Homeostatic Signature of Anabolic Steroids in Cattle Using 1Hâ13C

Homeostatic Signature of Anabolic Steroids in Cattle Using 1H-13C HMBC NMR Metabonomics Marc-Emmanuel Dumas,*,†,‡ Ce´ cile Canlet,‡ Joseph Vercauteren,§ Franç ois Andre´ ,| and Alain Paris‡ Biological Chemistry Section, Imperial College London, Sir Alexander Fleming Building, Exhibition Road, South Kensington, London SW7 2AZ, United Kingdom, UMR 1089 - Xeńobiotiques, INRA, BP 3, 31931 Toulouse Cedex 9, France, Laboratoire de Pharmacognosie, Universite´ Montpellier I, 15 Avenue Charles Flahault, B.P. 14491, 34093 Montpellier Cedex 5, France, LABERCA, Ecole Nationale Ve´te´rinaire, BP 50707, 44307 Nantes Cedex 3, France Received March 7, 2005

We used metabonomics to discriminate the urinary signature of different anabolic steroid treatments in cattle having different physiological backgrounds (age, sex, and race). 1H-13C heteronuclear multiple bonding connectivity NMR spectroscopy and multivariate statistical methods reveal that metabolites such as trimethylamine-N-oxide, dimethylamine, hippurate, creatine, creatinine, and citrate characterize the biological fingerprint of anabolic treatment. These urinary biomarkers suggest an overall homeostatic adaptation in nitrogen and energy metabolism. From results obtained in this study, it is now possible to consider metabonomics as a complementary method usable to improve doping control strategies to detect fraudulent anabolic treatment in cattle since the oriented global metabolic response provides helpful discrimination. Keywords: anabolic steroids • homeostatic adaptation • metabonomics • 1H-13C HMBC NMR spectroscopy • pattern recognition

Introduction The anabolic potency exhibited by steroids results in higher muscle accretion and reduced fat deposition. These macroscopic effects of steroids constitute a physiological model for hormonal disruption and correspond to a general modification of the metabolism of lipids, carbohydrates, and amino acids.1 Increasing nitrogen retention is a complex phenomenon involving numerous cooperatively regulated biochemical mechanisms, which involve steroid receptors present in muscle, heart, liver, and other tissues.2-4 All of these biochemical events are physiologically oriented toward anabolism when considering the whole organism, which results in a lower tyrosine aminotransferase activity (a biomarker of catabolism)3,5 and a higher net protein synthesis in muscle.6,7 In parallel, the efficiency of respiratory energy transduction8 should be cooperatively increased as a result of enhancement of mitochondrial proton pump energetic coupling by androgens. The consequences of biological action of anabolic steroids on metabolism, particularly on muscle mass increase, are the cause of their wide use in intensive cattle breeding9 as well as in highperformance sports,10,11 but are also responsible for their application for post-menopausal hormonal substitution12 and * To whom correspondence should be addressed. Tel: +44 207 594 3107. Fax: +44 027 594 3226. E-mail: [email protected]. † Biological Chemistry Section, Imperial College London. ‡ UMR 1089 - Xeńobiotiques, INRA. § Laboratoire de Pharmacognosie, Universite´ Montpellier. | LABERCA, Ecole Nationale Ve´te´rinaire. 10.1021/pr0500556 CCC: $30.25

 2005 American Chemical Society

aging prevention.13 However, numerous adverse effects including cardiomyopathy,14 cancer,14,15 altered reproductive functions,16 musculoskeletal injuries,16,17 cerebral dangers and psychosis,14,16 among others, which are related to a chronic use of anabolic steroids have been well documented for decades. Due to the pleiotropic effect of such hormones and the progressive integrated changes they induce on metabolism and physiology, a global analytical approach is sought for detecting the range of metabolic disruptions that are unequivocally related to the use of anabolics. Such an approach should also provide at the same time a more exhaustive insight on the different physiological targets involved in such endocrine disruption-mediated processes. Recently, patterns of skeletal muscle gene expression in healthy men treated with thyroid hormone has revealed that nearly 400 genes were up-regulated while only very few were down-regulated.18 Cluster analysis of triiodothyronine-regulated gene expression has shown that co-regulated genes were involved in a wide range of cellular functions such as transcription control, mRNA maturation, protein turnover, signal transduction, cellular trafficking, and energy metabolism. Until now, DNA microarray technology applied to knowledge of muscle physiology has been mainly used to characterize specific mRNA signatures in case of myopathies19,20 or senescence.21 In mouse, the study of expression patterns of different metabolic pathways has shown that expressed genes in a tissue-specific pattern were clustered into muscle-specific genes involved mainly in glycolysis, and into liver- or kidney-specific genes involved Journal of Proteome Research 2005, 4, 1493-1502

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research articles mainly in amino acid metabolism.22 In mouse, the proteomic analysis of androgen-regulated protein expression in a fetal vas deferens cell line differentially identified proteins which seem to be regulated by androgens at the post-translational level, possibly involving phosphorylation and may be associated with a cytoskeletal change that is involved in androgen-regulated gene expression.23 As an alternative to measurement of classical biochemical markers or to the more recent profiling techniques of mRNAs, metabonomics consists of a multivariate statistical analysis of NMR or MS spectroscopic data obtained from biofluids or organs without the need for a priori hypotheses concerning the molecular targets at the origin of the disturbances observed.24 Contrary to metabolomics, which is defined as the global characterization the metabolic content of cells, metabonomics is devoted to the functional integration level, at the organism scale, and requires a mathematical processing in order to unequivocally reveal biological signatures of perturbations of systems. [Metabonomics is deriving from the Greek root ‘metaboleˆ’, changing, and ‘nomos’, which means rule or law, whereas metabolomics is deriving from ‘metaboleˆ’ and ‘omos’, meaning assembly. One could also define metabolomics as the list of the structures of the cellular complement, and metabonomics as the functional study of such structures at the organism scale.] This methodology has already been widely used for tumor classification25 and toxicological monitoring.24 In a recent study, we validated 1H-13C heteronuclear multiple bonding connectivity (HMBC) NMR as a convenient metabonomic tool for studying subtle chronic physiological perturbations due to anabolic treatment.26 Our aim is to study the different disruptive metabolic responses to anabolic treatment in cattle by using metabonomics. More precisely, from identification of some relevant urinary biomarkers, we focus on the relationships between modification in nitrogen retention, the repartition of nitrogencontaining compounds excreted in urine, and the coordinated adaptation in energy metabolism. In conditions of growth stimulation, such an adaptation should be mainly realized by compensating for the relative deficiency in oxidative metabolism through an adaptation for a seemingly transitory hypoxia/ anaerobia state, and perhaps, through enhancement of intrinsic aerobic capabilities, resulting in a global adjustment of the intermediary metabolic network.

Materials and Methods Animals and Hormonal Treatments. This study was conducted in accordance with accepted standards of humane animal care as stated in the guidelines of the European Council on animals used in experimental studies. The local GLP committee approved the protocol before commencement of the study. Hereford steers aged from 1 to 2 years were treated with Revalor implants (Hoechst-Roussel Vet, Sommerville, NJ) containing 140 mg of trenbolone acetate (TBA) and 24 mg of 17β-estradiol (E2). These treatments were optimal in terms of biological effect.27,28 Implants were placed at the base of the ear. Animals were separated into four groups including control animals and animals treated with 1, 2, or 4 implants for a 90day period breeding assessment. The animals receiving four implants received a unique dose on the 1st day, whereas those treated with two implants were dosed on the 1st day and on the 45th day of experiment to simulate some existing misuse in breeding farms.29 Nevertheless, to perform the metabonomic exploration after a 90-day implantation period, all these treated 1494

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steers were classified in the same treated male group (MR). In addition, steers from other breeding origins have been used to increase the control group (MC) size. Cows from several genetic backgrounds (Normande, n ) 2; Holstein, n ) 3; Blonde d’Aquitaine, n ) 3) and variable ages (from 1 to 7 years) were subjected to a unique i.m. injection of Androtardyl (Schering, Lys-lez-Lannoy, France) containing 250 mg of testosterone enanthate (TE).30 The variability of the resulting dataset includes gender differences and anabolic treatment, which were statistically modeled, and other uncontrolled factors such as breed, age, farming, feeding, and the reproductive status, which were included in the statistical analysis to increase the residual (nonmodeled) variance and to improve discrimination robustness. All urine samples were collected in buckets on the 10th and 23rd day for males and females and on the 90th day for males, aliquoted in 15 mL Falcon tubes, centrifuged and frozen at -30 °C, and then submitted to freeze-drying for NMR analyses as described elsewhere.26 NMR Spectroscopy. Two-dimensional spectra were recorded on Bruker AMX-500 and Avance-500 spectrometers operating at 500.13 MHz 1H resonance frequency, with z gradient-field facility, inverse probe and temperature held at 303 ( 0.1 K. Metabolite assignments were obtained thanks to 1H experiments using WATER GrAdient Tailored Excitation (Watergate), 1 H-1H TOtal Correlation SpectroscopY (TOCSY), 1H-13C Heteronuclear Single Quantum Coherence (HSQC) experiments performed on representative urinary samples and 1H-13C HMBC parametrized for a 2-hour acquisition, as described previously.26 Integration of HMBC spectral cross-peaks was performed with Aurelia/Amix (v2.8.11, Bruker SA, Wissembourg, France), and files were exported by using a C2+ routine specifically developed for interfacing Aurelia 2D integration files and statistical software. Samples were normalized to 500 mg dried materials diluted in 1 mL of 90% H2O/10% D2O phosphate buffer (v/v) for deuterium lock plus trimethylsilylpropionate (1 mM) for internal chemical shift calibration reference. Urea was measured in 1H NMR spectra by calculating the integral value of the urea signal normalized to the total spectrum integral. Specific analyses were performed to check whether R-testosterone signals potentially overlap variables recorded in urine of females treated with high dosage of testosterone. Since similar classifications using linear discriminant analyses (LDA) showed no decisive influence on final classifications, those variables were kept in the dataset. Multivariate Analysis of Spectroscopic Data. Variables corresponding to 2D-NMR spot intensity, expressed in arbitrary units in a 107 dynamic range, were log-transformed before statistical treatment. Multivariate statistical analyses were performed using SAS (v8.01, SAS Institute Inc., Cary, NC), SIMCA-P (v8.0, Umetrics AB, Umea, Sweden) and S-Plus 2000 (v2.0, Mathsoft Inc., Seattle, WA) with MASS and Carlier libraries, respectively available at http://lib.stat.cmu.edu/DOS/S and http://www.lsp.ups-tlse.fr/Carlier/Logiciel.html. The dataset size was 182 individuals by 375 variables. One-way and twoway analyses of variance (ANOVA) and mean comparison Student-Neuman-Keuls (SNK) test were performed with a R-risk of 5%. Linear discriminant analyses (LDA) were performed with S-Plus 2000 using Venables & Ripley’s MASS library31 on a partition of primary instrumental variables. Variables were extracted by a specific variable selection algorithm26 that performs: (i) an ANOVA for selecting variables significantly affected by the factorial design (parametrized by a threshold P-value for the Fisher statistics, named P), and (ii) an analysis

Homeostatic Signature of Anabolic Steroids in Cattle

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of redundancy by a stepwise introduction of variables based on their partial correlations with the subspace selected and with the subsequent removal of variables, which are linearly correlated to the subspace already selected (parametrized by β). Optimum filtration parameters correspond to (P, β) ) (10-2, 0.9), the number n of variables being determined by a crossvalidation (CV) procedure or by graphical display of the discrimination of the different groups of animals.26 Canonical correlations between variables and linear discriminant axes describe the ability of variables to carry out the information handled by these factorial axes. Error rates of classification of unknown individuals were computed by 10-fold CV as described elsewhere.26 SIMCA models were performed with SIMCA-P software. The SIMCA algorithm32 results in independent principal component models of classes calculated by NIPALS. Each significant component is retained for the models. Normalized prediction distances (DmodX) gave distances to the models whose critical values (DCrit) were computed with 0.95 confidence intervals.

Results and Discussion 2D Proton (1H) Carbon (13C) Bidimensional NMR Spectroscopic Fingerprinting of Bovine Urine. The bidimensional 1H-13C HMBC spectra obtained from urine quantitatively describe the relative levels of low molecular weight analytes. This arises because each peak is located at the 13C and 1H chemical shifts of pairs of nuclei connected by long-range J (13C-1H) couplings over 2, 3, or 4 bonds (Figure 1). When combining the statistical approach inherent to the factorial design and the different structural assignments given by NMR coherences, we were able to identify several main metabolites in urine from cattle.26 The major metabolites identified in samples are citrate, glucose, dimethylamine (DMA), trimethylamine-N-oxide (TMAO), hippurate, creatine and creatinine, allantoin and urea (this latter is not observable by 1H-13C NMR, but only by 1D-NMR). Two other compounds were partially assigned and were noted A and B. Presence of glucose at significant levels was revealed by 1H-13C HMBC and confirmed by presence of both R and β anomeric signals in the 1H spectra (not shown). Indeed, employing 2D-NMR spectra as fingerprints enables us to emphasize quantitative variations of numerous signals. Nevertheless, when considering simultaneously all the urinary metabolic NMR variables selected among which only about a third were identified, some physiological hypotheses can be successively studied: (i) homogeneity of the basal metabolic status within the different male and female control groups, (ii) presence of a significant signature of hormonal treatments used, (iii) discrimination of the different metabolic responses when considering the progression of anabolic response in male and female treated groups. Homogeneity of Fingerprints Coming from Control Animals. To increase the biological variability of the control group that corresponds to an increase of the residual variance and a concomitant decrease of the modeled variance, we incorporated additional control animals to the experimental design. Therefore, the homogeneity of control groups had to be statistically assessed. Gender-group membership for control animals was tested in order to reject the first a priori null hypothesis consisting of an intrinsic homogeneity inside the respective control male and female groups (Supplemental Data 1). The nonlinear iterative partial least squares (NIPALS) algorithm independently models each control class. For the

Figure 1. 1H-13C-HMBC NMR spectrum and 1H NMR spectrum of urine at 303 K.

control female group (n ) 44), using the 2 components from the PCA in the score plot t1/t2, only three samples project outside the Hotelling’s T2 ellipse, which defines a significant distance from the model (P < 0.05). Yet, including 15 components, the distance to model (DmodX) plot clearly shows that all females are situated under the critical significant distance DCrit0.05 computed from T2, showing that the animals belong to a homogeneous group. All control males (n ) 32) project inside the Hotelling’s T2 ellipse using both representations. These tests, using metabonomic technology, confirm that no significant subclass structure can be evidenced within the control groups. Instead of undermining the approach, these extra animalssnot strictly related to the experimental design used to study the anabolic response after hormone treatments actually confirm the suitability of the approach and can be used to improve the robustness of multivariate analyses. Contrary to linear discriminant analysis (LDA) assuming that within-group variances are equal, the soft independent modeling by class analogy (SIMCA) algorithm takes into account the within-group variability, and hence can be used as a complementary factorial analysis. The Cooman’s plot represents control and treated animals for each sex (Figure 2). The Cooman’s plot on steers leads to a quite clear discrimination between control and treated animals (Figure 2a). These are projected in their respective class membership regions, whereas most of the control and treated females are located in the reject membership region, demonstrating that both control or treated females differ significantly in their urinary fingerprints from Journal of Proteome Research • Vol. 4, No. 5, 2005 1495

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Figure 2. Cooman’s plot showing the discrimination of treatments as a function of sex: (a) SIMCA model of steers, (b) SIMCA model of cows. Distances to the model are computed for each class and two critical distances DCrit0.05 (significant distance boundary) for each class. The two critical distances generate four quadrants: membership of class of control animals but not class of treated animals (upper left corner), membership of class of treated animals and not class of control animals (lower right corner), membership of both classes of control and treated animals (lower left corner), and exclusion of membership to both classes (upper right corner). MC, control males (n ) 32), FC control females (n ) 44), MR, Males treated with Revalor (n ) 74), FE, females treated with testosterone enanthate (n ) 32).

control and treated steers. A greater variability in the female groups should be attributed to the strong within-group biological variability in breed, age, feeding, and reproductive stage in addition to the anabolic treatment. There are neither false positive nor false negative castrated males regarding treatment used in castrated males, although some of them are projected in the common lower left corner. In a same way, the Cooman’s plot on females evidences some misclassified treated females from the corresponding control group (Figure 2b). In addition, some male animals were projected in the female group membership quadrants. However, most of steers are still rejected from control and treated female groups that are modeled by this plot. Metabonomic Characterization of Physiological Changes Due to Sex and Treatment. Variations of urea excretion were calculated by measuring urea abundance in 1H Watergate 1496


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Figure 3. Representation of the hormonal disruption by linear discriminant analysis performed on 106 variables: (a) LD1/LD2 plot, (b) LD2/LD3 plot. Note the loss of efficiency between the two maps for sex centroid (crosses) segregation by the discriminant boundaries from the LDA on sex (full lines), and the extensive complexity of the discriminant boundaries from the LDA on sex built with the control and treated groups (dashed lines) to segregate sex-groups. The female region becomes discontinuous. MC, control males (n ) 32), FC control females (n ) 44), MR, Males treated with Revalor implants (n ) 74), FE, females treated with testosterone enanthate (n ) 32).

spectra where the water resonance was not excited. This avoids the problem of cross saturation to the urea peak if conventional peak irradiation solvent suppression was used. Urinary urea concentrations are significantly lower in males than in females (-44%, P ) 6.8 × 10-12), and in treated animals than in control ones (-73%, P < 10-16) with a strict additive effect of the ‘gender’ and ‘treatment’ factors without any interaction between these two factors. For the metabonomic exploration of the different biological signatures, resulting from the different physiological disruptions, we performed a pattern recognition analysis on steers and cows belonging to control or treated groups (n ) 182). Then, a linear discriminant analysis (LDA) was performed on these 4 groups over the total of 106 variables selected (Figure 3, Table 1 and Supplemental Data 2). Canonical correlations between factorial axes (linear discriminant, LD) and some assigned variables corresponding to the geometric mean of initial variables characterizing a unique urinary

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Table 1. Canonical Correlations, ANOVA and Mean Comparison Test of Identified Discriminant Metabolites Determined from the LDA on the Overall Anabolic Factorial Design (control and treated animals) over the Total 106 Variables Initially Selected statistics one-way ANOVAc

relative mean values compoundsa

A B DMA Citrate Creatine Creatinine Hippurate TMAO Glucose

FCb

1.05 1.08 1.02 0.98 1.06 1.04 1.06 1.04 1.12

FEb

0.96 0.96 0.92 0.96 0.92 0.92 1.08 1.06 0.98

MCb

1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

MRb

0.97 0.92 0.93 1.03 0.90 0.90 1.03 1.07 0.99

LD1

-0.13 -0.25 0.00 0.28 -0.16 -0.11 -0.25 -0.17 -0.29

LD2

-0.46 -0.55 -0.54 0.13 -0.61 -0.50 0.02 0.26 -0.53

LD3

0.06 -0.10 -0.05 0.28 -0.06 -0.16 -0.02 0.25 0.15

two-way ANOVAd P4 groups

10-10

4.8 × < 10-16 3.9 × 10-13 3.7 × 10-07 < 10-16 7.9 × 10-13 1.2 × 10-02 1.6 × 10-06 < 10-16

Pgender

10-4

6.8 × 2.0 × 10-10 1.5 × 10-2 4.4 × 10-7 5.0 × 10-8 2.0 × 10-5 2.2 × 10-3 7.4 × 10-1 1.2 × 10-9

Ptreatment

10-8

5.6 × 3.0 × 10-12 1.4 × 10-13 2.8 × 10-1 5.5 × 10-16 2.2 × 10-11 2.1 × 10-1 2.6 × 10-6 2.3 × 10-9

Pinteraction

3.8 × 10-3 1.5 × 10-1 1.8 × 10-1 8.6 × 10-3 4.6 × 10-2 9.2 × 10-1 8.0 × 10-1 4.1 × 10-3 2.2 × 10-7

a Each compound variable is the geometric mean value of initial attributed variables. b Group-wise average values are reported in a 1 to 1 ratio compared to MC group average values. c One-way ANOVA tests the global factorial design with four groups. d Two-way ANOVA tests the effect of factors ‘sex’ and ‘treatment’, and their interaction. Group distributions: FC (n ) 44), FE (n ) 32), MC (n ) 32), MR (n ) 74).

metabolite as well as one-way (factor ‘group’) and two-way (factors ‘gender’, ‘treatment’, and their interaction) ANOVA were computed (Table 1 and Supplemental Data 2). For most of metabolites, results for both analyses (LDA and one-way or two-way ANOVA) are homogeneous. This can be explained by strong correlations between NMR signals assigned to the same urinary metabolite. The first linear discriminant (LD1) corresponds to 46.8% of information (between-group variance, P < 10-4) and segregates females (negative values) from castrated males (positive values) (Figure 3a). Since LD1 discriminates animals from their sex whatever the effect of the anabolic treatment, the ‘gender’ effect has been tested by a two-way ANOVA for ‘gender’ and ‘treatment’ effects, and their interaction. When analyzing canonical correlations, LD1 is mainly explained by metabolites such as TMAO, hippurate, creatinine, compound B, glucose, and creatine, the strongest urinary concentrations of which being correlated to the female status. Citrate is the best positively correlated metabolite to LD1 and consequently is mainly encountered in males (Table 1 and Supplemental Data 2). Results from ANOVA corroborate canonical correlations from LDA. Variables were selected for an effect of the factorial design (4 groups) with a R-risk less than 10-2 in the one-way ANOVA. All the variables selected are significantly affected by this criterion, although it is not necessarily the case for the ‘gender’ effect for TMAO in a two-way ANOVA, for instance. The other metabolites are both correlated to LD1 and significantly affected by the ‘gender’ effect in the two-way ANOVA (Table 1 and Supplemental Data 2). LD2 summarizes 33.4% (P < 10-4) of the between-group variance (Figure 3a). Only 2 from 31 interpreted variables display high canonical correlations but all the other assigned variables are significantly affected by the anabolic treatment. This axis discriminates control animals (negative values) from treated ones (positive values), regardless to both hormonal treatment and ‘gender’ factor considered. Indeed, this axis reveals the overall anabolic response. Although there are few positive correlations, hippurate, TMAO, and citrate are the urinary metabolites mainly correlated to LD2. On this axis, these metabolites are anticorrelated to DMA, creatinine, creatine, compound B and glucose, which are characteristic of control groups (Table 1 and Supplemental Data 2). LD3 explains the last 19.7% (P < 10-4) of the between-group variance (Figure 3b). Negative values on this discriminant axis correspond to projections of both control steers and treated

cows, whereas positive values correspond to projections of both control cows and treated steers. Such sex-dependent variations resulting from anabolic treatments revealed by this factorial analysis are equivalent to a significant interaction between the two factors, ‘gender’ and ‘treatment’, in a two-way ANOVA. In fact, 18 variables out of the 31 assigned variables are significantly affected by this interaction. Creatine, glucose, compounds A and B were principally encountered in TE treated females and control steers. Metabolites characterizing control females and steers treated with TBA+E2 are TMAO and citrate (Table 1 and Supplemental Data 2). Performing SNK tests reveals group means that are not significantly different between controls and treated animals from both sexes (i.e., from FC and MR on one hand, or MC and FE groups on the other hand). The special case of altered glucose seems to be linked to some control females and one TE-treated female, which presented characteristic HMBC glucose signals in urine just before treatment and on the 10th and 23rd day after TE administration. These signals clearly show an interaction between the ‘gender’ factor and the ‘anabolic treatment’ factor in a two-way ANOVA, as well as for those from TMAO, creatine and citrate (Table 1). Taking into account the interaction between the ‘gender’ and ‘anabolic treatment’ factors, excretion of citrate is higher in control steers than in control females. Anabolic treatment seems to enhance this different metabolic orientation with a higher excretion in steers treated by TBA+E2 and a lower excretion in cows treated by TE. Time Course Monitoring of Hormonally Induced Metabolic Disruptions. Given the wealth of the experimental design, gender-specific effects were investigated separately. The SIMCA algorithm takes into account the within-group variability, and hence can be used as a complementary factorial analysis, which softly filters out gender-specific interactions with a reduced overlapping between male and female models (Figure 2). However, the time-course variation of the metabolic response induced in females and males were assessed separately by LDA. In each case, the LD1 axis discriminates control animals from treated animals with a focus on the biggest time course variation, whereas upper order LDs sign effects related to transient responses at the different intermediary urine collection time points (Supplemental Data 3). Analysis of the time-course variations in metabolic response induced in cows (n ) 76) by TE treatment was achieved by LDA performed on 46 variables (Supplemental Data 3). When projected on LD1 (51.7% of information), control cows are Journal of Proteome Research • Vol. 4, No. 5, 2005 1497

research articles separated from treated ones, with a graduated response observed just before (control), or on the 10th and the 23rd day after i.m. injection. TMAO, creatine and compound A explain this discrimination (Supplemental Data 3), which is confirmed by a one-way ANOVA. LD2 explains 48.3% of the betweengroup variance. Since all correlations with initial variables are negative, this factor scales observations and describes heterogeneity between cows from TMAO, hippurate, compound A and creatine (Supplemental Data 3). LD2 as a scaling factor reveals a transitory physiological state with a transient decrease in concentrations of TMAO, compound A or hippurate examined on the 10th day, in contrast to control group and the group analyzed on the 23rd day postinjection, as evidenced by SNK test (Supplemental Data 3). Similarly, we have discriminated the time-dependent response to implantation with TBA + E2 on steers (n ) 106) using 55 selected variables (Supplemental Data 3). LD1 (41.8% of information) explains the opposition between control and steers implanted for 90 days. Besides, when projecting these four groups on the first discriminant axis, we can define a metabolic response gradient from the physiological status of controls to the fully hormonally disrupted one. Such a gradient is mainly explained by allantoin, hippurate and TMAO, which are correlated to LD1, whereas creatinine, compounds A and B or creatine are anticorrelated to this pseudo time-gradient axis. Such a significant time-dependent rise is also evidenced by SNK test for compound A and creatinine (Supplemental Data 3). LD2 segregates control steers from animals belonging to the 23th-day post-implantation group. SNK tests confirm the transient decrease after 23 days of treatment in mean values of compound B, creatine, DMA and glucose, whereas TMAO is significantly raised. Definition of Physiological Signatures of Anabolic Disruption. Factorial analysis of urinary 1H-13C HMBC NMR spectra clearly demonstrates the disruptive property of anabolic steroids on general metabolism (Figure 3, Table 1, and Supplemental Data 2), by discriminating specifically treated animals (steers and cows) from their respective controls on the second linear discriminant (LD2), whatever the overall basal sexdependent metabolic status displayed by the first LD. This can be attributed to an empirically adapted use of anabolic preparations for either sex, i.e., androgens such as TE were used for treating cows and the association of TBA and E2 was used for treating castrated males. This powerful exploration of metabolic contrasts elucidated by a metabonomic approach has been underlined previously in toxicological,24 and clinical33 studies. The residual information on LD3 displays a seeming ‘feminization’ process of steers that can be opposed to a relative ‘masculinization’ of females, revealing both a gender specificity and some sex-linked disorders resulting from potent anabolic disruptions. Consequently, from this quantitative exploration, anabolic preparations clearly modify the sex-dependent endocrine equilibrium of the general metabolism. Indeed, this should be considered as an additional specific anabolic effect that is measured on castrated males or on females when considering the specific hormonal treatment used for every gender. These effects can be interpreted as differential physiological adjustments of metabolic paths in response to anabolic treatment (Figures 4 and 5). Metabolic Homeostasis and Nitrogen Retention Increase. Both testosterone and trenbolone have an anti-catabolic effect through their anti-glucocorticoid activity.5 Testosterone also enhances the biosynthesis of myosin mRNA34 and protein.3,7 1498


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From the different variables analyzed in urine by 2D-NMR, among which nearly a third was assigned to known metabolites, LDA indicated clearly existence of the different metabolic paths, which were coordinately affected by the two anabolic treatments used (Table 1 and Supplemental Data 2 and 3). Among those metabolites, creatinine, and urea excretion in urine is reduced in response to both anabolic treatments, as a consequence of the overall increase in nitrogen retention (Figure 4). Morever, creatinine and creatine reveal a common adaptive variation in the elimination of some related metabolites. The biosynthetic origin of creatine is glycine, involving guanidoacetate N-methyltransferase activity.35 Since this activity is downregulated by estradiol,36 a lower excretion of creatinine and creatine could then explain a higher glycine bioavailability. This amino acid is involved in several biochemical pathways such as the biosynthesis of proteins, purines, collagen, bile salts, glutathione, and porphyrins leading to hemoglobin,36 and the conjugation of benzoate to give hippurate.37 This latter metabolite is considered as an alternative nitrogen excretion route occasioned by a downregulation of arginase activity and a lower urea cycle turnover.38 This then could explain the relative increase in hippurate excretion observed in treated animals (Supplemental Data 2). Concentrations of DMA seem rather higher in control animals in most analyses, whereas urinary concentrations of TMAO are higher in treated animals. Moreover, in the timecourse study performed on steers, these two metabolites are clearly anticorrelated (Supplemental Data 3). In vivo, trimethylamine (TMA) derives from choline and carnitine or from reduction of TMAO in gut.39 In human, TMA N-oxidation is obtained by flavin-containing monooxygenase isoform 3 (FMO3) in liver and kidney.40 In mice, expression of this orthologous form in liver is abolished by treatment with testosterone,41 but in human no gender difference has been revealed.42 In cattle, the respective implication of the FMO3 orthologous form, its possible hormonal control, and the gut microflora need to be studied more in details to know whether TMAO can be considered as a valuable biomarker of anabolic treatment. Consequences of Nitrogen Retention Increase on Electrolytes Status. The relative concentration in citrate is higher in urine of castrated males compared to females (Table 1 and Supplemental Data 2). In addition, citrate is increased in steers treated by TBA+E2, whereas it is decreased in cows treated by TE (Table 1). We can hypothesize that the decrease in citrate concentrations observed in cows is mainly due to androgen administration whereas the citrate increase observed in steers should be linked to administration of estrogens. Previous observations in humans have mentioned a higher excretion of citrate in female than in male.43 Indeed, citrate plays a critical role in preventing calcium precipitation in the urinary tract and in the rat kidney, as it prevents the formation of calcium oxalate deposits.44 The present results are in agreement to the fact that testosterone appears to promote stone formation in rat by suppressing osteopontin expression in the kidneys and by increasing urinary oxalate excretion, whereas estrogen appears to inhibit stone formation by increasing osteopontin expression in the kidneys and decreasing urinary oxalate excretion.45 Unfortunately, we are unable at the present time to select specific variables characterizing oxalate and to correlate them with a concomitant variation of citrate in urine. Bioenergetic and Osmolytic Consequences of the Homeostatic Adaptation for Nitrogen Retention. Muscle accretion is a consequence of the nitrogen retention increase induced by


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Figure 4. Map for metabolic pathway readjustments attributed to nitrogen retention in anabolic treatment.

anabolic steroids.7 Therefore, muscular and renal physiological modifications including osmotic homeostasis in kidney should also involve bioenergetic adaptations. First, a lowered creatinine excretion could result in a greater phosphocreatine bioavailability in muscle through the activity of creatine kinase (CK) (Figure 4). This phosphagen represents the main part of the total P-bonded energy and is instantaneously available for ATP regeneration.46 Thus, this could lead to a significant increase in the energetic storage pool in muscle. Testosterone treatment in male and female gonadectomized rats, and estradiol treatment in female castrated rats brought back the ATP content and creatine kinase and myokinase activities in skeletal muscle to normalcy, both hormones being effective in enhancing skeletal muscle energy metabolism.47 At this stage, we can query whether the increase in alactic anaerobic capabilities48 observed in intense physical exercise conditions and a possible adjustment of the phosphocreatine turnover could not be in fact coordinately adapted to a relative hypoxic status that could be present in case of anabolicsinduced growth response. Even if normoxic conditions may explain why no increase in hemoglobin concentrations were found in veal calves treated with trenbolone acetate and estradiol,49 this result does not consider the differential hemoglobin biosynthesis rates in growing animals due to different allometric parameters. So, the lower creatinine excretion in anabolized cattle would be consistent with a plausible increase in hemoglobin biosynthesis rate, since 5-aminolevulinate syn-

thase activity using glycine as substrate is stimulated by androgens50 and erythropoietin biosynthesis is stimulated by androgens (only in case of aplastic anemia),51 but is altered by a treatment with estradiol.52 Such a hypothesis should be tested. Further to this, some bioenergetic adaptations may be reinforced by the role of TMAO. This osmolyte is involved in the process of thermodynamic stabilization of proteins53 by counteracting the detrimental effects of urea on enzyme activities such as lactate dehydrogenase, creatine kinase, pyruvate kinase, and glutamate dehydrogenase.54 Here, the relatively higher TMAO urinary excretion seems to be linked to a decrease in urea excretion, which should reflect the lower urea concentration in plasma as already shown elsewhere in similar conditions.55 Consequently, transient and long-lasting variations of TMAO excretion could participate in a coordinated way to a physiological readjustment of counteracting osmolytes due to a reduction of urea stress in kidney medulla.56 In addition to its specific role in the regulation of calcium balance mentioned above, citrate is involved in energetic metabolism (Figure 5). Lactate dehydrogenase and pyruvate dehydrogenase activities are both increased by androgen treatment57,58 and isocitrate dehydrogenase, which is involved in the turnover regulation of the tricarboxylic acid (TCA) cycle, is down-regulated by androgens.59 Yet, concerning the hormonal control of citrate synthase activity (CS), involving condensation of acetyl-CoA and oxaloacetate to citrate and the regulation of acetyl-CoA entry in the TCA cycle, the literature Journal of Proteome Research • Vol. 4, No. 5, 2005 1499

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Figure 5. Map for metabolic pathway readjustments attributed to energetic adaptation in anabolic treatment.

is contradictory.59-61 Acetyl-CoA and citrate are also regenerated in connection with lipid catabolism, mainly under the control of malonyl-CoA bioavailability (Figure 5). Malonyl-CoA is synthesized in a cytosolic two-step reaction with citrate lyase and acetyl-CoA carboxylase, which seems to be under the control of sex hormones.62,63 Indeed, malonyl-CoA behaves like a molecular switch: at high concentrations, lipid synthesis is favored through the Wakil helix and lipid catabolism is inhibited through the carnitine palmityl-CoA transferase I transport system, whereas at low concentrations, lipid catabolism inhibition is impaired and lipid synthesis is not activated.64 All of these activities are highly dependent on the tissue, species and anabolics considered, and difficulties in explaining higher citrate levels in urine underline the lack of knowledge concerning the control of the turnover in the TCA cycle. Taken together, these arguments describe a coordinated response to anabolic steroids leading to an increase in nitrogen retention. Consequently, despite a lower protein turnover linked to the anti-glucocorticoid activity of androgens,5 a general strategy for control of energy demand seems to be activated, due to protein synthesis and amino acid mobilization,7 which should be triggered by a direct interaction of androgens and estrogens to their respective receptors.2,3 Specific biological signatures based on metabonomics performed on tissues should reveal this adaptive strategy, which could include mobilization of lipids and readjustment of enzymatic activity via protein stabilization by TMAO. 1500


Conclusion Clearly, the holistic approach characterizing hormonal disruptions obtained with anabolics, which is based on metabonomics combining 1H-13C-HMBC NMR profiling and statistical pattern recognition, can be considered as a very efficient analytical method to give pertinent biological signatures of inherent modifications of the general metabolism. This approach is characterized by the underlying serendipity principle, which is applied here to feature and elucidate the significant variations of the metabolic network. A significant part of NMR variables involved in the different discriminations can be assigned as relevant metabolites, which define quantitative biomarker sets useful to understand some metabolic homeostasis adjustments in this hormone-disrupted cattle model. Adaptation for increase in nitrogen retention induced by anabolic steroids also leads to major consequences on adaptation to energy metabolism. Nonetheless, this study emphasizes the proof of concept of a robust methodology for indirect diagnosis, monitoring or research purposes in animals, and possibly in humans, submitted to the use of anabolic steroids. Such a noninvasive methodology can easily be customized for several areas: monitoring the illegal use of anabolics in food science, helping in the fight against doping in sports and finding the metabolic consequences of hormone replacement therapies in medicine. However, complementary approaches performed at the tissue level involving other “-omics” biotechnologies such as transcriptomics,65,66 as recently shown with


xenobiotic metabolism, helps providing a more precise insight in adjustment of metabolic fluxes. Between gene expression probed by transcriptomics and the end-product metabolite changes revealed by metabonomics, a whole spectrum of feedback regulations makes direct interpretation rather difficult. There is no doubt that proteomics should play a major role in addressing key enzymatic67,68 and signaling regulations69,70 and, hence, help in developing a real integrated systems biology framework. This work is strongly indicative that the application of metabonomics to human testing could free doping control from searching for specific anabolic residuesssince the global metabolic response suffices to provide the required discriminationsand therefore should be properly tested in humans.

Acknowledgment. Dr. Daniel Maume and Dr. Bruno Le Bizec are thanked for providing samples. The authors would like to acknowledge Dr. Richard Barton for his critical reading of the manuscript and helpful comments. This study was funded by a grant from European Commission (B6-7920/98/ 000823). M.E.D. is currently supported by the Wellcome Trust Functional Genomics Initiative Grant (066786) for the Biological Atlas of Insulin Resistance Consortium (www.bair.org.uk). Supporting Information Available: Three Supporting Information documents available. Supplemental data 1: Accuracy verification of gender-grouping of control animals by NIPALS/SIMCA modeling. Supplemental data 2: Canonical correlations from the LDA, ANOVA and mean comparison tests. Supplemental data 3: Time-course variation of urinary metabolic signatures. This material is available free of charge via the Internet at http://pubs.acs.org. References (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20)

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Homeostatic Signature of Anabolic Steroids in Cattle Using 1Hâ13C

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