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STEROIDOMIC FOOTPRINTING BASED ON ULTRA-HIGH PERFORMANCE LIQUID CHROMATOGRAPHY COUPLED WITH QUALITATIVE AND QUANTITATIVE HIGH-RESOLUTION MASS SPECTROMETRY FOR THE EVALUATION OF ENDOCRINE DISRUPTING CHEMICALS IN H295R CELLS David Tonoli, Cornelia Fürstenberger, Julien Boccard, Denis Hochstrasser, Fabienne Jeanneret, Alex Odermatt, and Serge Rudaz Chem. Res. Toxicol., Just Accepted Manuscript • DOI: 10.1021/tx5005369 • Publication Date (Web): 31 Mar 2015 Downloaded from http://pubs.acs.org on April 16, 2015

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Chemical Research in Toxicology

STEROIDOMIC FOOTPRINTING BASED ON ULTRA-HIGH PERFORMANCE LIQUID CHROMATOGRAPHY COUPLED WITH QUALITATIVE AND QUANTITATIVE HIGHRESOLUTION MASS SPECTROMETRY FOR THE EVALUATION OF ENDOCRINE DISRUPTING CHEMICALS IN H295R CELLS

David Tonoli†,‡,§, Cornelia Fürstenberger§,#, Julien Boccard†, Denis Hochstrasser¦, Fabienne Jeanneret†,‡,§, Alex Odermatt§,#, Serge Rudaz†,§* †

School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, Geneva, Switzerland ‡

Department of Human Protein Sciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland

§

Swiss Centre for Applied Human Toxicology (SCAHT), Universities of Basel and Geneva, Switzerland

#

Division of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, Basel, Switzerland ¦

Department of Genetic and Laboratory Medicine, Geneva University Hospitals, Geneva, Switzerland

Address for correspondence: Prof. Serge Rudaz School of pharmaceutical sciences, University of Geneva 20 Bd d’Yvoy, 1211 Geneva 4, Switzerland Phone : + 41 22 379 65 72 Fax : + 41 22 379 68 08 email : [email protected]

Byline: Steroidomic footprinting to evaluate endocrine disrupting chemicals in H295R cells.

ACS Paragon Plus Environment

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TABLE OF CONTENT

Keywords: High-Resolution MS, Steroid, Triclocarban, Endocrine Disrupting Chemical, H295R, Steroidomics, Adrenal Toxicity.


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ABSTRACT The screening of endocrine disrupting chemicals (EDCs) that may alter steroidogenesis represents a highly important field mainly due to the numerous pathologies, such as cancer, diabetes, obesity, osteoporosis, and infertility that have been related to impaired steroid-mediated regulation. The adrenal H295R cell model has been validated to study steroidogenesis by the Organization for Economic Co-operation and Development (OECD) guideline. However, this guideline focuses solely on testosterone and estradiol monitoring, hormones not typically produced by the adrenals, hence limiting possible indepth mechanistic investigations. The present work proposes an untargeted steroidomic footprinting workflow based on Ultra-High Pressure Liquid Chromatography (UHPLC) coupled to high-resolution MS for the screening and mechanistic investigations of EDCs in H295R cell supernatants. A suspected EDC, triclocarban (TCC), used in detergents, cosmetics, and personal care products, was selected to demonstrate the efficiency of the reported methodology, allowing the simultaneous assessment of a steroidomic footprint and quantification of a selected subset of steroids in a single analysis. The effects of exposure to increasing TCC concentrations were assessed, and the selection of features with database matching followed by multivariate analysis has led to the selection of the most salient affected steroids. Using correlation analysis, 11 steroids were associated with a high, 18 with a medium and 8 with a relatively low sensitivity behavior to TCC. Amongst the candidates, 13 identified steroids were simultaneously quantified, leading to the evaluation and localization of the disruption of steroidogenesis caused by TCC upstream of the formation of pregnenolone. The remaining candidates could be associated with a specific steroid class (progestogens and corticosteroids, or


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androgens) and represent a specific footprint of steroidogenesis disruption by TCC. This strategy was devised to be compatible with medium/high-throughput screening and could be useful for the mechanistic elucidation of EDCs.


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INTRODUCTION Endocrine disrupting chemicals (EDCs) have been defined by the U.S. Environmental Agency (EPA) as “exogenous agents that interfere with the synthesis, secretion, transport, metabolism, binding action, or elimination of natural blood-borne hormones that are present in the body and responsible for homeostasis, reproduction, and the developmental process”.1 This rather vast definition has recently been revisited to focus on substances or mixtures that alter endocrine functions and consequently cause adverse health effects in an intact organism, its progeny or (sub)population.2 EDCs

include

natural

plant

compounds

and

synthetic

chemicals

such

as

pharmaceuticals, agricultural chemicals, compounds contained in consumer products and industrial chemicals. The identification of the alteration of steroidogenesis in humans is of growing importance, with the main reason being the numerous pathologies related to impaired steroid-mediated regulation such as cancer, diabetes, obesity, osteoporosis, and infertility.3-6 Moreover, a toxicological evaluation is critical for any new chemical entering the European Union (EU) domestic market since the establishment of regulations on the Registration, Evaluation, Authorization and Restriction of Chemicals (REACH). In this context, research to establish improved strategies and in vitro models for the identification of compounds disrupting steroidogenesis is pursued by regulatory authorities. Cellular models such as primary adrenal cells are not adequate as they are difficult to obtain, require fresh tissues and exhibit important variability between donors.7 Additionally, these cellular models do not allow medium- or high-throughput screening. The human adrenocortical carcinoma cell line H295R has been widely studied over the last 25 years.8-11 A steroidogenesis assay based on H295R cells has been validated in


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inter-laboratory studies,12,13 and their use resulted in a codified guideline issued by the Organization for Economic Cooperation and Development (OECD).14 Interestingly, this guideline was focused on the effect of exogenous chemicals on testosterone and estradiol production, although physiologically the human adrenal glands predominantly produce corticosteroids, progestogens and precursors of active androgens such as androstenedione and DHEA. This cell line has also been recognized as a valuable tool to study the mechanistic aspects of steroidogenesis7 because it expresses several functional enzymes involved in the synthesis of a wide range of steroids, including adrenal androgens, progestogens, glucocorticoids, and mineralocorticoids. Historically, steroids in human biological fluids or cell culture supernatants15 have been analyzed by immunoassays; unfortunately, due to their physico-chemical properties and structural similarities, cross-reactivity has been often reported.16-18 Separation techniques such as GC-MS were therefore considered as the gold standard for steroid analysis with the main drawbacks being the time-consuming and chemically based sample preparation (hydrolysis/derivatization) that is not compatible with conjugated steroid analysis. LCMS/MS based analysis in the selected reaction monitoring (SRM) mode is another standard in terms of sensitivity and versatility for steroid assays, with the main advantage of increasing sample preparation throughput.19 Several studies were published on H295R cells taking advantage of quantitative data generated using QqQ MS based LC-MS analytical methods.20-32 However, due to the targeted acquisition mode afforded by the MS/MS analysis (with either GC or LC as separation technique), the number of quantified steroids in these publications ranged from one, cortisol,21 to a maximum of 21 steroids,26,27 preventing further data investigation after acquisition.


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The quantitative and qualitative analysis of steroids produced by H295R cells, defined as steroidomic footprinting and representing a subset of metabolic footprinting as originally defined by Kell et al.,33 exhibits major analytical challenges despite the introduction of high-end targeted LC-MS/MS strategies. Mainly, steroid concentrations in cell culture media are very low and require a pre-concentration step prior to analysis. Moreover, the identification of steroids remains a critical issue as numerous isomers could be observed, and MS can fall short towards their elucidation. The recent development of high-resolution mass spectrometers, especially Orbitrap- and/or quadrupole time-of-flight (QTOF)-based analyzers, has opened new possibilities for the untargeted screening of steroid-like molecules.34-38 High-resolution strategies allow an untargeted analysis of samples and present sensitivities approaching those of QqQ instruments.39-41 In this context, this study proposes an untargeted steroidomic footprinting workflow for the screening and mechanistic investigation of EDCs in H295R cell culture supernatants. Triclocarban (3,4,4’-trichlorocarbanilide, TCC, Figure 1), an antibacterial agent used in detergents, cosmetics and personal care products, has been chosen as a model chemical, due to its suspected endocrine disrupting effects.42,43 Recently, the U.S. Food and Drug Administration (FDA) issued a rule requiring “manufacturers to provide more substantial data to demonstrate the safety and effectiveness of antibacterial soap” (which includes TCC) due to possible “unanticipated hormonal effects”.44 Concerns were raised from an environmental perspective as TCC was detected in surface waters and sewage sludge in the US.45 Furthermore, TCC is readily taken up by human organisms,


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and concentrations between 20-500 nM were measured in healthy volunteers after a single shower using TCC-containing soap.46 In the present study, untargeted data acquisition after sample preparation was performed using an UHPLC-QTOF-MS platform. The sequential profiling and classification of steroids with altered concentrations upon exposure to TCC based on single data acquisition were established on the supernatants of cultured H295R cells. Quantitative analysis of an identified subset of steroids was also simultaneously carried out in the TOF-MS mode. This analytical strategy allowed lending evidence towards the key steps of the steroidogenesis perturbation by TCC.


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EXPERIMENTAL PROCEDURES Chemicals 11β,21-dihydroxy-3,20-dioxo-4-pregnen-18-al (aldosterone), 4-androstene-3,17dione (androstenedione), 3-hydroxy-1,3,5(10)-estratrien-17-one (estrone, E1), 3,17βdihydroxy-1,3,5(10)-estratriene

(estradiol,

E2),

3β,16α,17β,trihydroxy-1,3,5(10)-

estratriene (estriol, E3), 4-pregnene-3,20-dione (progesterone), 4-pregnene-11β,21-diol3,20-dione

(corticosterone),

17β-hydroxy-4-androsten-3-one

(testosterone),

17α-

hydroxy-4-androsten-3-one (epitestosterone), 11β,17α,21-trihydroxypregn-4-ene-3,20dione (cortisol), 17α-hydroxy-4-pregnen-3,20-dione (17α-hydroxyprogesterone), 5pregnen-3β-ol-20-one (etiocholanolone),

(pregnenolone),

17α,21-dihydroxy-4-pregnen-3,20-dione

hydroxy-5-androsten-17-one dehydrate

(DHEAS),

(DHEA),

(cortisone),

11-DOC)

were

(11-deoxycortisol),

5-androsten-3β-ol-17-one

5α-androstan-17β-ol-3-one

pregnene-3,11,20-trione deoxycorticosterone,

3α-hydroxy-5β-androstan-17-one

(5α-DHT),

sulfate

from

sodium

17α,21-dihydroxy-4-

21-hydroxypregn-4-ene-3,20-dione purchased

3β-

Sigma-Aldrich

(11(Buchs,

Switzerland). 21-hydroxy-pregn-4-ene-3,11,20-trione (11-dehydrocorticosterone) and (3β)-3,17-dihydroxypregn-5-en-20-one (17α-hydroxypregnenolone) were obtained from LGC Standards GmbH (Wesel, Germany). Deuterated analogues cortisol-9,11,12,12-d4 and 17α-hydroxyprogesterone-2,2,4,6,6,21,21,21-d8 were purchased from Toronto Research Chemicals (Toronto, ON, Canada). 16,16,17-Testosterone-d3 was obtained from LGC Standards GmbH. TCC was purchased from Sigma-Aldrich. Zinc sulfate heptahydrate, dimethyl sulfoxide (DMSO) and formic acid (FA) were also obtained from


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Sigma-Aldrich. Acetonitrile (MeCN), water (H2O) and methanol (MeOH) were provided by Romil Ltd. (Waterbeach, UK). Cell culture H295R cells were grown in Dulbecco’s modified Eagle’s medium (DMEM) / Ham’s nutrient mixture F-12 (1:1, v/v) phenol red free medium (Life Technologies, Zug, Switzerland), supplemented with 2.5% (v/v) Nu-serum (Corning, Amsterdam, The Netherlands), 1% (v/v) ITS+Premix (BD Bioscience, Bedford, MA, USA) and 1% penicillin-streptomycin (Sigma-Aldrich). Cells at passages between 5-10 were seeded in 6 well dishes containing 2 mL of medium at a confluence of 700,000 cells per well. Cells were allowed to adhere for 24 h prior to incubation with vehicle (DMSO, final concentration 0.05% v/v), or TCC (final concentrations 500 nM, 1 µM, 2.5 µM, 5 µM, and 10 µM). After 48 h, the culture supernatant was collected and frozen at -80°C until analysis. Cell viability assay The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazoliumbromid (MTT) cell viability assay was performed as described by Nashev et al.47 Briefly, 200,000 cells per well containing 1 mL of medium were seeded in 24-well plates. After overnight incubation, the medium was replaced and cells were incubated with vehicle or TCC at three representative concentrations (500 nM, 1 µM, and 10 µM) for 24 h, followed by adding 5 mg/mL MTT for another 3 h. The medium was carefully removed and 200 µL DMSO were added to each well to dissolve the formazan crystals. After 30 min, the plate was


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measured at 670 nm using a reference filter at 650 nm. Three independent experiments, each conducted in triplicates, were performed. Standards and Quality Controls (QC’s) sample preparation Individual stock solutions of 15 steroids (i.e., testosterone, epitestosterone, androstenedione, 5α-DHT, etiocholanolone, cortisol, aldosterone, 11-DOC, 17αhydroxyprogesterone, progesterone, pregnenolone, 17α-hydroxypregnenolone, E1, E2, and E3) were prepared in MeOH at 1 mg/mL. Working solutions at appropriate concentrations (11 levels) were prepared by diluting in MeOH an intermediate stock solution of the 15 steroids at 10 µg/mL. Calibration standards were prepared by spiking 10 µL of working solutions in 990 µL of H295R cell medium prepared independently from cell culture experiments using different batches of starting materials (called hereafter “B1”) to obtain final concentrations ranging from 0.05 to 100 ng/mL. Spiked medium solutions were then kept at -20°C prior to sample preparation. Quality control (QC) samples at 5 levels (i.e., 0.25, 0.5, 1, 2.5, and 25 ng/mL) were prepared independently by spiking appropriate working solutions into the cell culture medium used for the in vitro experiments (called hereafter “B2”). Two series of validation experiments for quantification purposes were performed by preparing independent calibration and QC series as described above with concentrations ranging from 0.05 to 100 ng/mL (11 concentration levels) using an extended mixture of steroids included the 15 mentioned above added to the six found using

untargeted

analysis

(i.e.,

corticosterone,

11-dehydrocorticosterone,

11-

deoxycortisol, DHEA, DHEAS, and cortisone). Biological QCs (QC BIO) were prepared


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by thoroughly mixing equivolumes of each unknown sample, which were then aliquoted in 1 mL samples. Sample preparation Protein precipitation was performed by adding 2 mL of precipitation solution to 1 mL of cell culture supernatant. Precipitation solution was prepared by spiking 17αhydroxyprogesterone-d8 and cortisol-d4 at 1 µg/mL in MeOH, and testosterone-d3 at 1 µg/mL in MeOH (final concentrations equal to, respectively, 4, 4 and 2 ng/mL) to get a mixture of ZnSO4 at 50 g/L in H2O with MeOH (1:1, v/v). Samples were mixed on a rotator stirrer (RR70 Series, lbx instruments, Rungis, France) for 10 min at 70 rpm. Samples were centrifuged at 4°C and 12’000 x g for 10 min. Supernatant was supplemented with 4 mL of H2O before loading in Solid-Phase Extraction (SPE) Oasis HLB 96-well plate format (30 mg, 30 µm particle size, Waters, Milford, MA, USA). Prior to supernatant deposition, cartridges were conditioned with 1 mL of MeOH, dried for 10 min at 10 inHg, and equilibrated twice with 1 mL of H2O. Cartridges were then washed twice with 1 mL of H2O/MeOH (85:15, v/v) and dried for 1 min at 10 inHg. Samples were then eluted with 1 mL of MeOH. Fractions were transferred into 1.5 mL polypropylene tubes and evaporated to dryness under vacuum with a centrifugal evaporator (RC1022, Jouan, Instrumenten Gesellschaft AG, Zürich, Switzerland). Samples were reconstituted in 50 µL of H2O + 0.1% FA/MeCN + 0.1% FA (85:15, v/v) and homogenized in a thermomixer for 10 min at 20°C and 1400 rpm (Vaudaux-Eppendorf, Buchs, Switzerland) before injection of 5 µL into the LC-MS instrument. UHPLC conditions


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UHPLC separations were performed with an UPLC Acquity H-Class (Waters, Milford, MA, USA) including a quaternary solvent manager (QSM), a sample manager (SM-FTN) and a column manager (CM-A). The separation was performed on an Acquity UPLC BEH C18 column (1.0 x 150 mm, 1.7 µm) connected to an Acquity UPLC BEH C18 VanGuard pre-column (2.1 x 5 mm, 1.7 µm). Mobile phase A was H2O + 0.1% FA and mobile phase B was MeCN + 0.1% FA. The flow-rate was set at 60 µL/min. The gradient composition was set at 15% B for 0.5 min. The composition of mobile phase B was linearly increased up to 95% in 15 min, held at 95% B for 3 min, and then equilibrated back to the original mobile phase conditions in 0.1 min, which was followed by holding for 6.9 min. The total analysis time was 25.5 min per sample. The column was kept at 30°C during the analyses, while samples were kept at 4°C in the autosampler. The flow post-column was directed into a 6 port valve that was directed into a waste vessel between 0.06 and 0.43 min. During this period, a calibration solution used for automatic recalibration post-acquisition was infused at 100 µL/min into the electrospray source using a Sun Flow 100 HPLC pump (SunChrom). The calibration solution consisted of a 1 M sodium hydroxide solution diluted 100 times in H2O + 0.1% FA/isopropanol + 0.1% FA (50:50, v/v). Automatic calibration was performed with nine formate adducts spanning a m/z range of 158 to 702 using the high precision calibration (HPC) algorithm and calibration version 1.0 (Compass v1.5 SR3, Bruker, Bremen, Germany). Mass spectrometric conditions The MaXis 3G QTOF MS (Bruker) used was equipped with an electrospray ionization (ESI) source operated in the positive mode. The source parameters were


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optimized by infusing a mixture of testosterone and cortisol at 1 µg/mL in H2O + 0.1% FA/MeCN + 0.1% FA (70:30, v/v) using a tee-split and then adding it to a 57 µL/min flow of post-column H2O + 0.1% FA/MeCN + 0.1% FA (95:5, v/v) corresponding to the most aqueous composition of the mobile phase used in the gradient. The signal was monitored and averaged for 1 min at each of the monitored conditions. The end plate offset optimal value was found at -600 V with the capillary voltage at -5 kV and the nebulizer pressure at 1.3 bar. The optimal dry gas flow rate and temperature were found to be 4.0 L/min and 225°C, respectively. MS acquisition parameters were optimized by infusing a mixture of 15 steroids as described above. The optimized transfer time and pre-pulse storage were 40 µs and 7.0 µs, respectively, to allow for m/z cut-offs greater than 1,000 and hence increase the number of ion packages sent to the TOF per unit of time. The accumulation time was set at 1 s, and the mass range monitored was from m/z 50 to 1,000. The acquisition was performed in the profile mode, and the data were acquired using the Compass v1.5 SR3 software suite from Bruker and HyStar v3.2 SR2. The UHPLC was controlled using the plug-in for a Waters Acquity UPLC v.1.5. The MS/MS analysis of selected features was performed in the data-dependent acquisition (DDA) mode. The acquisition was performed with a reduced duty cycle time of 0.5 s for TOF MS analysis and a reduced acquisition range (m/z 50 to 500). MS/MS data acquisition was triggered using a precursor ion list (containing m/z and tR with input tolerances of ± 0.2 Da and ± 0.4 min, respectively) extracted from the candidates of interest. The threshold was set at 5,000 counts, and the cycle time for MS/MS was automatically adapted linearly depending on the precursor intensity (2 s for 10,000 count


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precursors and 1 s for 106 count precursors). The collision sweeping option was also selected to allow 20 % of the MS/MS time to be spent at 10 eV and 80 % of the MS/MS time to be spent at 20 eV. In empty intervals, other precursors were fragmented according to the sole criteria of abundance. Analytical sequence The first samples injected were blank cellular medium (cellular medium B1 00) and cellular medium spiked with only internal standard (ISTD) (cellular medium B1 0). These samples were followed by cellular medium B2 0 samples. Eleven calibration solutions were then injected followed by five QC B2 samples at different concentration levels. QC BIO were injected at the beginning (n = 6), every five samples, and at the end of the unknown sample sequence (n = 6). The biological samples were randomly analyzed using a random function generated in Excel 2013. Following the last QC BIO samples, another series of five QC B2 samples was injected. Cellular medium B1 00 samples were also analyzed in between calibration, QC and QC BIO samples. Data processing Data processing was performed using Progenesis CoMet (Version 2.0, 64-bit, Nonlinear Dynamics, Newcastle upon Tyne, UK). Bruker data files were first exported as CoMet readable datafiles with Bruker Compass Xtract (v. 3.1.5). A list of 9 potential adducts and neutral losses (i.e., H, Na, NH4, H+K, K, 2Na-H, and -1, -2, and -3 H2O) was used to deconvolute signals. No normalization nor retention time adjustments were performed. A minimum chromatographic peak width of 0.15 min was used, and the retention time limits were set between 1 and 22 min. No procedure of peak alignment


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was mandatory considering that the peak shift estimated by the extraction of several typical features from QC BIO samples was found to be negligible (< 0.05 min). Quantitative analysis was performed using QuantAnalysis (Version 2.2 build 383, 64-bit beta version, Bruker). Ion extraction was performed using a 20 mDa window and smoothing was performed 3 times with a Gaussian smooth width of 1 s. Peak detection algorithm used was version 3.0. Multivariate data analysis Principal component analysis (PCA) and orthogonal partial least squaresdiscriminant analysis (OPLS-DA) models were evaluated with the SIMCA-P software (version 13, Umetrics, Umeå, Sweden). The optimal model size and prediction ability of OPLS-DA models were assessed using a 7-fold cross-validation procedure. The validity of the model was verified using permutation tests and CV-ANOVA.48


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RESULTS AND DISCUSSION The purpose of this study was to develop a method that allows detecting disturbances in the production of steroid-like molecules in the human adrenal H295R cell model. The proposed methodology allows determining simultaneously a steroidomic fingerprint and to quantify a number of selected adrenal steroids, providing initial mechanistic insight into the mechanism underlying chemical-induced disruption of adrenal cell function. The current use of the H295R cells to identify chemicals interfering with steroidogenesis according to the OECD guideline focuses on the validated read-out of testosterone and estradiol. Interestingly, in a physiological context, human adrenal glands predominantly produce corticosteroids and precursors of active androgens such as DHEA. Steroidogenesis is a complex process involving many different enzymes, from uptake of extracellular cholesterol as well as intracellular cholesterol synthesis to the formation of the main precursor pregnenolone and downstream hormones such as progestogens, glucocorticoids, mineralocorticoids and androgen precursors such as androstenedione and DHEA. Therefore, covering a wide range of steroids extends the current protocol according to the OECD guideline and might provide additional mechanistic insight. The antibacterial agent TCC, a suspected EDC, was used as a model compound to test the methodology. The effects of different concentrations TCC on the production and metabolism of steroids by H295R cells were compared. Optimization of cell culture and sample preparation In order to achieve high reproducibility, H295R cells were exclusively used at passages 5-10, and the same batch of Nu-serum was used in all of the presented experiments. In addition, cell viability was assessed using the MTT assay. At the highest


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concentration of TCC used in the present study, cell viability tended to be decreased, although the value did not reach significance (Supplementary Materials Figure S1). However, cytotoxicity was observed at 20 µM (data not shown); therefore, 10 µM was the highest concentration chosen in this study. Given that an untargeted analysis of steroids was the main goal of these investigations, sample preparation was kept to be minimal and generic. The latter was mainly devised to concentrate analytes from the biological matrix because steroids are found at low concentrations in cell culture supernatants, i.e., in the low ng/mL range. SPE washing steps were evaluated using three MeOH concentrations (i.e., 5%, 15 % and 30%, v/v) on the subset of 15 representative steroid standards. During method development, the elution and washing fractions were systematically analyzed. Losses of aldosterone and cortisol in the 30% methanolic wash fraction were observed but did not significantly impact (< 5%) the recovery of the measured eluted fraction (results not shown). Therefore, a 15% MeOH in H2O (v/v) wash was used in order to prevent steroid loss. Elution was performed with MeOH as it has been demonstrated to be a suitable solvent for complete steroid elution.20 Exploratory analysis using an untargeted approach A C18 column was selected to provide sufficient retention of the relatively lipophilic steroids (range of log P values from 2.67 for estriol, the first known eluting steroid, to 4.15 for progesterone, the last one). Columns of 1.7 µm particles and 150 mm in length were selected to provide the optimal separation efficiency and peak capacity while maintaining an acceptable number of data points (≥ 12 data points per chromatographic peak) with the selected MS duty cycle (1 s). The separation of the


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steroids was performed in 18.5 min with a generic linear gradient to provide easy transferability capabilities towards different LC-MS systems. Acquired raw signals were extracted taking into account specific in-source losses of one, two and three molecules of water that are often observed in steroid MS analysis.49 A total of 7,437 features was obtained. A first data filtering was performed by removing features detected as doubly charged compounds (3,888 features left with z = 1). Prior to multivariate data analysis (MVA), a Pareto procedure was selected for variable scaling to reduce the influence of noise while retaining partial information related to the signal intensity. Data quality evaluation PCA was applied on the 3,888 features to obtain an initial evaluation of the dataset structure. The two first principal components summarized 42.5% of the total dataset and highlighted the TCC treatment as the major source of variability. Higher components were examined but only intra group variability was observed. The pattern expressed by the QC BIO samples initially revealed a small drift related to the analytical sequence. However, this unwanted source of variability was limited compared to the global biological variability (Supplementary Materials Figure S2). As the retention times were confirmed to be stable, this source of variability was related to the LC column during the sequence. While such a drift often constitutes a problem when performing long metabolomic sequences,50,51 its influence on further data analysis can be considered as negligible thanks to both the adapted sequence and post-processing. Chemical filtering for steroidomic footprinting analysis


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Chemical filtering based on automatic peak annotation37,52 was applied to extract potential steroid compounds using an in-house database created using accurate mass data extracted from HMDB53 and Lipid Maps54,55 and manually curated to remove duplicate and irrelevant entries (i.e., exogenous compounds). This database consisted of two series of steroids and steroid derivatives, excluding exogenous and plant or foodrelated compounds. The first series (n = 736) was extracted from HMDB, while the second (n = 242) was obtained from Lipid Maps (i.e., Steroids [ST02]) and was classified according to their C18, C19 and C21 carbon structures, which correspond to estrogens, androgens, and progestogens, mineralo- and glucocorticoids, respectively. The 3,888 automatically detected features in each sample were compared to the database according to the measured accurate mass (m/z tolerance of ± 5 ppm) and 156 putative steroid features matching an entry were kept. OPLS-DA and S-Plot Supervised OPLS modeling was carried out with the 156 selected steroid features to highlight steroid changes, i.e., putative markers of endocrine disruption due to the addition of different amounts of TCC into the cell culture medium (Figure 2). For this purpose, the concentration of TCC added in each sample was used as the response to be predicted (n = 5 for each group). A first model with two latent variables (one predictive and one orthogonal) was obtained with high fit and prediction ability indices (R2Y = 0.93, Q2Y = 0.91). It is important to note that the OPLS algorithm facilitates the interpretation of the results by separating predictive from non-predictive sources of variation (i.e., systematic variability unrelated to the experimental setup) in specific components. This allowed a clear pattern related to TCC effects to be observed on the


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OPLS score plot (Figure 2A). The predictive component summarized metabolic alterations occurring due to TCC toxicity starting from DMSO control samples (left side of the plot), progressing according to TCC concentrations and ending with the highest TCC content (i.e., 10 µM, right side of the plot). The orthogonal component was associated with intra-group biological variability. A S-Plot (Figure 2B) was used to highlight the contributions of ions to the predictive component related to TCC metabolic effects in terms of covariance (i.e., amplitude, p[1]) and correlation (i.e., reliability, p(corr)[1]). Ions located at the extremities of the S shape can be considered to be the most important ions in the context of TCC exposure and potential markers. It is interesting to note that relevant markers were located at the bottom left of the plot, and therefore they were related to a strict decrease in concentration. This observation is fully in line with prior knowledge of steroid metabolism in which, a decrease in steroid production detected in the cell supernatant could be related to TCC toxicity. A set of 50 features (representing then approximately one third of the 156 investigated features) was selected on the basis of the S-plot as the most promising steroid markers of TCC toxicity. A manual verification of the 50 features was then performed. 12 features were discarded due to problematic integration (very low intensity peaks close to noise level or too large peaks only partially integrated). One feature was also removed due to the detection of an unanticipated adduct ([M-H+2Na]+. 37 features were then kept for further analysis. This approach allowed steroid-like features with concentration levels closely related to the amount of TCC to be highlighted. However, this strategy focuses on variables behaving linearly with the response, i.e., metabolites characterized by levels proportionally increasing, or in the present case,


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decreasing with the TCC concentration. As biological processes are often dynamic and non-linear, an additional step of data mining was carried out to refine the results and elucidate the metabolic patterns related to TCC toxicity. Correlation matrices A correlation analysis was then performed on the 37 biomarker candidates selected by OPLS in regards to their sensitivity to disruption. For this purpose, the relative profile of each compound towards the TCC concentration was compared. Reference concentration patterns related to the three classes were defined as follows: i) the median area (n = 5) was used as a representative value for each assay, ii) areas were normalized to the DMSO control situation and relative changes were considered in the correlation analysis, and iii) high, medium and low sensitivity reference profiles were determined as being the 5th, 50th and 95th percentiles, respectively, of the normalized median peak areas. Correlations between the concentration profile of each of the 37 putative markers and the three reference patterns were then computed. With this procedure, the candidates were ranked in the following three classes: i) high sensitivity markers for compounds presenting a concentration profile affected by low amounts of TCC; ii) medium sensitivity markers, and iii) low sensitivity markers for compounds showing an altered profile for high TCC concentrations (Supplementary Materials Table S1). All of the selected compounds could therefore be attributed to one specific class according to their highest correlation score with a reference profile (Figure 3). By this method, 11 steroids were associated with having a high sensitivity behavior, 18 with a medium, and 8 with a relatively low sensitivity to TCC presence. Identification of the selected steroids


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Due to the fact that identification remains a critical aspect in untargeted approaches, the proposed analytical workflow aims to concentrate the efforts spent on identification towards the specific class of steroids. Although the developed data mining strategy led to the selection of steroid-like features, definite identification still remains a challenging task due to the following four main characteristics: i) steroids share the same core structure and therefore exhibit similar fragmentation spectra with hardly distinguishable m/z fragments, ii) several in-source water losses are often observed, making the evaluation of their composition in terms of hydroxyl and carbonyl functional groups difficult, even with soft ionization techniques, iii) numerous constitutional and/or isotopomers can be associated to a given molecular formula (e.g., for m/z 305.2111, 12 unique structures exist in the database) and iv) the acquisition of good quality MS/MS spectra can be challenging due to their low abundance in H295R cell culture supernatants. For the latter reason, the acquisition of MS/MS spectra was hence performed by reinjecting samples containing the most abundant ions of interest with scheduled precursor ion lists using a DDA strategy. MS/MS spectra were successfully acquired for twenty of them. For instance, the MS/MS spectrum of the unknown compound observed at tR = 17.03 min and m/z 289.2162 presents the following characteristic fragmentation pattern of steroids with 2 oxygen atoms present (Supplementary Materials Figure S3). However, even with a high quality MS/MS spectrum and the structural elucidation of fragments, a definitive identification could not be achieved when considering a series of three isomers (i.e., dehydroandrosterone, 5βandrostane-3,17-dione and 5α-androstane-3,17-dione) despite the similarity of its fragments to 5α-androstane-3,17-dione, the only compound with a MS/MS spectrum available in public databases.


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Among the 37 selected marker candidates, 12 were identified, namely aldosterone,

androstenedione,

hydroxyprogesterone,

progesterone,

17α-hydroxypregnenolone,

epitestosterone,

pregnenolone,

11-DOC,

17αDHEA,

DHEAS, 11-dehydrocorticosterone, and cortisone. The identification of these 12 steroids with retention times matching and MS and/or MS/MS spectra are definitive and comply with the Metabolomics Standards Initiative (MSI) recommendations.56,57 The remaining 25 unknown structures (6 categorized as highly sensitive, 16 as medium sensitive, and 3 as low sensitive) were tentatively identified by database matching (Supplementary Materials Table S2, the number of potential hits per compound is reported). Among these 25 unknown structures, 11 were putatively identified with a C21 structure (i.e., either progestogens or corticoids) and 11 with a C19 structure (i.e., androgens). One-third of the 37 candidates of interest were unequivocally identified using the proposed workflow. Of the remaining 25 candidates, 17 could be associated with a unique molecular formula, while 8 candidates could not be associated with a definite molecular formula due to ambiguity in-source neutral losses of water molecules (Supplementary Materials Table S2). Quantitative results In order to ensure the validity of the developed analytical strategy for the measurement of steroids in H295R cell culture medium, additional investigations were performed. First, the quantification capability towards the measurement of the selected set of 15 steroids added to the 6 additional steroids identified in the previous step using high-resolution TOFMS was assessed. The analysis of calibration and QC samples in different series of measurements (Supplementary Materials Table S3) has allowed the


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evaluation of key characteristics related to method validation. Parameters such as linearity, precision, accuracy, and the lower and upper limits of quantification (LLOQ and ULOQ, respectively) were obtained for this subset of steroids. The criteria for the precision and accuracy were set according to FDA guidelines for bioanalytical method validation,58 i.e., 15% for QC samples, except 20% at the LLOQ, and in the range of 85115% for QC samples and 80-120% at the LLOQ, respectively. The observed characteristics are reported in Table 1. ISTDs were chosen based on their similar structures and retention times (i.e., physico-chemical properties) to the different quantified analytes. Most of the selected analytes were quantified with a LLOQ at the low ng/mL level except for the estrogens (i.e., E1, E2, and E3) that are known to exhibit poor ionization properties in ESI. Another exception was observed for 17αhydroxypregnenolone (5 ng/mL), which has a lower response factor compared to the other quantified steroids. The LLOQs were found to be adequate for the quantification of 13 steroids in H295R cell culture medium using this experimental setup (Table 2). Using independent calibration curves and QC samples prepared in cell culture medium, quantification capabilities up to 2.5 orders of magnitude were observed using the TOF MS mode. The measurement of the QC 00 of the cell culture medium, which has not been in contact with cells, is essential to understanding the experimental starting point. Despite the detection of testosterone, progesterone, cortisol and cortisone in QC 00, all of the quantified steroids were below 20% of the LLOQ of our analytes (Figure 4). Quantification results are coherent with the results obtained by Hecker et al.59 that reported a measured concentration of 6.6 ng/mL of progesterone and pregnenolone,


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which were impossible to dissociate due to the cross-reactivity observed with the ELISA test, and a concentration of 1.7 ng/mL of testosterone (200,000 cells/24 well, 1 mL culture medium), while similar values of 6.7 ng/mL for progesterone + pregnenolone and 1.9 ng/mL for testosterone were obtained in our assays (700,000 cells/6 well, 2 mL culture medium). The rationalization and comparison of the quantitative steroid production in H295R cells remains a challenging task due to the different experimental conditions used in the studies published thus far. For example, the growth medium, the number of cells seeded and the volume of culture medium are often different from one experimental setting to another, hence making the comparison of the quantitative values reported using LC-MS challenging.23,26-29,59 It needs to be noted that, for instance, we observed important variations of steroid concentrations between inter-batch lots of NuSerum (data not shown). From the perspective of trying to achieve harmonized protocols allowing for inter-laboratory comparison, the OECD guideline first standardized the chemicals used for cell growth and the incubation time. Another critical part of the OECD guideline is the definition of the expected response to known inducers or inhibitors. This evaluation is based on the measurement of two steroids, testosterone and estradiol,14 and validated baseline values were reported. Estradiol remains, however, an analyte with a very low abundance, and its quantification using LC-MS/MS methods remains difficult.60 On the other hand, an overestimation of approximately 50% in the measurement of testosterone was observed using ELISA methods compared to LCMS/MS due to cross-reactivity. Van der Pas et al. also described a low conversion of 11deoxycortisol to cortisol due to the blockage of P450 11B1 expression in H295R cells,28 which can also be accompanied by a similar decrease in the production of corticosterone from 11-DOC as both substrates are metabolized by P450 11B1. This


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moderate conversion has also been observed in our experiments (Table 2). Despite the low efficiency of formation, corticosterone (only in DMSO control experiments) and cortisol were still above the LLOQ. In contrast, Schloms et al.27 reported a significantly higher production of corticosterone, 11-dehydrocorticosterone, aldosterone, cortisol and to a lesser extent, cortisone. Nevertheless, despite these differences, the concentrations of the upstream precursors 11-DOC and 11-deoxcortisol remained of the same order of magnitude in the study by Schloms et al. and in the present experiments. It has also to be noted that Xing et al.

29

observed that 11-deoxycortisol and androstenedione were

the most abundant steroid products present in the cell culture medium as it was also the case in this study. Nevertheless, in our opinion, because H295R cells are an adrenal cell model, their use to study potential EDCs should be revisited to focus on detecting disturbances

of

adrenal

steroids

including

progestogens,

glucocorticoids,

mineralocorticoids and precursors of active androgens. These inter-assay differences also highlight the importance of adequate controls in experiments using H295R cells. Localization of the disruption of steroidogenesis caused by TCC Thanks to the untargeted detection and classification of steroids by multivariate analysis and determined quantitative values using a single experiment, a scheme describing the observed perturbation of steroidogenesis by TCC can be proposed (Figure 5). The steroids most sensitive to increasing TCC concentrations mainly correspond to precursors of the active end products of steroidogenesis (i.e., pregnenolone, progesterone, 11-DOC, 17α-hydroxyprogesterone and DHEA). Also, marked decreases were found for 11-dehydrocorticosterone, androstenedione and DHEAS. Despite the


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relatively low conversion observed from 11-DOC and 11-deoxycortisol, small decreases in aldosterone and cortisone were observed. The presented scheme of perturbation caused by TCC indicates that an early step in steroid biosynthesis is affected. Possible candidates include P450scc (side-chain cleavage enzyme), which converts cholesterol to pregnenolone, and steroidogenic acute regulatory protein (StAR), regulating the transfer of cholesterol across the inner mitochondrial membrane, which is the rate-limiting step of steroidogenesis. Alternatively, TCC might cause an impairment of cholesterol synthesis, thus affecting downstream steroid production. New studies should therefore be designed in order to elucidate the mode of action and extend the number of steroids used in a post-targeted approach. The presented methodology was focused on adrenal steroidogenesis using H295R cells, and additional experiments should be performed to test whether similar effects are observed using cell models for gonadal steroidogenesis, i.e. theca and granulosa cells and Leydig cells, respectively. Moreover, the relevance of the observed in vitro effects of TCC needs to be further investigated in experimental animals and in clinical studies. Plasma TCC concentrations between 20-500 nM have been observed after a single shower using TCC containing soap.46 Considering the possible daily use of TCC containing soap and considering that pregnant women might use these products, it is of importance to evaluate whether the exposure to TCC leads only to transient disturbances of steroid homeostasis or whether it indeed causes adverse health effects.


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CONCLUSIONS This study presents the development of a steroidomic footprinting strategy based on the H295R cell model with UHPLC hyphenated to QTOF-MS, allowing the simultaneous acquisition of qualitative and quantitative data. The developed workflow demonstrates the potency of extracting steroid-like features from untargeted raw data using automatic peak annotation based on exact mass measurements. The developed method constitutes a potent approach to screen and classify potential or confirmed EDCs affecting adrenal steroidogenesis in a straightforward manner. The data mining strategy combining multivariate and correlation analysis was demonstrated as a relevant approach to extract and highlight marker candidates. In a single analysis, it was possible to quantify thirteen steroids and to evaluate and localize the disruption of steroidogenesis caused by TCC upstream of the formation of pregnenolone. As for most of the “-omics” strategies, compound identification constitutes the principal bottleneck of steroidomics. The definitive identification of additional molecular actors remains mandatory to improve the mechanistic understanding of EDCs. Further efforts in this area will be pursued to develop several models of perturbations of steroidogenesis using known EDCs along with strategies to improve metabolite identification using retention time modeling and in-depth studies of steroid MS/MS fragmentation mechanisms. These two strategies will be used concomitantly in order to increase the efficiency of the structural assignment of unknown molecular entities (i.e., with removal of inadequate candidates), using in silico retention time prediction combined with the use of MS/MS signature fragments. Furthermore, the presented methodology should prove useful for the investigation of EDCs acting on ovarian and testicular steroidogenesis, and data


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obtained from the analysis of adrenal, ovarian and testicular cell models may be combined to assess the in vitro effects of potential EDCs.


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FUNDING INFORMATION This study was supported by the Swiss Centre for Applied Human Toxicology (SCAHT). AO has a Novartis Chair for Molecular and Systems Toxicology. The authors declare no competing financial interest.


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ACKNOWLEDGMENTS DT, FJ and SR would like to acknowledge Dr. Peter Sander (Bruker) for providing the beta version of Quant Analysis software (x64), Ralf Hartmer and Thomas Zey (Bruker) for fruitful collaboration.


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SUPPORTING INFORMATION AVAILABLE Supplementary Figure S1, S2, and S3. Figure S1: MTT cell viability assay using a vehicle (DMSO) and TCC. Figure S2: PCA Score plot with 3,888 features (z = 1) extracted from raw data using Pareto scaling. PC1 represents 23.4 % of the total variability and PC2 represents 19.1 %. Figure S3: MS/MS spectrum of the unknown steroid at m/z 289.2162 and tR = 17.07 min and its potential structural assignments. Tables S1, S2, and S3. Table S1: Reference profiles and correlation table used to classify the 37 marker candidates into High, Medium and Low sensitivity profiles. Table S2: Features of interest extracted from S-Plot analysis with their putative identification. Table S3: QC values (n = 2) obtained for the quantification of the subset of 21 steroids. This information is available free of charge via the Internet at http://pubs.acs.org/.


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ABBREVIATIONS LIST DDA, Data-Dependent Acquisition; DMEM, Dulbecco’s Modified Eagle’s Medium; DMSO, Dimethyl Sulfoxide; EDC, Endocrine Disrupting Chemical; ESI, Electrospray Ionization; FA, Formic Acid; ISTD, Internal Standard, LLOQ, Lower Limit Of Quantification; MeCN, Acetonitrile; MeOH, Methanol; MSI, Metabolomics Standards Initiative;

MTT,

3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazoliumbromid;

MVA,

Multivariate Data Analysis; OPLS-DA, Orthogonal Partial Least Squares-Discriminant Analysis; PCA, Principal Component Analysis; QC, Quality Control; QC BIO, Biological QC; QqQ: triple quadrupole; OECD, Organization for Economic Co-operation and Development; QTOF, Quadrupole Time-Of-Flight; REACH, Registration, Evaluation, Authorization and Restriction of Chemicals; SPE, Solid-Phase Extraction; SRM, Selected Reaction Monitoring; TCC, Triclocarban; UHPLC, Ultra High Performance Liquid Chromatography; ULOQ, Upper Limit Of Quantification; XIC, Extracted ion chromatogram.


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Tables

Table 1: Subset of 21 steroids used for quantification with their respective m/z, tR, LLOQ, ULOQ and ISTD.

m/z monitored (neutral loss resulting in the monitored ion)

tR (min)

LLOQ (ng/mL)

ULOQ (ng/mL)

ISTD

Estriol (E3)

271.1693 (-H2O)

10.60

10

100

N/A

Aldosterone

361.2010

10.94

1.0

100

N/A

Cortisol-d4

367.2417

11.62

N/A

N/A

N/A

Cortisol

363.2166

11.64

2.5

100

Cortisol-d4

Cortisone

361.2010

11.77

0.25

100

Cortisol-d4

11-Dehydrocorticosterone

345.2060

12.74

0.25

100

Cortisol-d4

271.2056 (-HSO4)

12.80

2.5

100

N/A

Corticosterone

347.2217

13.10

0.25

100

Testosterone-d3

11-Deoxycortisol

347.2217

13.30

0.25

100

Testosterone-d3

b-Estradiol (E2)

255.1743 (-H2O)

14.31

0.50

100

Testosterone-d3

Testosterone-d3

292.2350

14.58

N/A

N/A

N/A

Testosterone

289.2162

14.63

0.25

100

Testosterone-d3

297.2213 (-2H2O)

14.96

5.0

100

Testosterone-d3

331.22677

15.00

0.25

100

Testosterone-d3

271.2056 (-H2O)

15.36

2.5

100

Testosterone-d3

Estrone (E1)

271.1693

15.38

2.5

100

17α-hydroxyprogesterone-d8

Androstenedione

287.2006

15.48

1.0

100

N/A

17α-Hydroxyprogesterone-d8

338.2697

15.55

N/A

N/A

N/A

17α-Hydroxyprogesterone

331.2268

15.60

0.25

100


Epitestosterone

289.2162

15.66

0.50

100


5-DHT

291.2319

16.11

1.0

100


Etiocholanolone

273.2213 (-H2O)

16.86

1.0

100

N/A

Pregnenolone

299.2369 (-H2O)

17.76

2.5

100


Progesterone

315.2319

17.91

0.25

50


Steroid

DHEAS

17α-Hydroxypregnenolone 11-DOC DHEA


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Page 46 of 53

Table 2: Quantitative values (median, standard deviation (SD) and fold changes calculated compared to cells incubated with DMSO) of the 13 quantified steroids.

Quantification values in ng/mL Steroid

DMSO

TCC 0.5 µM

TCC 1.0 µM

TCC 2.5 µM

TCC 5.0 µM

Median

SD

Median

SD

Pregnenolone

6.17

0.3

3.68

0.2

0.6

2.55

0.2

0.4

1.69

17α-hydroxypregnenolone

16.6

1.1

9.79

0.8

0.6

6.96

0.2

0.4

100

83.8

2.6

63.7

3.5

DHEAS

50.2

1.4

45.0

2.4

0.9

40.3

1.6

0.8

29.9

34.6

2

15.8

1.1

0.3

DHEA

6.22

0.4

4.05

0.5

0.7

2.56

0.4

0.5

Steroidomic Footprinting Based on Ultra-High ... - ACS Publications

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