Expression analysis of androgen-responsive genes in the prostate of veal calves treated with anabolic hormones

Domestic Animal Endocrinology 30 (2006) 38–55

Expression analysis of androgen-responsive genes in the prostate of veal calves treated with anabolic hormones L. Toffolatti a,b , L. Rosa Gastaldo a , T. Patarnello a , C. Romualdi b , R. Merlanti a , C. Montesissa a , L. Poppi a , M. Castagnaro a , L. Bargelloni a,∗ a

Dipartimento di Sanit`a Pubblica Patologia Comparata ed Igiene Veterinaria, Universit`a di Padova, Viale dell’universit`a 16, 35020 Legnaro (PD), Italy b Dipartimento di Biologia, Universit` a di Padova, 35121 Padova, Italy Received 7 April 2005; received in revised form 31 May 2005; accepted 31 May 2005

Abstract In order to identify indirect molecular biomarkers of anabolic treatments in veal calves, an animal experiment was performed using two combinations of growth promoters (consisting of boldenone undecylenate and estradiol benzoate, and of testosterone enantate and estradiol benzoate). We selected a set of 12 genes that are known to be androgen responsive in other mammalian species. The expression profile of this set of genes was analysed on prostate samples of veal calves using a real-time RTPCR approach. For each selected gene the corresponding bovine sequence was obtained and a gene specific real-time assay was optimised and validated. The amplification was shown to be highly specific, linear and efficient. High reproducibility (<1%) and low-test variability (<2.5%) were also been achieved. Messenger RNA levels were quantified in prostate samples, non-parametric analysis of variance showed significant up-regulation of three genes (MAF, ESR1 and AR) and significant down-regulation of four genes (HMGCS1, HPGD, DBI, and LIM) in treated samples when compared with untreated controls. To assess the possibility of identifying hormone-treated animals by molecular means we performed a discriminant analysis that was effective in classifying treated and non-treated samples with an accuracy of 93%. Our results indicate that identification of treatment with steroid

∗

Corresponding author. Tel.: +39 049 8272506; fax: +39 049 8272602. E-mail address: [email protected] (L. Bargelloni).

0739-7240/$ – see front matter © 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.domaniend.2005.05.008

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hormones in veal calves by means of gene expression analysis is a feasible approach and could be improved increasing both the number of genes and the number of controls analysed. © 2005 Elsevier Inc. All rights reserved. Keywords: Anabolic steroid hormones; Veal calves; Prostate; Androgen responsive genes; Real-time RT-PCR

1. Introduction The administration of health-risk related substances such as growth promoting agents and veterinary drugs is a recurring problem in animal production where these compounds are often used to increase the productivity and to reduce breeding costs. The use of natural and synthetic hormones for growth improvement in food producing animals is prohibited in the European Union [1,2]. Despite the ban, the use of anabolic steroids in cattle is still practiced. Therefore, diagnostic methods are required that are specific and sensitive in detecting anabolic treatments. Analytical controls in this field are traditionally carried out by biological and chemical methods: often a routine screening using immunoassay (RIA or ELISA) [3,4], thin layer chromatography or high-performance liquid chromatography, is followed by confirmation tests that use gas chromatography or liquid chromatography combined with mass spectrometry [5–7]. These are targeted approaches aimed at identifying predefined compounds. Recently, new validation criteria concerning the performance of analytical method and results interpretation were set by EU [8]. Due to continuing improvements in analytical techniques, very low detection limits for individual residues have been achieved. In response to these developments, cocktails composed of several steroids at low concentration are administered, achieving the desired cumulative anabolic effect while lowering the concentration of each individual substance. Moreover, new more effective synthetic steroids are used. Among these hormones boldenone, often administered as esterified molecules (boldenone undecylenate), has been illegally used as a performance enhancer in athletes [9] and race horses [10], and as growth promoter in meat production [11–13] where it affects the growth and food conversion of cattle [14]. Considering the number of possible residues, in-depth analysis using direct analytical methods is usually in conflict with the limited budget, number of samples, as well as the short response time required. Bioassays capable of detecting the integrated effect of cocktails might provide valuable additional tools in controlling the use of illegal anabolics. A possible approach is to identify indirect biomarkers of treatment in target tissues. To such aim a histological screening method was proposed [15,16]. It is based on the histopathological modifications induced by anabolic steroids on male and female calf target tissues, especially on the genital tract [17]. Indirect markers are not meant to substitute direct analytical methods, which should remain in use for official controls and law-enforcement by sanitary authorities. However, analysis of biomarkers might be of great interest for hazard analysis of critical control points (HACCP) procedures, as HACCP is increasingly used by producers, slaughterhouses, and retail-chains to improve the quality and safety of food products.

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L. Toffolatti et al. / Domestic Animal Endocrinology 30 (2006) 38–55

Androgens exert their biological action via activation of the androgen receptor (AR) that induce either transcriptional activation or repression of target genes, in responsive tissues [18]. Anabolic effects of steroid hormones operate directly in muscular tissue by a positive nitrogen balance and by increasing muscle mass [19], whereas indirectly by stimulating the growth hormone from hypophysis and other growth factors from liver [20]. Combinations of estrogens and androgens are often used for growth promoting purpose because their association shows increased potency while masking hormone negative effect on animal tissues. In several countries outside the EU these treatments are licensed and are usually carried out as subcutaneous implant preparations [21]. In the present study, two combinations of steroid hormones were investigated: the first one (ET) consisting of testosterone enantate and estradiol benzoate, the second one (EB) consisting of boldenone undecylenate and estradiol benzoate. While the EB combination was aimed at studying the anabolic properties of boldenone, the combination of natural hormones (ET) was also chosen, because it is described to be more efficient in producing lean meat, and to give less detrimental effects on beef carcass, and meat tenderness than synthetic hormones do [21]. In this context we assessed the expression pattern of a set of androgen responsive genes in bovine prostate tissues in order to identify possible indirect biomarkers of treatment. Androgens play a critical role in prostate growth and maintenance. Estrogens have been shown to potentiate androgen-mediated prostate growth [22]. Epidemiological evidence suggested that androgens might have a role in the genesis of human prostate cancer [23,24]. For this reasons several in vitro and in vivo studies (on human, rat, and murine cell lines or tissues) were performed to investigate the effects of androgens on prostate development and growth, as well as on neoplastic transformation. These studies identified a great number of androgen responsive genes [25–31]. Considering the evolutionary conservation of androgen action in the prostate we selected from the literature data a set of 12 androgen responsive genes and analysed the effects of the two steroid hormone treatments on the expression of the selected genes in the bovine prostate tissue using a real-time PCR technique.

2. Materials and methods 2.1. Experimental design Twenty-seven male veal calves (approximately, 4 months old) were divided in three treatment groups, homogeneous in weight: group C (n = 9) untreated controls; group ET (n = 9) treated with a cocktail of estradiol benzoate (10 mg) and testosterone enantate (200 mg); group EB (n = 9) treated with a cocktail of estradiol benzoate (10 mg) and boldenone undecylenate (200 mg). Animal weights expressed as mean value for each treatment class (before and after treatments) are reported in Table 1. Treatments of calves were carried out by four subcutaneous injections every 15 days for two months: the use of esterified molecules ensured a slow release of the hormones. Fifteen animals (five belonging to each group) were slaughtered six days after the last dose (group T1), the others 14 days after the last dose (group T2, n = 13). Selected tissues were dissected, immediately snap-frozen in liquid nitrogen and stored at −80 ◦ C.

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Table 1 Weight of veal calves averaged across animals of each treatment group Treatment group

C (n = 9) ET (n = 9) EB (n = 9) a

Weight of the animals (kg)a Before treatment

At slaughtering time

161.5 ± 13.75 167.56 ± 13.61 170.22 ± 10.02

226.6 ± 20.29 270.67 ± 17.32 264.67 ± 22.47

Values are given as the mean ± standard deviation.

2.2. Total RNA extraction and reverse transcription Tissue disruption and homogenisation was performed in a bead mill homogeniser in the presence of zirconia/silica beads 0.1 mm (BioSpec Products Inc., Bartlesville, OK). Total RNA was extracted and purified using a RNeasy Mini Kit (Qiagen, Valencia, CA) and any residual genomic DNA was removed by Rnase-Free DNase digestion (Qiagen) according to the manufacturer’s instruction. RNA integrity was confirmed by denaturing agarose gel electrophoresis, and the concentration was quantified spectrophotometrically by measuring the optical density at 260 nm. First strand complementary DNA (cDNA) was synthesized from 1 ␮g of total RNA. Briefly, 50 ng of random hexamers were annealed to 1 ␮g of RNA at 65 ◦ C for 5 min and chilled on ice. Then the reaction mix was added to obtain a final volume of 20 ␮l and a final concentration of 1× first strand buffer, 10 mM dithiothreitol (DTT), 0.5 mM each dNTPs, 40 U RNase Out (Invitrogen, Carlsbad, CA) and 200 U SuperScriptTM II Rnase H− Reverse Transcriptase (Invitrogen). The reaction was incubated at 25 ◦ C for 10 min, at 42 ◦ C for 50 min, and at 70 ◦ C for 10 min. 2.3. Selection of androgen responsive and housekeeping genes In order to identify a set of potentially androgen-regulated genes in the prostate of veal calves, expression data were collected from published studies that were performed on human prostate carcinoma cell lines or on rat and mouse prostate tissues. Choice-criteria were as follows: selected genes should be: (i) overexpressed after androgen treatment; (ii) described to be androgen responsive in different species; and (iii) reported in independent studies. Bibliographic references to the studies used for the selection of genes are reported in Table 2. In order to design a real-time assay for the selected genes we searched the corresponding Bos taurus cDNA sequences in GenBank (http://www.ncbi.nlm.nih.gov/). If the bovine sequence was not available in GenBank, the following approach was implemented: firstly, for each gene of interest, we identified highly conserved regions among different mammalian species (Homo sapiens, Rattus norvegicus, Mus musculus, Sus scrofa, and Canis familiaris). On these regions we designed PCR primers pair, spanning at least two exons. These primers were used to amplify bovine prostate cDNA. PCR reactions were carried out by AmpliTaq Gold (Applied Biosystems, Foster City, CA). After an activation step at 95 ◦ C for 10 min the reactions were continued for 40 cycles (95 ◦ C for 15 s and 60 ◦ C for 1 min). PCR products were resolved on 2% agarose gel. When a single band was obtained it was purified by ExoSAP-IT® (USB, Corporation, Cleveland, OH) and directly sequenced using BigDye terminator chemistry (Applied Biosystems, Foster City, CA).

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Gene

Name

Functional category

GenBank accession no.

References

HMGCS1 HPDG MAF CALR FKBP5 SMS PDEF ESR1 DBI GUCY1A3 LIM AR

3-Hidroxy-3-methylglutaryl-CoA synthase 1 15-Hydroxyprostaglandin dehydrogenase v-maf muscoloaponeurotic fibrosarcoma oncogene homolog Calreticulin FK506-binding protein 5 Spermine synthase SAM pointed domain-containing ETS transcription factor Estrogen receptor 1 Diazepam binding inhibitor Guanylate cyclase1, soluble, alpha-3 Enigma-like LIM domain protein Androgen receptor

Metabolism Metabolism Proliferation/differentiation Transcription factor/DNA binding protein Transport/trafficking Metabolism Transcription factor/DNA binding protein Receptor/transcription factor Metabolism Signal transduction Signal transduction Receptor/transcription factor

AY581197.1 CB431417 CB447356 NM 174000.2 CB442185 CB172364 AY862877 (this study) X66841.1 CB463590 X54014.1 AY862876 (this study) AY862878 (this study)

[25,26] [25,27] [25,26] [26,30] [25,27,30] [25,26] [25,26] [54,55] [25,27,31] [25,27] [25,28,31] [29,31]

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Table 2 Target genes selected to be quantified in real-time assay

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2.4. Primer design and optimisation and standard curves analysis Primers for quantitative RT-PCR were designed using the Primer ExpressTM software (version 2.0, Applied Biosystems) with the following settings: the melting temperature (TM) for primers was set between 58 and 60 ◦ C, the G/C content was kept in the 20–80% range, runs of identical nucleotides were avoided. Primers containing more than two G and/or C bases at the 3 end were also excluded. The specificity of PCR amplification for each primer pair was confirmed by agarose gel electrophoresis, and melting curve analysis. Each primer set was optimised with 900, 300 and 50 mM concentrations with and without template. This optimisation step identified primer concentrations that provided the highest sensitivity and specificity for each target sequence. Standard curves were generated from decreasing amounts of cDNA diluted at two-fold intervals to evaluate the efficiency of real-time RTPCR. The quality of each real-time assay can be judged from standard curves slopes and correlation coefficients (r). The PCR efficiency (Ex ) was determined using the equation: Ex = 10 −1/slope . Relative quantification of gene expression was made by means of ABI Prism 7000 SDS software (Applied Biosystems) according to the comparative threshold cycle (Ct ) method [32]. Intra-assay precision (among triplicates over standard curve molecular range) and inter-assay reproducibility (among three runs) were also evaluated as coefficient of variation (%CV). Before using the

Ct method for mRNA quantification, we performed a validation experiment to demonstrate that amplification efficiencies of target and reference genes are approximately equal. The absolute value of the slope of log input amount versus
Ct value should be <0.1. 2.5. SYBR® Green I real-time PCR quantification Quantification of genes of interest was carried out in ABI Prism 7000 Sequence Detection System (Applied Biosystems) programmed for an initial step of 2 min at 50 ◦ C and 10 min at 95 ◦ C, followed by 40 thermal cycles of 15 s at 95 ◦ C and 1 min at 60 ◦ C. The PCR reactions were carried out in 96-wells microtiter plates in a 20 ␮l reaction volume with SYBR Green Master Mix (Applied Biosystems) with optimised concentrations of specific primers and 5 ␮l of 1:100 diluted cDNA. Each plate included triplicate 100-fold dilutions of the calibrator cDNA, test cDNA samples, and no template controls. 2.6. Statistical analyses Statistical analyses were carried out using the software package SPSS-PC version 11.5 (Jandel Corp., San Rafael, CA). A Kolmogorov–Smirnov test indicated that the expression values in each group deviated significantly from normal distribution. Therefore, a non-parametric test (Mann–Witney test comparing two groups or Kruskal–Wallis test comparing three groups) was applied to evaluate differences of expression level for each gene across different treatment classes. Differences between groups were considered significant at p < 0.05. To assess the possibility of class prediction from gene expression data the method of nearest shrunken centroids was used, as implemented in the PAM program [33] (available on-line at http://www-stat.stanford.edu/∼tibs/PAM). The program first performs a discriminant

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Table 3 Housekeeping genes selected to be evaluated as reference genes in real-time assay Gene

Gene name

Functional category

GenBank accession no.

G6PDH GAPDH ACTB

Glucose-6-phosphate dehydrogenase Glyceraldehyde-3-phosphate dehydrogenase Actin, beta

Metabolism Metabolism Structural

AY862875 (this study) U85042.1 AY141970.1

analysis to choose the set of genes that provide the greatest accuracy of class prediction (the smallest misclassification error), then it tests the accuracy of predicting unknown samples through cross-validation (10% of samples are randomly extracted and classified based on the discriminant function calculated on the remaining cases).

3. Results 3.1. Gene selection and sequences Following the criteria described in Section 2.3, a set of 12 androgen-responsive genes belonging to different functional categories were selected (Table 2). Three housekeeping genes were also chosen to be used as reference in real-time assays (Table 3). Among the selected genes, four (AR, PDEF, LIM, G6PDH) were not available in public databases for the bovine genome. Using primers designed on highly conserved regions, fragments of each bovine homologous gene, spanning at least two exons, were amplified and sequenced. Primers used and fragment sizes are reported in Table 4. Sequence data were submitted to GenBank (AY862875, AY862876, AY862877, AY862878). 3.2. Specificity, linearity and efficiency of real-time PCR assays A single band of the predicted size in agarose gel (Fig. 1) was observed after PCR amplification with specific real-time primers and a single dissociation peak was obtained after dissociation analysis. These results mean that real-time PCR assays were gene-specific. Standard curve analysis showed high-test linearity (correlation coefficient 0.95 ≤ r ≤ 1). Table 4 B. aurus gene fragments amplified and sequenced in this study Gene

Primer sequences (5 → 3 )

Amplicon length (bp)

G6PDH

A: TGACCTGGCCAAGAAGAAGA B: TCCATGGCCACCAGACAC

649

PDEF

A: AAGGCCTTCCAGGAGCTG B: TCTTGTAATACTGGCGGATGG

386

LIM

A: CGATGTGGTTCTCAGCATTG B: GGCATCACTGTGAGTGGGTA

292

AR

A: TGTAAGGCAGTGTCGGTGTC B: GAAAGGATCTTGGGCACTTG

888

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Fig. 1. Three percent agarose gel electrophoresis of real-time RT-PCR products amplified from bovine prostate total RNA. Lane 1: 50 bp DNA ladder. Lanes 2–16: BACT (84 bp), G6PDH (75 bp), GAPDH (102 bp), DBI (102 bp), HPDG (83 bp), HMGCS (89 bp), MAF (68 bp), GUCY1A3 (70 bp), FKBP5 (79 bp), LIM (82 bp), SMS (51 bp), AR (71 bp), ESR1 (72 bp), CALR (87 bp), PDEF (76 bp).

Amplification efficiencies, calculated for each assay from the slope of the standard curve, ranged between 1.95 ≤ E ≤ 2.19. Primers pairs and the main real-time PCR parameters (efficiency, linearity, variation coefficients) are listed in Table 5. 3.3. Evaluation of housekeeping genes as internal controls Expression levels for three housekeeping genes were assessed in nine RNA samples (three belonging to each treatment class and three control samples) directly comparing the Ct values. To identify the most reliable gene to use as internal control, the Ct range for the three housekeeping genes selected was calculated as the difference between the lowest specific mRNA concentration (highest Ct value) and the highest specific mRNA concentration (lowest Ct value). The lowest range of mRNA concentration, which is a good indicator of constant RNA transcription over all samples, was observed for G6PDH (range = 1.57) followed by BACT (range = 2.19) and GAPDH (range = 2.6), as shown in Fig. 2. Therefore, G6PDH was used as internal control gene (reference) in all subsequent experiments. Once G6PDH was chosen as reference gene, we performed a validation experiment for all target genes (see Section 2.4). The absolute value of the slope was always <0.1 (data not shown). 3.4. Analysis of gene expression in different treatment groups We analysed the expression of 12 androgen responsive genes in 27 bovine prostate samples. Messenger RNA of every gene analysed could be detected in all samples. Relative expression levels for each gene are shown in Table 6. In order to evaluate the effect on bovine prostate gene expression in consequence of the administration of different androgens (boldenone or testosterone) as well as in relation to time elapsed after the last administration (6 or 14 days), expression data from different groups of animals were analysed. Firstly we compared controls (C, nine samples) against all

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Table 5 RT-PCR real-time assays: primers and main amplification parameters Primer sequences (5 → 3 )

G6PDH

A: GCAAAGAGATGGTCCAGAACCT B: TGTCCCGGTTCCAAATGG

GAPDH

A: ACACCCTCAAGATTGTCAGCAA B: TCATAAGTCCCTCCACGATGC

ACTB

Amplicon length (bp) 75

Melting temperature (◦ C) 80.2

PCR efficiency 2.19

Test linearity correlation (r) 0.98

Intra-assay variation (%) 0.29

Inter-assay variation (%) 0.61

102

83.5

2.01

0.98

0.91

1.18

A: GTCATCACCATCGGCAATGAG B: AATGCCGCAGGATTCCATG

84

83.4

1.95

0.98

0.64

1.66

HMGCS1

A: AAAGAGGAGGGGCGTGTTTT B: CAGACCCTGGTGTGGCATCT

89

80.8

2.11

0.98

0.32

0.70

HPGD

A: TTGGTTTCTGTGATCAGTGGAAC B: TTAATAATGATGCCACCTTCGC

83

78.8

2.09

0.99

0.27

0.88

MAF

A: GAGGAGGTGATTCGGCTGAA B: CGGCAGGACTGGGCATAG

68

81.5

2.04

0.95

0.24

1.19

CALR

A: TAAAGGCCTGCAGACTAGCCA B: ACCAGCGCTTGACCCTTGTT

87

83.4

2.06

0.99

0.43

1.36

FKBP5

A: TCTCTTTGACAATGGCAGCC B: AAGAGCTTCGAAAAGGCCAA

79

77.2

1.99

0.95

0.57

0.96

SMS

A: GTTCTTCATAGAGCGACAGGGC B: CACAGGGAAACTGTGTCAATTTGA

51

74.6

2.02

0.98

0.24

2.45

PDEF

A: CCCATCTGGACATCTGGAAATC B: GAGGCGCAGAAGTGAATTGC

76

80.5

2.06

0.98

0.83

1.31

ESR1

A: CTTGGACAGGAACCAGGGAAA B: GATGATGATGGCAGCAGCATGT

72

78.2

1.99

0.99

0.34

0.58

DBI

A: CTGCCAGCCATTCATTTCAC B: GCTGCTTTGAGCTTCTGGCTTA

102

75

2.06

0.99

0.32

1.11

GUCY1A3

A: GGAACTCTCTCATGAAGTCGTGTCT B: CCCAGAATGCAGCCCAAT

70

79.4

2.19

0.96

0.26

1.11

LIM

A: CTCAAGTCTAAAAGATGGTGGGAAA B: ACTTATTCCATCAATGCTGAGAACC

82

77.4

2.01

0.96

0.41

0.71

AR

A: CGGTCCTTCACCAATGTCAAC B: ATGCGGTACTCATTGAAAACCA

71

78.2

1.95

0.99

0.14

0.93

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Gene

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Fig. 2. Quantification of gene expression for GAPDH, ACTB, and G6PDH genes in nine samples of bovine prostate belonging to different treatment classes (C, ET, and EB), r values represent Ct range calculated as the difference between the highest Ct value and the lowest Ct value.

treated samples (T, 19 samples), then controls against either samples treated with estradiol benzoate and testosterone enantate (ET, nine samples), or samples treated with estradiol benzoate and boldenone undecylenate (EB, nine samples). Controls were also compared against treated animals sacrificed either six days (T1, 10 samples) or 14 days after the last dose (T2, eight samples). To test for differences in the expression levels of each gene among groups, we used a non-parametric test (Mann–Witney or Kruskal–Wallis). Comparing C/T we identified four genes, HMGCS1, HPDG, DBI, and LIM, which showed a significantly lower expression in treated samples, while one gene, namely AR, was significantly over-expressed. The comparison C/ET/EB revealed significant differences in mRNA levels of four genes: HMGCS1 (expression level of C > ET > EB), DBI (expression level of C > EB > ET), AR (expression level of EB > ET > C) and MAF (expression level of ET > EB > C). Finally, analysis of C/T1/T2 indicated six genes showing a significantly different expression level among groups: HMGCS1 (expression level of C > T2 > T1), HPDG, DBI and LIM (expression level of C > T1 > T2), ESR1 (expression level of T2 > C > T1), and AR (expression level of T1 > T2 > C). Associated probability values are reported in Table 7. Data from all 12 genes were then subjected to a class prediction analysis as implemented in the program PAM. Also, in this case, separate analyses were carried out comparing the following class of samples: C and T; C, ET, and EB; C, T1, and T2. The program firstly performs a training (discriminant) analysis that uses expression data attempting to assign each sample to the correct class. Then it performs a cross-validation analysis that estimates the discriminant function on 90% of the samples and then predicts the class of the remaining 10% samples, which are considered as unknown samples. This procedure is repeated 10 times, with random extraction of 10% of samples in each replicate. Prediction errors are averaged across all replicates to compute the overall cross-validation error. The frequency of correctly classified samples, test accuracy, and errors for a given threshold associated with training and cross-validation analysis are shown in Tables 8 and 9, respectively. This method also identifies the subset of genes that best characterise each class. Near all genes are used by the program to classify the different groups. Genes with higher average expression or lower average expression in samples belonging to one group as compared to others (i.e. the genes that best characterised the groups under study) are shown in Fig. 3.

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Table 6 Relative mRNA expression (2−∆∆Ct ) of the 12 genes in the 27 prostate samples analysed Gene expression level AR

MAF

FKBP5

HPDG

HMGCS

PDEF

DBI

SMS

LIM

ESR1

GUCY1A3

CALR

C C C C C C C C C ET ET ET ET ET EB EB EB EB EB ET ET ET ET EB EB EB EB

0.74 0.37 1.17 0.65 1.09 1.13 0.92 0.4 0.9 1.41 0.8 0.61 0.68 2.28 1.71 1.61 1.33 1.51 1.01 1.9 1.13 0.86 1.15 1.18 1.22 0.98 1.17

0.85 0.75 2.21 0.65 2.38 2.18 1.28 0.47 2.58 3.14 3.09 2.4 2.69 2.65 1.97 2.16 1.89 2.23 1.46 3.17 0.94 1.43 1.99 1.63 0.77 1.24 2.54

0.84 6.09 1.07 3.16 2.17 1.22 0.49 0.14 0.78 2.11 0.52 0.45 5.83 1.69 0.39 2.35 2.17 1.99 1.28 1.39 2.3 0.78 1.44 2.19 2.9 0.87 1.64

1.36 0.96 0.45 0.95 0.45 0.56 0.07 0.32 0.68 0.17 0.3 0.12 0.75 0.83 0.5 0.26 0.37 0.49 0.32 0.11 0.24 0.2 0.21 0.18 0.39 0.03 0.09

0.56 0.55 0.68 0.75 0.88 0.93 0.46 0.44 2.3 0.45 2.02 0.38 0.31 0.37 0.24 0.66 0.47 0.32 0.33 0.38 0.85 0.56 0.78 1.13 0.38 0.34 0.33

0.89 5.78 2.5 2.51 1.95 2.46 0.61 0.3 4.17 0.88 0.01 1.05 6.16 1.75 0.63 1.26 1.36 2.43 3.95 2.08 0.93 1.97 1.01 1.6 0.5 0.37 0.45

0.75 1.39 1.62 1.24 1.1 1.21 0.54 0.36 0.73 0.43 0.58 0.31 1.11 0.67 0.72 0.54 0.63 0.47 0.8 0.33 0.6 0.65 0.35 0.42 2.75 0.41 0.33

0.39 0.33 0.5 0.64 1.16 0.78 0.48 0.3 1.1 0.46 0.63 0.16 0.4 0.44 0.39 0.42 0.27 0.44 0.49 0.53 0.37 0.33 0.31 0.45 0.5 0.19 0.34

0.78 1.11 2.03 1.04 2.09 1.48 0.4 0.32 0.98 0.57 0.2 0.36 1.27 0.98 0.8 0.53 1.17 0.84 1.34 0.78 0.73 0.62 0.24 0.35 0.27 0.28 0.57

0.92 0.36 0.49 0.6 0.51 0.67 0.14 1.22 0.5 0.44 0.21 0.32 0.26 0.78 2.08 0.23 1.2 0.8 0.46 0.83 0.82 0.93 0.64 1.38 2.18 1.14 1.21

0.81 1.16 1.27 1.12 1.63 1.24 0.54 0.64 1.31 2.04 0.39 0.53 2.12 1.69 1.46 1.46 1.7 1.6 1.73 1 1.28 0.91 0.69 1.77 1.32 1.01 1.02

0.76 1.01 0.55 1.31 0.78 1.03 0.3 0.6 0.62 0.43 0.27 0.27 0.79 0.67 0.36 0.37 0.76 0.75 0.97 0.76 0.89 0.64 0.37 0.5 0.64 0.37 0.3

T1 T1 T1 T1 T1 T1 T1 T1 T1 T1 T2 T2 T2 T2 T2 T2 T2 T2

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Treatment group

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Table 7 Non-parametric analysis of gene expression data among different treatment groups Gene

HMGCS1 HPDG MAF CALR FKBP5 SMS PDEF ESR1 DBI GUCY1A3 LIM AR a

Treatment group comparison C/T

C/ET/EB

p-valuea

Expression level

p-valuea

Expression level

p-valuea

Expression level

0.029 0.023 0.093 0.123 0.532 0.073 0.212 0.320 0.017 0.190 0.037 0.014

C>T C>T

0.047 0.063 0.038 0.283 0.702 0.199 0.399 0.063 0.046 0.128 0.115 0.015

C > ET > EB

0.045 0.005 0.054 0.279 0.753 0.180 0.283 0.017 0.035 0.104 0.021 0.038

C > T2 > T1 C > T1 > T2

C>T C>T T>C

C/T1/T2

ET > EB > C

C > EB > ET

EB > ET > C

T2 > C > T1 C > T1 > T2 C > T1 > T2 T1 > T2 > C

Bold numbers indicate significant p-value (p < 0.05).

Table 8 Class prediction analysis: frequency of correctly classified samples, test accuracy, and training error associated with training analysis Frequency of correctly classified samples in each groups

Test accuracy (%)

Training error

C (n = 9) 7/9a

T (n = 18) 18/18a

C + T (n = 27) 25/27a

93

0.07

C (n = 9) 8/9a

EB (n = 9) 7/9a

ET (n = 9) 4/9a

C + EB + ET (n = 27) 19/27a

70

0.3

C (n = 9) 7/9a

T1 (n = 10) 9/10a

T2 (n = 8) 7/8a

C + T1 + T2 (n = 27) 23/27a

85

0.15

a

Correctly classified samples/total samples.

Table 9 Class prediction analysis: frequency of correctly classified samples, test accuracy, and misclassification error associated with cross-validation Frequency of correctly classified samples in each groups

Test accuracy (%)

Misclassification error

C (n = 9) 6/9a

T (n = 18) 17/18a

C + T (n = 27) 23/27a

85

0.15

C (n = 9) 7/9a

EB (n = 9) 5/9a

ET (n = 9) 4/9a

C + EB + ET (n = 27) 16/27a

60

0.4

C (n = 9) 7/9a

T1 (n = 10) 6/10a

T2 (n = 8) 4/8a

C + T1 + T2 (n = 27) 17/27a

63

0.37

a

Correctly classified samples/total samples.

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Fig. 3. Class prediction analysis: genes used by PAM program to classify samples in different treatment groups and the shrunken differences for the selected genes used for class prediction are shown. The size of the bars indicates relative distance from the centroid, with the larger bars having more significance in predicting the class. Genes that better characterised the class in C with T groups comparison (A), C with EB and with ET comparison (B), and C with EB and with ET comparison (C) are shown.

4. Discussion A set of 12 genes that are known to be androgen responsive in mammalian species were selected to study their expression pattern in prostate of veal calves treated with two combinations of growth promoters by using a real-time RT-PCR approach. Three housekeeping genes were also evaluated in order to choose the best reference gene to use in relative quantification assay. Ideally, expression level of the reference gene should not be influenced by the experimental procedure. BACT and GAPDH are the most commonly used genes in mRNA relative quantification studies. However, it was reported that BACT and GAPDH expression could vary considerably across samples, and consequently these might be unsuitable as reference genes [34–36]. For this reason, we decided to evaluate the expression of a third gene, G6PDH, that was described, in a recent in vitro study [37], as one of the most stably expressed genes also under stimulation with mitogenic substances. This evidence was confirmed in our preliminary analysis on nine prostate tissues samples. G6PDH showed smaller changes in the expression level as compared to two classical reference genes (BACT and GAPDH). Despite the large number of expressed sequences (ESTs) available for cattle (over 400,000), the sequences of four genes among those that we were interested to study are not publicly available. In order to obtain these sequences we applied a PCR-based approach to amplify and sequence the fragment of interest from total bovine prostate cDNA. Once obtained the sequences, we designed, optimised, and validated a real-time RT-PCR assay

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for each of 12 target genes and three housekeeping genes, using the SYBR Green I detection system. Amplification of the PCR products was shown to be highly specific, linear and efficient. High reproducibility (CV < 1%) and low-test variability (CV < 2.5%) could also be achieved. Once chosen the reference and optimised the RT-PCR assays, target genes could be quantified with accuracy and the expression values obtained were statistically analysed. Non-parametric analysis of variance showed statistically significant differences in the expression level for seven (HMGCS1, HPGD, MAF, DBI, ESR1, LIM, and AR) out of 12 genes analysed. Three of these genes, AR, ESR1, and MAF, showed a moderate upregulation in treated samples when compared with controls. AR presented a similar mRNA level in all treated animals, irrespective of either type of treatment or time after last dose. ESR1 was characterised by a higher mRNA level in T2 as compared to T1 samples, and in EB as compared to ET samples. The contrary was observed for MAF gene, characterised by a higher mRNA level in ET class as compared to EB, and in T1 as compared with T2. These results suggest that testosterone could be a stronger activator of MAF expression than boldenone and could contribute to identify different treatment classes. The remaining four genes with statistically different expression among groups were all down-regulated in treated samples when compared with controls. This is in contrast to the evidence available from the literature. However, all published studies analysed the effect of androgens action on prostate transcripts after 6–48 h after the last dose administration. Considering that in our experimental design the corresponding time is much longer (6 or 14 days), it might be possible that the observed down-regulation is due to a long-term negative feed-back on the same genes as a consequence of androgen administration. Alternatively, the interaction between estrogens and androgens might be responsible for the unexpected repression of genes that are generally up-regulated upon androgenic stimulation. Apart from the two nuclear hormone receptors (AR and ESR1), whose function is directly related to androgens and estrogens, the other genes showing significant variation in gene expression among groups belong to different functional categories. MAF is a gene encoding the Maf oncoprotein, identified as the transduced transforming component of avian musculoaponeurotic fibrosarcoma virus, AS42. Over-expression of Maf has been reported in multiple myeloma [38] and in melanoma cells [39]. Several classes of transcriptional regulators, including the bZip transcription factors Jun, Fos, and Bach1 [40] have been shown to interact with Maf family proteins. In addition to its role in oncogenesis, Maf mediates differentiation programs in specific cell types such as monocytes and T-helper cells [41]. It has been suggested that Maf and Maf-related family members form a network with other classes of transcription factors that guide cellular responses that either promote or inhibit specific differentiation or growth programs [19]. Because of its biological functions, Maf overexpression might be involved in the genesis of the observed prostate alterations in animals treated with anabolic steroids. These histological modifications include hypersecretion, cyst formation, and hyperplasia [15–17]. The protein encoded by LIM contains a cysteine-rich double zinc fingers domain (LIM domain) composed of 50–60 amino acids that are involved in protein–protein interactions. LIM domain-containing proteins are scaffolds for the formation of multi-protein complexes [42]. LIM proteins are involved in cytoskeleton organization, cell lineage specification, organ development, and oncogenesis [43]. They also interact selectively with protein kinase

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C [44]. It should be noted that two androgen receptor coactivators, ARA55 and FHL2, are LIM domain proteins [45]. The remaining three genes that are significantly regulated are involved in lipid metabolism. Diazepam binding inhibitor (DBI) has been isolated independently by five different research groups based on its ability to displace diazepam from the GABAA receptor [46], to affect cell growth, to induce medium-chain acyl-CoA-ester synthesis [47], to stimulate steroid hormone synthesis [48], and to affect glucose-induced insulin secretion [49]. HPGD (NAD-dependent 15-hydroxyprostaglandin dehydrogenase) is the main enzyme of prostaglandin degradation [50]. By catalysing the conversion of the 15-hydroxyl group of prostaglandin into a keto group, this ubiquitous enzyme strongly reduces the biological activity of these molecules. HMGCS1 is involved in acetyl-CoA metabolism, cholesterol biosynthesis, and more in general in lipid metabolism [51]. Our results therefore support previous findings that androgens regulate genes associated with the cholesterol and lipid pathway [52,53]. The use of hormonal anabolics in the fattening of cattle can lead to an increase of body weight as it was also observed in our animal experiment (Table 1). As mentioned above, an alternative approach to direct analytical techniques for the identification of illegal anabolic treatments might rely on the analysis of indirect biomarkers in target tissues. Indirect markers should allow detection of the integrated effect of anabolic cocktails and be effective in identifying the treatment regardless of the actual presence of hormones in the urine of animals. In this context a histological screening method based on the histophatological effects of anabolic treatment on target tissues has been developed and successfully applied [15–17]. Despite several advantages of this method, the histological screening requires highly-skilled pathologists, and it is potentially exposed to the risk of subjective interpretation of histological findings. In this work, the utility of molecular markers for the detection of illegal androgen administration in cattle was evaluated as a complementary or alternative method to histological analysis. The method we implemented relies on the analysis of expression levels for 12 androgen-responsive genes in prostate tissue samples. Although in the present study tissues were snap-frozen in liquid nitrogen immediately after dissection, molecular analysis of tissue samples collected and stored at 4 ◦ C in a non-toxic RNA-stabilizing buffer performed equally well (L. Toffolatti, unpublished data). Therefore, expression analysis can be carried out for screening of samples routinely collected in slaughterhouses. To assess our ability to identify treated and untreated samples based on gene expression data, we performed a class prediction analysis. The nearest shrunken centroid analysis as implemented in the PAM program was effective in discriminating between treated samples and untreated controls with an accuracy of 93% (training errorC/T = 0.07). Classification errors were higher when three classes were examined in the same test (training errorC/EB/ET = 0.3, training errorC/T1/T2 = 0.15). Cross validation errors presented a similar evidence (misclassification errorC/T = 0.15, misclassification errorC/EB/ET = 0.4, and misclassification errorC/T1/T2 = 0.37). Results of cross validation also indicate that single “unknown” samples collected during routine screening might be classified as “treated” or “untreated” using the discriminant function obtained from the analysis of samples from the animal experiment. However, error analysis suggests that sample size is an important parameter in correctly classifying samples. Therefore, increasing the size of control samples appears to be crucial in characterizing the physiological

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expression profile of prostate tissue. The results obtained with PAM also showed that expression data by near all genes contributed to class prediction and is in general agreement with the statistical analysis performed with non-parametric analysis in identifying the most significant genes. In conclusion, our results indicate that molecular identification of veal calves treated with steroid cocktails is a feasible approach by means of discriminant analysis based on real-time PCR data and that could likely be further improved by increasing both the number of genes and samples size, particularly the class of control samples.

Acknowledgements The authors thank Mauro Dacasto for critical reading of the manuscript. This work was partially funded by a Research Grant of Regione Veneto (2002): “Trattamenti illeciti nell’allevamento del vitello a carne bianca: marker biologici per il controllo di trattamenti ormonali” to C.M., and by a Research Grant of the University of Padova to L.B. (CPDA038593).

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