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Influence of glutathione S-transferases polymorphisms on biological monitoring of exposure to low doses of benzene
Toxicology Letters 213 (2012) 63–68
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Influence of glutathione S-transferases polymorphisms on biological monitoring of exposure to low doses of benzene Mariella Carrieri a , Giovanni Battista Bartolucci a,∗ , Maria Luisa Scapellato a , Giovanna Spatari b , Daniela Sapienza b , Leonardo Soleo c , Piero Lovreglio c , Giovanna Tranfo d , Maurizio Manno e , Andrea Trevisan a a
Department of Environmental Medicine and Public Health, University of Padova, Italy Department of Social and Territorial Medicine, University of Messina, Italy c Department of Internal Medicine and Public Health, University of Bari, Italy d Italian Workers’ Compensation Authority (INAIL), Monteporzio Catone (RM), Italy e Department of Preventive Medical Sciences, University of Napoli Federico II, Italy b
a r t i c l e
i n f o
Article history: Available online 7 December 2011 Keywords: Benzene t,t-Muconic acid S-phenylmercapturic acid Biological monitoring GST genotype
a b s t r a c t The environmental and biological monitoring of benzene exposure is crucial to prevent the toxic effects of this solvent in workers. The degree of correlation, however, between the two and of different biomarkers among them varies, particularly at low levels of exposure, depending on various factors, including variability in metabolizing enzymes and smoking habits. To investigate these further, a cohort of 28 petrochemical workers (6 smokers and 22 non smokers) was monitored throughout ten consecutive days, on two occasions, two years apart, by collecting in total 173 environmental and biological samples. The airborne benzene levels, the urinary t,t-muconic acid (t,t-MA) and S-phenylmercapturic acid (S-PMA) concentrations, and the glutathione S-transferases (GST) M1 and T1 genotypes were measured. S-PMA was the only metabolite statistically correlated with airborne benzene levels (r = 0.447, P < 0.0001), particularly in non smokers (r = 0.667, P < 0.0001), the smoking habit being the only variable influencing metabolite excretion. Finally, a reduced S-PMA excretion was found to be associated with the GSTT1, but not the GSTM1, null genotype. In conclusion, the results show that S-PMA, but not t,t-MA, is able to monitor exposure to low benzene concentrations and confirm that the GSTT1 null genotype has a significant influence on metabolite excretion. The influence of the GSTT1 null genotype, however, was low, even when studying each subject with several urine samples. © 2011 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Benzene, a genotoxic human carcinogen with no threshold dose, may cause bone marrow toxicity at high doses in chronically exposed workers and also leukaemia following occupational or environmental exposure, even to low concentrations (Schnatter et al., 2006). Environmental and, more recently, also biological monitoring are increasingly used as practical tools in the human health risk assessment of occupational and, sometimes, also environmental exposure to chemicals (Manno et al., 2010). The relevance of performing the environmental and biological monitoring of benzene exposure, therefore, is dual: the protection of the workers from the health effect of occupational exposure to the solvent
∗ Corresponding author at: Department of Environmental Medicine and Public Health, University of Padova, Via Giustiniani 2, I-35128 Padova, Italy. Tel.: +39 49 821 1365; fax: +39 49 821 2542. E-mail address: [email protected] (G.B. Bartolucci). 0378-4274/$ – see front matter © 2011 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.toxlet.2011.11.031
in the workplace and the prevention of the carcinogenic risk to the general population from exposure to low air levels of pollutant in the general environment. Benzene exposure in the general population is mainly due to outdoor air contamination from gasoline-driven vehicles and diesel exhaust and to indoor pollution from tobacco smoke, the main source of benzene in the non occupational environment (Fustinoni et al., 2005; Bono et al., 2005). The most common source of occupational exposure for the workers, however, is from employment in the petrol industry and in petrol distribution, mainly from gasoline pumps (Carrieri et al., 2006). Other common conditions of exposure are from working as a traffic policeman or as a driver of taxis, trucks, and coaches (Manini et al., 2006, 2010). The biological monitoring of occupational benzene exposure is usually made by dosing t,t-muconic acid (t,t-MA) and Sphenylmercapturic acid (S-PMA) in urine. These metabolites derive from two different metabolic pathways, both occurring in the liver and each involving two steps: i.e. the oxidative bioactivation of benzene to benzene oxide as the common, first step, and oxepin
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formation with either further opening of the aromatic ring, or conjugation with reduced glutathione by means of glutathione S-transferases (GST) for t,t-MA and S-PMA, respectively, as the second step (Rappaport et al., 2009). The levels of exposure to benzene in the workplace nowadays are generally low. As a result, the challenges for occupational health professionals in assessing the health risk from benzene exposure are twofold: to choose sensitive biomarkers of exposure and to avoid important confounding factors such as smoking habits. For instance, in a cohort of petrochemical workers exposed to low benzene concentration, a good correlation was observed recently in non-smokers, but only between S-PMA levels in urine and benzene concentrations in air, whereas the other urinary metabolite t,t-MA appeared to be correlated with exposure more weakly (Carrieri et al., 2010a). An additional variability factor in benzene metabolism is the influence of enzyme polymorphism. As previously mentioned, the metabolic pathway leading to the formation of S-PMA is via hepatic GST, whose classes M1, P1, and T1 are genetically polymorphic. Both GSTM1 and GSTT1 null genotypes are related to a low excretion of S-PMA in exposed subjects (Sørensen et al., 2004; Manini et al., 2006, 2010). GST enzymes, therefore, may play an important role in benzene toxicity and, interestingly, also in other, benzene unrelated diseases. The null genotype, indeed, has been shown to be associated with an increased risk of several types of cancer (Hengstler et al., 1998; Salagovic et al., 1998). The aim of the present work was to study the influence of the GST genotype on the urinary excretion of benzene metabolites, measured on different weekdays, in a cohort of petrochemical workers exposed to low air levels of benzene in a large industry of Northern Italy. 2. Materials and methods 2.1. Chemicals and suppliers The analytical reference standard of DL-S-PMA was purchased from Tokyo Kasei Cogio LTD (Tokyo, Japan). The internal standard, deuterated DL-S-PMA-3,3-d2 , was obtained from CDN Isotopes Inc. (Pointe-Claire, Quebec, Canada). Glacial acetic acid (100%) and ammonia have been purchased from Merck (Darmstadt, Germany). Purified water was obtained from a Milli-Q Plus system (Millipore, Milford, MA, USA). Methanol, formic acid (98%, purity), sulphuric acid (95% purity), the 50–52% (v:v) sodium hydroxide water solution, and carbon disulfide (purity >99.9%, low benzene content) have been purchased from Sigma–Aldrich (St. Louis, MO, USA). Control human urine samples, used to prepare standard calibration curves and quality control samples (QC), were obtained from non smoking healthy volunteers. SPE Vacuum Manifold and Sep-Pak Plus C18 (360 mg) cartridges for S-PMA analysis were supplied by Waters (Milford, MA, USA). SPE cartridges for t,t-MA analysis were supplied by Varian (EA Middelburg, The Netherlands). Anotop 10 LC® syringe filter devices (0.2 m pore size, 10 mm diameter) were purchased from Whatman Inc. (Maidstone, England). For S-PMA and t,t-MA analysis, a Supelco Discovery C18 HPLC column (150 mm × 4.6 mm, 5 m film thickness) and a Ultrasphere C18 HPLC column (250 mm × 4.6 mm, 5 m film thickness) were purchased from Sigma–Aldrich (Bellafonte, PA, USA) and Beckman Coulter (Fullerton, CA, USA), respectively. Diffusive air samplers (Radiello® ) were supplied by the Salvatore Maugeri Foundation–Environmental Research Centre (Padua, Italy).
2.2. Study population and sample collection Twenty eight male workers (22 non smokers and 6 smokers, mean age 41.8 ± 8.5 years) employed in outdoor operations at a petrochemical plant in Northern Italy were included in the study. Each worker provided one spot fresh urine sample at the end of the work-shift for benzene biomonitoring, and an oral mucosa cell sample for genotyping. Informed written consent to take part in the study was obtained from all subjects before enrollment. The environmental and biological monitoring procedures were carried out throughout ten consecutive days, on two different occasions with an interval of two years between the two sampling campaigns. A total of 173 environmental and biological samples (95 during the first and 78 during the second campaign) were collected, one to fourteen times for each worker. Benzene exposure was measured during the entire work-shift (approximately 8 h) in all subjects at the breathing zone level, using a personal diffusive sampler containing an active carbon cartridge (Radiello® ). On the same day of personal airborne benzene monitoring,
an urine sample was collected at the end of the work shift from each worker for measuring the metabolites. A buccal swab was used to obtain a sample of cells for DNA analysis. Swabs were collected in separate vials and kept at −20 ◦ C until DNA analysis using the standard Chelex® 100 method for DNA extraction. 2.3. Analytical methods for benzene and its metabolites The analysis of the airborne benzene samples was performed by GC–FID after desorption of benzene from the active carbon with carbon disulfide. The concentration of S-PMA in urine was determined according to a HPLC–MS/MS method that comprises an (quantitative) acidic hydrolysis of its urinary precursor pre-S-PMA (Paci et al., 2007). The detection was in negative ions, MRM mode, and the transitions were the following: −238.1 → −109.1 for S-PMA and −240.1 → −109.1 for the deuterium labelled internal standard. Urinary t,t-MA analysis was carried out, after solid phase extraction (SPE), by an HPLC–UV analytical method with detection at 264 nm, as described elsewhere (Carrieri et al., 2006). Values measured for both S-PMA and t,t-MA were adjusted to urinary creatinine concentration, based on basic-picrate Jaffe’s reaction (Jaffe, 1886). 2.4. GST polymorphism analysis GSTT1 and GSTM1 polymorphisms were analyzed in oral mucosa cells by standard polymerase chain reaction (PCR)-based methods. The genes of interest were amplified by PCR, using a “PCR sprint” (Hybaid) thermal-cycler. A multiplex system was created for the simultaneous amplification of the genetic loci of GSTM1 and GSTT1. To verify the correct completion of the PCR reaction, the gene of beta-globin, that is unrelated to GST and with a molecular weight clearly distinct from that of either the GSTM1 or GSTT1 genes, was amplified in the same system as a control. The oligonucleotide sequences of primers were: for GSTM1, forward primer: 5 -GAA CTC CCT GAA AAG CTA AAG C-3 and reverse primer: 5 -GTT GGG CTC AAA TAT ACG GTG G-3 ; for GSTT1, forward primer: 5 -TTC CTT ACT GGT CCT CAC ATC TC-3 ; reverse primer: 5 -TCA CCG GAT CAT GGC CAG CA-3 ; and for BETA-GLOBIN: forward primer: 5 -CAA CTT CAT CCA CGT TCA CC-3 ; reverse primer: 5 -GAA GAG CCA AGC ACA GGT AC -3 . Each PCR reaction was performed with the following: 2.5 ml cell extract (5–250 ng DNA), 0.5 mM of each primer, 2.5 ml Taq buffer (10xPCR Buffer II, Applied Biosystems), 2 ml MgCl2 25 mM (Applied Biosystems), 0.5 ml dNTPs mix (10 mM PCR Nucleotide Mix, Promega), and 1 U Taq polymerase (DyNAzyme II DNA Polymerase, Finnzymes) in a total volume of 25 ml. Thirty amplification cycles (denaturation at 95 ◦ C for 1 min, annealing at 60 ◦ C for 1 min, and extension at 72 ◦ C for 1 min) were performed (Walsh et al., 1991). Finally, the products of the reaction were vertically electrophoresed at 2000 V, max mA, max W for 150 min on 0.4 mm layer polyacrilamide denaturing gels (6% – urea 7 M) in TBE buffer 1×, and then revealed by silver staining (Budowle et al., 1991; Bassam et al., 1991). The migration bands of interest (480 bp for GSTT1, 215 bp for GSTM1) were identified by comparison with the molecular weights of a specific standard marker (DNA pGEM® marker, Promega). 2.5. Statistical analysis Statistical analysis was carried out using the StatsDirect statistical software (Statsdirect 2.7.7 version, Statsdirect Ltd., UK). Differences between groups were assessed using the Mann–Whitney U-test. Multivariate analysis was also employed to evaluate the influence of different variables on metabolite excretion. Linear regression coefficient was used to analyze the correlation between airborne benzene values and excretion of its metabolites. In all tests, a P value lower than 0.05 (two-tailed) was considered as statistically significant.
3. Results 3.1. Airborne benzene On average, exposure to benzene was very low (0.034 mg/m3 ), i.e. about two orders of magnitude lower than the ACGIH TLV 2011 of 1.6 mg/m3 , and variable (range 0.002–0.895 mg/m3 ), with the highest values about twofold lower than the TLV (Table 1). No statistically significant difference was observed between smokers and non smokers (results not shown). 3.2. Excretion of urinary metabolites Smokers had a significantly higher excretion of both urinary metabolites than non smokers. Particularly so for S-PMA (P < 0.0001), whose mean value was about four times higher in smokers than in non smokers (Table 1), but also for t,t-MA (P = 0.0003). Interestingly, airborne benzene levels were lower
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Table 1 Airborne benzene levels and metabolite excretion in the studied population of petrochemical workers (according also to smoking habit).
Benzene in air (g/m3 )
t,t-MA (g/g of creatinine)
S-PMA (g/g of creatinine)
Mean Median GM Range Mean Median GM Range Mean Median GM Range
All subjects (No. 28) 173 samples
Non smokers (No. 22) 137 samples
Smokers (No. 6) 36 samples
34.5 9.2 11.5 2.1–894.7 104.2 68.6 69.0 4.7–1168.3 1.52 0.54 0.62 0.05–18.63
37.7 10.3 12.2 2.1–894.7 98.8 56.9* 61.8 4.7–1168.3 0.88 0.34** 0.39 0.05–18.63
22.4 7.0 9.0 3–383.5 124.5 120.2* 104.7 8–264.1 3.94 3.53** 3.51 1.02–11.63
GM = geometric mean. * P = 0.0003 ** P < 0.0001 (Mann–Whitney U-test) smokers vs. non smokers.
for smokers than for non smokers, but the difference was not significant. In addition, a statistically significant correlation was observed between airborne benzene levels and S-PMA excretion in all subjects (smokers and non smokers together, r = 0.447, P < 0.0001, Fig. 1) and in non smokers (r = 0.667, P < 0.0001, Fig. 2), but not in smokers (results not shown). No such correlation was observed for t,t-MA. A preliminary multivariate analysis showed that smoking habit was the only variable among others (age and body mass index) significantly influencing excretion of benzene metabolites (data not shown). For this reason, only non smokers (22 subjects) were included in the genotyping study. The data obtained from these subjects were further pooled and the mean values of benzene exposure and metabolite excretion were calculated from
Fig. 1. Correlation between airborne benzene levels and concentration of the urinary metabolites t,t-MA (above) and S-PMA (below). The analysis was performed on all samples (173) collected on different days from all subjects (smokers and non smokers).
all samples collected for each subject. The statistical analysis of these mean values showed, again, a significant correlation between airborne benzene levels and S-PMA (r = 0.640, P = 0.0015, Fig. 3) but not t,t-MA urinary values (results not shown). Any correlation existing between urinary metabolites and benzene exposure was also investigated individually for each non smoker (except for one subject with just one sample). The correlation was found to be statistically significant in eight subjects for S-PMA and in three subjects only for t,t-MA (Table 2). 3.3. GST polymorphism Genotyping showed that the majority of subjects, smokers and non smokers together, had at least one or both null genotypes (60.7%, 59.1% in smokers and 66.7% in non smokers) (Table 3).
Fig. 2. Correlation between airborne benzene level and concentration of the urinary metabolites t,t-MA (above) and S-PMA (below). The analysis was performed on all samples (137) collected on different days from non smokers only.
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Table 2 Genotype, number of samples and correlation between airborne benzene level and urinary metabolite concentration in each non smoker. Subject no. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 * a
GSTT1 No null No null No null No null No null No null Null Null Null Null No null No null No null Null No null Null Null Null No null No null Null No null
GSTM1 Null No null No null Null No null No null Null Null No null Null No null No null No null Null Null Null Null Null Null No null No null No null
No. of samples
t,t-MA vs. benzenea
S-PMA vs. benzenea
*
10 4 7 1 3 7 3 5 6 14 6 4 6 8 5 5 4 10 9 8 6 6
0.82* −0.30 0.23 – 0.95 0.93* 0.91 0.98* 0.77 0.91* 0.98* 0.33 −0.59 0.18 0.93* 0.75 0.88 0.99* 0.86* 0.47 0.23 0.72
0.65 −0.59 0.22 – 0.40 −0.28 −0.54 0.98* 0.67 −0.33 0.92* 0.71 0.17 −0.01 −0.35 −0.35 0.90 −0.23 −0.31 −0.18 0.13 −0.76
P < 0.05 or higher statistical significance. Linear regression coefficient between air benzene levels and metabolite concentration in urine.
Considering the confounding effect of smoking on the biological monitoring of benzene, however, the possible influence of the GST genotype on metabolite excretion was only investigated in non smokers. Adopting the ratio between urinary metabolite concentration and airborne benzene level, as reported previously (Qu et al., 2005; Carrieri et al., 2010b), the GSTT1 genotype was found to influence S-PMA excretion (P = 0.0098), being the null genotype significantly associated with a lower metabolite excretion in comparison with the non null genotype (Fig. 4). No significant influence
of the GSTM1 null or both the GSTT1 and GSTM1 null genotypes was observed on S-PMA excretion (results not shown). The excretion of the t,t-MA appeared to be unaffected by either GST polymorphism, as expected.
4. Discussion The aim of the present study was to compare S-PMA and t,tMA as biomarkers of occupational exposure to low benzene levels in air, and to investigate the role, if any, of GST polymorphism in their excretion. A cohort of 28 workers of a petrochemical industry was monitored for their environmental exposure to benzene during the entire workshift, and by measuring the solvent’s urinary metabolites. Benzene and its metabolites were monitored at different times throughout two years. For each worker a total of 1–14 samples for measuring both benzene in air and the urinary metabolites were obtained. As depicted in Section 3, benzene exposure was very low, the airborne concentration being on the average, 35 fold lower than the current ACGIH TLV® . Interestingly, non smokers had higher levels of exposures than smokers (Table 1), an observation which confirms previous findings (Fracasso et al., 2010). At this low levels of exposure, a significant correlation between urinary metabolite concentration and airborne benzene level was only observed for S-PMA, showing a better correlation in non smoking workers, but not for t,t-MA, thus confirming previous results (Carrieri et al., 2010a). The lack of any such correlation observed in smokers, confirms the confounding effect of smoking on metabolite excretion. Indeed, in the multivariate analysis smoking habit appeared to be the only variable influencing metabolite excretion. According to the present results, therefore, it is reasonable to say that, for such low levels of exposure only S-PMA should be used as
Table 3 Genotype frequency in all subjects, smokers and non smokers together.
Fig. 3. Correlation between airborne benzene levels and concentration of the urinary metabolites t,t-MA (above) and S-PMA (below). The analysis was performed on non smokers pooled results using the mean values for air and urine samples collected for each subject.
Genotype
No.
Frequency
GSTM1/GSTT1 non null GSTM1/GSTT1 null GSTM1 non null/GSTT1 null GSTM1 null/GSTT1 non null
11 10 2 5
39.3% 35.7% 7.1% 17.9%
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Fig. 4. Different excretion of S-PMA in non smokers according to the GSTT1 genotype. *P = 0.0098, Mann–Whitney U-test.
a group exposure biomarker to monitor benzene exposure in the workplace, and in non smokers only. On the other hand, it is known that t,t-MA level in urine is not specific as a biomarker of benzene exposure. It may be affected, in fact, by the ingestion of sorbic acid, a common food preservative of which t,t-MA is a metabolite too (Pezzagno and Maestri, 1997; Pezzagno et al., 1999; Weaver et al., 2000; Negri et al., 2005). In this study the correlation between benzene exposure and t,t-MA excretion showed a high line intercept and a great dispersion of data, mainly at very low levels of exposure. This was probably due to the interference on t,t-MA excretion of the diet of the subjects examined. This finding was further confirmed by the results of metabolite excretion determined separately in individual subjects for a number of consecutive days (Table 2). In fact, a statistically significant correlation between benzene levels in air and levels of metabolites in urine was observed in eight cases for S-PMA but in three subjects only, for t,t-MA. The metabolic fate of benzene is complex. In the light of current knowledge it occurs via different pathways, all involving, as the first step, the formation of a reactive intermediate, benzene-oxide. The fate of this reactive metabolite is manifold, including activation and detoxication reactions: (1) the rupture of the instable C O C bond to form phenol, an early, classic biomarker at high levels of exposure, (2) the formation of an oxepin with the subsequent opening of the aromatic ring to form muconaldehyde and t,t-MA, (3) the conjugation of benzene-oxide with reduced glutathione, by means of GST, to form the mercapturic acid S-PMA, and (4) the formation of catechol, which is considered the main bioactivation pathway (Rappaport et al., 2009). Polymorphism of several drug metabolizing enzymes is known to modulate benzene metabolism and, therefore, its biological effects. Among those enzymes are NAD(P)H:quinone oxidoreductase 1, myeloperoxidase, cytochrome P450-2E1, GSTT1 and GSTM1, with only the last two isoforms showing consistent associations with biomarkers of exposure in urine and biomarkers of effects (Dougherty et al., 2008). The main aim of the present research was to investigate the role of GSTM1 and GSTT1 polymorphism on benzene metabolite excretion following exposure to low concentrations of the solvent. Under low exposure conditions, in fact, any effect of the genetic polymorphism of benzene metabolizing
enzymes may be expected to become more apparent than under heavy exposure conditions at or near saturating concentrations. Generally, the null genotype for GSTT1 and/or GSTM1 was found to be related to a low excretion of S-PMA (Sørensen et al., 2004; Manini et al., 2006, 2010). Interestingly, however, Verdina et al. (2001) showed that the urinary t,t-MA/blood benzene ratio, but not the S-PMA/blood benzene ratio, was partially modulated by the GST genotype, significantly higher values being found in null individuals (both null genotypes combined). The results of the present study in non smoking petrochemical workers exposed to low benzene concentrations show, in agreement with those of Qu et al. (2005), that only S-PMA levels in urine are significantly influenced by the GSTT1 null genotype. The influence of the GSTT1 null genotype on S-PMA excretion, however, was quantitatively low, even when studying each subject with several samples. These results are consistent with previous findings by other groups showing an association between a lower excretion of S-PMA and the GSTT1 null genotype (Sørensen et al., 2004; Manini et al., 2010), but not with those showing an influence also of the GSTM1 null genotype, as reported by others (Manini et al., 2006, 2010). In conclusion, our results showed that (1) under conditions of exposure to low benzene concentrations, such as those of the present study – which are currently found in most occupational settings – S-PMA is preferable to t,t-MA as the metabolite to be used as an indicator of group exposure in workers, and (2) only the GSTT1, but not the GSTM1, null genotype has a low, but statistically significant influence on the levels of metabolite excretion. As a result, despite S-PMA and, to a lesser extent, t,t-MA are commonly and successfully used as group indicators of occupational exposure to low levels of benzene in air, more sensitive and specific biomarkers are needed to perform, at the individual level, a reliable biological monitoring of exposure to these very low concentrations. Conflict of interest statement The authors declare no conflict of interest. Acknowledgement This work was supported by grant MIUR-PRIN 2006, no. 2006068922-001/004/005 and INAIL project no. 14/2009.
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Toxicology Letters journal homepage: www.elsevier.com/locate/toxlet
Influence of glutathione S-transferases polymorphisms on biological monitoring of exposure to low doses of benzene Mariella Carrieri a , Giovanni Battista Bartolucci a,∗ , Maria Luisa Scapellato a , Giovanna Spatari b , Daniela Sapienza b , Leonardo Soleo c , Piero Lovreglio c , Giovanna Tranfo d , Maurizio Manno e , Andrea Trevisan a a
Department of Environmental Medicine and Public Health, University of Padova, Italy Department of Social and Territorial Medicine, University of Messina, Italy c Department of Internal Medicine and Public Health, University of Bari, Italy d Italian Workers’ Compensation Authority (INAIL), Monteporzio Catone (RM), Italy e Department of Preventive Medical Sciences, University of Napoli Federico II, Italy b
a r t i c l e
i n f o
Article history: Available online 7 December 2011 Keywords: Benzene t,t-Muconic acid S-phenylmercapturic acid Biological monitoring GST genotype
a b s t r a c t The environmental and biological monitoring of benzene exposure is crucial to prevent the toxic effects of this solvent in workers. The degree of correlation, however, between the two and of different biomarkers among them varies, particularly at low levels of exposure, depending on various factors, including variability in metabolizing enzymes and smoking habits. To investigate these further, a cohort of 28 petrochemical workers (6 smokers and 22 non smokers) was monitored throughout ten consecutive days, on two occasions, two years apart, by collecting in total 173 environmental and biological samples. The airborne benzene levels, the urinary t,t-muconic acid (t,t-MA) and S-phenylmercapturic acid (S-PMA) concentrations, and the glutathione S-transferases (GST) M1 and T1 genotypes were measured. S-PMA was the only metabolite statistically correlated with airborne benzene levels (r = 0.447, P < 0.0001), particularly in non smokers (r = 0.667, P < 0.0001), the smoking habit being the only variable influencing metabolite excretion. Finally, a reduced S-PMA excretion was found to be associated with the GSTT1, but not the GSTM1, null genotype. In conclusion, the results show that S-PMA, but not t,t-MA, is able to monitor exposure to low benzene concentrations and confirm that the GSTT1 null genotype has a significant influence on metabolite excretion. The influence of the GSTT1 null genotype, however, was low, even when studying each subject with several urine samples. © 2011 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Benzene, a genotoxic human carcinogen with no threshold dose, may cause bone marrow toxicity at high doses in chronically exposed workers and also leukaemia following occupational or environmental exposure, even to low concentrations (Schnatter et al., 2006). Environmental and, more recently, also biological monitoring are increasingly used as practical tools in the human health risk assessment of occupational and, sometimes, also environmental exposure to chemicals (Manno et al., 2010). The relevance of performing the environmental and biological monitoring of benzene exposure, therefore, is dual: the protection of the workers from the health effect of occupational exposure to the solvent
∗ Corresponding author at: Department of Environmental Medicine and Public Health, University of Padova, Via Giustiniani 2, I-35128 Padova, Italy. Tel.: +39 49 821 1365; fax: +39 49 821 2542. E-mail address: [email protected] (G.B. Bartolucci). 0378-4274/$ – see front matter © 2011 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.toxlet.2011.11.031
in the workplace and the prevention of the carcinogenic risk to the general population from exposure to low air levels of pollutant in the general environment. Benzene exposure in the general population is mainly due to outdoor air contamination from gasoline-driven vehicles and diesel exhaust and to indoor pollution from tobacco smoke, the main source of benzene in the non occupational environment (Fustinoni et al., 2005; Bono et al., 2005). The most common source of occupational exposure for the workers, however, is from employment in the petrol industry and in petrol distribution, mainly from gasoline pumps (Carrieri et al., 2006). Other common conditions of exposure are from working as a traffic policeman or as a driver of taxis, trucks, and coaches (Manini et al., 2006, 2010). The biological monitoring of occupational benzene exposure is usually made by dosing t,t-muconic acid (t,t-MA) and Sphenylmercapturic acid (S-PMA) in urine. These metabolites derive from two different metabolic pathways, both occurring in the liver and each involving two steps: i.e. the oxidative bioactivation of benzene to benzene oxide as the common, first step, and oxepin
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formation with either further opening of the aromatic ring, or conjugation with reduced glutathione by means of glutathione S-transferases (GST) for t,t-MA and S-PMA, respectively, as the second step (Rappaport et al., 2009). The levels of exposure to benzene in the workplace nowadays are generally low. As a result, the challenges for occupational health professionals in assessing the health risk from benzene exposure are twofold: to choose sensitive biomarkers of exposure and to avoid important confounding factors such as smoking habits. For instance, in a cohort of petrochemical workers exposed to low benzene concentration, a good correlation was observed recently in non-smokers, but only between S-PMA levels in urine and benzene concentrations in air, whereas the other urinary metabolite t,t-MA appeared to be correlated with exposure more weakly (Carrieri et al., 2010a). An additional variability factor in benzene metabolism is the influence of enzyme polymorphism. As previously mentioned, the metabolic pathway leading to the formation of S-PMA is via hepatic GST, whose classes M1, P1, and T1 are genetically polymorphic. Both GSTM1 and GSTT1 null genotypes are related to a low excretion of S-PMA in exposed subjects (Sørensen et al., 2004; Manini et al., 2006, 2010). GST enzymes, therefore, may play an important role in benzene toxicity and, interestingly, also in other, benzene unrelated diseases. The null genotype, indeed, has been shown to be associated with an increased risk of several types of cancer (Hengstler et al., 1998; Salagovic et al., 1998). The aim of the present work was to study the influence of the GST genotype on the urinary excretion of benzene metabolites, measured on different weekdays, in a cohort of petrochemical workers exposed to low air levels of benzene in a large industry of Northern Italy. 2. Materials and methods 2.1. Chemicals and suppliers The analytical reference standard of DL-S-PMA was purchased from Tokyo Kasei Cogio LTD (Tokyo, Japan). The internal standard, deuterated DL-S-PMA-3,3-d2 , was obtained from CDN Isotopes Inc. (Pointe-Claire, Quebec, Canada). Glacial acetic acid (100%) and ammonia have been purchased from Merck (Darmstadt, Germany). Purified water was obtained from a Milli-Q Plus system (Millipore, Milford, MA, USA). Methanol, formic acid (98%, purity), sulphuric acid (95% purity), the 50–52% (v:v) sodium hydroxide water solution, and carbon disulfide (purity >99.9%, low benzene content) have been purchased from Sigma–Aldrich (St. Louis, MO, USA). Control human urine samples, used to prepare standard calibration curves and quality control samples (QC), were obtained from non smoking healthy volunteers. SPE Vacuum Manifold and Sep-Pak Plus C18 (360 mg) cartridges for S-PMA analysis were supplied by Waters (Milford, MA, USA). SPE cartridges for t,t-MA analysis were supplied by Varian (EA Middelburg, The Netherlands). Anotop 10 LC® syringe filter devices (0.2 m pore size, 10 mm diameter) were purchased from Whatman Inc. (Maidstone, England). For S-PMA and t,t-MA analysis, a Supelco Discovery C18 HPLC column (150 mm × 4.6 mm, 5 m film thickness) and a Ultrasphere C18 HPLC column (250 mm × 4.6 mm, 5 m film thickness) were purchased from Sigma–Aldrich (Bellafonte, PA, USA) and Beckman Coulter (Fullerton, CA, USA), respectively. Diffusive air samplers (Radiello® ) were supplied by the Salvatore Maugeri Foundation–Environmental Research Centre (Padua, Italy).
2.2. Study population and sample collection Twenty eight male workers (22 non smokers and 6 smokers, mean age 41.8 ± 8.5 years) employed in outdoor operations at a petrochemical plant in Northern Italy were included in the study. Each worker provided one spot fresh urine sample at the end of the work-shift for benzene biomonitoring, and an oral mucosa cell sample for genotyping. Informed written consent to take part in the study was obtained from all subjects before enrollment. The environmental and biological monitoring procedures were carried out throughout ten consecutive days, on two different occasions with an interval of two years between the two sampling campaigns. A total of 173 environmental and biological samples (95 during the first and 78 during the second campaign) were collected, one to fourteen times for each worker. Benzene exposure was measured during the entire work-shift (approximately 8 h) in all subjects at the breathing zone level, using a personal diffusive sampler containing an active carbon cartridge (Radiello® ). On the same day of personal airborne benzene monitoring,
an urine sample was collected at the end of the work shift from each worker for measuring the metabolites. A buccal swab was used to obtain a sample of cells for DNA analysis. Swabs were collected in separate vials and kept at −20 ◦ C until DNA analysis using the standard Chelex® 100 method for DNA extraction. 2.3. Analytical methods for benzene and its metabolites The analysis of the airborne benzene samples was performed by GC–FID after desorption of benzene from the active carbon with carbon disulfide. The concentration of S-PMA in urine was determined according to a HPLC–MS/MS method that comprises an (quantitative) acidic hydrolysis of its urinary precursor pre-S-PMA (Paci et al., 2007). The detection was in negative ions, MRM mode, and the transitions were the following: −238.1 → −109.1 for S-PMA and −240.1 → −109.1 for the deuterium labelled internal standard. Urinary t,t-MA analysis was carried out, after solid phase extraction (SPE), by an HPLC–UV analytical method with detection at 264 nm, as described elsewhere (Carrieri et al., 2006). Values measured for both S-PMA and t,t-MA were adjusted to urinary creatinine concentration, based on basic-picrate Jaffe’s reaction (Jaffe, 1886). 2.4. GST polymorphism analysis GSTT1 and GSTM1 polymorphisms were analyzed in oral mucosa cells by standard polymerase chain reaction (PCR)-based methods. The genes of interest were amplified by PCR, using a “PCR sprint” (Hybaid) thermal-cycler. A multiplex system was created for the simultaneous amplification of the genetic loci of GSTM1 and GSTT1. To verify the correct completion of the PCR reaction, the gene of beta-globin, that is unrelated to GST and with a molecular weight clearly distinct from that of either the GSTM1 or GSTT1 genes, was amplified in the same system as a control. The oligonucleotide sequences of primers were: for GSTM1, forward primer: 5 -GAA CTC CCT GAA AAG CTA AAG C-3 and reverse primer: 5 -GTT GGG CTC AAA TAT ACG GTG G-3 ; for GSTT1, forward primer: 5 -TTC CTT ACT GGT CCT CAC ATC TC-3 ; reverse primer: 5 -TCA CCG GAT CAT GGC CAG CA-3 ; and for BETA-GLOBIN: forward primer: 5 -CAA CTT CAT CCA CGT TCA CC-3 ; reverse primer: 5 -GAA GAG CCA AGC ACA GGT AC -3 . Each PCR reaction was performed with the following: 2.5 ml cell extract (5–250 ng DNA), 0.5 mM of each primer, 2.5 ml Taq buffer (10xPCR Buffer II, Applied Biosystems), 2 ml MgCl2 25 mM (Applied Biosystems), 0.5 ml dNTPs mix (10 mM PCR Nucleotide Mix, Promega), and 1 U Taq polymerase (DyNAzyme II DNA Polymerase, Finnzymes) in a total volume of 25 ml. Thirty amplification cycles (denaturation at 95 ◦ C for 1 min, annealing at 60 ◦ C for 1 min, and extension at 72 ◦ C for 1 min) were performed (Walsh et al., 1991). Finally, the products of the reaction were vertically electrophoresed at 2000 V, max mA, max W for 150 min on 0.4 mm layer polyacrilamide denaturing gels (6% – urea 7 M) in TBE buffer 1×, and then revealed by silver staining (Budowle et al., 1991; Bassam et al., 1991). The migration bands of interest (480 bp for GSTT1, 215 bp for GSTM1) were identified by comparison with the molecular weights of a specific standard marker (DNA pGEM® marker, Promega). 2.5. Statistical analysis Statistical analysis was carried out using the StatsDirect statistical software (Statsdirect 2.7.7 version, Statsdirect Ltd., UK). Differences between groups were assessed using the Mann–Whitney U-test. Multivariate analysis was also employed to evaluate the influence of different variables on metabolite excretion. Linear regression coefficient was used to analyze the correlation between airborne benzene values and excretion of its metabolites. In all tests, a P value lower than 0.05 (two-tailed) was considered as statistically significant.
3. Results 3.1. Airborne benzene On average, exposure to benzene was very low (0.034 mg/m3 ), i.e. about two orders of magnitude lower than the ACGIH TLV 2011 of 1.6 mg/m3 , and variable (range 0.002–0.895 mg/m3 ), with the highest values about twofold lower than the TLV (Table 1). No statistically significant difference was observed between smokers and non smokers (results not shown). 3.2. Excretion of urinary metabolites Smokers had a significantly higher excretion of both urinary metabolites than non smokers. Particularly so for S-PMA (P < 0.0001), whose mean value was about four times higher in smokers than in non smokers (Table 1), but also for t,t-MA (P = 0.0003). Interestingly, airborne benzene levels were lower
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Table 1 Airborne benzene levels and metabolite excretion in the studied population of petrochemical workers (according also to smoking habit).
Benzene in air (g/m3 )
t,t-MA (g/g of creatinine)
S-PMA (g/g of creatinine)
Mean Median GM Range Mean Median GM Range Mean Median GM Range
All subjects (No. 28) 173 samples
Non smokers (No. 22) 137 samples
Smokers (No. 6) 36 samples
34.5 9.2 11.5 2.1–894.7 104.2 68.6 69.0 4.7–1168.3 1.52 0.54 0.62 0.05–18.63
37.7 10.3 12.2 2.1–894.7 98.8 56.9* 61.8 4.7–1168.3 0.88 0.34** 0.39 0.05–18.63
22.4 7.0 9.0 3–383.5 124.5 120.2* 104.7 8–264.1 3.94 3.53** 3.51 1.02–11.63
GM = geometric mean. * P = 0.0003 ** P < 0.0001 (Mann–Whitney U-test) smokers vs. non smokers.
for smokers than for non smokers, but the difference was not significant. In addition, a statistically significant correlation was observed between airborne benzene levels and S-PMA excretion in all subjects (smokers and non smokers together, r = 0.447, P < 0.0001, Fig. 1) and in non smokers (r = 0.667, P < 0.0001, Fig. 2), but not in smokers (results not shown). No such correlation was observed for t,t-MA. A preliminary multivariate analysis showed that smoking habit was the only variable among others (age and body mass index) significantly influencing excretion of benzene metabolites (data not shown). For this reason, only non smokers (22 subjects) were included in the genotyping study. The data obtained from these subjects were further pooled and the mean values of benzene exposure and metabolite excretion were calculated from
Fig. 1. Correlation between airborne benzene levels and concentration of the urinary metabolites t,t-MA (above) and S-PMA (below). The analysis was performed on all samples (173) collected on different days from all subjects (smokers and non smokers).
all samples collected for each subject. The statistical analysis of these mean values showed, again, a significant correlation between airborne benzene levels and S-PMA (r = 0.640, P = 0.0015, Fig. 3) but not t,t-MA urinary values (results not shown). Any correlation existing between urinary metabolites and benzene exposure was also investigated individually for each non smoker (except for one subject with just one sample). The correlation was found to be statistically significant in eight subjects for S-PMA and in three subjects only for t,t-MA (Table 2). 3.3. GST polymorphism Genotyping showed that the majority of subjects, smokers and non smokers together, had at least one or both null genotypes (60.7%, 59.1% in smokers and 66.7% in non smokers) (Table 3).
Fig. 2. Correlation between airborne benzene level and concentration of the urinary metabolites t,t-MA (above) and S-PMA (below). The analysis was performed on all samples (137) collected on different days from non smokers only.
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Table 2 Genotype, number of samples and correlation between airborne benzene level and urinary metabolite concentration in each non smoker. Subject no. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 * a
GSTT1 No null No null No null No null No null No null Null Null Null Null No null No null No null Null No null Null Null Null No null No null Null No null
GSTM1 Null No null No null Null No null No null Null Null No null Null No null No null No null Null Null Null Null Null Null No null No null No null
No. of samples
t,t-MA vs. benzenea
S-PMA vs. benzenea
*
10 4 7 1 3 7 3 5 6 14 6 4 6 8 5 5 4 10 9 8 6 6
0.82* −0.30 0.23 – 0.95 0.93* 0.91 0.98* 0.77 0.91* 0.98* 0.33 −0.59 0.18 0.93* 0.75 0.88 0.99* 0.86* 0.47 0.23 0.72
0.65 −0.59 0.22 – 0.40 −0.28 −0.54 0.98* 0.67 −0.33 0.92* 0.71 0.17 −0.01 −0.35 −0.35 0.90 −0.23 −0.31 −0.18 0.13 −0.76
P < 0.05 or higher statistical significance. Linear regression coefficient between air benzene levels and metabolite concentration in urine.
Considering the confounding effect of smoking on the biological monitoring of benzene, however, the possible influence of the GST genotype on metabolite excretion was only investigated in non smokers. Adopting the ratio between urinary metabolite concentration and airborne benzene level, as reported previously (Qu et al., 2005; Carrieri et al., 2010b), the GSTT1 genotype was found to influence S-PMA excretion (P = 0.0098), being the null genotype significantly associated with a lower metabolite excretion in comparison with the non null genotype (Fig. 4). No significant influence
of the GSTM1 null or both the GSTT1 and GSTM1 null genotypes was observed on S-PMA excretion (results not shown). The excretion of the t,t-MA appeared to be unaffected by either GST polymorphism, as expected.
4. Discussion The aim of the present study was to compare S-PMA and t,tMA as biomarkers of occupational exposure to low benzene levels in air, and to investigate the role, if any, of GST polymorphism in their excretion. A cohort of 28 workers of a petrochemical industry was monitored for their environmental exposure to benzene during the entire workshift, and by measuring the solvent’s urinary metabolites. Benzene and its metabolites were monitored at different times throughout two years. For each worker a total of 1–14 samples for measuring both benzene in air and the urinary metabolites were obtained. As depicted in Section 3, benzene exposure was very low, the airborne concentration being on the average, 35 fold lower than the current ACGIH TLV® . Interestingly, non smokers had higher levels of exposures than smokers (Table 1), an observation which confirms previous findings (Fracasso et al., 2010). At this low levels of exposure, a significant correlation between urinary metabolite concentration and airborne benzene level was only observed for S-PMA, showing a better correlation in non smoking workers, but not for t,t-MA, thus confirming previous results (Carrieri et al., 2010a). The lack of any such correlation observed in smokers, confirms the confounding effect of smoking on metabolite excretion. Indeed, in the multivariate analysis smoking habit appeared to be the only variable influencing metabolite excretion. According to the present results, therefore, it is reasonable to say that, for such low levels of exposure only S-PMA should be used as
Table 3 Genotype frequency in all subjects, smokers and non smokers together.
Fig. 3. Correlation between airborne benzene levels and concentration of the urinary metabolites t,t-MA (above) and S-PMA (below). The analysis was performed on non smokers pooled results using the mean values for air and urine samples collected for each subject.
Genotype
No.
Frequency
GSTM1/GSTT1 non null GSTM1/GSTT1 null GSTM1 non null/GSTT1 null GSTM1 null/GSTT1 non null
11 10 2 5
39.3% 35.7% 7.1% 17.9%
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Fig. 4. Different excretion of S-PMA in non smokers according to the GSTT1 genotype. *P = 0.0098, Mann–Whitney U-test.
a group exposure biomarker to monitor benzene exposure in the workplace, and in non smokers only. On the other hand, it is known that t,t-MA level in urine is not specific as a biomarker of benzene exposure. It may be affected, in fact, by the ingestion of sorbic acid, a common food preservative of which t,t-MA is a metabolite too (Pezzagno and Maestri, 1997; Pezzagno et al., 1999; Weaver et al., 2000; Negri et al., 2005). In this study the correlation between benzene exposure and t,t-MA excretion showed a high line intercept and a great dispersion of data, mainly at very low levels of exposure. This was probably due to the interference on t,t-MA excretion of the diet of the subjects examined. This finding was further confirmed by the results of metabolite excretion determined separately in individual subjects for a number of consecutive days (Table 2). In fact, a statistically significant correlation between benzene levels in air and levels of metabolites in urine was observed in eight cases for S-PMA but in three subjects only, for t,t-MA. The metabolic fate of benzene is complex. In the light of current knowledge it occurs via different pathways, all involving, as the first step, the formation of a reactive intermediate, benzene-oxide. The fate of this reactive metabolite is manifold, including activation and detoxication reactions: (1) the rupture of the instable C O C bond to form phenol, an early, classic biomarker at high levels of exposure, (2) the formation of an oxepin with the subsequent opening of the aromatic ring to form muconaldehyde and t,t-MA, (3) the conjugation of benzene-oxide with reduced glutathione, by means of GST, to form the mercapturic acid S-PMA, and (4) the formation of catechol, which is considered the main bioactivation pathway (Rappaport et al., 2009). Polymorphism of several drug metabolizing enzymes is known to modulate benzene metabolism and, therefore, its biological effects. Among those enzymes are NAD(P)H:quinone oxidoreductase 1, myeloperoxidase, cytochrome P450-2E1, GSTT1 and GSTM1, with only the last two isoforms showing consistent associations with biomarkers of exposure in urine and biomarkers of effects (Dougherty et al., 2008). The main aim of the present research was to investigate the role of GSTM1 and GSTT1 polymorphism on benzene metabolite excretion following exposure to low concentrations of the solvent. Under low exposure conditions, in fact, any effect of the genetic polymorphism of benzene metabolizing
enzymes may be expected to become more apparent than under heavy exposure conditions at or near saturating concentrations. Generally, the null genotype for GSTT1 and/or GSTM1 was found to be related to a low excretion of S-PMA (Sørensen et al., 2004; Manini et al., 2006, 2010). Interestingly, however, Verdina et al. (2001) showed that the urinary t,t-MA/blood benzene ratio, but not the S-PMA/blood benzene ratio, was partially modulated by the GST genotype, significantly higher values being found in null individuals (both null genotypes combined). The results of the present study in non smoking petrochemical workers exposed to low benzene concentrations show, in agreement with those of Qu et al. (2005), that only S-PMA levels in urine are significantly influenced by the GSTT1 null genotype. The influence of the GSTT1 null genotype on S-PMA excretion, however, was quantitatively low, even when studying each subject with several samples. These results are consistent with previous findings by other groups showing an association between a lower excretion of S-PMA and the GSTT1 null genotype (Sørensen et al., 2004; Manini et al., 2010), but not with those showing an influence also of the GSTM1 null genotype, as reported by others (Manini et al., 2006, 2010). In conclusion, our results showed that (1) under conditions of exposure to low benzene concentrations, such as those of the present study – which are currently found in most occupational settings – S-PMA is preferable to t,t-MA as the metabolite to be used as an indicator of group exposure in workers, and (2) only the GSTT1, but not the GSTM1, null genotype has a low, but statistically significant influence on the levels of metabolite excretion. As a result, despite S-PMA and, to a lesser extent, t,t-MA are commonly and successfully used as group indicators of occupational exposure to low levels of benzene in air, more sensitive and specific biomarkers are needed to perform, at the individual level, a reliable biological monitoring of exposure to these very low concentrations. Conflict of interest statement The authors declare no conflict of interest. Acknowledgement This work was supported by grant MIUR-PRIN 2006, no. 2006068922-001/004/005 and INAIL project no. 14/2009.
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