Fast and sensitive LC MS/MS method measuring human mycotoxin exposure using biomarkers in urine

Transcription

1 DOI /s ANALYTICAL TOXICOLOGY Fast and sensitive LC MS/MS method measuring human mycotoxin exposure using biomarkers in urine B. Huybrechts J. C. Martins Ph. Debongnie S. Uhlig A. Callebaut Received: 22 January 2014 / Accepted: 28 August 2014 / Published online: 11 September 2014 Springer-Verlag Berlin Heidelberg 2014 Abstract A direct, fast and sensitive LC MS/MS method was developed to measure biomarkers for mycotoxin exposure in human urine. In total, 32 biomarkers were quantitatively or semi-quantitatively measured in 32 urine samples of Belgian volunteers using two injections. All urine samples contained deoxynivalenol-15-glucuronide, the major detoxification metabolite of deoxynivalenol, in the ng/ml range. Also deoxynivalenol-3-glucuronide and de-epoxydeoxynivalenol-glucuronide were present in, respectively, 90 and 25 % of the samples, while deoxynivalenol was detected in 60 % of the samples, in lower concentrations. Deoxynivalenol glucuronides were the major biomarkers for deoxynivalenol exposure. Ochratoxin A was detected in 70 % of the samples in pg/ml. Citrinin and/or dihydrocitrinone were detected in 90 % of the samples, also in concentrations of pg/ml. The presence of ochratoxin A and citrinin was confirmed by a second method using sample cleanup by immunoaffinity columns, followed by LC MS/ MS. Our data show that humans are much more exposed to citrinin than realized before and suggest further work on citrinin exposure in relation with ochratoxin A exposure, as both mycotoxins are nephrotoxic. B. Huybrechts P. Debongnie A. Callebaut (*) Unit Toxins and Natural Components, Veterinary and Agrochemical Research Centre (CODA-CERVA), Leuvensesteenweg 17, 3080 Tervuren, Belgium alfons.callebaut@coda cerva.be J. C. Martins Department of Organic Chemistry, Organic and Macromolecular Chemistry, Ghent University, Krijgslaan 281, S4, 9000 Ghent, Belgium S. Uhlig Section for Chemistry and Toxicology, Norwegian Veterinary Institute, P.O. Box 750, Sentrum, 0106 Oslo, Norway Keywords Biomarkers Urine Mycotoxin exposure Glucuronides LC MS/MS Introduction Many mycotoxins are known to cause subacute and chronic health problems in humans and animals (Bennett and Klich 2003; Bryden 2007; Richard 2007; Shephard 2008a; Pestka 2010; Wild and Gong 2010; Maresca 2013). The real exposure of humans and animals to mycotoxins is difficult to measure by an indirect approach based on mycotoxin occurrence data in food/feed, combined with food/ feed consumption data. The heterogeneous distribution of mycotoxins, the effect of food/feed processing on the final mycotoxin concentration, under- and overestimation of food consumption data, the presence of masked mycotoxins and individual differences in absorption, distribution, metabolism and excretion (ADME) all influence the individual exposure (Shephard 2008b; Cano-Sancho et al. 2013; Berthiller et al. 2013). An alternative to indirect approaches is the quantification of specific biomarkers of exposure in body fluids. Thus, several laboratories already developed a biomarker approach to measure mycotoxin exposure (Scott 2005; Cano-Sancho et al. 2010; Baldwin et al. 2011; Leong et al. 2012; Turner et al. 2012; Warth et al. 2013a, b). Urine is the body fluid that is most often used for measuring mycotoxin exposure, but also blood (plasma, serum) has been used (Duarte et al. 2011; Märtlbauer et al. 2009). Measuring mycotoxins and their metabolites in urine requires sensitive methods as biomarkers usually are present at very low levels. In the reported methods, samples are often precleaned prior to analysis either using solid-phase extraction (SPE), liquid liquid extraction (LLE) and/or immunoaffinity

2 1994 Arch Toxicol (2015) 89: columns (IAC) (Solfrizzo et al. 2011; Lattanzio et al. 2011; Rubert et al. 2011; Njumbe Ediage et al. 2012; Song et al. 2013; Ahn et al. 2010). However, sample cleanup makes the analysis more expensive and time-consuming and introduces the risk of losing important biomarkers as the chemical properties of the molecules are quite different. Furthermore, the use of specific enzymes such as β-glucuronidases was necessary as there are no commercially available standards for the conjugated metabolites (e.g., glucuronides) (Meky et al. 2003; Turner et al. 2010), which makes these methods more elaborate and difficult to validate. Deoxynivalenol (DON) is one of the major mycotoxins in diets in Western Europe. As stated by Turner et al. (2011), the focus with DON is on the balance between the toxicity of the parent compound and any detoxification metabolites. Glucuronic acid (GlcA) conjugates are the main phase II metabolites of DON and de-epoxydeoxynivalenol (DOM- 1), itself a DON metabolite. Turner et al. (2008) found a strong correlation between the sum of urinary DON and its glucuronidated metabolites, and the cereal intake of the study population. Biomarkers of exposure for ochratoxin A (OTA) in blood or urine have been described in many papers (Gilbert et al. 2001; Pena et al. 2006; Manique et al. 2008; Muñoz et al. 2010). Recently, the frequent occurrence of citrinin (CIT) and its metabolite dihydrocitrinone (HO-CIT) in humans has been reported (Blaszkewicz et al. 2013). Comparatively, low levels (2 5 ng/ml) of CIT were detected previously in rat urine (Phillips et al. 1980). Zearalenone (ZEN), a common mycotoxin in cereals and derived products, is mainly reduced during metabolization, resulting in the formation of α-zearalenol (α-zel) and/or β-zel. All these forms can be further metabolized by conjugation with glucuronic acid (Zinedine et al. 2007). Validated biomarkers for aflatoxin B 1 (AFB 1 ) exposure are described (Groopman and Kensler 2005; Wild and Gong 2010). The metabolites of AFB 1 detected in human urine include aflatoxin P 1 (AFP 1 ), aflatoxin Q 1 (AFQ 1 ), aflatoxin M 1 (AFM 1 ) and a DNA adduct (AFB 1 -N7Guanine) (Kemilainen et al. 2005). In vitro studies using primate and human liver microsomes have demonstrated that AFQ 1 is a major AFB 1 metabolite. In contrast, the long known hydroxylated AFB 1 metabolite AFM 1 constituted <10 % of the total metabolized AFB 1 (Ramsdell et al. 1991; Neal et al. 1998). AFQ 1 was suggested as a predictive marker for AFB 1 exposure (Mykkänen et al. 2005) but the use of this metabolite is strongly compromised as no commercial standards are available for the moment. Fumonisins have a low oral bioavailability, and in humans, only the parent molecule fumonisin B 1 (FB 1 ) is important as biomarker (Gong et al. 2008; Van Der Westhuizen et al. 2011). It has been reported that the exposure to FB 1 has to be very high to be detected in urine (Ahn et al. 2010; Silva et al. 2010). As mentioned above, using an extensive cleanup as part of a multi-mycotoxin biomarker LC MS/MS method is not only time-consuming but it might also lead to analyte loss, as the chemical properties of possible biomarkers are different. Therefore, the use of direct LC MS/MS methods is a better alternative. Warth et al. (2011) focused on the direct determination of DON together with the phase II metabolite, DON-3-glucuronide (DON-3-GlcA) in urine, while no other biomarkers were included in this method. The same group reported recently that DON-15-GlcA is the most important DON-glucuronide (Warth et al. 2012a). We developed a direct LC MS/MS method using electrospraynegative ionization for glucuronides of DON, DOM-1, ZEN and electrospray-positive ionization for glucuronides of 3- or 15-acetylDON (AcDON), α- and β-zel and OTA. In total, 32 biomarkers were monitored. The optimized method was used to analyze samples obtained from 29 volunteers working at our institute (CODA-CERVA). Materials and methods Reagents and materials ULC grade glacial acetic acid, ULC grade ammonium acetate, ULC MS grade methanol, AR-grade acetone, ARgrade dimethyl sulfoxide (DMSO), AR-grade hydrochloric acid (HCl 37 %) and HPLC-S grade acetonitrile were purchased from Biosolve (Valkenswaard, The Netherlands). Purified water was from a MilliQ system (Millipore, Overijse, Belgium). All fractional percentages of solvent concentration are volumetric percentages unless stated otherwise. Two-milliliter disposable syringes and 2-mL disposable centrifugation tubes were purchased from VWR (Haasrode, Belgium). Two-milliliter amber glass vials with and without insert were purchased from Waters (Waters Corp., Milford, MA. USA). Fifteen-millimeter syringe filters with a 0.2-μm regenerated cellulose (RC) membrane were purchased from Phenomenex (Utrecht, Netherlands). For the IAC experiments OchraTest and CitriTest, columns were used for OTA and CIT verification, both from Vicam (Cerealtester, Belgium). The presence of AFM 1 was verified using EASI-EXTRACT Aflatoxin columns from R-Biopharm (Glasgow, UK). Phosphate-buffered saline (PBS, powder, ph 7.4) was purchased in pouch form from Sigma-Aldrich (Bornem, Belgium). Acidified acetone was prepared by adding 1-mL hydrochloric acid (37 %) to 99-mL acetone. This solution was prepared daily before use. Standards Standards of FB 1, FB 2 and FB 3 were purchased from Cfm Oskar Tropitzsch (Marktredwitz, Germany) in powder

3 form. DOM-1, AFB 1, AFB 2, AFG 1, AFG 2, DON, OTA, ZEN and T2 were purchased in powder form from Romer Labs (Tulln, Austria). 3-AcDON, 15-AcDON, α-zel and β-zel and HT2 were purchased from Sigma-Aldrich. These were dissolved in pure acetonitrile or in a mixture of acetonitrile/water (1:1) (fumonisins). Their concentration was verified by an in-house validated UHPLC MS/MS method using certified reference solutions (Romer Labs). HO-CIT was purchased from AnalytiCon Discovery GmbH (Potsdam, Germany) and dissolved in acetonitrile. AFM 1, diacetoxyscirpenol (DAS), CIT, ochratoxin-alpha (OTα) and fusarenon X (FusX) were purchased from Romer Labs as certified calibrant solutions. These solutions were stored at 20 C. DON-3-GlcA was synthesized as described in Versilovskis et al. (2012) and the structure confirmed from NMR analysis (see NMR spectroscopy of DON-3-GlcA ). The synthesis and structure confirmation of DON-15-GlcA was described in Uhlig et al. (2013). The structures of all other glucuronides (DOM-GlcA, ZEN-14-GlcA, β-zel-14-glca, α-zel-7- GlcA, α-zel-14-glca, 15-AcDON-3-GlcA, 3-AcDON- 15-GlcA) were tentatively identified as described in Versilovskis et al. (2012). From the individual stock solutions, a standard mixture was prepared at the following concentrations: AFB 1, AFB 2, AFG 1, AFG 2, OTA, AFM 1, (4 ng/ml); DAS, FusX, 3-AcDON, 15-AcDON, β-zel, α-zel, CIT, OTα, DOM- 1, FB 1, FB 2, FB 3, DON, ZEN, T2, HT2, DON-3-GlcA, DOM-GlcA, ZEN-14-GlcA, β-zel-7-glca, β-zel-14- GlcA, α-zel-7-glca, α-zel-14-glca, 15-AcDON-3- GlcA, 3-AcDON-15-GlcA (40 ng/ml). Starting from this mixture a calibration curve with six points, excluding the blank was made each time an analysis was started. NMR spectroscopy of DON 3 GlcA To confirm successful synthesis, NMR spectra were recorded using a Bruker AVANCE II 700 MHz spectrometer, operating at 1 H and 13 C frequencies of and MHz, respectively. Samples were prepared by adding 20 µl of MeOH-d4 to the dry compound, after which 10 µl was transferred into a 1-mm capillary for measurement using a 1-mm TXI 1 H, 13 C, 15 N TXI-Z probe head. Experiments were performed using standard pulse sequences from the Bruker library and included a quantitative 1D 1 H, COSY, 100 ms TOCSY, a 1 H-{ 13 C} sensitivity-enhanced HSQC with multiplicity information and 2 1 H-{ 13 C} HMBC optimized for couplings of 4.5 and 8 Hz, respectively. Following analysis, complete and independent assignment of nonexchangeable (i.e., non-oh) 1 H resonances was achieved, while most 13 C resonances were inferred from the 2D 1 H 13 C correlation spectra, the quantity being insufficient for direct 13 C measurement. When 1995 compared to the assignment reported by Uhlig et al. (2013), complete agreement was found, with 1 H and 13 C chemical shifts within 0.05 and 0.1 ppm, respectively, from the values published. The successful introduction of the aglycon by means of a glycosidic bond linking the anomeric C1 of the glucuronide ring and the C3 carbon of DON was clearly evident from 3 J HC correlations in the HMBC spectra connecting H1 to C3 as well as H3 to C1. The concomitant upfield shift of the C3 from to ppm is also typical for a glycosidic bond at this position. Confirmation of the β-configuration was inferred from the chemical shift of the H1 (4.43 ppm) and the 3 J H1 H2 scalar coupling (7.81 Hz) both having typical values for the β-configuration. Samples Several employees from CODA-CERVA kindly volunteered to provide first morning urine samples. Informed consent was obtained from all the volunteers. Urine was stored in plastic containers at 20 C within 2 h after collection. In total, 32 samples were collected from 29 volunteers (3 volunteers provided 2 times a urine sample). All volunteers were adults, and the samples were equally divided over female and male volunteers. We did not collect any information on the diet of the participants. Sample preparation Direct method Prior to analysis, samples were thawed at room temperature. After agitation, 2 ml of sample was centrifuged at 16,800g for 5 min to remove the majority of the solid matter. Subsequently, an aliquot of the supernatant was filtered through an RC syringe filter directly into an autosampler vial. Ten microliters was injected into the chromatographic system. IAC cleanup Only for analysis of OTA, CIT and AFM 1, we developed a second method using IAC cleanup. PBS was dissolved in MilliQ water according to the instructions of the manufacturer and prepared freshly every second day. Samples were allowed to reach room temperature, and after agitation, aliquots of 10 ml were centrifuged and filtered as described above. Aliquots of 6 ml were diluted with 24 ml PBS buffer, and 25 ml of the mixtures was passed through IAC columns that were prewashed with 20 ml of PBS, under gravity. After application of samples, the columns were washed with 10 ml of PBS followed by 10 ml of water. The loading protocol was identical during cleanup of OTA, CIT and AFM 1. OTA and AFM 1 were eluted using 2 ml

4 1996 Arch Toxicol (2015) 89: of acetone, while CIT was eluted with 2 ml acetone acidified with 0.1 mol/l HCl. These eluents were mixed with 100 μl of DMSO (keeper solvent), resulting in a total concentration factor of 50 after evaporation, and the acetone evaporated under a gentle nitrogen stream at 45 C in an amber glass vial. The remaining DMSO solution was centrifuged for 10 min at 16,800g and transferred to an autosampler vial insert. Aliquots of 10 μl were injected into a chromatographic system using the same gradient as for the direct method. LC MS conditions The UHPLC system consisted of an Acquity UPLC H-Class (Waters, Milford, MA, USA) equipped with a quaternary solvent manager and a flow through needle sample manager. The analytical column used was a Waters Acquity UPLC HSS T mm, 1.8 µm column kept at 40 C preceded by a Waters Acquity UPLC BEH C18 VanGuard, 1.7 µm, 2.1 mm 5 mm precolumn. The gradient program started at 2.5 % methanol; after a plateau for 2 min at initial conditions, it increased to subsequently 4 % in 6 min, 10 % at 10 min and reached 75 % at 27 min. The column was then washed with 99 % methanol for 1 min and equilibrated at initial conditions for 2 min resulting in a total run time of 30 min. The flow rate used was set at 500 μl/min. Four mobile phases were used : Mobile phase A was water, mobile phase B was methanol, mobile phase C was 10 % acetic acid in water and mobile phase D was 500-mM ammonium acetate in water acidified with 5 % acetic acid. For the analysis in negative mode, mobile phase C was continuously added at a rate of 1 % resulting in a constant acid concentration of 0.1 % throughout the run. For the analysis in positive mode, mobile phase D was continuously added at a rate of 1 % resulting in a final buffer concentration of 5 mm ammonium acetate with 0.05 % acetic acid (ph ± 5.4). A XEVO TQ-S (Waters, Manchester, UK), equipped with an ESI source, was used as detector. Experiments were carried out in the multiple reaction monitoring mode (MRM). Cone voltage (V) and collision energy (CE) were optimized for each component during tuning by infusion of standard solutions with a flow rate of 20 µl/min into a column flow rate of 200 µl/min at 50 % methanol/water buffered with ammonium acetate (5 mmol) and acetic acid (0.05 %) using the integrated syringe pump. At least two product ion transitions for each analyte were selected in the final method. The most abundant product ion was used for quantification, unless it was due to nonspecific fragmentation (e.g., water loss), while a secondary product ion was used for confirmation (qualifier). The infusion experiments were also used to determine whether positive or negative mode gave the highest signal-to-noise ratio. The source temperature was set at 150 C while the capillary desolvation heater was set at 450 C. The capillary voltage used was 0.5 kv, both in positive and negative mode. The drying gas was nitrogen at a flow rate of 1,000 L/h. Both quadrupoles were operated at unit mass resolution while the collision cell was operated at an argon pressure of mbar. These parameters were used for analyses in both the positive and negative ion mode. The MRM data are summarized in Tables 1 (negative mode) and 2 (positive mode). Calibration standards were injected at the end of each sequence in order to avoid carryover of the fumonisins. Additionally, each sequence included a procedure blank. At the beginning, the end and at regular intervals (after six samples) in between, a pure standard solution containing OTA and CIT was injected as system performance test. The LC MS was operated using MassLynx 4.1 software. Method validation European Commission Decisions 2002/657/EC and 401/2006/EC were used as guidelines for the validation studies. Raw data were processed using TargetLynx 4.1. All analytes were quantified using external calibration. Individual six-point calibration plots were obtained from injection of pure standards in methanol/water (25/75, v/v). A logarithmic calibration was used. The result was calculated using the chromatographic peak area corrected for the suppression or enhancement in matrix as determined during the validation of the method (see also Table 3). The results from the validation study are summarized in Table 3. Linearity (of the log log-transformed response) was verified by injecting a calibration curve in both solvent and in urine. The two main criteria for positive identification were (a) retention time (must lie within ±2.5 %) and (b) ion ratios defined as qualifier area/quantifier area. Acceptable results had to fall within ±20 % if the ion ratio was between 0.5 and 1 or ±25 % if lower than 0.5. The three highest levels of the standards were used to establish reference criteria. The calibration curve included the following 6 concentrations for the calibration range indicated as (Table 3): 0, 0.5, 1, 2.5, 5, 10, 20 ng/ml or the concentrations 0, 0.05, 0.1, 0.25, 0.5, 1, 2 for the calibration range indicated as The matrix effect was assessed by the standard addition method, according to Matuszewski et al. (2003). From four spiked blank urines, an average matrix effect was calculated and used to correct the determined concentrations. If the matrix effect is <100 % (Table 3), ion suppression occurs; if the matrix effect is >100 %, ion enhancement is taking place.

5 1997 Table 1 MS parameters in negative mode Component Precursor (m/z)/[adduct] Cone voltage (V) Quantifier (m/z)/ce (ev) Qualifier (m/z)/ce (ev) DON-3-GlcA 471 [M H] /25 175/40 DON-15-GlcA 471 [M H] /25 175/40 DOM-GlcA 455 [M H] /20 113/25 FusX 353 [M H] /10 187/25 HO-CIT 265 [M H] /16 177/20 OTα 255 [M H] /15 167/25 ZEN-14-GlcA 493 [M H] /25 175/15 CIT 281 [M H+CH 3 OH] /15 205/25 α-zel-7-glca 495 [M H] /80 174/80 β-zel 319 [M H] /25 160/30 α-zel 319 [M H] /25 160/30 ZEN 317 [M H] /20 175/25 Table 2 MS parameters in positive mode Component Precursor (m/z)/[adduct] Cone voltage (V) Quantifier (m/z)/ce (ev) Qualifier (m/z)/ce (ev) DON 297 [M + H] /10 203/15 3-AcDON-15-GlcA 532 [M + NH 4 ] /20 199/20 DOM [M + H] /10 215/10 15-AcDON-3-GlcA 532 [M + NH 4 ] /20 321/20 3-AcDON 339 [M + H] /15 213/15 15-AcDON 339 [M + H] /10 137/10 β-zel-14-glca 514 [M + NH 4 ] /20 303/20 AFG [M + H] /28 245/38 AFM 29 [M + H] /25 229/25 AFG 29 [M + H] /25 200/37 AFB [M + H ] /25 259/28 AFB 13 [M + H] /19 241/35 DAS 384 [M + NH 4 ] /10 247/15 α-zel-14-glca 514 [M + NH 4 ] /20 303/20 HT2 442 [M + NH 4 ] /13 245/12 OTA 404 [M + H] /20 221/30 FB [M + H] /35 334/35 T2 484 [M + NH 4 ] /15 215/20 FB [M + H] /35 318/35 FB [M + H] /35 318/35 The limit of detection (LOD) and limit of quantification (LOQ) were determined by spiking four different blank urine samples with known amounts of the components at one determined level indicated as spike in Table 3. Table 3 summarizes the LOD and LOQ for the different components as calculated by TargetLynx defining a minimum signal-tonoise ratio (S/N) of 3 and 10, respectively. The spiked urines (1 level) as indicated in Table 3 were used to determine the following parameters: matrix effect, LOD and LOQ. The repeatability RSD r and within-lab reproducibility RSD Rw were determined by spiking a blank urine sample at a known level (see Table 3) and analyzing the spiked urines 6 times on 3 different days (18 repetitions in total). Results and discussion Optimization of LC/MS conditions Optimization of the LC separation parameters and MS detection conditions was performed with matrix spikes, which is the best method to identify interferences and is

6 1998 Arch Toxicol (2015) 89: Table 3 Retention time, calibration range, spike level, coefficient of determination (R 2 ) in solvent, variability between four different spiked blank urines (VAR), matrix effect and limits of detection (LOD) and quantification (LOQ) of different analytes Nr. Analyte Retention time (min) Calibration range (ng/ ml) R 2 Spike (ng/ VAR (%) Matrix ml) a effect (%) LOD b (pg/ LOQ b (pg/ ml) ml) RSD r c (%) (n = 6) RSD c Rw (%) (n = 3) 1 DON-3-GlcA DON-15-GlcA DON DOM-GlcA AcDON-15-GlcA DOM AcDON-3-GlcA FusX HO-CIT AcDON AcDON β-zel-14-glca OTα AFG AFM AFG AFB AFB DAS α-zel-14-glca CIT ZEN-14-GlcA HT α-zel-7-glca OTA β-zel FB T α-zel ZEN FB FB a Spike level was used to determine the following parameters: matrix effect, LOD, LOQ, VAR: applied to four different urines, RSD r and RSD Rw b Numbers for LOD and LOQ were rounded to one significant digit c RSD r and RSD Rw determined in 1 urine and analyzed 18 times in total imperative when using the direct approach (no sample cleanup). The retention time of the most polar metabolites (DON-glucuronides) was greatly influenced by the ph of the mobile phase. The use of an ammonium acetate/acetic acid buffer leads to a significant shorter retention time of DON-3-GlcA. Thus, using only acetic acid as a mobile phase additive improved retention of DON-3-GlcA. Furthermore, the S/N ratio for other compounds analyzed in the negative ionization mode was comparable when acetic acid was used instead of an acetate buffer. In the positive ionization mode, the addition of ammonium acetate leads to a significantly higher signal for all components, despite decreasing the retention of OTA and fumonisins. Moreover, it was essential to support the formation of [M + NH 4 + ] adducts for T2, HT2 and 15-AcDON-3-GlcA, 3-AcDON- 15-GlcA, β-zel-14-glca and α-zel-14-glca. For most

7 1999 Table 4 Biomarker contamination profile in urine samples Analyte of the analytes, the use of ammonium acetate as an additive in the mobile phase gave a significantly higher signal compared to ammonium formiate. Validation Positive samples (%) Average (pg/ml) Range (pg/ml) DON ,000 DON-3-GlcA 91 10, ,000 DON-15-GlcA ,620 3, ,000 DOM-GlcA 25 4, ,400 OTA CIT HO-CIT Calibration curves generated R 2 values of at least Visual inspection of the residuals revealed no nonrandom scattering of the residuals, indicating the regression model used is adequate (Neter et al. 1996). Although a long gradient run was used to give the best possible separation within an acceptable time range, we observed a strong matrix effect (either enhancement or suppression) for almost half of the components. In extreme cases, the matrix effect exceeded 50 % (<50 or >150 % in Table 3). There was no indication that components analyzed in negative mode suffered less from detrimental matrix effects than components analyzed in positive mode. As it would be difficult to spike every sample to determine the matrix effect, it was decided to use a solvent-based calibration curve and correct the response for the matrix effect as described in Table 3. The RSD Rw values were <20 % for 25 analytes, 23 % for DON, and higher than 30 % for 4 analytes only (DOM-1, 3-AcDON-15-GlcA, 15-AcDON-3-GlcA and β-zel-14- GlcA with RSD Rw between 31 and 41 %). Already during tuning it was noticed that these mainly formed sodium adducts in the positive ionization mode, which were not suitable for MS fragmentation. Therefore, the less intensive proton or ammonium adduct had to be used as a precursor ion in MRM. LOQs were in the sub ng/ml range and are comparable with most of the methods published using more extensive sample cleanup. confirmed by LC-diode array detection (DAD), using the 218-nm peak area of a certified solution of DON as reference. Our data report the concentration of DON-15-GlcA based on a calibration curve using DON-3-GlcA. A difference in MS response (1.88) of both DON-glucuronides has been reported by Warth et al. (2012a). All other glucuronides were quantified semi-quantitatively, as not enough pure compounds were available for weighing. Sample analysis The method presented in this study was designed for the analysis of 12 mycotoxin biomarkers in the ESI and 20 mycotoxin biomarkers in the ESI + mode. In total, seven biomarkers were detected in urine samples of Belgian volunteers (Table 4): DON-3-GlcA, DON-15-GlcA, DON, CIT, HO-CIT, OTA and DOM-GlcA. In our survey, each urine sample contained at least one biomarker. Recently, Njumbe Ediage et al. (2012) analyzed 40 urine samples from Belgian volunteers and detected only nine positive samples. DON-15-GlcA was present in all samples and was also the major DON biomarker, as earlier suggested by Warth et al. (2012a). Even in samples where no DON aglycone could be detected, DON-15-GlcA (and sometimes also DON-3-GlcA) was present. Figure 1 clearly shows a strong correlation between the presence of DON and its glucuronides, suggesting that humans are efficiently conjugating DON with glucuronic acid. Our results confirm that DON-glucuronides are more important as urinary biomarkers than DON itself (Table 5). Almost 90 % of the DON-equivalents (the amount of DON in DON, DON-3- GlcA and DON-15-GlcA) in human urine are present as DON-15-GlcA, confirming the observations with Austrian volunteers. Turner et al. (2008), using an indirect method, Quantification of DON glucuronides Our DON-3-GlcA standard was quantified in two ways. First, it was measured by UV-spectroscopy using an ε = 4,715 L/cm/mol (Wu et al. 2007). Then, it was Fig. 1 Concentration of DON-15-GlcA (ng/ml) and DON-3-GlcA (ng/ml) plotted against DON, both in logarithmic scale, detected in the urine samples

8 2000 Arch Toxicol (2015) 89: Table 5 Detected amounts of DON, DON-3-GlcA, DON-15-GlcA, the total DON equivalent present in each sample, DOM-GlcA, CIT, HO- CIT and OTA in the 32 urine samples Nr. DON (ng/ml) DON-3-GlcA (ng/ml) DON-15-GlcA (ng/ml) DON-equivalents (ng/ml) DOM-GlcA (ng/ ml) CIT (pg/ml) HO-CIT (pg/ml)ota (pg/ml) <LOQ <LOQ 5.2 <LOQ <LOQ <LOQ 15.7 <LOQ <LOQ <LOQ <LOQ 5 <LOQ <LOQ <LOQ 18.6 <LOQ 6 <LOQ <LOQ 6.2 <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ 13 <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ 8.7 <LOQ <LOQ 42.2 <LOQ <LOQ 10.5 <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ <LOQ Average detected DON or its glucuronide metabolites in 98.7 % of the analyzed samples. It was already suggested by Meky et al. (2003) that the combined measure of free DON and its glucuronides is necessary for measuring DON exposure in humans. Turner et al. (2010) further confirmed the use of DON and DON-glucuronides as biomarker for exposure to DON, especially for exposure during the previous h (Turner et al. 2009). In an Austrian pilot survey, DONglucuronides were detected in 96 % of the samples (Warth et al. 2012a), whereby on average 86 % of the total DON was present as either DON-3-GlcA or DON-15-GlcA. The data of Warth et al. (2012a) and our data clearly confirm that glucuronidation of DON at the 15-position is the major detoxification route of DON in humans. Although we could not detect DOM-1, it was determined in human urines of French (34 %) (Turner et al. 2010) and Egyptian (2 out of 69) volunteers (Piekkola et al. 2012). DOM-glucuronide was detected in 25 % of our samples. None of our volunteers were farmers, and our results suggest that detoxification of DON in humans can proceed via the formation of DOM-1 by intestinal gut microbiota followed by glucuronidation. There is no correlation between the concentration of DOM-GlcA and the concentration of DON or DON-glucuronides, further suggesting that

9 microbial conversion is responsible for glucuronidation of DOM-1. The DOM-GlcA in the analyzed samples amounts from 0.4 to 14 % of the DON-equivalents (DON aglycone + DON-glucuronides). Turner et al. (2011) reported that the DOM-1 equivalents represented about 1 % of the total DON-equivalents. Our data suggest that glucuronide metabolites are the main metabolites for DOM-1 as well as for DON. This can explain the lack of or the low presence of DOM-1 in human urine samples (Turner et al. 2011; Piekkola et al. 2012). Njumbe Ediage et al. (2012) analyzed 40 urine samples of Belgian volunteers but did not detect any DON-glucuronide. We think that extensive cleanup as used by Njumbe Ediage (2012) and Song et al. (2013) might be responsible for these results, perhaps in combination with the use of less sensitive LC MS/MS equipment as reported also by Warth et al. (2013a). Although the fragmentation of both glucuronides is slightly different, both DON-glucuronides can be detected using the transition 471 > 265 (Table 1). Although IAC cleanup is a selective method for enriching a target molecule, to our knowledge, there is no specific antibody for DON-3-GlcA or DON-15-GlcA available. Therefore, cleanup for analysis of DON and its glucuronides using IAC is not feasible. Also, it is likely difficult to design an antibody that is equally selective for both DON- 3-GlcA and DON-15-GlcA. OTA was detected in nearly 70 % of the samples at very low levels (pg/ml). This toxin has previously been detected at similar concentrations in urine samples, and positive samples were found as often as in our study (Gilbert et al. 2001; Ahn et al. 2010; Duarte et al. 2011; Klapec et al. 2012). Interestingly, at least tenfold higher levels of OTA were earlier reported in Belgian urine samples (Njumbe Ediage et al. 2012) and by Muñoz et al. (2010) in urine from German volunteers. Although the transitions for OTA-glucuronide were included in our method based on tuning of this molecule obtained from microsome incubations (Han et al. 2013), OTA-glucuronide was not detected in any of the samples. Also, OTA-glucuronide has not been detected in other studies (Muñoz et al. 2010). The OTA biotransformation product OTα could not be detected by our direct method, in agreement with Ahn et al. (2010). However, other studies showed the presence of OTα in human urine (Muñoz et al. 2010; Coronel et al. 2011; Klapec et al. 2012; Njumbe Ediage et al. 2012). Furthermore, the elsewhere reported substantial inter-individual variation on human ability for OTA detoxification might greatly compromise the use of OTα as an alternative or simply additional biomarker (Duarte et al. 2011). Apparently, OTα can be conjugated rapidly, but we did not look for possible OTα conjugates in this study. Interestingly, 59 % of the samples contained CIT while 66 % of the samples contained HO-CIT, a major hepatic 2001 citrinin metabolite in the rat (Dunn et al. 1983). This metabolite was recently characterized as a detoxification product of CIT (Föllmann et al. 2014). In fact, based on both CIT and HO-CIT analysis, 90 % of the analyzed human urine samples showed evidence for CIT exposure. Blaszkewicz et al. (2013) were the first to report the presence of CIT or its metabolite in all urine and blood samples from German volunteers in a small monitoring program. That study reported concentrations up to 790 pg/ml of CIT in urine, while the detected maximum concentration in our study was 117 pg/ml for CIT, with similar differences for its metabolite HO-CIT. As OTA and CIT were both detected at comparable and rather low levels, it was decided to verify their concentration by inclusion of an IAC cleanup prior to LC MS/MS, resulting in a 50-fold concentration of the analyte compared to the direct method. The results are shown in Fig. 2. The good agreement between the results of the direct method and the IAC cleanup method confirmed their presence and concentration in urine. The results of this study and a recent mini-survey on the presence of CIT in food (data unpublished) indicate that humans are much more exposed to CIT as previously known, which was already suggested by Blaszkewicz et al. (2013). These results are further confirmed by an ongoing study including a much larger group of volunteers (unpublished data). The obvious common exposure to CIT might be of concern, especially as CIT is often co-occurring with OTA. However, toxicity studies generally do not target combinations of these nephrotoxic mycotoxins. Clearly, more attention should be given to CIT (EFSA 2012; Flajs and Peraica 2009; Pepeljnjak and Klaric 2010). To our knowledge, our method is presently the most sensitive direct method (no cleanup) for analyzing CIT and HO-CIT in urine (Fig. 3). Validated biomarkers of exposure for the carcinogen AFB 1 have been reported in the literature (Kensler et al. Fig. 2 OTA and CIT concentration in 11 samples of urine as determined by both the direct method and the method with IAC cleanup

10 2002 Arch Toxicol (2015) 89: (a) (b) (c) (d) (e) (f) Fig. 3 Urine sample spiked at the level indicated in Table 3 with AFB 1, AFB 2, AFG 1, AFG 2 and OTA (a), DAS, FB 1, FB 2, FB 3, T2, HT2, 3-AcDON, 15-AcDON, DON and DOM-1; (b) FusX, ZEN-14- GlcA, α-zel-7-glca, DON-3-GlcA and DOM-GlcA; (c) β-zel, α- ZEL, ZEN, CIT, HO-CIT, OTα; (d) 3-AcDON-15-GlcA, 15-AcDON- 3-GlcA, β-zel-14-glca and α-zel-14-glca (e). f Urine sample naturally containing DON-3-GlcA and DON-15-GlcA 2011). In human urine, AFM 1 was the main excretion product and a dose-dependent relationship was shown between AFB 1 intake and the presence of the metabolite. While AFB 1 itself can be found in urine, it does not correlate with AFB 1 food intake (Turner et al. 2012). Urinary AFM 1 is often reported as biomarker for AFB 1 exposure, mainly in urines of African or Chinese people (Zhu et al. 1987; Piekkola et al. 2012; Warth et al. 2011). Ahn et al. (2010) used IAC for AFM 1 cleanup, but could not detect any AFM 1 in Korean urine samples, which could indicate a lower exposure of the Korean population compared to the population in some Chinese or African regions. However,

11 Ahn et al. (2010) reported the presence of a peak that could easily be mistaken for AFM 1, as it has three identical MRM transitions and even acceptable ion ratios. We encountered a similar interference in the AFM 1 channel in some urines. At least three transitions were similar, but in our case the interfering peak can easily be rejected as the ion ratios were not correct. After purification of urine samples containing the AFM 1 interference using IAC, the interfering peak had disappeared, which made it possible to identify in some urines AFM 1, but at rather low concentrations (pg/ml) that cannot be detected using our direct method without IAC cleanup. Although ZEN, α-zel and β-zel-glucuronides were detected in human urine after ingestion of 100 mg of ZEN (Mirocha et al. 1981), no ZEN or any of its metabolites could be detected in our urine samples. Warth et al. (2012b, 2013b) detected ZEN-14-GlcA in human urines. Urinary FB 1 has recently been correlated with FB 1 food intake (Gong et al. 2008; Van Der Westhuizen et al. 2011; Xu et al. 2010; Desalegn et al. 2011; Riley et al. 2012). In our urine samples, no fumonisins were detected above our LOQ, which can be explained by the lower intake of fumonisins by the Belgian volunteers. Conclusions We developed a direct LC MS/MS method that fulfilled the criteria to detect and quantify 32 mycotoxin biomarkers of exposure in human urine samples. All Belgian urine samples contained DON-15-GlcA. The latter appears to be the principal DON metabolite as 90 % of the total DON was detected as DON-15-GlcA. DON-3-GlcA was present in 90 % of our samples, while DON aglycone was less often detected. Another DON metabolite, DOM-GlcA was detected in 25 % of our samples, but not DOM-1 itself. This emphasizes the importance of glucuronidation for detoxification of DON in humans. Exposure to OTA appears also to be relatively common, as it was detected in 70 % of our samples. In 59 % of the samples, CIT was detected while 66 % contained HO-CIT. These data show that about 90 % of the volunteers have been exposed to CIT. This should stimulate further research as OTA and CIT are both nephrotoxic mycotoxins and synergistic effects are generally not taken into account during risk assessment. Acknowledgments The 700-MHZ equipment was funded through the FFEU-ZWAP initiative of the Flemish Government. We thank Dr A Versilovskis for the synthesis of DON-3-GlcA. A. C. thanks the Federal Public Service Health, Food Chain Safety and Environment for financial support (Project RT 11/2). Conflict of interest The authors declare that they have no conflict of interest. References 2003 Ahn J, Kim D, Kim H, Jahng KY (2010) Quantitative determination of mycotoxins in urine by LC MS/MS. 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