Identification of Lipidomic Biomarkers for ... - ACS Publications

Stefan Kalkhof, Franziska Dautel, Salvatore Loguercio, Sven Baumann, Saskia Trump, Harald Jungnickel, Wolfgang Otto, Susanne Rudzok, Sarah Potratz, ...
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Identification of Lipidomic Biomarkers for Coexposure to Subtoxic Doses of Benzo[a]pyrene and Cadmium: The Toxicological Cascade Biomarker Approach Harald Jungnickel,*,† Sarah Potratz,† Sven Baumann,‡,§ Patrick Tarnow,† Martin von Bergen,‡,∥,⊥ and Andreas Luch† †

Department of Product Safety, German Federal Institute for Risk Assessment (BfR), Max-Dohrn-Strasse 8-10, 10589 Berlin, Germany ‡ Department of Metabolomics, UFZ − Helmholtz-Centre for Environmental Research, Permoserstrasse 15, 04318 Leipzig, Germany § Institute of Pharmacy, Faculty of Biosciences, Pharmacology and Psychology, University of Leipzig, 04103 Leipzig, Germany ∥ Department of Proteomics, UFZ − Helmholtz-Centre for Environmental Research, Permoserstrasse 15, 04318 Leipzig, Germany ⊥ Department of Biotechnology, Chemistry and Environmental Engineering, Aalborg University, 9000 Aalborg, Denmark S Supporting Information *

ABSTRACT: The search for model bioassay systems indicating activation of different toxicological signaling pathways is one of the paramount goals of modern toxicology. Especially coexposure scenarios need to be investigated with respect to synergistic and interdependent effects for the activation of toxicological signaling pathways. The present study introduces an experimental in vitro model system for nontoxic and low-dose coexposures of human mammary carcinoma MCF-7 cells against polycyclic aromatic hydrocarbons (PAHs) such as benzo[a]pyrene (BP) and heavy metals such as cadmium. For the first time, a multivariate model that identifies 18 metabolic biomarkers has been shown to be sufficient to separate BP-treated cells from coexposed or control cells. A “toxicological pathway color code model” is introduced to visualize the results. Different biomarker subsets can be associated with specific HER2 signaling steps. A tiered cascade biomarker approach is proposed that could be used to identify profiles associated with tumorigenic potency of environmental toxicants in coexposure scenarios, including possible synergistic or additive effects.

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INTRODUCTION Polycyclic aromatic hydrocarbons (PAHs) and heavy metals are environmental contaminants that interact either cumulatively, synergistically, or antagonistically with intracellular targets such as the aryl hydrocarbon receptor (AHR). The AHR, or socalled “dioxin receptor”, is a cytosolic and basic helix−loop− helix Per-Arnt-Sim transcription factor that is activated by dioxin, certain PAHs, and a wide range of other environmental toxicants. The AHR is a highly conserved motif in nature with structural similarities to the Drosophila developmental regulators Per and Sim.1 In the activation process, the environmental toxicant binds to the AHR present in the cytosol.2 Upon agonist binding, the activator−receptor complex translocates into the nucleus, where it binds to the AHR nuclear translocator (ARNT) to form a heterodimer complex. This complex interacts with specific DNA promoter sequences, that is, xenobiotic responsive elements (XREs), which finally trigger the upregulation of drug-metabolizing enzymes of phase I, such as cytochrome P450-dependent monooxygenase 1A1 (CYP1A1), CYP1A2, and CYP1B1, and enzymes of phase II, © XXXX American Chemical Society

such as NAD(P)H:quinone oxidoreductase 1 (NQO1), glutathione S-transferases (GSTs), cytosolic aldehyde dehydrogenase, and UDP-glucuronosyltransferases (UGTs), respectively.3 In particular, coexposure scenarios are difficult to evaluate in environmental risk assessment but are more the rule than the exception under real-life conditions. Up-to-date risk assessments are predominantly based on single-compound exposure scenarios only. However, single-compound exposure scenarios are only valid if the underlying mechanism reveals additive behavior. Conversely, they do not account for synergistic or antagonistic mechanisms of multicomponent exposures. Therefore, the identification of biomarkers indicating coexposure scenarios in combination with toxicity data are needed to estimate environmental threshold levels for compound mixtures and to exactly assess the health risks Received: May 17, 2014 Revised: July 31, 2014 Accepted: August 5, 2014

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were quenched using 0.5 mL of ice-cold HPLC-grade methanol (Sigma-Aldrich, St. Louis, MO). The methanol solution containing the quenched cells was pipetted into a 2 mL centrifuge tube for extraction and diluted to give a final concentration of 2 × 106 cells/mL. Methanol (0.5 mL) was transferred into an Eppendorf vial, and the cells were extracted for 3 min using a Labsonic ultrasonic homogenizer. The methanol extract was removed and stored at −80 °C until it was subjected to chemical analysis. Metabolite Quantification. The AbsoluteIDQ kit p150 (Biocrates, Innsbruck, Austria) was prepared as described by the manufacturer (for a full list of all metabolites analyzed, see Supplementary Table S1 in the Supporting Information). Brief description: (a) 100 μL of cell extract was pipetted onto the filter inserts of the 96-well kit plate (containing stable isotopelabeled internal standards); (b) samples were dried using N2; (c) metabolites and internal standards were extracted with 5 mM ammonium acetate in methanol, centrifuged, and diluted. The final extracts were analyzed using FIA-MS/MS on an Agilent 1100 series binary HPLC system (Agilent Technologies, Waldbronn, Germany) coupled to a 4000 QTrap mass spectrometer (AB Sciex, Concord, Canada) equipped with a Turbolon spray source. The standard flow injection method comprising two 20 μL injections (one for positive and one for negative electrospray ionization mode) was applied for all measurements. Quantification was achieved by multiple reaction monitoring (MRM) detection in combination with the use of stable isotope-labeled and other internal standards as described previously.9,10 Data evaluation for quantification of metabolite concentrations was performed with the MetIQ software package. The precision was 20 μM, respectively, after 48 h of exposure (data not shown). Cell cultures were also pretreated for 6 h with either O-tricyclo[5.2.1.02,6]dec-9-yl dithiocarbonate potassium salt (D609) (50 μg/mL) or 3-{1-[3-(amidinothio)propyl]-1H-indol-3-yl}-3-(1-methyl-1H-indol-3-yl)maleimide (Ro 31-8220) (1 μM). Cell cultures in which cells were preincubated for 6 h with 1 μM α-naphthoflavone (α-NF) were also conducted with all treatment regimes. Extraction. After the culture medium was removed from the culture dish (6 cm diameter), cells were quickly washed twice with ice-cold phosphate-buffered saline (PBS) (pH 7.4). Cells B

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Figure 1. Metabolic changes in lipid composition of MCF-7 cells after treatment with benzo[a]pyrene (BP) and BP plus cadmium (Cd). Shown are values of the discriminant scores obtained from Fisher’s discriminant analysis of 18 MCF-7 samples for 18 principal compounds, which were selected to discriminate between cells treated with BP (1 μM) and BP (1 μM) plus Cd (0.1 nM). All three groups can be separated from each other. The model was evaluated using the “leave-one-out” formalism (100% correct grouping of ungrouped cases). The inset diagrams show MS data for reference peaks and flow-injection analysis (FIA) experiments. The x-axes show the acquisition time (2 min for each diagram).

equals control levels, “group 2” contains mean values in the interval [(meancontrol + 2stdev); (meancontrol + 3stdev)], and so on, where meancontrol denotes the mean of the untreated group. Each ranking group including controls was assigned to a specific color [group 1 (controls) = green, group 2 = light orange, etc.] and arranged in a “toxicological pathway color code map”. No negative metabolite changes were observed for the 18 metabolites used in our model. Negative metabolite changes would have been assigned to new groups (e.g., group −1) and new colors (e.g., bright yellow). A model combining p values with a given fold-change cutoff was chosen because fold-change cutoffs do not take variability into account and p values result in a high false-discovery rate, as was successfully shown previously for the TREAT model.14

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Supplementary Figure S1). All 18 metabolites were tested with Student’s t test and found to be significantly different from the controls when the cells were treated with BP plus Cd (95% confidence interval). Sixteen metabolites were significantly different from the controls when the cells were treated with BP alone (95% confidence interval). The Student’s t test results can be used as a means to evaluate the “toxicological pathway color code model”. All differences in colors show significance (p < 0.05) in the Student’s t test. Hence, the color code was used to visualize all of the changes for these 18 metabolites (see Supplementary Figures S1 and S2). Treatment of MCF-7 cells with BP and Cd yielded four distinct cellular responses in the metabolome (see Supplementary Figure S1). Coexposure of BP-treated cells to Cd resulted in a further increase in the levels of 11 different metabolites (see Supplementary Figure S1a) while two compounds showed the opposite behavior (see Supplementary Figure S1b) when compared with cells treated with BP only. Interestingly, two compounds showed elevated levels only under cotreatment conditions and were not found to be elevated in cells treated with BP alone (see Supplementary Figure S1c). Finally, the metabolic levels of three compounds were found to be similarly enhanced in cells treated with BP alone and cells cotreated with BP and Cd (see Supplementary Figure S1d). Cells treated with Cd alone showed no significantly different levels compared to solvent-treated

RESULTS

Comparison of metabolic profiles for cells treated with BP alone, cells treated with BP plus Cd, and untreated (solvent control) cells resulted in the identification of 18 compounds that could be used in a multivariate model to distinguish between these three treatment regimes (Figure 1). The levels of amino acids, acylcarnitines, carnitines, and the sum of hexoses did not change significantly among the investigated sample sets. Samples treated with Cd2+ alone showed no significant differences in metabolite levels compared to the controls (see C

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MCF-7 cells are known to grow estrogen dependently, all experiments were performed in medium supplemented with charcoal-treated (hormone-free) FBS to minimize ER activation. Under these conditions, any crosstalk between the AHR and ER can be assumed to be neglectable with regard to the metabolite alterations observed. Moreover, it should be noted that under hormone-starved conditions MCF-7 cells are synchronized in the G1 phase of the cell cycle. On the basis of similar coexposure experiments with AHR-null MCF-7-300 (i.e. MCF-7AHR200) cells, all of the identified lipid biomarkers were also revealed as indicators for AHR activation (see Supplementary Figure S2c). A toxicological pathway color code model has been introduced to visualize possible additive and synergistic effects under coexposure conditions (see Supplementary Figure S1). This model was also used to visualize the modes of action of chemical inhibitors of particular toxicological signaling pathways (see Supplementary Figure S2d). Here we identified HER2 signaling triggered through the activation of PC-PLC as a key signaling pathway responsible for the alterations in the metabolic pattern of MCF-7 cells during coexposure to BP and Cd by using the specific inhibitors D609 (for PC-PLC) and Ro 31-8220 (for c-Fos and MKP-1). Metabolic changes of the 18 lipid biomarker compounds could be used in a multivariate model to differentiate between cells treated simultaneously with BP and Cd from those cells in which either the PC-PLC activity or c-Fos and MKP-1 signaling was inhibited16,17 (see Supplementary Figure S3). Activation of c-Fos indicates upregulation of Bcl-2 and survivin expression, which not only protects breast cancer cells from apoptosis18,19 but is also responsible for enhanced cancer progression and decreased efficiency of most cancer chemotherapy regimes that are based on apoptosis induction.20 The toxicological pathway color code model visualizes metabolite changes in inhibitortreated cells compared with cells coexposed to BP and Cd but not treated with any inhibitor (see Supplementary Figure S2d). Subsequently, from this color code a cascade biomarker approach for coexposure of MCF-7 cells to BP and Cd was deduced (Figure 2). Correlation of Identified Biomarker Profiles to Known Disease Markers. Three of the affected phosphatidylcholines (PC aa C34:1, PC aa C36:1, and PC aa C36:2) were identified as possible biomarkers in cigarette smokers in the frame of the Cooperative Health Research in the Region of Augsburg, Germany (KORA) study.21 They were all increased in smokers compared with nonsmokers. Since BP is a carcinogenic compound that is also present in cigarette smoke,22,23 this result confirms the validity of these three metabolites as biomarkers for the exposure of cells against BP and probably also other carcinogenic PAHs. On the basis of these data, these three phosphatidylcholines, which elicit the same distinct alterations in human blood plasma and in human mammary carcinoma MCF-7 cells as a response to exposure to BP, may serve as general lipid biomarkers applicable in biomonitoring of possible risks mediated by carcinogenic PAHs. Cigarette smoke also contains Cd and is therefore to be considered as one of the major sources for the coexposure of human tissues to BP and Cd.24 Interestingly four of the identified biomarkers, namely, the sphingomyelins SM 16:0 and SM 16:1 and the two phosphatidylcholines PC aa C34:1 and PC aa C36:2, were also identified as biomarkers for the Niemann−Pick type B disease.25 For this kind of human disease, which belongs to the

controls for all 18 metabolites (see Supplementary Figure S1). Pretreatment of BP-exposed cells with α-NF for 6 h revealed no significant change for two biomarkers [SM(OH) C22:1; SM 24:0], a return back to control cell conditions for seven biomarkers, and a significant decrease in the levels of five biomarkers compared with cells treated with BP only (see Supplementary Figure S2a). Conversely a further increase in the metabolite levels of four biomarkers was also noticed, while one of these four compounds (lyso PC C16:1) was not significantly enhanced in cells when treated with BP alone (see Supplementary Figure S2a). On the other hand, pretreatment with α-NF resulted in similar biomarker levels in cells cotreated with BP and Cd compared to the untreated controls (see Supplementary Figure S2b). Experiments conducted with AHR-null MCF-7-300 (i.e. MCF-7AHR200) cells showed that the levels of all 18 metabolites investigated remained unchanged compared to control cells no matter whether treatment was performed with BP plus Cd or BP alone (see Supplementary Figure S2c). Specific inhibition of phosphatidylcholine-specific phospholipase C (PC-PLC) with the inhibitor D609 resulted in the reversal of aberrant metabolic patterns for 15 of the 18 biomarker compounds (see Supplementary Figure S2d). On the other hand, Ro 31-8220-mediated inhibition of the signaling molecules c-Fos and MKP-1, both of which are triggered via epidermal growth factor receptor (EGFR) activation15 and HER2 signaling, reversed the aberrant metabolic patterns of only four metabolites (see Supplementary Figure S2d). The two metabolites SM(OH) C22:1 and lyso PC C16:1, which showed elevated metabolite levels in cells coexposed to BP and Cd but not in those treated with BP only (see Supplementary Figure S1c), revealed similar levels as the controls upon treatment with D609 or Ro 31-8220 (see Supplementary Figure S2d). Since PC-PLC activation and subsequent c-Fos and MKP-1 activation are all part of the HER2 signaling pathway, this pathway can be considered as one of the key toxicological pathways triggered in MCF-7 cells during coexposure to BP and Cd (see Supplementary Figure S2d). Conversely, compounds that showed similarly enhanced metabolite levels in cells treated with BP only compared to those cotreated with BP and Cd (SM 24:0, lyso PC C16:0, and PC aa C36:1; see Supplementary Figure S1d) as well as compounds that showed decreased metabolite levels upon coexposure to BP and Cd (SM 26:0 and SM 16:1; see Supplementary Figure S1b) were still found to be enhanced in comparison with the control cells upon pretreatment with Ro 31-8220 (see Supplementary Figure S2d). Among them, however, lyso PC C16:0, PC aa C36:1, and SM 16:1 were returned back to control-cell levels by pretreatment with D609.

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DISCUSSION Time-dependent regulation of the HER2 signaling pathway in MCF-7 cells influences apoptosis and cancer development and thus may also affect cancer treatment strategies.15,16 We tested whether metabolic subsets can be identified in the HER2 signaling cascade using specific signaling pathway inhibitors and whether the AHR is involved. For the first time, a set of 18 lipid biomarkers could be identified in MCF-7 cells exposed to BP and Cd at subtoxic concentrations, and the same biomarkers could be used in a multivariate statistical model to separate coexposed cells from cells treated with BP or solvent only (Figure 1). Since the AHR is known to influence estrogen receptor (ER) activity and D

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tidylcholine.27 Only one of the four subisoforms, CCTα, is located in the nucleus in an inactive form.28 After release from the nucleus and membrane association, the enzyme CCTα becomes fully activated. The translocation of the regulatory transcription factor Nrf-2 to the nucleus, which is promoted in the presence of Cd, may cause an increase in the release of CCTα from the nucleus or an increased nuclear redistribution, as observed in MDKC cells under stress conditions.29 This process can be counteracted by the production of lysophosphatidylcholine, which inhibits CCTα. In that way, AHR signaling may be involved in stabilizing the phosphatidylcholine pool within cells. Bach1, a ubiquitously expressed mammalian transcription factor of the cap’n’collar B-zip family of proteins30 that actively competes with Nrf-2 to regulate ARE-mediated gene expression,31 is actively exported from the nucleus when Cd ions are present within cells.32 While Nrf-2 enhances AREmediated gene expression, Bach1, when present in the nucleus, inhibits it. Bach1 is also responsible for downregulating antioxidizing enzymes after an oxidative burst within cells. This suggests that phospholipids that are only upregulated in the presence of Cd are markers for enhanced oxidative stress. Differential Regulation during Coexposure: Four Distinct Biomarker Sets. Our experiments demonstrated that four different mechanisms could be separated for MCF-7 cells coexposed to BP and Cd (see Supplementary Figure S1a− d). For each mechanism, a specific set of biomarkers could be detected. Biomarker Set 1. Simultaneous exposure of MCF-7 cells to BP and Cd resulted in enhanced levels of two metabolites (see Supplementary Figure S1c) that were not elevated when cells were exposed to BP or Cd alone. This clearly indicates a synergistic effect of BP and Cd in cells upon coexposure. Biomarker Set 2. Compared with cells treated with BP only, 11 metabolites revealed higher levels (see Supplementary Figure S1a) and two biomarkers revealed lower concentrations (see Supplementary Figure S1b) in response to simultaneous exposure to BP and Cd. No significant changes in metabolite concentrations were observed in control cells exposed to Cd only. Together with biomarker set 1 (see above), our results demonstrate that 15 different metabolites were affected synergistically in cells by coexposure to BP and Cd. In contrast, three of the investigated compounds were found to be similarly enhanced regardless of whether the cells were treated with BP alone or BP in the presence of Cd (see Supplementary Figure S1d). Biomarker Set 3. Ten metabolites (all of the investigated sphingomyelins and lysophosphatidylcholines together with PC aa C32:2 and PC aa C42:2) that were not reduced to control levels by α-NF pretreatment in cells exposed to BP alone became α-NF-dependent with the addition of Cd (see Supplementary Figure S2a,b). In light of the observation that AHR-null MCF-7-300 (i.e. MCF-7AHR200) cells showed no increases in the levels of these 10 metabolites in coexposure experiments (see Supplementary Figure S2c), these results suggest AHR-dependent regulation. α-NF is an inhibitor of the AHR and thus of CYP1A1 gene expression in MCF-7 cells.33 α-NF exhibits antagonistic and agonistic activity when interacting with the AHR.33,34 The 10 metabolites identified (see Supplementary Figure S2a) could therefore be used to assess AHR-dependent but CYP1A1independent signaling pathways involved in cellular responses to BP exposure. Interestingly, these 10 compounds exhibited CYP1A1-dependent behavior when the cells were coexposed to

Figure 2. Scheme showing the metabolic biomarkers that indicate treatment of MCF-7 cells with BP alone or with BP plus Cd. Specific biomarker subsets could be identified for PC-PLC activation and c-Fos and MKP-1 activation. SM = sphingomyelin; lyso PC = lysophosphatidylcholine; PC = phosphatidylcholine; SM(OH) = hydroxysphingomyelin.

group of lipid storage disorders, significant increases in the same four lipid biomarkers were detected in affected patients in contrast to healthy humans. The disease manifests itself in downregulation of the gene SMPD-1, which encodes for acid sphingomyelinase (SMase). A significant decrease in SMase activity could also be confirmed in cultured skin fibroblasts from patients with Niemann−Pick disease, resulting in increased sphingomyelin and phosphatidylcholine levels within cells.25 Accumulation of sphingomyelins is also characteristic of other lysosomal storage diseases such as I-cell disease and lactosylceramidosis.26 The increase seen for all four biomarkers in cells treated with BP or BP plus Cd is suppressed by addition of α-NF 6 h before exposure and thus through inhibition of AHR signaling (see Supplementary Figure S2a,b). Coexposure to BP and Cd Influences Phosphatidylcholine and Subsequent Sphingomyelin Biosynthesis in Cells. Our results show a significant increase in phosphatidylcholine biosynthesis in cells treated with BP along with an increase of sphingomyelins. CTP-phosphocholine cytidylyltransferase (CCT), a rate-limiting enzyme in this biosynthesis pathway, may be involved in regulation processes. CCT is an amphitropic protein whose activity is influenced not only by ceramide but also by protein kinase C-α (PKC-α), p38 mitogen-activated protein kinase (p38 MAPK), cytosolic phospholipase A2 (cPLA2), 5-lipoxygenase, and lysophosphaE

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that these effects are even intensified upon coexposure to Cd. For the first time, specific metabolic profiles consisting of 15 lipid biomarkers could be associated with PC-PLC activation and possible HER2 heterodimer formation in the plasma membrane of MCF-7 cells. This biomarker set thus might be suitable to assess possible tumorigenicity and synergistic or additive activity of metal compounds when accompanied by PAHs. In light of the literature data cited above, our results from coexposure experiments with BP and Cd regarding elevated levels of lipid biomarkers become explainable. BP activates PCPLC by inducing its translocation to the cell membrane, where it promotes the formation of HER2 dimer complexes. Cd has recently been shown to activate EGFR signaling pathways via interaction with preformed HER2 dimers in the cell membrane of mesangial cells.36 The lack of HER2 dimers in the cell membrane of cells not treated with BP may also explain why Cd exposure alone (see Figure 2 and Supplementary Table S2) does not show any effect on the levels of the biomarkers identified. This result clearly demonstrates the need to establish model bioassays capable of addressing possible synergistic modes of action triggered by different environmental toxicants under coexposure conditions. Identification of Biomarker Sets in Cells Coexposed to BP and Cd. Our results for the first time associate a specific set of 15 metabolites with PC-PLC activation, and among them, four metabolites are associated with c-Fos and MKP-1 activation, which result from HER2 activation (see Figure 3d). Exposure to cigarette smoke, which contains both BP and Cd, has also been reported to activate c-Fos.44 Coexposure to BP and Cd results in an increase of lyso PC C16:1 (see Supplementary Figure S1c), which indicates that enhanced DNA repair occurs only under conditions where Nrf-2 nuclear translocation becomes increased. Conversely, cells solely exposed to BP already produce an excess of lyso PC C16:0 (see Supplementary Figure S1d ). This effect relies only on increased PC-PLC activity (inhibitable by D609) and not on cFos and MKP-1 activation (see Supplementary Figure S2d). The “Cascade Biomarker Set”. A cascade biomarker set containing 18 biomarkers that can be used to discriminate between cells treated with BP alone, BP plus Cd, or just solvent was identified (Figure 2). Among these markers, 15 can be associated with PC-PLC activation (inhibitable by D609), and of these, four can be associated with MKP-1 and c-Fos activation (inhibitable by Ro 31-8220). The latter group includes both biomarker compounds that are only altered under coexposure conditions [i.e., lyso PC C16:1 and SM(OH) C22:1]. In light of these results, HER2 regulation can be considered as a biomarker three-step cascade converging in four key metabolites indicative of HER2 signaling via c-Fos and MKP-1 activation. The Three-Step Tiered Screening Approach. Here we suggest a specific three-step tiered cascade biomarker approach that could be used to identify possible proliferative and carcinogenic potencies of environmental toxicants exerted during multicomponent exposures. Furthermore, this approach could also highlight possible synergistic or even antagonistic effects, which are not covered with single-compound toxicological assays to date. The experiments could be used as a blueprint to decide whether BP and other heavy metals or even mixtures of other PAHs and heavy metals show alterations in metabolic patterns similar to those for BP and Cd. The results from these assays may then be used for a better

Cd, indicating a mechanism dependent on drug-metabolizing enzymes. Studies have shown that HO-1 activity is inhibited by α-NF35 but increased by Cd.36 Therefore, Cd coexposure seems capable of activating alternative metabolic pathways such as oxidative stress, drug metabolism, or immune response pathways, which are then reversed by pretreatment with α-NF. The increase in the levels of lysophosphatidylcholines (lyso PC C16:0 and lyso PC C16:1) points toward the activation of regulatory pathways involved in DNA synthesis,37 thus indicating the onset of DNA repair induced by DNA-damaging metabolites of BP in the absence or presence of Cd-mediated Nrf-2 nuclear translocation. Biomarker Set 4. Eight compounds [SM(OH) C22:1 and all of the phosphatidylcholines except PC aa C32:1 and PC aa C42:2] were found to be dependent on both AHR and CYP1A1 activity since they could be completely downregulated in cells through pretreatment with α-NF (see Supplementary Figure S2a). Activation of HER2 Signaling in MCF-7 Cells Coexposed to BP and Cd. Elevated metabolite levels for 15 of the 18 investigated compounds could be reversed to normal levels by pretreatment with the PC-PLC inhibitor D609 (see Supplementary Figure S2d), thus indicating that upregulation of PC-PLC is a key mechanism following coexposure to BP and Cd. Aberrant phosphatidylcholine metabolism associated with elevated PC-PLC activity has been reported in epithelial ovarian cancer cells, 38 higher phosphatidylcholine levels thereby indicating ovary tumor progression. The increase in sphingomyelin levels may be attributed to increased activity of the enzyme sphingomyelin synthase (EC 2.7.8.27), which has been correlated to increases in PC-PLC activity37 and inhibition of sphingomyelin phosphodiesterase (EC 3.1.4.12).39 Sphingomyelin synthase is responsible for the conversion of ceramides to sphingomyelins, which results in higher overall sphingomyelin levels in cells, similar to our observations in this study. Activation of PC-PLC results in its translocation to the plasma membrane and colocalization with the human epidermal growth factor HER2 in raft regions of MCF-7 cell membranes.40 HER2 monomers are stored in lysosomes and upon activation by PC-PLC are transported to the cell membrane. Lysosomes are one of the target organelles for enhanced AHR activity. AHR activity significantly influences the membrane permeability of lysosomes in murine hepatoma 1c1c7 and Tao cells.41 Lysosomes are also the destination for certain polyaromatic hydrocarbons after cellular uptake.40 In lysosomes, sphingomyelin metabolism is also regulated via sphingomyelin phosphodiesterase.39 PC-PLC stabilizes HER2 when integrated into the plasma membrane and causes heterodimer formation of HER2 with other members of the EGFR receptor family.42 The resulting heterodimers activate specific cell-signaling pathways that finally contribute to abnormal cell growth and proliferation of cancer cells in humans, thereby causing an aggressive and metastasizing spread of such cells in the human body.43 Conversely, PC-PLC inhibition results in the removal of HER2 from the plasma membrane (internalization) and enhanced degradation in the lysosomes, effectively diminishing its presence at the surface of cells.44 The Biomarker Set Suggests Enhanced Carcinogenic and Metastasizing Activity of Cells Coexposed to BP and Cd. Our results suggest that elevated lipid biomarker levels induced by BP treatment might contribute to enhanced carcinogenicity and metastasizing activity of MCF-7 cells and F

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Figure 3. Tiered approach suggested to identify possible modes of action in the chemically triggered signaling cascade of HER2. Tier 1 accounts for overall HER2 activation. Tier 2 identifies possible c-Fos and MKP-1 activation only, while tier 3 accounts for additive or synergistic HER2 activation or c-Fos, MKP-1, and PC-PLC inhibition.

with Student’s t test to verify the significance of the observed differences. The first tier results in a “yes/no” decision, where similar patterns as observed with BP and Cd result in “yes” and movement to the second tier, while different patterns result in “no”, assuming no influence on HER-mediated c-Fos and MKP-1 activity for the coexposed compound at a given concentration. If a “yes” decision is made in the first tier, the test compound is automatically processed to the second tier, where an additional 11 compounds are quantified and the toxicological color code maps are obtained and compared with the results of BP and Cd coexposure experiments. Test compounds that also pass the second tier are transferred to the third tier, where a third set of three compounds is evaluated. If automatically processed directly after the mass spectral analysis, the results can be automatically converted into easy-to-read result tables that indicate c-Fos and MKP-1 activation only or PC-PLC activation without or with synergistic effects. This procedure would help to avoid cost- and labor-intensive data interpretation steps, as currently used in the Biocrates kit. In addition, by the use of these 18 compounds, a multivariate model could separate coexposed cells that were pretreated or untreated with inhibitors of PC-PLC or c-Fos and MKP-1. If the model achieves complete comparability, the outcome of the test tier will be regarded as “positive” and the compound will be marked for synergistic effects with regard to carcinogenicity and

prediction of possible synergistic and antagonistic effects of compound mixtures and subsequently could be applied in toxicological risk evaluation, thereby possibly influencing the assessment of total daily intake levels. A possible bioassay for chemical coexposure indicating HER2 signaling with simultaneous AHR activation could focus on a three-step tiered screening approach (Figure 3). For the screening approach, a concentration of the test compound would be used that is unable to change any metabolite level among the 18 compounds that have been tested in the tiered approach. The assay would focus on 18 lipid metabolites analyzed simultaneously in a high-throughput environment in one run with subsequent automated analysis for results interpretation. The methodology would significantly reduce analysis time by avoiding time-intensive derivatization reactions of amino acids (quantified with the Biocrates p150 kit) and also reduce costs for expensive labeled amino acid reference standards, thereby significantly decreasing the overall cost of the assay. After the analysis, automated data interpretation would quantify for the first tier only four key metabolites of the unexposed controls, cells exposed to BP alone, cells coexposed to BP and the test compound, and cells pretreated with inhibitors of c-Fos and MKP-1. The results would then be compared with those obtained by coexposure of cells to BP and Cd using the toxicological pathway color model in combination G

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cell-proliferating capacity in coexposure scenarios with carcinogens such as PAHs and especially BP. The results could also be processed directly after mass spectral analysis in an automated way, presenting all of the information in easy-toread tables where the analyzed compounds are directly classified as having synergistic effects or not. Overall, we have developed a multivariate statistical model by using concentration pattern differences of 18 specific metabolites to differentiate human mammary carcinoma MCF-7 cells treated with BP alone from those coexposed to BP and Cd and from untreated cells. Among these 18 biomarkers, a subset of 15 that indicate PC-PLC activation was identified, and of these, a subset of four that are likely to indicate specificity for c-Fos and MKP-1 activation was identified. Applying this approach, we identified AHR-dependent lipid biosynthesis regulation as an important mechanism initiated upon exposure to environmental chemicals such as BP and Cd.

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ASSOCIATED CONTENT

S Supporting Information *

Tables S1 and S2, Figures S1−S3, and a list of abbreviations used in the text. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS

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REFERENCES

The authors acknowledge intramural funding from Grants BfR1322-434 and BfR-1329-483.

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