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Impact of dioxins on antipyrine metabolism in firefighters
Accepted Manuscript Title: Impact of dioxins on antipyrine metabolism in firefighters Author: Yury I. Chernyak Alla P. Merinova Andrey A. Shelepchikov Sergey I. Kolesnikov Jean A. Grassman PII: DOI: Reference:
S0378-4274(16)30058-3 http://dx.doi.org/doi:10.1016/j.toxlet.2016.04.006 TOXLET 9346
To appear in:
Toxicology Letters
Received date: Revised date: Accepted date:
20-1-2016 31-3-2016 7-4-2016
Please cite this article as: Chernyak, Yury I., Merinova, Alla P., Shelepchikov, Andrey A., Kolesnikov, Sergey I., Grassman, Jean A., Impact of dioxins on antipyrine metabolism in firefighters.Toxicology Letters http://dx.doi.org/10.1016/j.toxlet.2016.04.006 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Impact of dioxins on antipyrine metabolism in firefighters Yury I. Chernyak1*, Alla P. Merinova1, Andrey A. Shelepchikov2, Sergey I. Kolesnikov1 and Jean A. Grassman3 1
East-Siberian Institute of Medical and Ecological Research, P.O. Box 1170, Angarsk, 665827,
Russia 2
A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, 33, Leninskiy
prosp., Moscow, 119071, Russia 3
Brooklyn College-CUNY, 2900 Bedford Avenue, Brooklyn, NY 11210-2889, USA
*Corresponding author: Yury I. Chernyak, East-Siberian Institute of Medical and Ecological Research, P.O. Box 1170, Angarsk, 665827, Russia Tel.: +07(3955)510415; Fax: +07(3955)554077; e-mail: [email protected]
Highlights:
Firefighter exposure to dioxins was assessed using a metabolic test.
Current firefighters excreted metabolites consistent with CYP1A2 induction.
Recent, but not former exposure, alters the metabolism of firefighters.
Abstract Antipyrine (AP) metabolism was used to assess factors associated with the activity of hepatic oxidative enzymes in firefighters. Emphasis was placed on 3-hydroxymethylantipyrine (3HMAP), the metabolite with the greatest dependence on dioxin-inducible cytochrome P4501A2 (CYP1A2) activity. AP urinary metabolites were measured by HPLC in 38 male subjects from Eastern Siberia. Subjects were divided into three groups having similar ages and BMIs: current firefighters (n = 11); former firefighters (n = 17) and non-firefighters (n = 10). Multiple regression models were constructed using the three major AP metabolites as dependent variable to assess the influence of age, smoking as urinary cotinine concentration, dioxin exposure (as either WHO-TEQ or body burden), group, and CYP1A2*F (-163C>A) genotypes. Models for the proportion of dose excreted as the metabolite 3HMAP produced the best fit (adjusted R2 = 0.46, p <0.05). When the models
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were restricted to current firefighters, only those based on 3HMAP were statistically significant (adjusted R2 of 0.80 (p <0.002)) due to contributions from urinary cotinine (β = 0.56, p <0.01) and dioxin expressed as body burden (β = 0.55, p = 0.014). These results indicate that the antipyrine test can be used as metabolic probe of biological response to recent dioxin exposure provided the impact of smoking is carefully controlled.
Key words: dioxin exposure, firefighters, antipyrine metabolism, cytochrome P4501A2 (CYP1A2), urinary cotinine level
1. Introduction Dioxins are considered to be one of the most toxic classes of anthropogenic compounds because of their ability to produce a wide variety of alterations of human homeostasis and health. Dioxins are potent cellular dysregulators which have been extensively studied as carcinogens, developmental toxicants, and endocrine disruptors (White and Birnbaum, 2009). In particular dioxin-like compounds have been implicated in diabetes, cardiovascular disease, testicular cancer, prostate cancer and non-Hodgkin's Lymphoma (IARC, 2010) and other effects. Dioxins are typically formed when organic materials are burned or heated in the presence of chlorine as happens during waste combustion, metal or cement production, and electrical generation at coal power plants (Vikelsoe and Johansen, 2000; Dopico and Gómez, 2015). Some congeners such as 2,3,7,8tetrachlorodibezo-p-dioxin
(TCDD),
and
2,3,4,7,8-pentachlordibenzodioxin
(PeCDD)
have
biological half lives lasting for years; for example, 7 years for TCDD and 15.7 years for PeCDD (Flesch-Janys et al., 1996). Moreover, the biological half life of a congener can be influenced by age, percent body fat, smoking status and breast feeding (Milbrath et al., 2009). Most of the effects of dioxins are mediated by the Ah-receptor (AhR) which plays a central role in the induction of the Phase I enzymes and acts as a modulator of cellular signaling pathways (Whitlock, 1993). Firefighter exposure to dioxins is usually accompanied by a complex mixture of other compounds (Edelman et al., 2003; Laitinen et al., 2012).
High affinity dioxins may
successfully compete for the AhR and in doing so may alter the toxicity of other agents such as polycyclic aromatic hydrocarbons whose conversion to active metabolites is dependent on CYP1
2
family (Denison and Nagy, 2003). The resultant mix of exposure agents means that some, but not all of the toxicity experienced by firefighters is mediated by the AhR pathway. CYP1A2, along with CYP1A1 and CYP1B1, is an AhR-responsive oxidative enzyme whose dose dependent induction by dioxins occurs in both humans and rodents. In rodents, this induction results in the sequestration of TCDD and PeCDD in the liver (Diliberto et al., 1997). Furthermore, Grassman et al. (2002) found high and significant correlation to the total TEQ with the expression of CYP1A2 in human liver samples taken from post-mortem donors. CYP1A2 is predominantly a hepatic enzyme and as such, CYP1A2 mRNA is not detectable in lymphocytes (Krovat et al., 2000). Because of this, expression of the enzyme is assessed indirectly through the measurement of the products of CYP1A2-dependent metabolism. Both caffeine (Halperin et al., 1995; Abraham et al., 2002) and antipyrine (AP) (Ostashevsky et al., 1994) have been used as metabolic probes in dioxin exposed populations. AP was used to evaluate CYP1A2-dependent activity in residents of South Vietnam living in regions that had been sprayed with Agent Orange (Ostashevsky et al., 1994). Although dioxin body burdens were not measured, a significant correlation between the pattern of urinary AP metabolites and CYP1A1 activity (benzopyrene-hydroxylase) in blood lymphocytes of humans was found. Halperin et al. (1995) examined the effect of occupational exposure to chemicals contaminated with 2,3,7,8-TCDD on CYP1A2 activity by using caffeine as a substrate and subsequently measuring the caffeine metabolite ratio (CMR). The authors did not find a significant association between the CYP1A2 activity and serum TCDD, whereas cigarette smoking induced CYP1A2. Abraham et al. (2002) measured caffeine demethylation in two individuals who were highly exposed to TCDD. Eighteen months after the presumed time of intoxication, they found a more than 10-fold induction of hepatic CYP1A2 enzymes. Specifically, the two poisoned individuals had CMRs of 39.3 and 29.8, compared with a mean CMR of 3.65 in 30 nonsmokers. A positive association between the serum concentration of PCB-105 (one of three dioxin-like congeners that were investigated) and CMR was found in a study by Petersen et al. (2006). Lambert et al. (2006) used the caffeine breath test to measure CYP1A2 activity in members of the Yucheng cohort who were exposed to PCBs at levels far above those measured by Petersen and team when they consumed contaminated rice oil. After 16-17 years of exposure, they had levels of CYP1A2 activity that were double the levels measured in breath samples and were correlated with serum TEQ, consisting predominantly of PCDFs and PCBs (Lambert et al., 2006). Current evidence demonstrates that firefighters are occupationally exposed to dioxins (Kelly et al., 2002; Schecter et al., 2002; Edelman et al., 2003; Hsu et al., 2011; Chernyak et al., 2012; 3
Shaw et al., 2013). Consequently, there is a need for methods that measure the biological response of workers, which is determined not only by the toxicant dose, but also by the activity of enzymes involved in its biotransformation. The purpose of this study is to employ antipyrine metabolism as a metabolic probe to measure factors that influence the activity of hepatic oxidative enzymes, predominantly CYP1A2, in current, former, and non-firefighters.
2. Materials and Methods 2.1. Selection of the cohort and blood donors In 2009-2010 we examined dioxin levels in 40 men, thirty of whom were recruited from a cohort of 165 firefighters originally assembled in 2003 to study dioxin exposure and health effects following the 1992 Shelekhov fire (Chernyak et al., 2004). This examination included an additional 10 men who were recruited to serve as non-firefighter controls. The thirty firefighters were grouped according to their status as current or former firefighters. The formation of the firefighter groups was complicated due to the limited number and inaccessibility of candidates for a variety of reasons, including change of residence and death. Approximately 20 of those contacted declined to participate. Men were screened to ensure their body mass index (BMI) and ages were comparable. All participants are from similar economic strata within Irkutsk Oblast. Information on demographic, familial, occupational, and personal characteristics including smoking habits, diet, hobbies, and illnesses was obtained through an oral questionnaire. Informed consent, which included authorization for blood and urine sampling and banking, was provided by all participants. The study protocol was approved by the Biomedical Ethics Committee of East-Siberian Scientific Center of Siberian Branch of Russian Academy of Medical Sciences in Irkutsk and the Brooklyn CollegeCUNY Institutional Review Board.
2.2. Measurement of serum dioxin concentrations After overnight fasts, each participant provided 40-50 ml of blood from which serum was obtained using a standard procedure. Seven polychlorinated dibenzo-p-dioxin (PCDD), 10 polychlorinated dibenzofuran (PCDF), and 12 polychlorinated biphenyl (PCB) congeners were analyzed by gas chromatography/high-resolution mass spectrometry in each of the samples at the A.N. Severtsov Institute of Ecology and Evolution (Moscow) according to the protocol previously described (Chernyak et al., 2012). The system of toxicity equivalence factors developed by the WHO in 2005 was used to calculate total toxicity equivalent (TEQ) (Van den Berg et al., 2006). Measurements below the limit of detection (LOD) were assigned a value representing the level of 4
detection divided by the square root of 2 as recommended by the US Centers for Disease Control and Prevention (Hornung and Reed, 1990). Results are expressed as WHO-TEQ and as body burden. Dioxin exposure as body burden was calculated using the measured TEQ and the following formula for percent of lipids in the body: %Lipids = 495/(1,0324 – 0,19077(log(waist – neck)) + 0,15456(log(height))) – 450) (Hodgdon and Beckett, 1984).
2.3. Antipyrine test The current study used antipyrine (AP) (pharmaceutical purity, Fluka Chemical Co., Milwaukee, WI, USA) as a metabolic probe to measure the activity of hepatic oxidative enzymes. Because AP has not been used as an analgesic in recent years, there was no possibility that participants were exposed outside of the study. AP metabolism was assessed by HPLC performed on urine samples obtained for 38 from 40 participants, two individuals from current firefighters group were excluded because of medical conditions. The subjects were instructed to abstain from ingesting medication 48 hours before the AP dosage until the end of the urine collection. After voiding the bladder, urine was collected for 24 hours following the ingestion of 18 mg AP/kg body weight. Each 24-hour sampling was collected in a vessel containing 200 mg of Na2S2O5 as a stabilizer. After mixing, two 1 ml samples of the collected urine were obtained and stored at -20˚С before analysis. Samples for liquid chromatography were prepared as previously described by enzymatically hydrolyzing the conjugate metabolites with Type H-3 β-glucuronidase from Helix pomatia (Sigma Aldrich Chemical Co., St. Louis, MO) (Rakhmanov et al. 1989; Teunissen et al. 1983). After hydrolysis, a two stage extraction was performed. The first stage extracted the metabolites 4-hydroxyantipyrine (4HAP) and norantipyrine (NAP) with dichloromethane : n-pentane (3:7, v/v).
The second stage used
dichloromethane to extract 3-hydroxymethylantipyrine (3HMAP) and AP. Urinary AP and metabolites were analyzed by HLPC ("Milichrom-A02", EcoNova, Russia) using a Silasorb SPH C18, 5 μm, 2 х 75 mm column. Detection was at λ = 244 nm at 45˚С. Phenacetin (Sigma Aldrich Chemical Co., St. Louis, MO) was used as internal standard. Elution was performed using eluent А, methanol with 0.05 M phosphate buffer, pH 6.7 (10:90), and eluent B, 90% methanol. The eluent flow rate was 200 μl/min. The components were resolved under isocratic conditions with 7% eluent B for 10 minutes followed by gradient elution from 7 to 100% eluent В for 4.5 minutes. In some samples, a non-identified impurity interfered with the resolution of the phenacetin peak. In these cases, repeated isocratic chromatography with 20% eluent В for 10 minutes successfully resolved the phenacetin and the impurity. A four-point standard curve was used to calculate the concentration 5
of АР and the metabolites. The standard curves were linear and had correlation coefficients of at least R2 > 0.98. Urinary AP metabolites levels are presented as percent of the total ingested AP dose.
2.4. Genotype analyses Venous blood was collected for studies of CYP1A2 gene polymorphisms during the medical examination. DNA was extracted using the "DNA-sorb-B" commercial kit (Institute of Epidemiology, Moscow). The CYP1A2*F (-163C>A) genotypes (CC, CA, and AA for CYP1A2 low, medium, and high activity, respectively) was carried out by restriction fragment-length polymorphism analysis as described previously (Chida et al., 1999). The primers (Medigene, Novosibirsk) F: 5’-CCC AGA AGT GGA AAC TGA GA-3’and R: 5’-GGG TTG AGA TGG AGA CAT TC-3’, and ApaI restriction endonuclease (Thermo Fisher Scientific Inc.) was used for genotyping. The results of electrophoresis in 1.5% agarose gel were evaluated in transmitting UV light after ethidium bromide staining.
2.5. Measurement of tobacco exposure Smoking status was determined by the excretion of urinary cotinine measured with a Cotinine Direct ELISA Kit (Bio-Quant, Inc.) using a universal microplate reader ELx800 (Bio-Tek Instruments, Inc.). Urine samples for cotinine assessment (approximately 30 ml from every participant) were obtained prior to the blood collection and the ingestion of AP. 2.6. Statistical analysis We used Statistica software package (v. 6.1, StatSoft Inc., OK, USA) to perform all statistical analysis. Data were tested for normality by Shapiro-Wilk’s W test and log-transformations or nonparametric tests were used when appropriate. Differences between the three groups were assessed using one-way analysis of variance (Median Test / Kruskal-Wallis ANOVA) and followed by Mann–Whitney U non-parametric tests. Multiple linear regression analysis was performed for 38 individuals and for every group subjects to evaluate the validity of the metabolic test indices as potential biomarkers of effect. Antipyrine test indices were the dependent variables and potentially modifying factors were added to the models as predictor variables: age, cotinine level (decimal-logtransformation), dioxin exposure (WHO-TEQ or body burden), group (two dummy variables (nonfirefighters (0/0), current firefighters (1/0), and former firefighters (0/1)), CYP1A2*F (-163C>A) genotype (genotype codes 0 (CC), 1 (CA), and 2 (AA)). The distribution of residuals was examined for each model. A confidence level of 0.05 was used as the criteria for statistical significance.
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3. Results 3.1. The study participants Table 1 shows the characteristics of the 38 male participants based on their employment status as current, former, or non-firefighters. The three employment-based groups were similar in age and in BMI. Fourteen out of the 17 former firefighters were disabled at the time of examination. Current and former firefighters had similar years of professional experience. Smokers made up 5 (45.4 %), 10 (58.8 %) and 4 (40 %) of the current, former, and non-firefighters, respectively. Average urinary concentrations of cotinine, the most abundant metabolite of nicotine, did not differ between groups but showed considerable inter-individual variability: 2235-24734 ng/ml for smokers and 0.9-158 ng/ml for nonsmokers. Dioxin exposure was higher in firefighters than in non-firefighters. When expressed as body burden, the difference was statistically significant for the three groups (p = 0.013) although neither of the two group-wise comparisons were significant (p = 0.078 and p = 0.056 for current and former firefighters compared to non-firefighters, respectively). The three antipyrine metabolites, NAP, 4HAP, and 3HMAP, were excreted in similar proportions by each of the groups. The frequency distribution of CYP1A2*1F (-163C>A) genotypes in the cohort did not deviate from the HardyWeinberg equilibrium (χ2 = 0.43, p > 0.05).
3.2. Models for AP metabolites The impact of dioxin exposure and potential modifying factors are shown in Table 2. Each of the three major antipyrine metabolites were used as dependent variables; age, log transformed concentration of urine cotinine, dioxin exposure (TEQ or body burden), two dummy variables for group, and CYP1A2 genotypes were used as independent variables. Each model incorporated 4 to 6 of the independent variables. The best fit was obtained with models for 3HMAP (adjusted R2 = 0.46, p <0.01), the metabolite with the greatest dependence on CYP1A2 activity. Smoking, measured as urinary cotinine, was a significant contributor to the formation of 4HAP and 3HMAP (beta regression coefficients (β): 0.50 to 0.52, p <0.01 and 0.68 to 0.71, p <0.01 respectively) but not NAP. Urinary AP metabolites levels were not affected by group, level of dioxin exposure, or CYP1A2 genotype in any of the models.
3.3. Models for AP metabolites in current firefighters Models using only data from current firefighters were explored to more fully examine the impact of recent dioxin exposure. Four models of antipyrine metabolite excretion based on urinary 7
cotinine, dioxin exposure expressed as TEQ or body burden, and either age or CYP1A2 genotype were studied (Table 3). Only models based on 3HMAP were statistically significant and urinary cotinine was significant only in these models. The model with the best fit expressed exposure as body burden with an adjusted R2 of 0.80 (p <0.002). The regression coefficient for this exposure variable was also substantial and significant (β = 0.549, p = 0.0138). The same model was characterized by the best distribution of residuals (specifically, Durbin-Watson d = 2.34, ShapiroWilk W = 0.917 p < 0.297, evidence of homoscedasticity). For all 3HMAP models the effect of smoking was significant (β ranging from 0.56 to 0.77, р <0.05). Neither age nor CYP1A2 genotype were significant in any of models.
4. Discussion The current study describes the application of the AP metabolic test to detect dioxindependent variations in hepatic oxidative metabolism in male workers having distinct occupational histories as current firefighters, former firefighters, and non-firefighters. The AP test is used to assess CYP1A2, an hepatic oxidative enzyme which is sensitive to induction by environmental agents such as cigarette smoke (Pavanello et al., 2002) and dioxins (Budinsky et al., 2010). Metabolism-based, rather than transcript-based measures are required because CYP1A2 is not expressed in easily accessible biological targets such as peripheral blood monocytes or epithelial cells. A person’s enzyme activity can be assessed by measuring the proportion of three major products of oxidative AP metabolism excreted in the urine after administration of a known dosage. CYP1A2 contributes to the formation of about 50% of the 3HMAP, 30% of the 4HAP and 20-25% of the NAP (Pelkonen et al., 1998). Other metabolic tests such as the caffeine test are prone to interference due to the ubiquitous consumption of caffeine and related chemicals. The exposure of this cohort to a recognized inducer of CYP1A2, cigarette smoke (Pavanello et al., 2002), provided an opportunity to verify that the metabolites measured by the AP test are consistent with CYP1A2 enzyme activation. This was apparent both with our full cohort of 158 firefighters where exposure to cigarette smoke was self reported and also in the present study where cigarette smoke was assessed by measuring the urinary excretion of cotinine. In the full cohort of firefighters who self-identified as smokers were found to metabolize a larger proportion of the dose than non-smokers, specifically because they converted more of the AP to 4HAP and 3HMAP rather than NAP (Chernyak et al., 2005). The difference between smokers and non-smokers was detectable even though, due to the self-reported nature of these data, the results do not account for nicotine content of cigarette brand, and inter-individual differences in smoking technique such as number of 8
inhalations per cigarette, inhalation volume, percentage of smoke inhaled into the lung, and amount of time smoke is held in the lung before exhaling. In addition, non-smokers are likely to have substantial exposure to at least some second-hand smoke given the prevalence of smoking in the region. In the current study, urinary excretion of cotinine was most correlated with the 3HMAP, somewhat correlated with 4HAP and not related to NAP. Together, these observations demonstrate that the AP assay is responsive to smoking status and detects a pattern of urinary metabolites that is consistent with CYP1A2 induction. Occupational status is associated with different levels of dioxin exposure, a finding that is based on the current study as well as a previous analysis that included members of the cohort who had a group of neurological syndromes related to their participation in the 1992 Shelekhov fire (Shelekhov syndromes) (Chernyak et al., 2011). The present study found that current firefighters have higher dioxin exposures than either former firefighters or men who are not employed as firefighters. This difference reaches significance only when dioxins are expressed body burden and based on previous analyses, is attributable to higher levels of PCBs and PCDFs (Chernyak et al., 2012). The lowest dioxin exposures are found in men who do not work as firefighters with the levels in former firefighters being intermediate. Our studies also suggest a tendency for dioxin exposure to correlate with formation of 3HMAP. In the present study, although not statistically significant, the current workers had the highest dioxin body burdens and the highest proportion of 3HMAP; non-firefighters had the lowest of both. An earlier analysis of a different subcohort of workers who were grouped according to their occupation as firefighters and whether they suffered from Shelekhov syndromes found that current firefighters metabolize a larger proportion of the AP dose to 3HMAP (Chernyak et al., 2011). Almost all models indicate a slight inverse association (not always statistically significant) between main AP metabolites level and the age. This is consistent with the recognized age-related decrease of functional activity of most CYPs, especially CYP1A2 (Doki et al. 2009). The absence of significance may be due to the limited variation of age among the 38 participants. Others have found that the CYP1A2*1F polymorphism has an inducing effect on CYP1A2 activity (Gunes et al., 2009; Dobrinas et al., 2011). This study found no similar effect possibly because the effect was too small to be detected in a study of this size. Although efforts were made to control for non-dioxin related factors that influence the level of CYP1A2 enzyme, they do not account for the pattern of exposure giving rise to the measured serum dioxin levels. Former and current firefighters in this cross sectional study had similar body burdens of dioxins in 2010 stemming from distinctly different exposure histories. Both groups have 9
had extensive opportunity for exposure to combustion products since the region of Russia where they are employed undergoes a remarkable number of annual wildland fires. The dioxin exposure of the former firefighters incorporates two phenomena: first, a “spike” of exposure stemming from their participation in the 1992 Shelekhov fire and secondly, the gradual decline expected after periods of accumulation during active firefighting. In contrast, current firefighters may or may not have had the initial spike related to the Shelekhov fire but they have continued to be exposed to combustion products, including dioxin-like compounds due to their continued employment. These temporal differences in the patterns may obscure the relationship between dioxin exposure and the production of CYP1A2 dependent metabolites because be the lack of recent occupational exposure capable of inducing the dioxin signaling pathway among the former firefighters. Epidemiological studies and in vitro models suggest that the expression of dioxin signaling pathway, of which CYP1A2 is a constituent, may lose responsiveness in the absence of new exposures (Landi et al., 2003). In an effort to examine the dioxin signaling pathway in a manner that captures the impact of recent exposures, the multiple regression analysis was performed using only the results from current firefighters. For current firefighters, body burden was significantly related to the formation of 3HMAP (β = 0.55, p = 0.014) whereas this relationship was not evident in former firefighters which lack recent exposures to dioxins (β = -0.14, p = 0.550). Other investigators have observed a similar lack of relationship among long term workers exposed to TCDD 15-37 years before the examination (Halperin et al., 1995). Assessment of AP metabolism augments current serum measurements since it reflects the impact of congeners currently not incorporated into the TEQ. Examples include the CYP1A2inducing polybrominated dibenzo-p-dioxins, dibenzofurans, and some dioxin-like biphenyls that have been detected in firefighters (Shaw et al., 2013). Firefighters currently engaged in firefighting activities are exposed to dioxin-like compounds that distinguishes them from the general human population having only environmental exposure. Consequently, assessment of their risk would be more informative by the inclusion of brominated analogues in addition to the chlorinated congeners (van den Berg et al., 2013). This will require developing methods to detect the TEQ contribution of brominated dioxins within mixtures of halogenated congeners. A more comprehensive measure of TEQ will improve assessments of the relationship between dioxin exposure and CYP1A2 activity. Despite the significance of our findings, the present study has a number of limitations which should be taken into account. Primarily, it included a relatively small number of subjects. Realizing this, we sought to minimize the number of independent variables (in this case, up to three) in the analysis focused on current firefighters. As stated earlier, we used a non-specific substrate for 10
measuring CYP1A2 activity. Based on the significance of these findings, this study should be replicated using current firefighters from a different population and a comparison with other CYP1A2 substrates.
Conclusion These results support the conclusion that dioxins induce the metabolism of CYP1A2 in individuals who are currently experiencing active bouts of exposure. This observation is contingent upon dioxin being expressed as body burden rather than TEQ and uses the formation of 3HMAP as a surrogate for direct measurement of CYP1A2. The effect of dioxin is only evident when the level of smoking, detected as urinary excretion of cotinine, is controlled in the regression model. Moreover, a
statistically significant association between dioxin exposure as body burden and
3HMAP level in urine, AP metabolite most dependent on induction of dioxins CYP1A2 was found when the analysis focused only on current firefighters, presumably because the contemporary nature of their exposure results in an activated signaling pathway. Overall, this pattern is consistent with but not diagnostic of CYP1A2 activity. These results will enhance the ability to link urinary 3HMAP formation, an intermediate phenotype, with health outcomes in future studies of populations with recent exposure to dioxins. This work contributes to the understanding of the factors contributing to the modification of the hepatic Phase I oxidative enzymes activity, the mechanism of toxicity, and the integrated impact of all compounds acting through the dioxinsignaling pathway.
Conflict of interest statement The authors declare no conflicts of interest.
Acknowledgements This work was supported in part by the Russian Foundation for Basic Research (project no. 08-04-91119) and U.S. Civilian Research & Development Foundation (project no. RUB1-2917-AN07). The authors are grateful to physicians Irina N. Kodinets and Svetlana A. Vasilieva for medical assistance during conducting of the AP test. We especially thank the firefighters and the controls, who participated in the examinations.
11
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Halperin, W., Kalow, W., Sweeney, M.H., Tang, B.K., Fingerhut, M., Timpkins, B., Wille, K., 1995. Induction of P-450 in workers exposed to dioxin. Occup. Environ. Med. 52, 86-91. Hodgdon, J.A., Beckett, M.B., 1984. Prediction of percent body fat for U.S. Navy men from body circumferences and height. Report no. 84-11, Naval Health Research Center, San Diego, CA. Hornung, R.W., Reed, L.D., 1990. Estimation of average concentration in the presence of nondetectable values. App. Occup. Environ. Hyg., 5, 46-51. Hsu, J.-F., Guo, H.-R., Wang, H.W., Liao, C.-K., Liao, P.-C., 2011. An occupational exposure assessment of polychlorinated dibenzo-p-dioxin and dibenzofurans in firefighters. Chemosphere. 83, 1353-1359. IARC, 2010. Painting, firefighting, and shiftwork. IARC Monographs on the Evaluation of Carcinogenic Risks to Humans, vol. 98, pp. 9 –764. Kelly, K.J., Byrne, M., Connelly, E., Reinhold, G.A., Preznat, D.J., 2002. Assessment of health effects in New York City firefighters after exposure to polychlorinated biphenyls (PCBs) and polychlorinated dibenzofurans (PCDFs): The Staten Island Transformer Fire Health Surveillance Project. Arch. Environ. Health. 57, 282-293. Krovat, B.C., Tracy, J.H., Omiecinski, C.J., 2000. Fingerprinting of cytochrome P450 and microsomal epoxide hydrolase gene expression in human blood cells. Toxicol. Sci. 55, 352360. Laitinen, J., Mäkelä, M., Mikkola, J., Huttu, I., 2012. Firefighters’ multiple exposure assessments in practice. Toxicol. Lett. 213, 129-133. Lambert, G.H., Needham, L.L., Turner, W., Lai, T.J., Patterson, D.G. Jr, Guo, Y.L., 2006. Induced CYP1A2 activity as a phenotypic biomarker in humans highly exposed to certain PCBs/PCDFs. Environ. Sci. Technol. 40, 6176-6180. Landi, M.T., Bertazzi, P.A., Baccarelli, A., Consonni, D., Masten, S., Lucier, G., Mocarelli, P., Needham, L., Caparaso, N., Grassman, J., 2003. TCDD-mediated alterations in the AhRdependent pathway in Seveso, Italy, 20 years after the accident. Carcinogenesis. 24, 673-680. Milbrath, M.O., Wenger, Y., Chang, C.-W., Emond, C., Garabrant, D., Gillespie, B.W., Jolliet, O., 2009. Apparent half-lives of dioxins, furans, and polychlorinated biphenyls as a function of age, body fat, smoking status, and breast-feeding. Environ. Health Perspect. 117, 417– 425. Ostashevsky, V.A., Gerasimov, K.E., Tsyrlov, I.B., Roumak, V.S., 1994. Investigation of monooxygenase oxidation enzymes in lymphocytes and human liver exposed to chlorophenoxyherbicides [in Russian]. Trudi Rus Acad Nauk: Biol. Byull. 1, 56-62. Pavanello, S., Simioli, P., Lupi, S., Gregorio, P., Clonfero, E., 2002. Exposure Levels and Cytochrome P450 1A2 Activity, but not N-Acetyltransferase, Glutathione S-Transferase (GST) M1 and T1, Influence Urinary Mutagen Excretion in Smokers. Cancer Epidemiol. Biomarkers Prev. 11, 998-1003. Pelkonen, O., Mäenpää, J., Taavitsainen, P., Rautio, A., Raunio, H., 1998. Inhibition and induction of human cytochrome P450 (CYP) enzymes. Xenobiotica. 28, 1203-1253. Petersen, M.S., Halling, J., Damkier, P., Nielsen, F., Grandjean, P., Weihe, P., Brøsen, K., 2006. Caffeine N3-demethylation (CYP1A2) in a population with an increased exposure to polychlorinated biphenyls. Eur. J. Clin. Pharmacol. 62, 1041-1048. Rakhmanov, I.A., Semenyuk, A.V., Slyn’ko, N.M., Sviridov, A.V., Kolesnikov, S.I., 1989. Quantitative determination of antipyrine and its metabolites in urine by the method of microcolumn HPLC. Pharm. Chem. J., 23, 253-256. Schecter, A., Pavuk, M., Amirova, D.A., Grosheva, E.I., Päpke O., Ryan, J.J., Adibi, J., Piskac, A.L., 2002. Characterization of dioxin exposure in firefighters, residents, and chemical workers in the Irkutsk Region of Russian Siberia. Chemosphere, 47, 147-156. Shaw, S.D., Berger, M.L., Harris, J.H., Yun, S.H., Wu, Q., Liao, C., Blum, A., Stefani, A., Kannan, K., 2013. Persistent organic pollutants including polychlorinated and polybrominated 13
dibenzo-p-dioxins and dibenzofurans in firefighters from Northern California. Chemosphere, 91, 1386-1394. Teunissen, M.W.E., Meerburg-Van der Torren, J.E., Vermeulen, N.P.E., Breimer, D.D., 1983. Automated high-performance liquid chromatographic determination of antipyrine and its main metabolites in plasma, saliva and urine, including 4,4’-dihydroxyantipyrine. J. Chromatogr. 278, 367-378. Van den Berg, M., Birnbaum, L.S., Denison, M., De Vito, M., Farland, W., Feeley, M., Fiedler, H., Hakansson, H., Hanberg, A., Haws, L., Rose, M., Safe, S., Schrenk, D., Tohyama, C., Tritscher, A., Tuomisto, J., Tysklind, M., Walker, N., Peterson, R.E., 2006. The 2005 World Health Organization Reevaluation of Human and Mammalian Toxic Equivalency Factors for Dioxins and Dioxin-Like Compounds. Toxicol. Sci. 93, 223–241. Van den Berg, M., Denison, M.S., Birnbaum, L.S., Devito, M.J., Fiedler, H., Falandysz, J., Rose, M., Schrenk, D., Safe, S., Tohyama, C., Tritscher, A., Tysklind, M., Peterson, R.E., 2013 Polybrominated dibenzo-p-dioxins, dibenzofurans, and biphenyls: inclusion in the toxicity equivalency factor concept for dioxin-like compounds. Toxicol. Sci. 133, 197-208. Vikelsoe, J., Johansen, E., 2000. Estimation of dioxin emission from fires in chemicals. Chemosphere. 40, 165–175. White, S.S., Birnbaum, L.S., 2009. An overview of the effects of dioxins and dioxin-like compounds on vertebrates, as documented in human and ecological epidemiology. J. Environ. Sci. Health., Part C: Environ. Carcinog. Ecotoxicol. Rev. 27, 197–211. Whitlock, J.P., 1993. Mechanistic aspects of dioxin action. Chem. Res. Toxicol. 6, 754-763.
14
Table 1. Characteristics of the study participants (Mean, min–max). current former firefighters firefighters n=11 n=17 Parameters Age, yrs 42.5 (33-51) 45.0 (35-54) BMI, kg/m2 Disabled, # b Operational experience as firefighters, yrs Smoking Current smokers, n (%) Urine cotinine, ng/ml
non-firefighters n=10 45.4 (38-52)
0.97 8 26.8 (24.1-29.6) 27.4 (21.7-36.0) 25.4 (21.1-30.3) 0.52 8 0 14 0 16.4 (9-29)
14.4 (9-25)
0
5 (45.4) 5634 (6.618354)
10 (58.8) 7733 (4.924734)
4 (40) 4369 (0.918792)
Dioxin exposure TEQ, pg/g lipids
23.1 (9.2-46.0)
21.0 (12.0-34.3)
17.3 (6.1-34.5)
body burden, ng
425 (173-1148)
410 (165-877)
285 (100-695)
AP metabolites NAP c 4HAP c 3HMAP c
Рa
13.6 (11.0-16.6) 13.5 (11.1-18.0)
0.60 0 0.33 0 0.01 3
13.0 (8.7-18.6)
0.80 8 25.4 (20.0-33.4) 27.3 (21.2-33.5) 26.4 (14.0-39.8) 0.99 7 23.9 (14.4-31.8) 26.0 (16.3-27.9) 18.3 (14.7-26.5) 0.48 8
CYP1A2*F (-163C>A) genotype, n (%) CC 1 (9) 3 (18) 1 (10) CA 4 (36) 7 (41) 4 (40) AA 6 (55) 7 (41) 5 (50) a - Significance level (p) in intergroup comparison: one-way analysis of variance (Median Test ANOVA); b – the number of the firefighters whose disability is related to the fire suppression at the cable factory in 1992; c – NAP - norantipyrine, 4HAP - 4-hydroxyantipyrine and 3HMAP - 3-hydroxymethylantipyrine as % of total AP dose, the data are expressed as the median value (Me) and interquartile range (LQ- UQ: upper limit of the lower quartile and lower limit of the upper quartile).
15
Table 2. Multiple regression models for AP metabolites in urine (n=38)
Independent variables Exposure - TEQ, pg/g lipids Exposure - body burden, ng Age, yrs
NAP 0.03 NI
4HAP -0.03
3HMAP -0.00
NAP NI
NI
NI
0.18
0.32 0.13
-0.18
-0.05
0.35* 0.07
Models Dependent variables a 4HAP 3HMAP NAP 4HAP NI NI -0.05 0.01 0.01 0.02 NI NI -0.19
-0.06
0.33 0.13
-0.17
3HMAP 0.05
NAP NI
4HAP NI
3HMAP NI
NI
0.18
-0.02
0.03
-0.10
0.35* 0.07
-0.18
-0.09
Urine cotinine, ng/ml b 0.52** 0.71** 0.51** 0.71** 0.50** 0.69** 0.50** 0.68** c Group current vs non0.08 -0.09 0.18 0.02 -0.10 0.18 NI NI NI NI NI NI firefighters former vs non0.04 -0.14 -0.06 -0.00 -0.15 -0.06 NI NI NI NI NI NI firefighters CYP1A2 genotype d NI NI NI NI NI NI 0.07 0.06 0.18 0.05 0.07 0.18 2 Adjusted R 0.00 0.17 0.46 0.03 0.17 0.46 0.03 0.18 0.46 0.06 0.19 0.46 (F statistic) (1.0) (2.6)* (7.3)** (1.2) (2.5)* (7.3)** (1.3) (3.2)* (8.9)** (1.6) (3.1)* (8.8)* Beta coefficients are presented: * p < 0.05, ** p < 0.01; a as % of total AP dose; b log transformed; c Group – two dummy variables: non-firefighters (0/0), current firefighters (1/0), and former firefighters (0/1); d The following genotype codes were used: 0 (CC), 1 (CA), and 2 (AA) for CYP1A2*F(-163C>A) NI – variable not included in model
16
Table 3. Multiple regression models AP metabolites in the urine of current firefighters (n=11) Models Dependent variables a Independent variables NAP 4HAP 3HMAP NAP 4HAP 3HMAP NAP 4HAP 3HMAP NAP 4HAP 3HMAP Exposure - TEQ, pg/g -0.14 0.23 NI NI NI 0.10 0.48 NI NI NI lipids 0.35 0.02 Exposure - body burden, NI NI NI -0.13 0.37 NI NI NI 0.24 0.18 0.55* ng 0.18 Age (y) NI NI NI NI NI NI -0.34 -0.38 -0.62 -0.36 -0.32 0.50 Urine cotinine, ng/ml b 0.28 0.77* 0.32 0.67* 0.25 0.71** -0.32 0.19 0.56** 0.15 0.10 0.22 CYP1A2 genotype c 0.33 0.33 0.19 0.37 0.36 0.09 NI NI NI NI NI NI 2 Adjusted R nd nd 0.63 nd nd 0.70 nd nd 0.69 0.002 nd 0.80 (F statistic) (0.7) (0.7) (6.6)* (0.3) (0.7) (8.8)** (0.8) (0.6) (8.5)** (1.0) (0.6) (14.4)** Beta coefficients are presented: * p < 0.05, ** p < 0.01; a as % of total AP dose; b log transformed; c The following genotype codes were used: 0 (CC), 1 (CA), and 2 (AA) for CYP1A2*F(163C>A) NI – variable not included in model nd - R2 could not be calculate
17
S0378-4274(16)30058-3 http://dx.doi.org/doi:10.1016/j.toxlet.2016.04.006 TOXLET 9346
To appear in:
Toxicology Letters
Received date: Revised date: Accepted date:
20-1-2016 31-3-2016 7-4-2016
Please cite this article as: Chernyak, Yury I., Merinova, Alla P., Shelepchikov, Andrey A., Kolesnikov, Sergey I., Grassman, Jean A., Impact of dioxins on antipyrine metabolism in firefighters.Toxicology Letters http://dx.doi.org/10.1016/j.toxlet.2016.04.006 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Impact of dioxins on antipyrine metabolism in firefighters Yury I. Chernyak1*, Alla P. Merinova1, Andrey A. Shelepchikov2, Sergey I. Kolesnikov1 and Jean A. Grassman3 1
East-Siberian Institute of Medical and Ecological Research, P.O. Box 1170, Angarsk, 665827,
Russia 2
A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, 33, Leninskiy
prosp., Moscow, 119071, Russia 3
Brooklyn College-CUNY, 2900 Bedford Avenue, Brooklyn, NY 11210-2889, USA
*Corresponding author: Yury I. Chernyak, East-Siberian Institute of Medical and Ecological Research, P.O. Box 1170, Angarsk, 665827, Russia Tel.: +07(3955)510415; Fax: +07(3955)554077; e-mail: [email protected]
Highlights:
Firefighter exposure to dioxins was assessed using a metabolic test.
Current firefighters excreted metabolites consistent with CYP1A2 induction.
Recent, but not former exposure, alters the metabolism of firefighters.
Abstract Antipyrine (AP) metabolism was used to assess factors associated with the activity of hepatic oxidative enzymes in firefighters. Emphasis was placed on 3-hydroxymethylantipyrine (3HMAP), the metabolite with the greatest dependence on dioxin-inducible cytochrome P4501A2 (CYP1A2) activity. AP urinary metabolites were measured by HPLC in 38 male subjects from Eastern Siberia. Subjects were divided into three groups having similar ages and BMIs: current firefighters (n = 11); former firefighters (n = 17) and non-firefighters (n = 10). Multiple regression models were constructed using the three major AP metabolites as dependent variable to assess the influence of age, smoking as urinary cotinine concentration, dioxin exposure (as either WHO-TEQ or body burden), group, and CYP1A2*F (-163C>A) genotypes. Models for the proportion of dose excreted as the metabolite 3HMAP produced the best fit (adjusted R2 = 0.46, p <0.05). When the models
1
were restricted to current firefighters, only those based on 3HMAP were statistically significant (adjusted R2 of 0.80 (p <0.002)) due to contributions from urinary cotinine (β = 0.56, p <0.01) and dioxin expressed as body burden (β = 0.55, p = 0.014). These results indicate that the antipyrine test can be used as metabolic probe of biological response to recent dioxin exposure provided the impact of smoking is carefully controlled.
Key words: dioxin exposure, firefighters, antipyrine metabolism, cytochrome P4501A2 (CYP1A2), urinary cotinine level
1. Introduction Dioxins are considered to be one of the most toxic classes of anthropogenic compounds because of their ability to produce a wide variety of alterations of human homeostasis and health. Dioxins are potent cellular dysregulators which have been extensively studied as carcinogens, developmental toxicants, and endocrine disruptors (White and Birnbaum, 2009). In particular dioxin-like compounds have been implicated in diabetes, cardiovascular disease, testicular cancer, prostate cancer and non-Hodgkin's Lymphoma (IARC, 2010) and other effects. Dioxins are typically formed when organic materials are burned or heated in the presence of chlorine as happens during waste combustion, metal or cement production, and electrical generation at coal power plants (Vikelsoe and Johansen, 2000; Dopico and Gómez, 2015). Some congeners such as 2,3,7,8tetrachlorodibezo-p-dioxin
(TCDD),
and
2,3,4,7,8-pentachlordibenzodioxin
(PeCDD)
have
biological half lives lasting for years; for example, 7 years for TCDD and 15.7 years for PeCDD (Flesch-Janys et al., 1996). Moreover, the biological half life of a congener can be influenced by age, percent body fat, smoking status and breast feeding (Milbrath et al., 2009). Most of the effects of dioxins are mediated by the Ah-receptor (AhR) which plays a central role in the induction of the Phase I enzymes and acts as a modulator of cellular signaling pathways (Whitlock, 1993). Firefighter exposure to dioxins is usually accompanied by a complex mixture of other compounds (Edelman et al., 2003; Laitinen et al., 2012).
High affinity dioxins may
successfully compete for the AhR and in doing so may alter the toxicity of other agents such as polycyclic aromatic hydrocarbons whose conversion to active metabolites is dependent on CYP1
2
family (Denison and Nagy, 2003). The resultant mix of exposure agents means that some, but not all of the toxicity experienced by firefighters is mediated by the AhR pathway. CYP1A2, along with CYP1A1 and CYP1B1, is an AhR-responsive oxidative enzyme whose dose dependent induction by dioxins occurs in both humans and rodents. In rodents, this induction results in the sequestration of TCDD and PeCDD in the liver (Diliberto et al., 1997). Furthermore, Grassman et al. (2002) found high and significant correlation to the total TEQ with the expression of CYP1A2 in human liver samples taken from post-mortem donors. CYP1A2 is predominantly a hepatic enzyme and as such, CYP1A2 mRNA is not detectable in lymphocytes (Krovat et al., 2000). Because of this, expression of the enzyme is assessed indirectly through the measurement of the products of CYP1A2-dependent metabolism. Both caffeine (Halperin et al., 1995; Abraham et al., 2002) and antipyrine (AP) (Ostashevsky et al., 1994) have been used as metabolic probes in dioxin exposed populations. AP was used to evaluate CYP1A2-dependent activity in residents of South Vietnam living in regions that had been sprayed with Agent Orange (Ostashevsky et al., 1994). Although dioxin body burdens were not measured, a significant correlation between the pattern of urinary AP metabolites and CYP1A1 activity (benzopyrene-hydroxylase) in blood lymphocytes of humans was found. Halperin et al. (1995) examined the effect of occupational exposure to chemicals contaminated with 2,3,7,8-TCDD on CYP1A2 activity by using caffeine as a substrate and subsequently measuring the caffeine metabolite ratio (CMR). The authors did not find a significant association between the CYP1A2 activity and serum TCDD, whereas cigarette smoking induced CYP1A2. Abraham et al. (2002) measured caffeine demethylation in two individuals who were highly exposed to TCDD. Eighteen months after the presumed time of intoxication, they found a more than 10-fold induction of hepatic CYP1A2 enzymes. Specifically, the two poisoned individuals had CMRs of 39.3 and 29.8, compared with a mean CMR of 3.65 in 30 nonsmokers. A positive association between the serum concentration of PCB-105 (one of three dioxin-like congeners that were investigated) and CMR was found in a study by Petersen et al. (2006). Lambert et al. (2006) used the caffeine breath test to measure CYP1A2 activity in members of the Yucheng cohort who were exposed to PCBs at levels far above those measured by Petersen and team when they consumed contaminated rice oil. After 16-17 years of exposure, they had levels of CYP1A2 activity that were double the levels measured in breath samples and were correlated with serum TEQ, consisting predominantly of PCDFs and PCBs (Lambert et al., 2006). Current evidence demonstrates that firefighters are occupationally exposed to dioxins (Kelly et al., 2002; Schecter et al., 2002; Edelman et al., 2003; Hsu et al., 2011; Chernyak et al., 2012; 3
Shaw et al., 2013). Consequently, there is a need for methods that measure the biological response of workers, which is determined not only by the toxicant dose, but also by the activity of enzymes involved in its biotransformation. The purpose of this study is to employ antipyrine metabolism as a metabolic probe to measure factors that influence the activity of hepatic oxidative enzymes, predominantly CYP1A2, in current, former, and non-firefighters.
2. Materials and Methods 2.1. Selection of the cohort and blood donors In 2009-2010 we examined dioxin levels in 40 men, thirty of whom were recruited from a cohort of 165 firefighters originally assembled in 2003 to study dioxin exposure and health effects following the 1992 Shelekhov fire (Chernyak et al., 2004). This examination included an additional 10 men who were recruited to serve as non-firefighter controls. The thirty firefighters were grouped according to their status as current or former firefighters. The formation of the firefighter groups was complicated due to the limited number and inaccessibility of candidates for a variety of reasons, including change of residence and death. Approximately 20 of those contacted declined to participate. Men were screened to ensure their body mass index (BMI) and ages were comparable. All participants are from similar economic strata within Irkutsk Oblast. Information on demographic, familial, occupational, and personal characteristics including smoking habits, diet, hobbies, and illnesses was obtained through an oral questionnaire. Informed consent, which included authorization for blood and urine sampling and banking, was provided by all participants. The study protocol was approved by the Biomedical Ethics Committee of East-Siberian Scientific Center of Siberian Branch of Russian Academy of Medical Sciences in Irkutsk and the Brooklyn CollegeCUNY Institutional Review Board.
2.2. Measurement of serum dioxin concentrations After overnight fasts, each participant provided 40-50 ml of blood from which serum was obtained using a standard procedure. Seven polychlorinated dibenzo-p-dioxin (PCDD), 10 polychlorinated dibenzofuran (PCDF), and 12 polychlorinated biphenyl (PCB) congeners were analyzed by gas chromatography/high-resolution mass spectrometry in each of the samples at the A.N. Severtsov Institute of Ecology and Evolution (Moscow) according to the protocol previously described (Chernyak et al., 2012). The system of toxicity equivalence factors developed by the WHO in 2005 was used to calculate total toxicity equivalent (TEQ) (Van den Berg et al., 2006). Measurements below the limit of detection (LOD) were assigned a value representing the level of 4
detection divided by the square root of 2 as recommended by the US Centers for Disease Control and Prevention (Hornung and Reed, 1990). Results are expressed as WHO-TEQ and as body burden. Dioxin exposure as body burden was calculated using the measured TEQ and the following formula for percent of lipids in the body: %Lipids = 495/(1,0324 – 0,19077(log(waist – neck)) + 0,15456(log(height))) – 450) (Hodgdon and Beckett, 1984).
2.3. Antipyrine test The current study used antipyrine (AP) (pharmaceutical purity, Fluka Chemical Co., Milwaukee, WI, USA) as a metabolic probe to measure the activity of hepatic oxidative enzymes. Because AP has not been used as an analgesic in recent years, there was no possibility that participants were exposed outside of the study. AP metabolism was assessed by HPLC performed on urine samples obtained for 38 from 40 participants, two individuals from current firefighters group were excluded because of medical conditions. The subjects were instructed to abstain from ingesting medication 48 hours before the AP dosage until the end of the urine collection. After voiding the bladder, urine was collected for 24 hours following the ingestion of 18 mg AP/kg body weight. Each 24-hour sampling was collected in a vessel containing 200 mg of Na2S2O5 as a stabilizer. After mixing, two 1 ml samples of the collected urine were obtained and stored at -20˚С before analysis. Samples for liquid chromatography were prepared as previously described by enzymatically hydrolyzing the conjugate metabolites with Type H-3 β-glucuronidase from Helix pomatia (Sigma Aldrich Chemical Co., St. Louis, MO) (Rakhmanov et al. 1989; Teunissen et al. 1983). After hydrolysis, a two stage extraction was performed. The first stage extracted the metabolites 4-hydroxyantipyrine (4HAP) and norantipyrine (NAP) with dichloromethane : n-pentane (3:7, v/v).
The second stage used
dichloromethane to extract 3-hydroxymethylantipyrine (3HMAP) and AP. Urinary AP and metabolites were analyzed by HLPC ("Milichrom-A02", EcoNova, Russia) using a Silasorb SPH C18, 5 μm, 2 х 75 mm column. Detection was at λ = 244 nm at 45˚С. Phenacetin (Sigma Aldrich Chemical Co., St. Louis, MO) was used as internal standard. Elution was performed using eluent А, methanol with 0.05 M phosphate buffer, pH 6.7 (10:90), and eluent B, 90% methanol. The eluent flow rate was 200 μl/min. The components were resolved under isocratic conditions with 7% eluent B for 10 minutes followed by gradient elution from 7 to 100% eluent В for 4.5 minutes. In some samples, a non-identified impurity interfered with the resolution of the phenacetin peak. In these cases, repeated isocratic chromatography with 20% eluent В for 10 minutes successfully resolved the phenacetin and the impurity. A four-point standard curve was used to calculate the concentration 5
of АР and the metabolites. The standard curves were linear and had correlation coefficients of at least R2 > 0.98. Urinary AP metabolites levels are presented as percent of the total ingested AP dose.
2.4. Genotype analyses Venous blood was collected for studies of CYP1A2 gene polymorphisms during the medical examination. DNA was extracted using the "DNA-sorb-B" commercial kit (Institute of Epidemiology, Moscow). The CYP1A2*F (-163C>A) genotypes (CC, CA, and AA for CYP1A2 low, medium, and high activity, respectively) was carried out by restriction fragment-length polymorphism analysis as described previously (Chida et al., 1999). The primers (Medigene, Novosibirsk) F: 5’-CCC AGA AGT GGA AAC TGA GA-3’and R: 5’-GGG TTG AGA TGG AGA CAT TC-3’, and ApaI restriction endonuclease (Thermo Fisher Scientific Inc.) was used for genotyping. The results of electrophoresis in 1.5% agarose gel were evaluated in transmitting UV light after ethidium bromide staining.
2.5. Measurement of tobacco exposure Smoking status was determined by the excretion of urinary cotinine measured with a Cotinine Direct ELISA Kit (Bio-Quant, Inc.) using a universal microplate reader ELx800 (Bio-Tek Instruments, Inc.). Urine samples for cotinine assessment (approximately 30 ml from every participant) were obtained prior to the blood collection and the ingestion of AP. 2.6. Statistical analysis We used Statistica software package (v. 6.1, StatSoft Inc., OK, USA) to perform all statistical analysis. Data were tested for normality by Shapiro-Wilk’s W test and log-transformations or nonparametric tests were used when appropriate. Differences between the three groups were assessed using one-way analysis of variance (Median Test / Kruskal-Wallis ANOVA) and followed by Mann–Whitney U non-parametric tests. Multiple linear regression analysis was performed for 38 individuals and for every group subjects to evaluate the validity of the metabolic test indices as potential biomarkers of effect. Antipyrine test indices were the dependent variables and potentially modifying factors were added to the models as predictor variables: age, cotinine level (decimal-logtransformation), dioxin exposure (WHO-TEQ or body burden), group (two dummy variables (nonfirefighters (0/0), current firefighters (1/0), and former firefighters (0/1)), CYP1A2*F (-163C>A) genotype (genotype codes 0 (CC), 1 (CA), and 2 (AA)). The distribution of residuals was examined for each model. A confidence level of 0.05 was used as the criteria for statistical significance.
6
3. Results 3.1. The study participants Table 1 shows the characteristics of the 38 male participants based on their employment status as current, former, or non-firefighters. The three employment-based groups were similar in age and in BMI. Fourteen out of the 17 former firefighters were disabled at the time of examination. Current and former firefighters had similar years of professional experience. Smokers made up 5 (45.4 %), 10 (58.8 %) and 4 (40 %) of the current, former, and non-firefighters, respectively. Average urinary concentrations of cotinine, the most abundant metabolite of nicotine, did not differ between groups but showed considerable inter-individual variability: 2235-24734 ng/ml for smokers and 0.9-158 ng/ml for nonsmokers. Dioxin exposure was higher in firefighters than in non-firefighters. When expressed as body burden, the difference was statistically significant for the three groups (p = 0.013) although neither of the two group-wise comparisons were significant (p = 0.078 and p = 0.056 for current and former firefighters compared to non-firefighters, respectively). The three antipyrine metabolites, NAP, 4HAP, and 3HMAP, were excreted in similar proportions by each of the groups. The frequency distribution of CYP1A2*1F (-163C>A) genotypes in the cohort did not deviate from the HardyWeinberg equilibrium (χ2 = 0.43, p > 0.05).
3.2. Models for AP metabolites The impact of dioxin exposure and potential modifying factors are shown in Table 2. Each of the three major antipyrine metabolites were used as dependent variables; age, log transformed concentration of urine cotinine, dioxin exposure (TEQ or body burden), two dummy variables for group, and CYP1A2 genotypes were used as independent variables. Each model incorporated 4 to 6 of the independent variables. The best fit was obtained with models for 3HMAP (adjusted R2 = 0.46, p <0.01), the metabolite with the greatest dependence on CYP1A2 activity. Smoking, measured as urinary cotinine, was a significant contributor to the formation of 4HAP and 3HMAP (beta regression coefficients (β): 0.50 to 0.52, p <0.01 and 0.68 to 0.71, p <0.01 respectively) but not NAP. Urinary AP metabolites levels were not affected by group, level of dioxin exposure, or CYP1A2 genotype in any of the models.
3.3. Models for AP metabolites in current firefighters Models using only data from current firefighters were explored to more fully examine the impact of recent dioxin exposure. Four models of antipyrine metabolite excretion based on urinary 7
cotinine, dioxin exposure expressed as TEQ or body burden, and either age or CYP1A2 genotype were studied (Table 3). Only models based on 3HMAP were statistically significant and urinary cotinine was significant only in these models. The model with the best fit expressed exposure as body burden with an adjusted R2 of 0.80 (p <0.002). The regression coefficient for this exposure variable was also substantial and significant (β = 0.549, p = 0.0138). The same model was characterized by the best distribution of residuals (specifically, Durbin-Watson d = 2.34, ShapiroWilk W = 0.917 p < 0.297, evidence of homoscedasticity). For all 3HMAP models the effect of smoking was significant (β ranging from 0.56 to 0.77, р <0.05). Neither age nor CYP1A2 genotype were significant in any of models.
4. Discussion The current study describes the application of the AP metabolic test to detect dioxindependent variations in hepatic oxidative metabolism in male workers having distinct occupational histories as current firefighters, former firefighters, and non-firefighters. The AP test is used to assess CYP1A2, an hepatic oxidative enzyme which is sensitive to induction by environmental agents such as cigarette smoke (Pavanello et al., 2002) and dioxins (Budinsky et al., 2010). Metabolism-based, rather than transcript-based measures are required because CYP1A2 is not expressed in easily accessible biological targets such as peripheral blood monocytes or epithelial cells. A person’s enzyme activity can be assessed by measuring the proportion of three major products of oxidative AP metabolism excreted in the urine after administration of a known dosage. CYP1A2 contributes to the formation of about 50% of the 3HMAP, 30% of the 4HAP and 20-25% of the NAP (Pelkonen et al., 1998). Other metabolic tests such as the caffeine test are prone to interference due to the ubiquitous consumption of caffeine and related chemicals. The exposure of this cohort to a recognized inducer of CYP1A2, cigarette smoke (Pavanello et al., 2002), provided an opportunity to verify that the metabolites measured by the AP test are consistent with CYP1A2 enzyme activation. This was apparent both with our full cohort of 158 firefighters where exposure to cigarette smoke was self reported and also in the present study where cigarette smoke was assessed by measuring the urinary excretion of cotinine. In the full cohort of firefighters who self-identified as smokers were found to metabolize a larger proportion of the dose than non-smokers, specifically because they converted more of the AP to 4HAP and 3HMAP rather than NAP (Chernyak et al., 2005). The difference between smokers and non-smokers was detectable even though, due to the self-reported nature of these data, the results do not account for nicotine content of cigarette brand, and inter-individual differences in smoking technique such as number of 8
inhalations per cigarette, inhalation volume, percentage of smoke inhaled into the lung, and amount of time smoke is held in the lung before exhaling. In addition, non-smokers are likely to have substantial exposure to at least some second-hand smoke given the prevalence of smoking in the region. In the current study, urinary excretion of cotinine was most correlated with the 3HMAP, somewhat correlated with 4HAP and not related to NAP. Together, these observations demonstrate that the AP assay is responsive to smoking status and detects a pattern of urinary metabolites that is consistent with CYP1A2 induction. Occupational status is associated with different levels of dioxin exposure, a finding that is based on the current study as well as a previous analysis that included members of the cohort who had a group of neurological syndromes related to their participation in the 1992 Shelekhov fire (Shelekhov syndromes) (Chernyak et al., 2011). The present study found that current firefighters have higher dioxin exposures than either former firefighters or men who are not employed as firefighters. This difference reaches significance only when dioxins are expressed body burden and based on previous analyses, is attributable to higher levels of PCBs and PCDFs (Chernyak et al., 2012). The lowest dioxin exposures are found in men who do not work as firefighters with the levels in former firefighters being intermediate. Our studies also suggest a tendency for dioxin exposure to correlate with formation of 3HMAP. In the present study, although not statistically significant, the current workers had the highest dioxin body burdens and the highest proportion of 3HMAP; non-firefighters had the lowest of both. An earlier analysis of a different subcohort of workers who were grouped according to their occupation as firefighters and whether they suffered from Shelekhov syndromes found that current firefighters metabolize a larger proportion of the AP dose to 3HMAP (Chernyak et al., 2011). Almost all models indicate a slight inverse association (not always statistically significant) between main AP metabolites level and the age. This is consistent with the recognized age-related decrease of functional activity of most CYPs, especially CYP1A2 (Doki et al. 2009). The absence of significance may be due to the limited variation of age among the 38 participants. Others have found that the CYP1A2*1F polymorphism has an inducing effect on CYP1A2 activity (Gunes et al., 2009; Dobrinas et al., 2011). This study found no similar effect possibly because the effect was too small to be detected in a study of this size. Although efforts were made to control for non-dioxin related factors that influence the level of CYP1A2 enzyme, they do not account for the pattern of exposure giving rise to the measured serum dioxin levels. Former and current firefighters in this cross sectional study had similar body burdens of dioxins in 2010 stemming from distinctly different exposure histories. Both groups have 9
had extensive opportunity for exposure to combustion products since the region of Russia where they are employed undergoes a remarkable number of annual wildland fires. The dioxin exposure of the former firefighters incorporates two phenomena: first, a “spike” of exposure stemming from their participation in the 1992 Shelekhov fire and secondly, the gradual decline expected after periods of accumulation during active firefighting. In contrast, current firefighters may or may not have had the initial spike related to the Shelekhov fire but they have continued to be exposed to combustion products, including dioxin-like compounds due to their continued employment. These temporal differences in the patterns may obscure the relationship between dioxin exposure and the production of CYP1A2 dependent metabolites because be the lack of recent occupational exposure capable of inducing the dioxin signaling pathway among the former firefighters. Epidemiological studies and in vitro models suggest that the expression of dioxin signaling pathway, of which CYP1A2 is a constituent, may lose responsiveness in the absence of new exposures (Landi et al., 2003). In an effort to examine the dioxin signaling pathway in a manner that captures the impact of recent exposures, the multiple regression analysis was performed using only the results from current firefighters. For current firefighters, body burden was significantly related to the formation of 3HMAP (β = 0.55, p = 0.014) whereas this relationship was not evident in former firefighters which lack recent exposures to dioxins (β = -0.14, p = 0.550). Other investigators have observed a similar lack of relationship among long term workers exposed to TCDD 15-37 years before the examination (Halperin et al., 1995). Assessment of AP metabolism augments current serum measurements since it reflects the impact of congeners currently not incorporated into the TEQ. Examples include the CYP1A2inducing polybrominated dibenzo-p-dioxins, dibenzofurans, and some dioxin-like biphenyls that have been detected in firefighters (Shaw et al., 2013). Firefighters currently engaged in firefighting activities are exposed to dioxin-like compounds that distinguishes them from the general human population having only environmental exposure. Consequently, assessment of their risk would be more informative by the inclusion of brominated analogues in addition to the chlorinated congeners (van den Berg et al., 2013). This will require developing methods to detect the TEQ contribution of brominated dioxins within mixtures of halogenated congeners. A more comprehensive measure of TEQ will improve assessments of the relationship between dioxin exposure and CYP1A2 activity. Despite the significance of our findings, the present study has a number of limitations which should be taken into account. Primarily, it included a relatively small number of subjects. Realizing this, we sought to minimize the number of independent variables (in this case, up to three) in the analysis focused on current firefighters. As stated earlier, we used a non-specific substrate for 10
measuring CYP1A2 activity. Based on the significance of these findings, this study should be replicated using current firefighters from a different population and a comparison with other CYP1A2 substrates.
Conclusion These results support the conclusion that dioxins induce the metabolism of CYP1A2 in individuals who are currently experiencing active bouts of exposure. This observation is contingent upon dioxin being expressed as body burden rather than TEQ and uses the formation of 3HMAP as a surrogate for direct measurement of CYP1A2. The effect of dioxin is only evident when the level of smoking, detected as urinary excretion of cotinine, is controlled in the regression model. Moreover, a
statistically significant association between dioxin exposure as body burden and
3HMAP level in urine, AP metabolite most dependent on induction of dioxins CYP1A2 was found when the analysis focused only on current firefighters, presumably because the contemporary nature of their exposure results in an activated signaling pathway. Overall, this pattern is consistent with but not diagnostic of CYP1A2 activity. These results will enhance the ability to link urinary 3HMAP formation, an intermediate phenotype, with health outcomes in future studies of populations with recent exposure to dioxins. This work contributes to the understanding of the factors contributing to the modification of the hepatic Phase I oxidative enzymes activity, the mechanism of toxicity, and the integrated impact of all compounds acting through the dioxinsignaling pathway.
Conflict of interest statement The authors declare no conflicts of interest.
Acknowledgements This work was supported in part by the Russian Foundation for Basic Research (project no. 08-04-91119) and U.S. Civilian Research & Development Foundation (project no. RUB1-2917-AN07). The authors are grateful to physicians Irina N. Kodinets and Svetlana A. Vasilieva for medical assistance during conducting of the AP test. We especially thank the firefighters and the controls, who participated in the examinations.
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Table 1. Characteristics of the study participants (Mean, min–max). current former firefighters firefighters n=11 n=17 Parameters Age, yrs 42.5 (33-51) 45.0 (35-54) BMI, kg/m2 Disabled, # b Operational experience as firefighters, yrs Smoking Current smokers, n (%) Urine cotinine, ng/ml
non-firefighters n=10 45.4 (38-52)
0.97 8 26.8 (24.1-29.6) 27.4 (21.7-36.0) 25.4 (21.1-30.3) 0.52 8 0 14 0 16.4 (9-29)
14.4 (9-25)
0
5 (45.4) 5634 (6.618354)
10 (58.8) 7733 (4.924734)
4 (40) 4369 (0.918792)
Dioxin exposure TEQ, pg/g lipids
23.1 (9.2-46.0)
21.0 (12.0-34.3)
17.3 (6.1-34.5)
body burden, ng
425 (173-1148)
410 (165-877)
285 (100-695)
AP metabolites NAP c 4HAP c 3HMAP c
Рa
13.6 (11.0-16.6) 13.5 (11.1-18.0)
0.60 0 0.33 0 0.01 3
13.0 (8.7-18.6)
0.80 8 25.4 (20.0-33.4) 27.3 (21.2-33.5) 26.4 (14.0-39.8) 0.99 7 23.9 (14.4-31.8) 26.0 (16.3-27.9) 18.3 (14.7-26.5) 0.48 8
CYP1A2*F (-163C>A) genotype, n (%) CC 1 (9) 3 (18) 1 (10) CA 4 (36) 7 (41) 4 (40) AA 6 (55) 7 (41) 5 (50) a - Significance level (p) in intergroup comparison: one-way analysis of variance (Median Test ANOVA); b – the number of the firefighters whose disability is related to the fire suppression at the cable factory in 1992; c – NAP - norantipyrine, 4HAP - 4-hydroxyantipyrine and 3HMAP - 3-hydroxymethylantipyrine as % of total AP dose, the data are expressed as the median value (Me) and interquartile range (LQ- UQ: upper limit of the lower quartile and lower limit of the upper quartile).
15
Table 2. Multiple regression models for AP metabolites in urine (n=38)
Independent variables Exposure - TEQ, pg/g lipids Exposure - body burden, ng Age, yrs
NAP 0.03 NI
4HAP -0.03
3HMAP -0.00
NAP NI
NI
NI
0.18
0.32 0.13
-0.18
-0.05
0.35* 0.07
Models Dependent variables a 4HAP 3HMAP NAP 4HAP NI NI -0.05 0.01 0.01 0.02 NI NI -0.19
-0.06
0.33 0.13
-0.17
3HMAP 0.05
NAP NI
4HAP NI
3HMAP NI
NI
0.18
-0.02
0.03
-0.10
0.35* 0.07
-0.18
-0.09
Urine cotinine, ng/ml b 0.52** 0.71** 0.51** 0.71** 0.50** 0.69** 0.50** 0.68** c Group current vs non0.08 -0.09 0.18 0.02 -0.10 0.18 NI NI NI NI NI NI firefighters former vs non0.04 -0.14 -0.06 -0.00 -0.15 -0.06 NI NI NI NI NI NI firefighters CYP1A2 genotype d NI NI NI NI NI NI 0.07 0.06 0.18 0.05 0.07 0.18 2 Adjusted R 0.00 0.17 0.46 0.03 0.17 0.46 0.03 0.18 0.46 0.06 0.19 0.46 (F statistic) (1.0) (2.6)* (7.3)** (1.2) (2.5)* (7.3)** (1.3) (3.2)* (8.9)** (1.6) (3.1)* (8.8)* Beta coefficients are presented: * p < 0.05, ** p < 0.01; a as % of total AP dose; b log transformed; c Group – two dummy variables: non-firefighters (0/0), current firefighters (1/0), and former firefighters (0/1); d The following genotype codes were used: 0 (CC), 1 (CA), and 2 (AA) for CYP1A2*F(-163C>A) NI – variable not included in model
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Table 3. Multiple regression models AP metabolites in the urine of current firefighters (n=11) Models Dependent variables a Independent variables NAP 4HAP 3HMAP NAP 4HAP 3HMAP NAP 4HAP 3HMAP NAP 4HAP 3HMAP Exposure - TEQ, pg/g -0.14 0.23 NI NI NI 0.10 0.48 NI NI NI lipids 0.35 0.02 Exposure - body burden, NI NI NI -0.13 0.37 NI NI NI 0.24 0.18 0.55* ng 0.18 Age (y) NI NI NI NI NI NI -0.34 -0.38 -0.62 -0.36 -0.32 0.50 Urine cotinine, ng/ml b 0.28 0.77* 0.32 0.67* 0.25 0.71** -0.32 0.19 0.56** 0.15 0.10 0.22 CYP1A2 genotype c 0.33 0.33 0.19 0.37 0.36 0.09 NI NI NI NI NI NI 2 Adjusted R nd nd 0.63 nd nd 0.70 nd nd 0.69 0.002 nd 0.80 (F statistic) (0.7) (0.7) (6.6)* (0.3) (0.7) (8.8)** (0.8) (0.6) (8.5)** (1.0) (0.6) (14.4)** Beta coefficients are presented: * p < 0.05, ** p < 0.01; a as % of total AP dose; b log transformed; c The following genotype codes were used: 0 (CC), 1 (CA), and 2 (AA) for CYP1A2*F(163C>A) NI – variable not included in model nd - R2 could not be calculate
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