◦ C for 20 s, and 72 ◦ C for 5 min. The PCR products were digested with 10 units of Rsa I restriction enzymes to generate a fragment of 210 bp in the case of wild-type, and 163 bp and 47 bp in the case of mutant. Subjects were classified into three groups according to the expected mEH enzyme activity (Sarmanova et al., 2000). The carriers with combinations of His/His-His/His, His/His-His/Arg, Tyr/His-His/His, and His/His-Arg/Arg were classified as low mEH activity; combinations of Tyr/Tyr-

His/His, Tyr/His-His/Arg and Tyr/His-Arg/Arg were classified as medium activity, and combinations of Tyr/Tyr-Arg/Arg and Tyr/Tyr-His/Arg were classified as high activity. Genotyping for GSTM1 and GSTT1 were conducted by a multiplex PCR method (Arand et al., 1996). The ␤-globin gene was used as a positive control in the GSTM1 and GSTT1 assays to verify the presence of amplified DNA. The PCR mixtures (50 ␮L) consisted of 200 ng genomic DNA, 10× PCR buffer, 2.5 mM dNTPs, 50 ␮M of each GSTM1 primer (5 -GAACTCCCTGAAAAGCTAAAGC3 and 5 -GTTGGGCTCAAATATACGGTGG-3 ), 50 ␮M of each of GSTT1 primer and 5 -TTCCTTACTGGTCCTCACATCTC-3 ), (5 -TCACCGGATCATGGCCAGCA-3 50 ␮M of each of ␤-globin primer (5 -CAACTTCATCCACGTTCACC-3 and 5 GAAGAGCCAAGGACAGGTAC-3 ), and 2.5 units Taq DNA-polymerase in water. Both genes were denatured at 95 ◦ C for 5 min, amplified at 94 ◦ C for 30 s, 64.8 ◦ C for 30 s, and 72 ◦ C for 30 s for 35 cycles, and elongated at 72 ◦ C for 5 min. The amplification product of the positive-type GSTT1 gene fragment was 480 bp and that of the GSTM1 gene fragment was 215 bp. For quality control, 10% of the samples were genotyped twice and the consistency of results was 100%. 2.6. Statistical analysis Because the distributions of the concentrations of three urinary metabolites were skewed to the right, their concentrations were log-transformed for further statistical analyses. Student’s t test was applied to evaluate the differences between exposed workers and controls in age, body mass index (BMI), working years, and the levels of the three urinary metabolites. The 2 test or Fisher’s exact test was performed to testify the differences in habitual and dietary status and to examine the distributions of subjects with the polymorphic genotypes between these two groups. Correlations between AA exposures and the post-shift urinary AAMA or GAMAs (the summations of GAMA2 and GAMA3) levels and the association between the AAMA and GAMAs levels were evaluated with the Spearman correla-

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Fig. 1. Representative LC–MS/MS chromatograms generated from analysis of AA metabolites in a urine sample collected from an AA-exposed worker. Urinary GAMA2, GAMA3, AAMA and internal standards were quantitated under the MRM (A) GAMA2: 239 ng/ml, (B) 13 C3 -GAMA2: 400 ng/ml, (C) GAMA3: 1345 ng/ml, (D) 13 C3 -GAMA3: 289 ng/ml, (E) AAMA: 51037 ng/ml and (F) 13 C3 -AAMA: 1200 ng/ml. The double peaks at 6.9 and 7.4 min in (A) and (C) might possibly be the sulfoxide analogue of AAMA. tion coefficient. The Kruskal–Wallis test was used to testify the differences between polymorphic genotypes with regard to airborne AA levels and urinary metabolites levels. Linear regression was used to evaluate the relationships of the post-shift urinary AAMA level, GAMAs level, and GAMAs/AAMA ratio with personal AA exposures, consumption of fried food on the preceding day and smoking status. Multivariate regression analyses were used to analyze the post-shift level of urinary AAMA, GAMAs, and GAMAs/AAMA ratio as the dependent variables to adjust for confounding factors. The significant associations of possible predictors, including personal AA exposure, mEH exon4 and GSTM1 with the urinary metabolites were evaluated with Spearman correlation or Kruskal–Wallis test. The effects of CYP2E1 polymorphisms, tobacco smoking, and intake of fried food on the formation of AA metabolites were also analyzed with multivariate regression. For those samples with urinary metabolites below the detectable limit, their contents were designated as one-half of the

limit of detection (LOD). All p-values were two-sided. Statistical analyses were conducted using the Statistical Package for the Social Sciences (SPSS) version 11.0, (SPSS Inc., Chicago, IL, USA).

3. Results 3.1. Characteristics of the study population For this study, 85 workers (51 AA-exposed workers and 34 controls) were recruited. The demographic characteristics of our study subjects are presented in Table 1, including age, BMIs, working his-

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tory, alcohol consumption, preference for fried food, and intake of fried-food on the preceding day. Most of these characteristics did not differ significantly between exposed and the control groups, whereas gender and smoking status did. After analysis of genetic polymorphisms of CYP2E1, mEH (in exon 3 and exon 4), GSTT1, and GSTM1, their distributions of genotypes are also summarized in Table 1. The frequency of our study subjects with CYP2E1, mEH in exon 4, GSTM1, and GSTT1 allele are similar to those reported elsewhere for Taiwanese and Chinese populations, except for mEH in exon 3 (Stephens et al., 1994; Hildesheim et al., 1995; Lin et al., 2000; Yin et al., 2001; Lei et al., 2002; Wu et al., 2002). The frequency of the His (variant) allele at mEH exon 3 in the controls was 20.6%, which value was unexpected and different from the frequencies 57.4–71.9% reported for the Taiwan/China population (Lin et al., 2000; Yin et al., 2001; Lei et al., 2002). The frequency of the His/His allele in exon 3 of mEH was significantly lower in the AA-exposed workers than in the controls (p = 0.028). 3.2. Concentrations of urinary metabolites An LC–MS/MS method was successfully established to simultaneously analyze AAMA, GAMA2, and GAMA3 in urine samples collected from our study subjects. Fig. 1 shows representative chromatograms of urinary AAMA, GAMA2, and GAMA3 generated from analysis of an exposed sample. The chromatograms show double peaks at the same transition m/z 251 → 162 for GAMA2 and m/z 251 → 123 for GAMA3 and consistent with previous studies (Boettcher and Angerer, 2005; Boettcher et al., 2005; Fennell et al., 2006; Kopp et al., 2008; Hartmann et al., 2008, 2009). The retention time of AAMA-sulfoxide was at 6.9 and 7.4 min (Fig. 1). Although AAMA-sulfoxide is isomeric and would have the same m/z value as GAMA2 and GAMA3 (Fennell et al., 2006; Kopp et al., 2008), the AAMA-sulfoxide was completely separated from GAMA2 and GAMA3 (Fig. 1). Analysis of all samples shows that AAMA, GAMA2, and GAMA3 were detectable in 95.2%, 35.2%, and 40.0% of exposed samples, 88.6% of the control samples for AAMA while GAMA2 and GAMA3 were all below LODs in the control samples. Data are summarized in Table 2 with percentage of detectable AAMA, GAMA2, and GAMA3 and their corresponding geometric mean (GM) in AA-exposed workers and controls. The pre- and post-shift GM of urinary AAMA levels for AA-exposed workers were 848.9 (ND–41685.4) ␮g/g and 555.4 (ND–115297.9) ␮g/g creatinine appeared significantly greater than 103.2 (ND–634.7) ␮g/g and 114.6 (ND–661.7) ␮g/g creatinine for controls (p < 0.001). But, the difference in pre-shift and post-shift urinary AAMA levels for AA-exposed workers was not significant (p = 0.365). Similar results were also observed for GAMA2 and GAMA3. The pre- and post-shift levels of GAMA2 were 41.6 (ND–227.5) ␮g/g creatinine and 37.1 (ND–342.7) ␮g/g creatinine, and those of GAMA3 were 98.2 (ND–1307.0) ␮g/g creatinine and 100.3 (ND–1759.1) ␮g/g creatinine for the exposed group. The median (range) ratio of GAMA2 to GAMA3 levels was 0.50 (0.07–0.79). The median GAMAs/AAMA ratio, which potentially representing the proportion of AA metabolized to GA, was 0.05 (range, 0.003–0.456) in our study subjects. 3.3. Association between AA exposures and urinary metabolites The associations of urinary AAMA and GAMAs levels with personal AA exposures were analyzed using Spearman correlation coefficients. As shown in Fig. 2, significant correlations between their exposures (8 h time-weighted average (TWA) AA level) and the post-shift urinary AAMA (r = 0.732, p < 0.001) and GAMAs levels (r = 0.797, p < 0.001). However, there was no significant correlation between personal AA exposure with either urinary GAMA2 or GAMA3 levels. The correlation between AAMA and GAMAs lev-

Fig. 2. Correlations between AA exposure levels and the post-shift urinary metabolites, AAMA and GAMAs in 51 AA-exposed workers. A, AA exposure level vs urinary AAMA level; B, AA exposure level vs urinary GAMAs level.

Fig. 3. Correlation between urinary AAMA and GAMAs levels in AA-exposed workers.

els was highly significant: increased urinary excretion of AAMA was associated with increased urinary excretion of GAMAs (Fig. 3; Spearman correlation coefficient r = 0.707, p < 0.001). 3.4. Effects of genotypes on AA metabolites The AA exposures, AAMA levels, GAMAs levels, and GAMAs/AAMA ratios were stratified by CYP2E1, mEH (in exon 3 and exon 4), GST genotypes, and mEH activity, as shown in Table 3. Both the AAMA level and the GAMAs levels of the exposed workers with the mEH His/Arg genotype in exon 4 significantly exceeded those of workers with the His/His genotype. For the GSTM1 genotype, the AAMA levels, the GAMAs levels and the GAMAs/AAMA ratio differed significantly between subjects with positive and deficient genotype. The post-shift urinary metabolites (AAMA, GAMAs, and GAMAs/AAMA ratio) versus intake of fried food on the preceding day and smoking status were analyzed using simple linear regression. Our results showed no significant correlations between post-shift urinary metabolites and food consumption or self-reported smoking. There were no significant differences in GM (GSD) urinary AAMA and GAMAs levels between the exposed smokers (790.1 (8.5) ␮g/g creatinine and 145.4 (3.3) ␮g/g creatinine) and the exposed nonsmokers (576.6 (7.3) ␮g/g creatinine and 109.3 (4.5) ␮g/g creatinine). These data were further analyzed with multivariate regression analyses to adjust polymorphic genotypes, fried-food consumption, and smoking status. As shown in Table 4, personal AA exposure was a significant and positive predictor for post-shift urinary AAMA levels after adjusting for polymorphic genotypes, fried-food consumption, and smoking

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Table 2 Summary of urinary GAMA2, GAMA3, and AAMA levels in study subjects. GAMA2

GAMA3

%>LOD

␮g/L

␮g/g Cr.

58.3 (2.9) NDa -1107.0 57.8 (2.6) ND–364.0 58.8 (3.2) ND–1107.0

39.2 (2.9) ND–342.7 41.6 (2.8) ND–227.5 37.1 (3.0) ND–342.7

AAMA

%>LOD

␮g/L

␮g/g Cr.

133.0 (4.2) NDb –5682.0 115.0 (4.3) ND–1599.0 158.8 (4.3) ND–5682.0

99.1 (4.0) ND–1759.1 98.2 (3.9) ND–1307 100.3 (4.2) ND–1759.1

␮g/L

␮g/g Cr.

GM (GSD) range GM (GSD) Range GM (GSD) Range

697.2 (9.6) NDc -372411.8 852.9 (7.8) ND–54571.6 578.6 (11.5) ND–372411.8

682.3 (7.9) ND–115297.9 848.9 (7.0) ND–41685.4 555.4 (8.9) ND–115297.9

GM (GSD) Range GM (GSD) Range GM (GSD) Range

83.1 (3.2) ND–1282.1 70.1 (3.5) ND–1282.1 101.0 (2.8) ND–906.6

108.5 (2.3) ND–661.7 103.2 (2.5) ND–634.7 114.6 (2.0) ND–661.7

%>LOD

Exposure Total

35.2

Pre-shift

34.6

Post-shift

35.8

GM (GSD) Range GM (GSD) Range GM (GSD) Range

40.0

44.2

35.8

GM (GSD) range GM (GSD) Range GM (GSD) Range

95.2

92.3

98.1

Control Total

0

0

88.6

Pre-shift

0

0

87.1

Post-shift

0

0

88.9

a b c

ND, not detectable (below 10 ␮g/L). ND, not detectable (below 20 ␮g/L). ND, not detectable (below 5 ␮g/L).

Table 3 Personal AA exposures, urinary GAMAs/AAMA ratio, AAMA, and GAMAs concentrations stratified by genotype polymorphism and AA exposure level. Genotype

n

AA (mg/m3 ) Mean ± SD

GAMAs/AAMA Mean ± SD

AAMA (␮g/g creatinine) GM (GSD)

CYP2E1 C1/C1 C1/C2 C2/C2 p

26 20 2

0.033 ± 0.108 0.022 ± 0.031 0.008 ± 0.011

0.106 ± 0.118 0.078 ± 0.101 0.237 ± 0.226 0.124

386.47 (12.81) 808.22 (7.91) 397.53 (21.41) 0.656

35.85 (5.22) 39.37 (3.74) 69.79 (6.76) 0.731

mEH3(Tyr113His) Tyr/Tyr Tyr/His His/His p

41 5 2

0.029 ± 0.088 0.019 ± 0.008 0.011 ± 0.002

0.112 ± 0.123 0.035 ± 0.026 0.037 ± 0.017 0.346

433.09 (11.52) 2417.77 (3.02) 629.33 (4.99) 0.274

36.59 (4.62) 69.74 (4.22) 22.16 (3.11) 0.511

mEH4(His139Arg) His/His His/Arg p

41 7

0.029 ± 0.087 0.015 ± 0.011

0.111 ± 0.122 0.033 ± 0.014 0.218

453.43 (10.06) 1257.84 (13.30) 0.040*

33.82 (4.74) 79.73 (2.42) 0.042*

mEH activity Low Medium High p

4 40 4

0.012 ± 0.002 0.030 ± 0.088 0.007 ± 0.005

0.043 ± 0.026 0.111 ± 0.124 0.039 ± 0.011 0.886

887.06 (3.70) 496.83 (10.79) 554.10 (26.94) 0.385

33.38 (4.91) 36.35 (4.72) 74.83 (2.42) 0.370

GSTM1 Deficient Positive p

23 25

0.003 ± 0.005 0.050 ± 0.109

0.152 ± 0.134 0.048 ± 0.065 <0.001*

196.25 (5.79) 1303.71 (12.06) 0.001*

21.53 (2.50) 65.14 (5.70) 0.031*

GSTT1 Deficient Positive p

25 23

0.016 ± 0.026 0.039 ± 0.115

0.069 ± 0.086 0.131 ± 0.136 0.302

644.76 (9.50) 421.88 (12.00) 0.241

43.43 (4.45) 33.46 (4.63) 0.483

*

GAMAs (␮g/g creatinine) GM (GSD)

p < 0.05.

status (p = 0.001). However, GSTM1 was marginally associated with post-shift urinary AAMA levels (p = 0.057). AA exposure (p < 0.001) and mEH in exon 4 (p = 0.047) were significant predictors for the post-shift urinary GAMAs levels after adjustments for other factors. GSTM1 was a significant predictor for GAMAs/AAMA ratios after adjustments for other factors (p = 0.014). The GAMAs/AAMA ratios in the GSTM1-positive workers were significantly lower than that in the GSTM1-deficient workers.

4. Discussion 4.1. Urinary AA metabolites as exposure biomarkers Several LC–MS/MS methods have been developed to quantify urinary AAMA, GAMA2 and GAMA3. Compared with their LODs in the ranges from 1 to 80 ␮g/L for AAMA (Bjellaas et al., 2005; Boettcher and Angerer, 2005; Li et al., 2005; Fennell et al., 2006),

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Table 4 Multivariate regression analysis: predictors of urinary AAMA, GAMAs, and ratio of GAMAs/AAMA in 51 AA-exposed workers. Predictors

Intercept Airborne AA (mg/m3 ) CYP2E1 (c1/c1 vs c1/c2 + c2/c2) GSTM1 (positive vs deficient) mEH4 (His/His vs His/Arg) Smoking (yes vs no) Intake of fried-food on the preceding day (yes vs no) *

GAMAs/AAMA

Log AAMA (␮g/g creatinine)

Log GAMAs (␮g/g creatinine)

B

(95%CI)

p-Value

B

(95%CI)

p-Value

B

(95%CI)

p-Value

0.158 −0.156

(0.069 to 0.246) (−0.598 to 0.287)

<0.001* 0.479

5.189 13.956

(3.705 to 6.673) (6.483 to 21.430)

<0.001* 0.001*

3.268 10.469

(2.303 to 4.233) (5.611 to 15.326)

<0.001* <0.001*

−0.008

(−0.083 to 0.067)

0.831

−0.433

(−1.672 to 0.806)

0.484

−0.304

(−1.109 to 0.501)

0.450

−0.097

(−0.174 to −0.021)

0.014

1.204

(−0.038 to 2.446)

0.057

0.572

(−0.235 to 1.379)

0.160

−0.053

(−0.165 to 0.059)

0.346

0.875

(−0.885 to 2.635)

0.321

1.158

(0.014 to 2.302)

0.047*

0.047

(−0.023 to 0.118)

0.181

−0.701

(−1.882 to 0.481)

0.238

−0.120

(−0.888 to 0.647)

0.753

−0.024

(−0.084 to 0.036)

0.421

0.799

(−0.171 to 1.770)

0.104

−0.200

(−0.831 to 0.43‘)

0.526

*

p < 0.05.

from 3 to 5 ␮g/L for GAMA2 (Bjellaas et al., 2005; Fennell et al., 2006), and from 1.5 to 5 ␮g/L for GAMA3 (Bjellaas et al., 2005; Boettcher and Angerer, 2005; Fennell et al., 2006), our method with LODs at 5 ␮g/L for AAMA, 10 ␮g/L for GAMA2, and 20 ␮g/L for GAMA3 is comparable with the previous methods, may not be sensitive to quantify GAMAs. Only 40% of exposed samples with detectable GAMA3 levels suggested that AA exposures for most of our study subjects were relatively low. These observations are consistent with our personal exposure data with a mean at 15.4 ␮g/m3 and only 17.6% of the personal exposures were greater than the current TWA at 30 ␮g/m3 (Wu et al., 2010). Most studies had investigated AAMA and GAMA3 levels in urine for the general populations that might be exposed to AA via tobacco smoke and/or certain high-temperature processed foods (Bjellaas et al., 2005, 2007a; Boettcher et al., 2005; Li et al., 2005; Fennell et al., 2006; Kellert et al., 2006; Urban et al., 2006; Huang et al., 2007; Hartmann et al., 2008; Kopp et al., 2008). This is the first study to simultaneously analyze urinary AAMA, GAMA2, and GAMA3 for exposed workers. Our results revealed that the GM of pre-shift AAMA, GAMA2, and GAMA3 levels in exposed workers were 848.9, 41.6, and 98.2 ␮g/g creatinine, respectively. Their corresponding post-shift levels were 555.4, 37.1, and 100.3 ␮g/g creatinine. These numbers are significantly greater than those in the control group since GAMA2 and GAMA3 were not detectable for all control samples (Table 2). However, the observation of no significant differences in pre- and post-shift urinary AAMA, GAMA2, and GAMA3 probably could be explained with relatively low airborne AA exposures and half lives of urinary AAMA, GAMA2, and GAMA3 greater than 10 h leading to the accumulation of these metabolites over one shift. Exposed workers excreted higher AAMA and GAMAs levels than smokers or nonsmokers of the control group (data not shown). The GM of pre- and post-shift urinary AAMA and GAMA3 levels in our control group were consistent with previous studies, which reported the median urinary AAMA and GAMA3 levels in the range of 24–42 ␮g/L and 3–17 ␮g/L for nonsmokers, and 29–337 ␮g/L and 10–111 ␮g/L for smokers, respectively (Bjellaas et al., 2005, 2007a; Boettcher et al., 2005; Li et al., 2005; Kellert et al., 2006; Urban et al., 2006; Huang et al., 2007; Hartmann et al., 2008; Kopp et al., 2008). This suggested that AA exposures contributed by tobacco smoke could be negligible when compared with occupational exposures. These results demonstrated that factory workers could have been exposed to significantly higher concentrations of AA than the general population. Although several studies have reported that urinary AAMA and GAMA3 levels in smokers were around 1–14 times and 3–37 times higher, respectively, than those of nonsmokers (Bjellaas et al., 2005, 2007a; Boettcher et al., 2005; Kellert et al., 2006; Urban et al., 2006; Kopp et al., 2008),

the effect of AA exposures from tobacco smoke on the formation of urinary AAMA and GAMAs were insignificant in this study when compared with occupational exposures (Table 4; p = 0.238 and p = 0.753, respectively). The urinary AAMA and GAMAs levels were also compared with personal AA exposures in a previous study (Huang et al., 2010). Analysis of data using both Spearman correlation and multivariate linear regression analyses show that the airborne AA exposure was a significant predictor of post-shift urinary AAMA and GAMAs levels after adjustments were made for other confounding factors (Fig. 2 and Table 4). But, either GAMA2 or GAMA3 was not significantly correlated to personal AA exposures. One of the reasons could be that GAMA2 and 3 were not detectable in a great proportion of samples, and the other could be the longer half life of urinary GAMA2 and GAMA3 (19 h) than that of urinary AAMA (11 h) (Hartmann et al., 2009). Since AAMA was more abundant than GAMA2 or GAMA3 and detectable in all the post-shift urine samples, these results suggest that urinary AAMA could be a better biomarker for current AA exposures than either GAMA2 or GAMA3. 4.2. Association between CYP2E1, mEH, GSTT1, and GSTM1 genotypes and urinary AA metabolites As detoxified metabolites of AA, urinary AAMA and GAMAs can also serve as biomarkers for the study of interactions between environmental exposure to AA and individual genetic susceptibility, statistical analysis showed that mEH4 and GSTM1 might have significantly modified effects on the formation of AAMA and GAMAs (Table 3). These observations are consistent with a previous study, but inconsistent with significant association of GSTT1 on the formation of these metabolites (Duale et al., 2009). Since GA is the active metabolite of AA, the GAMAs/AAMA ratio could be used as an indicator of the percentage of absorbed AA metabolized to GA. Our results showed the ratios were highly variable (median: 0.05, range: 0.003–0.456) and suggested dramatic interindividual differences in the metabolism of AA to GA among our study subjects. The associations between genetic polymorphisms of CYP2E1, mEH (in exon3 and exon 4), GSTT1, and GSTM1 with the GAMAs/AAMA ratios were further evaluated. Our results show that the GAMAs/AAMA ratio was associated with genetic polymorphism of GSTM1 (Table 3). Further analysis of data using multivariate regression analyses also shows that mEH (exon 4) was a significant predictor for the post-shift urinary GAMAs level after adjusting for confounding factors. Moreover, GSTM1 was a significant predictor for GAMAs/AAMA ratios after further adjustments were made for other covariates. However, GSTM1 was marginally associated with the post-shift urinary AAMA levels after adjustments for other

Y.-F. Huang et al. / Toxicology Letters 203 (2011) 118–126

covariates (p = 0.057) (Table 4). No effects of CYP2E1, mEH in exon 3, and GSTT1 polymorphisms on the formation of AA metabolites were observed. According to the reports of Duale et al. (2009) and Doroshyenko et al. (2009), the lack of effect in CYP2E1 might be attributed to the fact that CYP2E1 only accounts for one fourth of the primary AA metabolism in humans. Therefore, mEH in exon 4 and GSTM1 polymorphisms might have modified effects on the metabolism of AA among the exposed workers. Two genetic polymorphic sites of mEH have been identified (Hassett et al., 1994). A T-to-C mutation in exon 3 of the mEH gene changes tyrosine residue 113 to histidine, reducing enzyme activity by at least 40–50% (low allele). Furthermore, an A to G mutation in exon 4 changes histidine residue 139 to arginine, increasing enzyme activity by at least 25% (high allele). Subjects with the mEH (exon 4) His/Arg genotype, corresponding to increased enzyme activity, should have significantly lower GAMAs level than did subjects with the His/His genotype. These observations are similar to those in a recent study by Duale et al. (2009), who found that individuals with the mEH (exon 4) 139Arg (high activity) alleles had significantly lower AAVal level and higher GAVal/AAVal ratios than those with the 139His (low activity) alleles. The net effect of amino acid exchanges in both mEH axons somewhat lowered the enzyme activity (Sarmanova et al., 2000). This classification was used to separate exposed workers into low, medium and high mEH activity groups (Sarmanova et al., 2000), but only five and four subjects exhibited low and high mEH activity. Our results revealed that the predicted mEH activity might have no significant effect on the levels of urinary AA metabolites. The reason could probably be that the relatively small sample size in the low and high mEH activity resulted in low statistical power in the determination of the effects of mEH activity. The GSTs are multifunctional enzymes that catalyze several reactions between glutathione and electrophilic compounds. To date, several GST isoenzymes have been identified in humans and polymorphisms of GSTM1 and GSTT1 genes have been extensively investigated. Both GSTM1 and GSTT1 genes exhibit deletion polymorphism. Homozygous deletions of the GSTM1 gene (GSTM1deficient genotype) and the GSTT1 gene (GSTT1-deficient genotype) result in the absence of enzyme activity (Strange et al., 2000). This study shows that exposed workers with the GSTM1-deficient genotype have lower urinary AAMA levels, to a degree that was almost statistically significant (p = 0.057). The exposed workers with GSTM1-deficient genotype had a significantly higher GAMAs/AAMA ratio. This observation is consistent with a previous study (Duale et al., 2009), which demonstrated a significant association between the GSTM1 genotype and GAVal/AAVal ratio. These results may indicate that a higher percentage of AA was metabolized to GA for individuals with GSTM1-deficient genotypes than those with GSTM1-positive genotypes. Therefore, individuals with the GSTM1-deficient variants should reveal a significantly higher GAMAs/AAMA ratio than those with the positive genotypes. A previous study concluded that GSTP1, GSTM1 and GSTT1 enzymes played no major role in the AA and GA conjugation with glutathione in humans (Doroshyenko et al., 2009). However, an in vitro study reported that the polymorphisms in the GSTM1 or GSTT1 gene did not affect the levels of Hb adducts after exposure of human blood samples to AA and GA (Paulsson et al., 2005). This indicates that further studies will be needed to disclose the roles of these phase II enzymes in metabolism of AA. Although this study was also limited by the facts that the sample size was not large enough, and sensitivity of the analytical method for the GAMA2 and GAMA3 was not sufficient, the major GA metabolite, glyceramide, was not analyzed (Fennell et al., 2005), a very detailed exposure assessment for each study subject might have gained some advantages in statistical resolution power (Wu et al., 2010). We could still detect the potential modifying effects of GSTM1 genetic polymorphism on the levels of

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urinary AAMA and GAMAs/AAMA ratio and mEH4 on the levels of urinary GAMAs. In conclusion, this study explored the interactions between AA exposures and genetic polymorphisms of some phase I and phase II genes in the formation of urinary AA metabolites. Statistical analysis suggests that mEH in exon 4 and GSTM1 genetic polymorphisms significantly modify the levels of the post-shift urinary GAMAs and the GAMAs/AAMA ratio after adjustment for other covariates, such as fried-food consumption, and smoking status although personal AA exposure is the major factor. These observations are somewhat in accordance with the results of Duale et al. (2009). Additionally, GSTM1 was marginally associated with the post-shift urinary AAMA level after adjusting for other confounding factors (p = 0.057). Future studies on AA metabolite profiles with excellent sensitivity and specificity and with a larger study group will be needed to shed light on the involvement of phase I and II enzymes in AA metabolism in human. Conflict of interest statement None. Acknowledgements This research was supported in part by a grant from the Division of Environmental Health and Occupational Medicine, National Health Research Institute (EO-095-PP-02), a grant from National Science Council (NSC 95-2314-B-400-004-MY3), and a grant from Institute of Occupational Safety and Health (IOSH95-A319), Taiwan. References Arand, M., Mühlbauer, R., Hengstler, J., Jäger, E., Fuchs, J., Winkler, L., Oesch, F., 1996. A multiplex polymerase chain reaction protocol for the simultaneous analysis of the glutathione-S-transferase GSTM1 and GSTT1 polymorphisms. Anal. Biochem. 236 (1), 184–186. Besaratinia, A., Pfeifer, G.P., 2005. DNA adduction and mutagenic properties of acrylamide. Mutat. Res. 80, 31–40. Bjellaas, T., Janak, K., Lundanes, E., Kronberg, L., Becher, G., 2005. Determination and quantification of urinary metabolites after dietary exposure to acrylamide. Xenobiotica 35 (10–11), 1003–1018. Bjellaas, T., Stølen, L.H., Haugen, M., Paulsen, J.E., Alexander, J., Lundanes, E., Becher, G., 2007a. Urinary acrylamide metabolites as biomarkers for short-term dietary exposure to acrylamide. Food Chem. Toxicol. 45 (6), 1020–1026. Bjellaas, T., Olesen, P.T., Frandsen, H., Haugen, M., Stølen, L.H., Paulsen, J.E., Alexander, J., Lundanes, E., Becher, G., 2007b. Comparison of estimated dietary intake of acrylamide with hemoglobin adducts of acrylamide and glycidamide. Toxicol. Sci. 98 (1), 110–117. Boettcher, M.I., Angerer, J., 2005. Determination of the major mercapturic acids of acrylamide and glycidamide in human urine by LC-ESI–MS/MS. J. Chromatogr. B 824, 283–294. Boettcher, M.I., Schettgen, T., Kutting, B., Pischetsrieder, M., Angerer, J., 2005. Mercapturic acids of acrylamide and glycidamide as biomarkers of the internal exposure to acrylamide in the general population. Mutat. Res. 580 (1–2), 167–176. Bull, R.J., Robinson, M., Laurie, R.D., Stoner, G.D., Greisiger, E., Meier, J.R., Stober, J., 1984. Carcinogenic effects of acrylamide in sencar and A/J mice. Cancer Res. 44, 107–111. Calleman, C.J., Wu, Y., He, F., Tian, G., Bergmark, E., Zhang, S., Deng, H., Wang, Y., Crofton, K.M., Fennell, T., Costa, L.G., 1994. Relationships between biomarkers of exposure and neurological effects in a group of workers exposed to acrylamide. Toxicol. Appl. Pharmacol. 126 (2), 361–371. Chevolleau, S., Jacques, C., Canlet, C., Tulliez, J., Debrauwer, L., 2007. Analysis of hemoglobin adducts of acrylamide and glycidamide by liquid chromatographyelectrospray ionization tandem mass spectrometry, as exposure biomarkers in French population. J. Chromatogr. A 1167 (2), 125–134. Doerge, D.R., Young, J.F., McDaniel, P., Twaddle, N.C., Churchwell, M.I., 2005. Toxicokinetics of acrylamide and glycidamide in Fischer 344 rats. Toxicol. Appl. Pharmacol. 208, 199–209. Doroshyenko, O., Fuhr, U., Kunz, D., Frank, D., Kinzig, M., Jetter, A., Reith, Y., Lazar, A., Taubert, D., Kirchheiner, J., Baum, M., Eisenbrand, G., Berger, F.I., Bertow, D., Berkessel, A., Sörgel, F., Schömig, E., Tomalik-Scharte, D., 2009. In vivo role of cytochrome P450 2E1 and glutathione-S-transferase activity for acrylamide toxicokinetics in humans. Cancer Epidemiol. Biomarkers Prev. 18 (2), 433–443.

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