Tracking main environmental factors masking a minor steroidal doping effect using metabolomic analysis of horse urine by liquid chromatography

Abstract

There is an urgent need to implement holistic and untargeted doping control protocols with improved discriminatory power, compared to conventional methods that only target doping agents. Metabolomics, which aims to characterize all metabolites present in biological matrices, could fulfill this need. In this context, the aim of this study was to evaluate the impact of environmental factors on the ability to obtain a metabolic signature of stanozolol administration in horse doping situation. Urine samples from 16 horses breeded in two different places were collected over a one-year period, before, during and seven months after the administration of stanozolol, a horse doping agent. Metabolomic analysis was performed using ultra-high pressure reverse phase liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry (MS). Results showed a major impact of the nutritional regimen, drug administration (for de-worming purpose) and breeding place on the metabolite profiles of horse urines, which hampered the detection of metabolic perturbations induced by stanozolol administration. After having used MS/MS experiments to characterize some MS features related to these environmental factors, we showed that highlighting and then removing the features impacted by these confounding factors before performing supervised multivariate statistical analyses could address this issue. In conclusion, adequate consideration should be given to environmental and physiological factors; otherwise, they can emerge as confounding factors and conceal doping administration.

Keywords

Metabolomics high-resolution mass spectrometry doping urine horses confounding factors

Introduction

The great potential of metabolomic studies already demonstrated in the field of medicine^1,2 was recently recognized likewise in anti-doping field.^3–6 Dumas et al. were first to reveal the relevance of metabolomics as a screening tool for the detection of anabolic steroid use in cattle with a further potential application in sports and horseracing.⁷ More recently, metabolomics successfully highlighted metabolic perturbations associated with reGH treatment in horses.^8,9

There are several reasons for intensifying development of metabolomic approaches in doping control laboratories: (i) most of existing methods are based on target analyses of known compounds and approved therapeutics; thus, even small modifications in chemical structure of substances (e.g., designer drugs) may result in void analysis; (ii) the direct detection of the administration of “endogenous-like” substances, such as some androgenic anabolic steroids or a full set of peptide hormones (e.g., GH, IGF-1, EPO), is still difficult; (iii) only prohibited substances are monitored, but not their effects specifically resulting from a chronic exposition, knowing that some drug effects are potent even after drug elimination from plasma or urine; and (iv) tracking only a specific metabolic pathway provides a narrow part of the relevant biological picture in athlete's organism after the administration of doping substances.

Metabolomics as a comprehensive analytical approach is nonselective and ubiquitous; given the ultimate aim to identify and quantify all detectable metabolites of a biological system, it becomes thus a sound alternative to the detection of any doping substance abuse. The profile of metabolites in a biological matrix represents the signature of a functional state, that needs to be understood in physiological (homeostatic) terms, principle which still stays true even in case of a misuse of prohibited substances. In addition, this fingerprint also contains an additional metabolic information, which is related to environmental conditions, i.e. feed or season.

Providing a holistic view of all metabolic pathways connected in one common network is a huge advantage of the metabolomic approach over the more conventional linear “reading” of multiple isolated metabolic pathways. However, its statistical modeling is difficult, especially when the co-called “normal” conditions are encountered. With so many possible environmental and physiological factors which influence the resulting metabolite fingerprints, comparison of a “normal” fingerprint defining a seemingly control pattern to either a treated-one or a fully suspicious one is clearly challenging.

In this context and in order to deepen the knowledge of horse urine metabolome with its unavoidable variations and perturbations, the aim of our study was to highlight environmental circumstances that may cause deep changes in supposed “reference” equine urine fingerprints and thus mask a potential metabolic signature of a steroid-based doping. Indeed, some physiological factors such as gender, age, and body mass index,¹⁰ timing of the urine sample collection,^11,12 but also regimen^13–16 were already pointed out for their significant impact on urine metabolic fingerprint properties.

In the present study, horse urine metabolic fingerprints were obtained by liquid chromatography (LC) connected to high-resolution mass spectrometry (HRMS) using electrospray ionization (ESI) either in positive or negative mode. Variation in food, season, and regional location of breeding were controlled in order to help in detecting the main features related to these environmental factors thanks to multivariate statistical analyses. Such a procedure was reinforced by an in-depth mass spectrometric analysis of some of these features. Last, we showed that highlighting and then removing the features impacted by these confounding factors help to reveal a significant metabolomic signature of stanozolol administration.

Materials and methods

Animal study

Fourteen horses were followed during one year before stanozolol administration to assess the different possible environmental factors that may influence the general metabolism and to compare their relative impact on the metabolic changes induced by the administration of stanozolol. Simultaneously, to evaluate the impact of geographical location of animals on the urine horse metabolome, two additional horses breeded in a second experimental center were included in our experimental protocol (see Scheme 1s in the supplementary material section for more information). The animal study was thus conducted according to an ethically approved protocol at two different places: Chamberet and Coye-la-Forêt.

The total duration of the animal phase was 19 months and only females were enrolled in the study. Mares were not reproductively active racing horses, but were in good physical condition. They have been routinely de-wormed (twice per year) and vaccinated (once per year).

Regarding the study conducted at Chamberet, the 14 anglo-arab female horses were four years old and weighted 520 ± 60 kg. At this place, horses were at shelter from mid-November to mid-April and were in pasture for the remaining of the year. In the winter period, horses were fed twice a day with hay and complement feed.

The study conducted at Coye-la-Forêt involved two 6-year-old thoroughbred females, weighting 450 and 550 kg. These horses stayed in box bedded with straw, during the entire experiment. They were fed twice a day the same diet comprising hay and complement feed. Horses were moderately exercised for about one hour every day. Water was provided ad libitum. Sampling consisted in urine collection spontaneously voided.

Of note, differences in horse age and breeds between the two facilities were assumed not to be a matter of concern for this study. Indeed, horses are considered as adults usually from three years old, and the age difference of the horses included in our protocol from one facility to the other (i.e., four vs. six years old) is small compared to their life expectancy (around 30 years old). Furthermore, the Anglo-Arab breed is considered to be close to the breed of French race horses (i.e., Thoroughbred and French Trotter).

Evaluation of the impact of environmental factors on urinary metabolomic profiles

To evaluate the impact of seasonal and other environmental factors on urinary metabolomic profiles, sample collection has been conducting for one year. Urine samples (about 200 mL) were collected from all mares every two weeks in the morning at the same time. Sample pH was measured (8–9) before freezing and storage at −20°C until analysis. All individual veterinary treatments or other special happening were reported.

Evaluation of the impact of stanozolol administration on urinary metabolomic profiles

Stanozolol (17α-methyl-5α-androstano-[3,2-c]-pyrazol-17β-ol) is a synthetic anabolic androgenic steroid (AAS) classified as a class III drug by the Association of Racing Commissioners International. In-house preparation (9 mL) of stanozolol for chronic intramuscular administration consisted in dissolution of stanozolol in a mixture of sesame oil/isoamyl alcohol (7/2). Vehicle of stanozolol administration was injected to five control horses (i.e., four and one from Chamberet and Coye-la-Forêt, respectively). The stanozolol treatment consisted in four injections every four days of 0.12 mg/kg or 0.31 mg/kg doses. The lower dose treatment (i.e., total of 0.48 mg/kg) was given to five mares located in Chamberet. The higher dose treatment (i.e., total of 1.24 mg/kg) was given to a total of six mares (five in Chamberet and one in Coye-la-Forêt). Since the experiment took place at the beginning of March, all mares were housed in box during the drug administration. Urine was collected before the beginning of stanozolol administration. At the end of the treatment, urine was collected every 4 days for 16 days, then once a week for seven months, thus corresponding to 32 collection time points (see Scheme 1s for more information). The urinary pH was measured and samples were stored at −20°C until analysis.

Chemicals

Stanozolol, metformine, amiloride, imipramine, prednisone, colchicine, and 2-aminoanthracene were purchased from Sigma-Aldrich (Saint-Quentin-Fallavier, France). Sesame oil was obtained from Cooper (Melun, France) and isoamyl alcohol at VWR (Fontenay-sous-Bois, France).

HPLC grade acetonitrile (MeCN) and formic acid were purchased from Carlo Erba Reactifs (SDS, Peypin, France). Deionized water was produced using an ultra-pure water system (Milli-Q, Millipore Corporation, Billerica, MA, USA).

Sample preparation

Urine samples (400 µL) were centrifuged at 12,000 r/min for 30 min at 20°C; 200 µL aliquots of the supernatant were harvested for further analyses. Quality control (QC) samples were obtained by pooling a 50-µL aliquot of every sample to be analyzed. Furthermore, metformine, amiloride, imipramine, prednisone, colchicine and 2-aminoanthracene were added to QC samples at concentrations of 5 μg · mL⁻¹ in order to check for consistency of analytical results in terms of signal and retention time stability throughout the experience. Then, acetonitrile (1 mL) was added to 200 µL of urine samples. After centrifugation at 12,000 r/min for 30 min at 20°C, supernatants were harvested and transferred in vials evaporated to dryness at 60°C. Finally, 200 µL of acetonitrile/water mixture (50/50, v/v) was added to each sample before transfer to LC vials.

LC-HRMS analysis

Liquid chromatography

For mass spectrometry (MS) fingerprinting analysis, chromatographic separation was performed with an Ultimate 3000 (Dionex, Sunnyvale, USA) pump on a reversed phase Uptisphere Strategy C18 NEC column (2.1 mm × 100 mm, 2.2 µm particle size; Interchim, Montluçon, France).

Analytes were eluted using a 25-min gradient, which started at 100% of mobile phase A (water + 0.1% formic acid) for 2 min, changed to 100% of mobile phase B (acetonitrile + 0.1% formic acid) for 18 min, maintained at 100% of B for 5 min, and then returned to the initial condition for equilibration for 2 min. The flow rate was 0.25 mL·min⁻¹ and the column temperature was maintained at 25°C. Autosampler was set at 4°C for the duration of the analysis, and 15 µL of samples were injected.

Regarding metabolite identification by MS/MS analysis, the liquid chromatography system was a Prominence UFLCXR (Shimadzu, Japan) equipped with a Sunfire C18 column (2.1 mm × 150 mm, 3.5 µm particle size) from Waters (Saint-Quentin-en-Yvelines, France) maintained at 35°C. The mobile phases were identical as for fingerprinting analysis. The chromatographic flow was set to 0.3 mL·min⁻¹. The 30-min LC gradient started at 80% of A for 5 min, then linearly changed to 50% for 15 min and lastly changed to 100% of B for 5 min. Initial conditions equilibration recovery was obtained for 5 min; 5 µL of sample were injected for analysis. The same urine sample was injected in the two LC/HRMS systems used for this study so that it was possible to link the two retention times related to the features to be characterized.

ESI-HRMS

High-resolution mass spectra for metabolomic profiles were acquired on a quadrupole-time of flight analyzer (MicroToF Q II, Bruker, Bremen, Germany) operated in positive and negative ionization modes. The spectrometer parameters corresponding to capillary voltage, capillary temperature, nebulizer gas flow and dry gas flow were set as following: −4.5 kV (negative ionization mode) and 3.8 kV (positive ionization mode), 180°C, 2.4 bar, 8 L·min⁻¹, respectively. External mass calibration of the instrument was performed using a solution of lithium cluster (16 mM lithium formiate in isopropanol/water) at the beginning of the chromatographic gradient using a diverting valve and a separate pump. Mass accuracy of the m/z calibration standard was below 3 ppm for the positive mode and below 2 ppm for the negative one. Centroid mass spectra were acquired in the m/z 50–1000 range. Hystar (Bruker) software was used for system controlling and data acquisition.

MS² analyses were performed on a Q-Exactive mass spectrometer (Thermo Fisher Scientific, San Jose, USA) equipped with a heated electrospray ionization (HESI) probe. The instrument was operated in positive–negative polarity switching mode. Temperatures were set at 250°C for heated auxiliary gas, and 300°C for ion transfer capillary. ESI needle spray voltages were set at 3.5 kV or −3.5 kV for the positive or negative ion modes, respectively. Nitrogen sheath gas and auxiliary gas were maintained at 35 and 2 arbitrary units, respectively. The automatic gain control (AGC) and resolution parameters were set from 200,000 to 3,000,000, and from 35,000 to 140,000 at full width at half of the maximum, respectively. MS/MS data were acquired using an external calibration and processed using Xcalibur software version 2.2. The orbitrap was calibrated with Pierce LTQ Velos ESIpos and MSCAL6-1EA for positive and negative ion modes, respectively. The calibration was performed when the daily results of the mass accuracy exceeded 2 ppm.

Data pre-processing

Data files generated after LC-HRMS analysis were converted to the NetCDF format (“.cdf”) using a conversion function from “Data Analysis” software program (Bruker). The converted data were exported in the open-source XCMS software for subsequent data processing based on several steps: peak picking, peak grouping, and retention time alignment.¹⁷ XCMS matched filter algorithm was used with default values for all parameters, except for fwhm, step, steps, mzdiff, mzwid, and minfrac which were, respectively, set at 10, 0.1, 5, 0.1, 0.1, and 0.2 for both group functions. Grouping of features was performed using the CAMERA software.¹⁸ Detected features from all samples were combined in a single dataset according to the following characteristics: accurate mass, retention time and peak intensity.

Metabolite annotation and identification

Features were annotated by matching their accurate measured masses ± 20 ppm with theoretical masses contained in biochemical and metabolomic databases by using an informatics tool developed in R language. The used databases were as follows: KEGG (Kyoto Encyclopedia of Genes and Genomes, www.genome.jp/kegg), HMDB (Human Metabolome Database, www.hmdb.ca), METLIN (Scripps Center for Metabolomics, http://metlin.scripps.edu/), Humancyc (Encyclopedia of Homo Sapiens Genes and Metabolism, http://humancyc.org/), ChemSpider (the free chemical structure database, www.chemspider.com/), Drug Bank (Open Data Drug&Drug Target Database, www.drugbank.ca), and MZedDB (tools for the annotation of High Resolution MS metabolomics data, http://maltese.dbs.aber.ac.uk:8888/hrmet/index.html).

Features were also annotated according to accurate measured masses and chromatographic retention times using the spectral database developed at CEA-Saclay¹⁹ which contained over 400 compounds at the time of this study.

As proposed by the Metabolomics Standards Initiative,²⁰ to be identified (level 1), ions had to match at least two orthogonal criteria among the following criteria: accurate measured mass, isotopic pattern, MS/MS spectrum or retention time, to those of an authentic chemical standard analyzed under the same analytical conditions. In absence of any available authentic chemical standard, other metabolites of interest were putatively annotated, based on accurate measured mass and interpretation of the MS/MS spectra when available.

Statistical analysis

SIMCA-P + (v. 12.0, Umetrics, Sweden) software and R (http://www.r-project.org/) free software environment were used for multivariate data analysis. Principal Component Analysis (PCA) and Partial Least Squares Regression (PLS) were applied to build some descriptive models. The various mass peaks constituting the mass fingerprints (i.e., couples of chromatographic retention time and m/z ratio) were considered as independent variables. All variables were Pareto scaled (i.e., centered and divided by the square root of their standard deviation) prior to multivariate analyses.

Results and discussion

The great interest of metabolomics as a screening tool in doping control is based on its potential to display global biological effects induced by the use of a prohibited substance, raising the possibility to access to both effect biomarkers and exposition signature, i.e., monitoring of the drug and its metabolites. This property answers perfectly the need arising in some recent anti-doping techniques to track known doping agents, but also some unknown ones, which should be banned given the high risk to cause deep functional disruptions translating abnormal effects detected in athletes. However, these atypical biological effects resulting from exposition to doping substances have to be highlighted among many other sources of variation.

In this context, the aim of the study is to evaluate the impact of some awaited confounding factors, such as season, breeding place and biases brought by the analytical method on the ability to detect significant metabolic changes induced by stanozolol administration. Stanozolol, an exogenous AAS, was selected to apprehend physiological perturbations induced by this steroid misuse and to get a sound hierarchy between variances explained by this metabolome disturbance and the ones explained by the most common environmental causes. This steroid has been often fraudulently used in sport by athletes, man^21,22 and horses.^23–25 Analytical properties have been extensively studied by Poelmans et al.²⁶ It is also one of the rare registered veterinary anabolic steroids authorized for equine therapeutic use out of competition. The same experimental design used on 14 horses at Chamberet was also applied simultaneously on 2 horses from the Coye-la-Forêt experimental center to evaluate impact of the breeding place and season on the metabolomic fingerprints (Scheme 1s).

Evaluation of the impact of environmental factors on horse urine metabolome

Impact of environmental factors was evaluated using 104 urine samples collected over a one-year period from 16 horses (i.e., 14 and 2 mares at Chamberet and Coye-la-Forêt, respectively) and corresponding to seven different time points regularly distributed all along the year (refer to supplementary material, Scheme 1s for further information). LC/MS analyses were performed in both positive and negative ESI modes.

Chemometric analyses

LC/MS analyses of the 104 samples in positive and negative ionization modes led to two datasets which were processed using XCMS software for automatic peak detection, alignment and integration. The two initial data matrices contain 5306 ions and 3374 ions obtained in the positive and negative ionization modes, respectively. They were used for the preprocessing step (see experimental section). Therefore, 2154 positively and 1520 negatively charged analytically relevant ions were obtained and then used for multivariate data analyses.

PCAs were first performed to visualize how datasets are structured. The score plots related to data sets obtained from acquisitions in positive and negative ionization modes are displayed in Figure 1(a) and 1(b), respectively. Considering the variance explained by the first axes, there are clear separations between samples collected from horses staying in pasture and horses housed in boxes. This could be easily explained by differences in nutritional regimens, already pointed-out as a factor having a huge impact on the metabolome.²⁷ Indeed, from April to November, the feeding program in the experimental center in Chamberet changed, as the horses were in the meadow and were allowed to graze ad libitum.

The season effect was discarded since two horses from the experimental center in Coye-la-Forêt stay in their box all along the year and there is no differentiation between months or between warm and cold periods of the year. Any differential effect related to physical activity of horses was discarded since all horses were submitted to the constant and well-balanced physical exercise over all this period.

A further exploration of PCA scores plots showed a tight cluster within samples from the two horses stayed in boxes in Chamberet (Figure 1(a) and (b), samples T19). Interestingly, from metadata recorded in the specification book, these samples have been collected after the administration of an anthelmintic drug (moxidectin), except for one horse, which received this drug only the day after, and surprisingly does not cluster with other T19 samples. When data analysis is restricted to samples collected from horses staying in boxes (Figure 1s), the second factor responsible for the discrimination is linked to the geographical location of horse sampling since horses from the two experimental centers are clearly separated on the first PCA axis, whereas the impact of the anthelmintic treatment could be visualized (Figure 1s).

To summarize, PCA highlighted the main sources of variation in data sets which are (i) nutritional regimen changes, (ii) deworming and (iii) geographical location of experimental places. Then, supervised PLS-based multivariate statistical analyses were implemented to improve the prior clustering obtained with PCA and to select variables responsible for such discriminations.

Several supplementary PLS analyses were performed to investigate the impact of (i) the nutrition regimens (i.e., “box” vs. “pasture”, Figure 2), (ii) the geographical location of experimental places (i.e., “Chamberet” vs. “Coye-la-Forêt”, Figure 2s(A) and (B)), and (iii) the anthelmintic treatment (Figure 2s(C) and (D)). Significant discriminations were observed for all these three factors, as shown in Table 1. These models were validated using ANOVA of the cross-validated residuals (CV-ANOVA) with p-values far lower than 0.05. The permutation tests were also conclusive, as shown in insets of Figures 2 and 2s.

Table 1.

Performance of the PLS models.

Factor	Feeding regimen	Geographical origin	Anthelmintic treatment
Number of ions	765	598	546
ESI positive mode	R²(Y) = 0.977	R²(Y) = 0.970	R²(Y) = 0.972
ESI positive mode	Q²(Y) = 0.963	Q²(Y) = 0.931	Q²(Y) = 0.913
ESI negative mode	R²(Y) = 0.982	R²(Y) = 0.968	R²(Y) = 0.974
ESI negative mode	Q²(Y) = 0.978	Q²(Y) = 0.951	Q²(Y) = 0.955

R²(Y) corresponds to the proportion of the variance of the response variable (i.e., Y, or the factor) that is explained by the model. Q²(Y) corresponds to the fraction of the total variation of Y that can be predicted by the model.

Selection of features that are the most involved in the discrimination between the two groups in the different PLS models was performed using the S-plot representation, which highlights ions with higher correlation to the first discriminant component, as displayed on Figure 3(a) for the regimen effect. The most significant variables highlighted from positive and negative mode experiments and their putative annotations are displayed in Figure 3(b) and (c), respectively. Taking into account their high-intensity response, these ions could potentially alter the performance of statistical models aimed at discriminating doped from non-doped horses. Thus, a special attention is paid to the nutritional factor, which demonstrated here the highest impact on the horse urine metabolome, because this kind of lifestyle and feed management is recurrent in many horse stables.

Concerning metabolic changes obtained after the deworming period noticed here, it was demonstrated that ions, which are the most relevant to support the discrimination between treated horses from untreated ones, are not related to drugs or drug metabolites since they are present in both populations. No mass spectrometric analysis has been further performed to characterize such ions, which may find their origin in the horse microbiote compartment and which levels could be altered consecutively to oral administration of anthelmintic drugs.

At last, it is difficult to explain the discrimination observed regarding the geographical location of experimental places. Such a discrimination could be based on a combination of several aspects such as nutritional regimen, breeding genetics or regional location and its associated local climate. To enable a more systematic deconvolution of these factors, changing the sample size of experimental design and including higher number of horses than in current study is thus required.

MS/MS investigation of three annotated features

In a previous work, about one hundred of metabolites were identified or putatively identified in horse urine matrix.²⁸ Unfortunately, in the present study, only few of them were found as being statistically significant to support effects explained by one of the three environmental factors characterized here. Clearly, metabolites resulting from changes in environmental factors are largely absent from present databases. Nevertheless, three features (M243T479, M781T513, and M617T847) were investigated because they were annotated by public databases and they exhibit a high statistical significance in our study (Figure 3(b) and (c)).

This feature, which was detected in the positive ESI mode, was annotated as equol from public databases. Equol, a nonsteroidal estrogen, was isolated from pregnant mare urine in 1932²⁹ with the observation that, in the autumn, equol amounts declined, and, by winter, it was impossible to isolate it from urine.³⁰ Twenty years later, it was demonstrated that equol, formed by bacteria from rumen, is metabolically coming from the ingestion of several species of clover.³¹

The M243T479 feature was grouped with 14 others by the CAMERA software (Figure 4(a)). All these features, which were generated during the electrospray process, could originate from a single metabolite. Indeed, as shown in Figure 4(b), these features were eluted at the same retention time (8 min). Moreover, they exhibit similar intensity ratios and all of them highly contribute to the PLS model built to investigate the effect of nutritional regimen.

Figure 4(b) displays the annotation of the 15 features grouped by the CAMERA software. Besides the M243T479, the second most intense feature was M419T479 and, probably, corresponds to equol glucuronide, already reported in literature.^32,33 This feature with a nominal mass m/z 419 is considered as [M+H]⁺. This protonated form is consistent with the presence of various cationized molecules displayed in Figure 4(b) (i.e., m/z 436, m/z 441 and m/z 457, corresponding to the [M+NH₄]⁺, [M+Na]⁺ and [M+K]⁺ ions, respectively, within accuracy less than 5 ppm). In addition, several fragment ions (i.e., m/z 123 and m/z 107) produced by “in-source” CID processes can be considered as diagnostic ions of equol and could correspond to the heterocyclic competitive cleavage as reported in the Scheme 1. The consecutive losses of H₂O ([MH-nH₂O]⁺ with n = 1 to n = 4) suggest the presence of four labile hydroxyl groups. Note that from [MH-2H₂O]⁺ (m/z 383) the 44 u release is observed at m/z 339 within 2 ppm error which can be explained by a CO₂ loss. Consequently, m/z 243 bears a carboxylic group and at least four hydroxyl sites. It is noteworthy that ion corresponding to the counterpart of charged m/z 243, i.e., m/z (175 ± 1) is not observed in the CID spectra. The small size neutral releases (i.e., several molecules of water and carbon dioxide) could be consistent with the presence of a glucuronic ester. This was confirmed by the interpretation of the ESI mass spectrum recorded in negative mode at this retention time.

The corresponding deprotonated form of equol glucuronic ester structure (m/z 417 ion) is characterized by a CID spectrum which displays a series of product ions at m/z 175.0238, m/z 129.0181, m/z 113.0231 and m/z 85.0280 corresponding to the following elemental compositions: C₆H₇O₆, C₅H₅O₄, C₅H₅O₃ and C₄H₅O₂, respectively, with 0.2, 1.4, −2.4, and −5.4 ppm errors (Figure 5). The m/z 175 ion is complementary to m/z 241, which is formed competitively from cleavage of [M-H]⁻. It can be attributed to a dehydrated glucuronic acid fragment (Scheme 2s).

To summarize, the M243T479 feature, annotated as equol by public databases, is in fact an in-source product ion of equol glucuronide (M419T479, protonated form), which was further characterized using MS/MS experiments. Of note, our experimental data cannot provide any information regarding the location of the glucuronic ester bond. For this reason, as it is not possible to discriminate between positional isomers of equol glucuronide from our experiments, the M419T479 feature is annotated as level 3 according to the recommendations of the Metabolomics Standards Initiative.²⁰ Interestingly, this feature was not among the most significant variables highlighted by the S-plot.

M781T513

The second group of features corresponds to a chromatographic peak at a retention time of 9.9 min. The corresponding positive ion ESI mass spectrum displays a series of peaks at m/z 179, m/z 408, m/z 781 and m/z 798 with intensities higher than 10% of this of the base peak at m/z 408. The presence of ions at even nominal m/z ratio values indicates that the metabolite of interest very likely bears an odd number of nitrogen atom(s). The weakly abundant ions are observed at m/z 781, m/z 803 and m/z 819. This suggests that the ion at m/z 781 is a protonated form, whereas the two others are cationized by Na⁺ and K⁺, respectively. Consecutively, the m/z 798 ion could be the ammonium adduct of m/z 781, owing to the mass difference of 17.02526 consistent with NH₃ addition to m/z 781. However, the large abundance of the ion at m/z 798 compared to other adduct ions is unusual. As a consequence and conversely, the ion at m/z 781 could correspond to an in-source fragment ion of m/z 798.

From database annotation, the molecular weight of 780 u may correspond to digoxin (a glycoside present in the leaves of Digitalis lanata), composed by the steroid aglycone part and a triglucidic moiety. This hypothesis is reinforced by the presence of the ion at m/z 391 in the mass spectrum (7% of the base peak m/z 408). This ion could correspond to digoxigenin, the aglycone part of digoxin. Unfortunately, the elemental composition of the ion detected at m/z 391 (i.e., m/z 391.2321, C₁₉H₃₅O₈) is different from that of digoxigenin (i.e., m/z 391.2479, C₂₃H₃₄O₅). Furthermore, no diagnostic product ions of digitoxose molecules (i.e., three consecutive losses of 130 u corresponding to m/z 651, m/z 521 and m/z 391, as shown in Figure 3s) were observed in the CID spectrum. As consequence, M781T513 cannot be annotated as digoxin.

The CID spectrum of m/z 798 shows that this precursor ion is very unstable since it disappears to yield m/z 408 as a base peak and a series of weak intensity peaks at m/z 391, m/z 373, m/z 355 and m/z 337. The ion at m/z 391 could have been produced from an ion at m/z 408, whereas the three latter ion could correspond to serial losses of H₂O from the ion at m/z 391 (Figure 6). All fragment ions being characterized by odd m/z ratio values indicate that the nitrogen atom was released in the neutral fragment. Such behavior is rarely observed since the majority of the fragment ions preserved the nitrogen group(s) in the product ions because of its (their) high proton affinity.

Consequently, this trend reinforces our initial hypothesis that the m/z 798 and m/z 408 ions could be ammonium adducts of m/z 781 ion and m/z 391 ion, respectively. The absence of product ions in the 410-780 Th range also suggests that the m/z 798 ion is an ammonium adduct of a dimeric structure (m/z 781). This hypothesis directed us towards a molecule of 390 Da ruling out again digoxine as a possible compound. A database search (the list of queried databases is provided in the materials and methods section) provided 16 glyco-conjugates, 3 macrocyclic systems, 2 long chain alkyl benzoic acids, 4 triterpenes and steroids, 22 tripeptides, 2 fatty ester structures and 9 molecules classified in divers families, all matching with such a molecular mass. The CID spectrum of m/z 798 submitted to different resonant excitations (normalized collision energies from 15% to 35%, Figure 4s) presents a series of product ions at m/z values lower than that of m/z 408 (Table 2). With aforementioned fragmentations, the glycoconjugate structures were ruled out because their dissociation differs from this of the studied molecule. The macrocyclic polyethylenes were also ruled out since the 44 Da consecutive losses were not detected. All others proposed structure for m/z 391.2321 were not consistent since they could not release several molecules of water. Finally, the most consistent structure, which could produce a non-covalent dimer associated to ammonium, was (Z)-2,3,4,5-tetrahydroxy-6-tridecanoyloxy-hex-2-enoic acid (ChemSpider ID accession: 22378074). As expected, a fast ammonia release takes place from m/z 408 to generate an ion at m/z 391, and consequently releases three molecules of water (m/z 373, m/z 355, m/z 337) as described in Scheme 2. These losses are produced mainly from labile hydroxyl groups. Furthermore, the occurrence of a series of alken ions in the CID spectrum (at m/z 137, 123, 109 and 95) could be explained by serial decompositions of an acylium ion involved in an ion dipole complex. Taken together, these results suggest that M781T513 could be a linear ether fatty acid structure. In the absence of any additional spectral information such as a reference MS/MS spectrum of (Z)-2,3,4,5-tetrahydroxy-6-tridecanoyloxy-hex-2-enoic acid, this feature is annotated as level 3 according to the recommendations of the Metabolomics Standards Initiative.²⁰

Table 2.

Elemental composition of the main product ions displayed on the CID spectrum of m/z 798 recorded in the positive electrospray mode.

m/z experimental	Molecular formula	Error ppm
408.2587	C₁₉H₃₈O₈N	−1
391.2321	C₁₉H₃₅O₈	−2
373.2216	C₁₉H₃₃O₇	−1
355.2110	C₁₉H₃₁O₆	−2
337.2004	C₁₉H₂₉O₅	−2
197.1899	C₁₃H₂₅O	−1
179.1792	C₁₃H₂₃	−2

M617T847

The third metabolite highlighted by statistical analysis had a retention time of 14.5 min and was observed in both ionization modes as m/z 617 in positive mode and m/z 615 in negative mode (Figure 3(b) and (c)). Only one structure was proposed by public databases for this molecular mass: avermectin A2 aglycone. MS/MS experiments were required to confirm this annotation hypothesis. Since the intensity of this feature in positive mode was insufficient to undertake MS/MS experiments, it was only possible to acquire the CID spectrum of m/z 615 (i.e., [M-H]⁻) recorded in the negative ionization mode. It displays a base peak at m/z 477.2492 and a series of fragment ions at m/z 175, m/z 157, m/z 129, m/z 113, m/z 85 and m/z 75, which suggest the presence of glucuronic ester conjugate (i.e., level 3 according to the recommendations of the Metabolomics Standards Initiative²⁰), not present in the proposed molecule. Consequently, we did not succeed to get a structure consistent with the CID spectrum and, hence, to annotate this compound more in depth.

Finally, all these examples illustrate the complexity of the metabolite identification process and the necessity to perform a careful interpretation of MS spectra in order to select the true feature of interest. Furthermore, de novo annotation of metabolites on the sole MS and MS/MS data remains time consuming and challenging.

Chemometric investigation of urine metabolome after stanozolol administration

The metabolic variability due to environmental factors is often assumed less important than the metabolic perturbations observed in response to drug administration. However, in our study, it was observed that, even after chronic administration of the anabolic steroid stanozolol, only some of the factors discussed previously were sufficiently thorough to be detectable in PCA analyses (Figure 7). If we visualize the two PCA scores plot obtained after positive and negative ionization modes, it is apparent that the first principal component is based on the variation between samples collected from horses staying in pasture and horses housed in box and the second principal component distinguishes samples from the two experimental installations.

To highlight the impact of stanozolol administration on the metabolic fingerprints, it was necessary to remove the main features that were impacted by the two main environmental factors, that are the nutritional regimen (box/pasture) and the breeding place (Chamberet/Coye-la-Forêt) and, subsequently, to use a supervised multivariate analysis such as projection to latent structure discriminant analysis (PLSDA). Figure 8(a) displays the PLSDA model obtained on the remaining 614 features among the 1935 initial ones recorded in the positive ionization mode. This PLSDA model was validated by cross-permutation test (Figure 8(b)). This finally led to the selection of 221 ions that were used to get an excellent descriptive OPLSDA model accounting for the specific administration of stanozolol with R²(Y) and Q²(Y) of 0.935 and 0.789, respectively (Figure 8(c)). Surprisingly, although stanozolol and 16β-hydrostanozolol have been detected using a targeted LC-MS/MS approach (data not shown), we failed to detect the main stanozolol metabolites, i.e., hydroxystanozolol, its sulfated and glucuronide metabolites, in the frame of our untargeted metabolomics approach, suggesting that these 221 ions are either unknown stanozolol metabolites or are not at all structurally related to this drug. So, it is highly probable that this “cleaned” metabolic signature of the chronic exposure of mares to stanozolol is more related to a fully endogenous metabolic disruption than to a residual but still explicit xenometabolomic one.

Conclusion

Several factors influencing the metabolite profiles of horse urine have been tracked in this study. Their nature (environment, genetics, and drug treatment) and extent of their involvement in the metabolic perturbations were discussed. Contrary to toxicological studies for which the impact of drug treatment is prominent due to the high dosing of administered drugs, we observed here that the global anabolic effect of stanozolol is less pronounced than, for example, the effect of the nutritional regimen. Consequently, this latter effect can emerge as a confounding factor and needs to be overcome when the aim is to put in evidence the anabolic steroid administration. In this context, we showed that highlighting and then removing the features impacted by confounding factors before performing supervised multivariate statistical analyses could help to get a metabolic signature of the impact of stanozolol administered to horses in doping situation conditions. We also emphasized that feature annotation remains the main bottleneck of metabolomics studies applied to doping control. Although accurate mass measurements at high resolution together with MS/MS experiments were mandatory to remove ambiguity when several metabolite annotation proposals were provided by databases, they were not sufficient to identify metabolites according to the criteria established in the frame of the metabolite standard initiative.²⁰ This time-consuming step, with the lack of metabolite standards is one of the principal reasons why, until now, the metabolomic approach is not the part of routine analysis in doping control laboratories. Decoding the metabolome is mandatory for achieving so-called “normal” fingerprint that will be compared to the suspicious sample. This representative control pattern requires metabolomic fingerprinting of samples collected under different physiological and environmental conditions that need to be rigorously mastered (e.g., gender, age, pregnancy and castration status, breed, geographical region of breeding, physical conditions, stress status, veterinary treatments, nutritional regimen, hydration, housing, season, or time of sampling among the most evident ones). The data reported here reflect a well-controlled²¹ study with minimized physiological variations (e.g., same gender, age, weight), but many other factors that could also affect horse urine fingerprint were not addressed in this study. Some of them are difficult to control (e.g., well-being, hormonal influences), but need to be taken into consideration as well to build a robust and relevant metabolomic model. Anyway, the capability of HRMS-based metabolomic analysis to differentiate various normal physiological states is demonstrated and its potential as a powerful screening tool for anti-doping purposes is encouraging.

Supplemental Material

Supplemental Material1 - Supplemental material for Tracking main environmental factors masking a minor steroidal doping effect using metabolomic analysis of horse urine by liquid chromatography–high-resolution mass spectrometry

Supplemental material, Supplemental Material1 for Tracking main environmental factors masking a minor steroidal doping effect using metabolomic analysis of horse urine by liquid chromatography–high-resolution mass spectrometry by Natali Stojiljkovic, Fanny Leroux, Saša Bubanj, Marie-Agnès Popot, Alain Paris, Jean-Claude Tabet and Christophe Junot in European Journal of Mass Spectrometry

Supplemental Material

Supplemental Material2 - Supplemental material for Tracking main environmental factors masking a minor steroidal doping effect using metabolomic analysis of horse urine by liquid chromatography–high-resolution mass spectrometry

Supplemental material, Supplemental Material2 for Tracking main environmental factors masking a minor steroidal doping effect using metabolomic analysis of horse urine by liquid chromatography–high-resolution mass spectrometry by Natali Stojiljkovic, Fanny Leroux, Saša Bubanj, Marie-Agnès Popot, Alain Paris, Jean-Claude Tabet and Christophe Junot in European Journal of Mass Spectrometry

Footnotes

Acknowledgements

This work is dedicated to the memory of Dr Yves Bonnaire, Director of the Laboratoire des Courses Hippiques, who deceased on 31 December 2017. The authors are indebted to “la station expérimentale de Chamberet” where one part of the animal phase took place. The authors also thank to the veterinary surgeon, Dr J-J Garin and his staff, for assistance with the animal phase taking place at Coye-la-Forêt, France.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was financially supported by the ANRT (National Association of Research and Technology) and IFCE (French Institute for the horse and horse riding).

Supplemental material

Supplemental material for this article is available online.

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