Open Access

Transfection of Sertoli cells with androgen receptor alters gene expression without androgen stimulation

  • D. Fietz1Email author,
  • M. Markmann2,
  • D. Lang1,
  • L. Konrad3,
  • J. Geyer1,
  • S. Kliesch4,
  • T. Chakraborty2,
  • H. Hossain2 and
  • M. Bergmann1
Contributed equally
BMC Molecular Biology201516:23

https://doi.org/10.1186/s12867-015-0051-7

Received: 3 August 2015

Accepted: 9 December 2015

Published: 29 December 2015

Abstract

Background

Androgens play an important role for the development of male fertility and gained interest as growth and survival factors for certain types of cancer. Androgens act via the androgen receptor (AR/Ar), which is involved in various cell biological processes such as sex differentiation. To study the functional mechanisms of androgen action, cell culture systems and AR-transfected cell lines are needed. Transfection of AR into cell lines and subsequent gene expression analysis after androgen treatment is well established to investigate the molecular biology of target cells. However, it remains unclear how the transfection with AR itself can modulate the gene expression even without androgen stimulation. Therefore, we transfected Ar-deficient rat Sertoli cells 93RS2 by electroporation using a full length human AR.

Results

Transfection success was confirmed by Western Blotting, immunofluorescence and RT-PCR. AR transfection-related gene expression alterations were detected with microarray-based genome-wide expression profiling of transfected and non-transfected 93RS2 cells without androgen stimulation. Microarray analysis revealed 672 differentially regulated genes with 200 up- and 472 down-regulated genes. These genes could be assigned to four major biological categories (development, hormone response, immune response and metabolism). Microarray results were confirmed by quantitative RT-PCR analysis for 22 candidate genes.

Conclusion

We conclude from our data, that the transfection of Ar-deficient Sertoli cells with AR has a measurable effect on gene expression even without androgen stimulation and cause Sertoli cell damage. Studies using AR-transfected cells, subsequently stimulated, should consider alterations in AR-dependent gene expression as off-target effects of the AR transfection itself.

Keywords

Transfection Gene expression analysis Androgen receptor Sertoli cells

Background

Androgens play a pivotal role for the development of the male phenotype, the initiation and maintenance of spermatogenesis and therefore male fertility (for review see [1]). The action of the most important androgens testosterone (T) and dihydrotestosterone (DHT) is mediated by the androgen receptor (AR/Ar). It is a ligand-activated transcriptional factor belonging to the nuclear receptor superfamily. The AR/Ar gene is located on the X chromosome and consists of eight exons, coding for the N-terminal transcription regulation domain, the DNA binding domain (DBD) in the middle of the protein and the C-terminal ligand binding domain (LBD). The DBD as well as the LBD are highly conserved throughout species (for review see [2]). Bound to its ligand, the androgen-AR complex is translocated into the nucleus, binds to the DNA (androgen responsive elements, AREs) and is able to activate or repress gene expression by recruiting co-activators or co-repressors (for review see [3]). The activity of steroid hormone receptors is also regulated by post-transcriptional modifications. In case of AR/Ar, a great variety of these modifications has been described, i.e. phosphorylation, acetylation, ubiquitination and also methylation (for review see [4]).

The AR/Ar is expressed in all tissues except the spleen (for review see [2]). In the testis, it is expressed in interstitial Leydig cells and endothelial cells, as well as in peritubular myoid cells and tubular Sertoli cells [5], for review see [1]. Since germ cells do not express AR/Ar, the androgen action has to be mediated towards the germ cells by Sertoli cells. These somatic cells have been described as branched cells surrounding all germ cell stages [6, 7]. As was shown by Willems et al. [8], a selective ablation of Ar in mouse Sertoli cells (SCARKO) leads to a disturbed Sertoli cell maturation including a delayed and defective establishment of the blood-testis barrier. Moreover, no meiotic germ cells were observed in SCARKO mice, showing the importance of a functional AR/Ar on Sertoli cell biology and for the development of germ cells.

To examine the role of the AR/Ar in different biological processes such as cell growth and survival as well as AR/Ar-dependent gene expression, cell culture systems are needed. Therefore, administration of T and/or the more efficient metabolite DHT has widely been used to investigate the effect of androgens and AR/Ar, respectively, in diverse cultured cells such as human breast cancer cells, adrenocortical carcinoma cells, murine skeletal muscle cells or liver carcinoma cells [912]. Additionally, AR/Ar-deficient cell lines were used, e.g. AR-deficient MCF-7 breast cancer cells, to examine the effect on estrogen administration in a system lacking AR [13]. Szelei et al. [14] transfected AR-deficient MCF-7 breast cancer cells with human AR and showed an inhibition of proliferation. Also prostate cancer cells devoid of AR were transfected with human AR and showed a decreased proliferation rate [15]. The question is, whether the transfection procedure itself might have led to an altered expression of AR/Ar-dependent and AR/Ar-independent genes. Xiao et al. [16] demonstrated equal concerns in Amh-Cre-transfected mouse, where an increase of oxidative stress and lipid peroxidation in Sertoli cells was detected even without stimulation.

Beside “classical” androgen-dependent prostate cancer development, also androgen-independent signalling pathways gained increasing interest as shown recently by Li et al. [17]. The authors describe a persistent transcriptional activity in castration-resistant prostate cancer cell lines in the absence of androgens. This transcriptional activity was mediated by a truncated AR protein lacking the LBD. This raises the question, whether an androgen-independent AR/Ar action is always important in cell biology and which genes might be expressed or repressed by AR/Ar presence alone.

For this purpose, we transfected rat Sertoli cells which have been shown to be deficient of Ar with full length human AR DNA. After transfection, we performed genome-wide microarray analysis and compared the gene expression pattern with non-transfected Sertoli cells to identify a possible “intrinsic” activity of AR/Ar without androgen administration. We found significantly altered gene expression in transfected compared with non-transfected cells, possibly influencing Sertoli cell function.

Results

Transfection of 93RS2 cells with the human AR

Performing RT-PCR with primers specific for mouse and rat Ar, respectively, rat Sertoli cells (93RS2, [18]) proved to lack endogenous Ar (Fig. 1) and were therefore chosen for further experiments.
Fig. 1

Expression of androgen receptor (Ar) mRNA in Sertoli cell cultures. To find an appropriate cell culture system for our planned transfection studies, RT-PCR with specific primers for mouse and rat Ar was performed. Testis homogenate from rat and mouse served as positive control, whereas water was used as no template control (NTC) samples. We tested two mouse (WL3 and SK-11) as well as two rat Sertoli cell lines (SCIT-C8 and 93RS2). The latter revealed no expression of intrinsic Ar and were therefore used for further experiments

Success of transfection with full length human AR CDS was validated by immunofluorescence (IF, Fig. 2a), Western Blot (Fig. 2b) and RT-PCR (Fig. 2c). As the commercially available human AR was introduced in a GFP-coupled vector system, we used a rabbit anti-GFP antibody for IF experiments in transfected cells whereas non-transfected cells were used as internal negative control. Using PAGE, we were able to show the CAG repeat length of 17 to be stable throughout different settings (Fig. 2d).
Fig. 2

Transfection control of 93RS2 Sertoli cells. a 24 h after transfection, transfected (a) and non-transfected (b) cells as negative control were fixed for IF experiments. left Incubation with rabbit anti-GFP antibody showed successful transfection of almost 80 % of cells. right No staining signal was detectable in non-transfected cells. Scale bars in main image: 200 µm, detail: 25 µm. DAPI counterstain. b Western Blot analysis revealed AR protein in transfected Sertoli cells at approx. 135 kDa (1) and in human testis tissue at the expected molecular weight of 110 kDa (2). The higher protein weight measured in transfected cells is due to coupling of AR with GFP. c Expression of human AR mRNA was tested in human testis homogenate (1), transfected (2) and non-transfected cells (3). AR mRNA was detected in the positive control and transfected 93RS2hAR17 cells, but not in non-transfected cells and the NTC (lane 4). d To control the CAG repeat length in transfected 93RS2 cells, we performed high-resolution PAGE. Three different passages of 93RShAR17 cells (lanes 1–3) were analysed and revealed a band for human AR at 185 bp by using two different DNA ladders. By sequencing, 185 bp was shown to be typical for the presence of 17 CAG repeats. Lane 4 no template control (NTC)

Microarray analysis revealed an altered gene expression in transfected 93RS2 cells

Microarray analysis revealed 672 significantly regulated genes (p < 0.01 and fold change (FC) >2.0). Of these, 200 genes showed higher gene expression values, whereas 472 revealed a lower gene expression in 93RShAR17 cells compared with non-transfected cells.

Hierarchical clustering of the 672 significantly regulated genes shows two clusters clearly differentiating between transfected and non-transfected cells (Fig. 3). Three biological replicates have been tested and show a homogeneous expression pattern, indicating high reproducibility of microarray results. An overview of the ten highest regulated genes for down- and up-regulation is given in Table 1. Complete array data may be found following the link provided [19].
Fig. 3

Hierarchical clustering of 672 significantly altered genes. Genes are depicted in rows and samples in columns. Blue indicates downregulation whereas red shows upregulation. Clustering was done using “Pearson correlation” and “complete linkage”. The tree on the left reflects the distances between gene profiles based on this algorithm

Table 1

Overview of ten highest ranked up- and down-regulated genes

Regulation

Identifier

Symbol

EntrezID

FDR

FC

Gene name

Comment

Down

Idx_R307_C32

Cybrd1

295,669

0.001

−107,712

Cytochrome b reductase 1

Expression of the ferric reductase is regulated by intracellular iron concentration and other facilitators of iron absorption, indicating that it responds to iron demand

Down

Idx_R293_C42

Tmsbl1

286,978

0.003

−71,936

Thymosin beta-like protein 1

Actin cytoskeleton organization

Down

Idx_R29_C52

Nnat

94,270

0.001

−50,214

Neuronatin

The effects of Nnat on inflammatory pathways in vitro and in vivo suggest a pathophysiological role of this new gene in diabetic vascular diseases

Down

Idx_R245_C71

Fam46a

300,870

0.006

−42,921

Family with sequence similarity 46, member A

 

Down

Idx_R259_C49

Ctsz

252,929

0.003

−39,163

Cathepsin Z

Accounts for the lysosome’s capacity to digest polyQ sequences. Cathepsins L and Z are important in defending against the accumulation and toxicity of polyQ proteins

Down

Idx_R322_C43

Slc24a3

85,267

0.003

−38,220

Solute carrier family 24 (sodium/potassium/calcium exchanger), member 3

 

Down

Idx_R196_C66

Nudt7

361,413

0.004

−36,487

Nudix (nucleoside diphosphate linked moiety X)-type motif 7

 

Down

Idx_R200_C18

Marveld1

309,375

0.001

−34,855

MARVEL domain containing 1

 

Down

Idx_R240_C21

Tpp1

83,534

0.001

−32,489

Tripeptidyl peptidase I

 

Down

Idx_R245_C74

Tpp1

83,534

0.001

−31,626

Tripeptidyl peptidase I

 

Down

Idx_R47_C36

Bhlhb9

317,407

0.001

−30,042

Basic helix-loop-helix domain containing, class B, 9

 

Up

Idx_R117_C7

Irf7

293,624

0.003

5540

Interferon regulatory factor 7

The crucial regulator of type I interferons (IFNs) against pathogenic infections, which activate IRF7 by triggering signaling cascades from pathogen recognition receptors (PRRs) that recognize pathogenic nucleic acids

Up

Idx_R14_C99

Apol9a

503,164

0.003

5611

Apolipoprotein L 9a

 

Up

Idx_R252_C110

Usp18

312,688

0.003

5976

Ubiquitin specific peptidase 18

 

Up

Idx_R317_C53

Usp18

312,688

0.003

6264

Ubiquitin specific peptidase 18

 

Up

Idx_R74_C32

Wfdc18

171,059

0.004

6291

WAP four-disulfide core domain 18

 

Up

Idx_R53_C102

Ripk4

304,053

0.001

6479

Receptor-interacting serine-threonine kinase 4

 

Up

Idx_R278_C80

Ccl4

116,637

0.006

7177

Chemokine (C–C motif) ligand 4

 

Up

Idx_R188_C91

Oas1b

246,268

0.003

7827

2–5 oligoadenylate synthetase 1B

 

Up

Idx_R192_C96

Ffar4

294,075

0.004

9720

Free fatty acid receptor 4

 

Up

Idx_R299_C11

Il33

361,749

0.002

9759

Interleukin 33

IL-33 is a dual function protein that may function as a proinflammatory cytokine and an intracellular nuclear factor with transcriptional regulatory properties

Up

Idx_R66_C107

Il33

361,749

0.001

10,690

Interleukin 33

Up

Idx_R102_C39

Mx1

24,575

0.004

12,708

Myxovirus (influenza virus) resistance 1

The human myxovirus resistance protein 1 is a key mediator of the interferon-induced antiviral response against a wide range of viruses. MxA may form oligomeric rings around tubular nucleocapsid structures. As a consequence, these viral components are trapped and sorted to locations where they become unavailable for the generation of new virus particles

“Development”, “Hormone response” and “Immune response” are the predominant functions of the differently regulated genes

Of 370 annotated down-regulated genes, 330 could be assigned to DAVID functional categories, and 124 out of 142 annotated up-regulated genes, respectively.

An overview of the functional categories that have been inferred with DAVID is given in Table 2. Down-regulation is predominant in “Cell development/Cell contact”, “Response to hormone stimulus” and “Nucleotide catabolic process”, whereas regulation is evenly distributed in “Immune response”. The highest score values are achieved by four significantly overrepresented gene ontology (GO) categories clustered under “Biological adhesion”, whereas the highest number of genes is assigned to 15 GO categories grouped as a cluster named “Epithelium development”. More than half of the functional assigned groups belong to cell development and cell contact while 25 % of the functionally assigned genes are related to immune response. 36 genes can be attributed to “Hormone stimulus” and a minority of 12 genes contributes to “Nucleotide catabolic process”.
Table 2

Overview of functional gene ontology categories according to their pattern of significantly regulated genes

Group

Cluster#

Cluster of GO categories

Score

 

Symbols

Cell development/cell contact [106]

1

Biological adhesion (4) [25]

2.27

Up

Vnn1, Amigo2, Bcam, Cdh2, Ceacam1, Col12a1, Col14a1, Col16a1, Dsg2, Gpc1, Mcam, Omd, Sned1, Col18a1, Ctgf, Gpr56, Ncam1, Igfbp7

Down

Itgb8, F5, Pcdh1, Pcdh18, Plcxd2, Ptprm, Ctgf

3

Axonogenesis (13) [36]

1.89

Up

Aldh1a2, Apbb1, Apoe, Boc, Cd24, Cdkn1c, Chn2, Col18a1, Col18a1, Cxcl12, Efna2, Efnb1, Fgfr2, Gli2, Gpc2, H19, Hoxc10, Krt19, Lpar3, Nnat, Nrep, Obsl1, Pmp22, Ppp1r9a, Prickle2, Sdc2, Sema4f, Shroom3, Sox5, Uchl1

Down

Ptprm, Epha7, Dpysl3, Mtss1, Nes, Sgk1

4

Retinoid metabolic process (5) [8]

1.75

Up

Akr7a3, Aldh1a2, As3mt, Ldhb, Rarres2, Rbp1

Down

Crabp2, Rbp2

7

Epithelium development (15) [50]

1.47

Up

Acp5, Adamts1, Adck3, Aldh1a2, Celsr1, Col18a1, Col1a1, Col4a1, Cxcl12, Disp1, Efna2, Efnb1, Fbn1, Fgfr2, Foxe1, Foxl2, Gli2, H19, Hmx2, Hoxc10, Irf6, Kazn, Mgp, Mn1, Mycn, Pgf, Plce1, Serpinf1, Sfrp2, Shroom3, Sox5, Spry1, Srgn, Tbx18, Tbx4, Tek, Tgfb1i1, Tgm2, Upk1b

Down

Ctgf, Crabp2, Fst, Ptger2, Rsad2, Cdx2, Hoxb6, Krt14, Ptgs2, Foxp2, Myc

H [36]

2

Response to steroid hormone stimulus (10) [36]

1.90

Up

Acp5, Adamts1, Adck3, Aldh1a2, Apoe, Boc, Cd24, Celsr1, Col1a1, Cxcl12, Disp1, Efna2, Efnb1, Fgfr2, Gli2, Gpr56, H19, Igfbp7, Krt19, Lpar3, Mgp, Ncam1, Nnat, Pgf, Plce1, Sdc2, Serpinf1, Sfrp2, Tek, Tgfb1i1, Tgm2

Down

Foxp2, Myc, Nes, Ptgs2, Sgk1

Immune response [55]

5

Innate immune response (4) [34]

1.69

Up

Acp5, Adck3, Afap1l2, Apbb1, C2, Cd24, Cxcl12, Cyp4f6, Il27ra, Masp1, Ptpn6, RT1-DMb, Tf, Tgm2, Tinagl1, Tlr2, Vnn1, Zfr2

Down

A2 m, C3ar1, Ccl2, Ccl4, Ereg, F2rl1, Gch1, Il1rl1, Irf7, Irgm, Nppb, Oas1b, Oasl2, Prg4, Ptgs2, Rsad2

8

Cell surface receptor linked signal transduction (3) [25]

1.35

Up

Adamts1, Adck3, Apoe, Boc, Cd24, Celsr1, Cxcl12, Disp1, Efna2, Efnb1, Fgfr2, Gli2, Gpr56, Lpar3, Ncam1, Plce1, Sfrp2, Tek, Tgfb1i1, Tgm2

Down

Ctgf, Epha7, Fst, Itgb8, Ptger2

N [12]

6

Nucleotide catabolic process (7) [12]

1.47

Up

Akr7a3, Ampd3, Gucy1b3, Nt5e, Nudt7, Pde4a, Pde4b, Prodh

Down

Gch1, Nppb, Ppat, Upp1

Numbers in normal brackets denote the number of grouped GO categories. Absolute numbers of regulated genes per main group are given in squared brackets, examples of regulated genes are shown for up- and down-regulated genes

H hormone stimulus, N Nucleotide Catabolic Process

Upstream regulation analysis identified more activation than de-activation

Upstream regulation analysis with IPA is based on gene expression patterns and predicts activation or deactivation of regulators of the differentially regulated genes. The results show that more upstream regulators are predicted to be activated (n = 51) than inhibited (n = 20).

These predictions are based on 220 genes from which 95 contributed to activation as well as to deactivation. The proportion of overall down- and up-regulation is mirrored in these genes with more down-regulation in inhibition as well as in activation (Tables 3, 4, 5, 6). The majority of deactivated upstream regulators (8 out of 20) are classified as transcription regulators. Activation is mainly predicted for cytokines (14 out of 51).
Table 3

Upstream regulator analysis with IPA: types of predicted upstream regulators

Activation (n = 51)

Inhibition (n = 20)

Cytokines/group of cytokines

14

Transcription regulator

8

Others/complex of others

8

Cytokine

2

Kinases, group of kinases

8

Enzyme

2

Growth factors/complex of growth factors

6

Other

2

Transcription regulator

6

G-protein coupled receptor

1

Transmembrene receptors

4

Growth factor

1

Enzymes

3

Ligand-dependent nuclear receptor

1

Ligand-dependent nuclear receptor

1

Peptidase

1

Peptidase

1

Phosphatase

1

  

Transporter

1

Summarizing the regulator according to their type revealed a high proportion of possibly activated cytokines, whereas transcription regulators play a major role in inhibition

Based on gene expression patterns, predictions are made on activation or inactivation of known upstream regulators. Absolute activation z-scores of higher than 2.0 are considered to be highly significant. We found more than twice as much regulators predicted to be activated as compared to inhibited. These tables show the predicted upstream regulators with an absolute z-score above 2.0 in detail—some are in fact complexes or groups. The prediction is opposed to the real measurement on the micro array (rightmost columns), as far as the respective genes have passed QC and is otherwise marked as “not measured”. Mean expression per group is given as logarithm of the intensity to base 2. Reasonably high expression values are in bold face. The column “regulation AR17” denotes if the respective gene is contained in the set of regulated genes (level = L1) or at least close to significance (level = L2/L3) which holds true for the minority of genes. Activation or inhibition is not necessarily reflected by significant change of gene expression, since processes not measurable on a micro array, like for example phosphorylation, are more likely to be responsible for that

Table 4

Upstream regulator analysis with IPA: proportion of up- and downregulated genes

Gene pattern

Activation only

Inhibition only

Both

Down regulation

64

28

50

Up regulation

28

5

45

The gene expression patterns upon which the prediction is made is constituted by both up-regulated and down-regulated genes. The predicted activation and inhibition is either based on two third down regulated (n = 114/n = 78) and one third upregulated genes (n = 73/n = 50). 50 downregulated genes and 45 upregulated genes contribute likewise to activation and inhibition (The details of the contributing gens are not shown here)

Based on gene expression patterns, predictions are made on activation or inactivation of known upstream regulators. Absolute activation z-scores of higher than 2.0 are considered to be highly significant. We found more than twice as much regulators predicted to be activated as compared to inhibited. These tables show the predicted upstream regulators with an absolute z-score above 2.0 in detail—some are in fact complexes or groups. The prediction is opposed to the real measurement on the micro array (rightmost columns), as far as the respective genes have passed QC and is otherwise marked as “not measured”. Mean expression per group is given as logarithm of the intensity to base 2. Reasonably high expression values are in bold face. The column “regulation AR17” denotes if the respective gene is contained in the set of regulated genes (level = L1) or at least close to significance (level = L2/L3) which holds true for the minority of genes. Activation or inhibition is not necessarily reflected by significant change of gene expression, since processes not measurable on a micro array, like for example phosphorylation, are more likely to be responsible for that

Table 5

Upstream regulator analysis with IPA: predicted activated regulators

IPA–prediction

Micro array analysis

Upstream regulator

Molecule type

z-score

FDR

FC

Mean AR17

Mean noAR

Regulation AR17 [level]

Ahr

Ligand-dependent nuclear receptor

3.185

0.895

−1.017

−1.219

−1.194

 

Bmp6

Growth factor

2.791

0.011

−1.483

2.595

3.164

 

Ccl5

Cytokine

2.190

0.016

2.529

2.922

1.583

[Up L3]

Ddx58

Enzyme

2.789

0.019

2.096

3.316

2.249

[Up L3]

Dock8

Other

2.530

0.010

−1.656

2.545

3.272

[Down L2]

Egf

Growth factor

2.539

 

<not measured>

  

Erk:

Group of kinases (n=7)

2.372

  

<group>

  

Mapk1

Kinase

 

0.009

−1.019

3.670

3.697

 

Mapk3

Kinase

 

0.027

−1.280

5.646

6.002

 

Mapk4

Kinase

 

0.701

1.086

−2.321

−2.440

 

Mapk6

Kinase

 

0.037

1.131

5.152

4.975

 

Mapk7

Kinase

 

0.758

−1.047

2.862

2.928

 

Mapk12

Kinase

 

0.018

−2.005

0.121

1.125

[Down L3]

Mapk15

Kinase

 

0.208

1.217

0.507

0.224

 

Mek:

Group of kinases (n=7)

2.942

  

<group>

  

Map2k1

Kinase

 

0.105

1.182

4.651

4.410

 

Map2k2

Kinase

 

0.177

1.090

4.816

4.691

 

Map2k3

Kinase

 

−1.066

−0.092

4.360

4.452

 

Map2k4

Kinase

 

1.023

0.032

3.336

3.303

 

Map2k5

Kinase

 

−1.058

−0.082

3.327

3.408

 

Map2k6

Kinase

 

1.177

0.235

3.760

3.525

 

Map2k7

Kinase

 

−1.125

−0.170

−1.445

−1.275

 

P38 Mapk:

Group of kinases (n= 5)

2.624

  

<group>

  

Mapk1

Kinase

 

0.009

−1.019

3.670

3.697

 

Mapk11

Kinase

 

0.087

−1.343

0.139

0.565

 

Mapk12

Kinase

  

<see above>

  

Mapk13

Kinase

  

<not measured>

  

Mapk14

Kinase

 

0.046

−1.276

3.242

3.594

 

Mapk2/1: group of

Kinases (n= 2)

2.401

  

<group>

  

Map2k1

Kinase

  

<see above>

  

Map2k2

Kinase

  

<see above>

  

F7

Peptidase

2.592

 

<not measured>

  

Fgf2

Growth factor

2.085

0.122

1.178

0.417

0.180

 

Fos

Transcription regulator

2.086

0.069

−1.425

2.972

3.482

 

Hras

Enzyme

3.258

 

<not measured>

  

Ifn / Ifn alpha:

Group of groups

      

Ifn:

Group of cytokines

2.429

  

<group>

  

Ifn alpha:

Group of cytokines

2.228

  

<group>

  

Ifna1

Cytokine

 

0.104

1.306

2.728

2.343

 

Ifna2

Cytokine

2.448

 

<not measured>

  

Ifna4

Cytokine

2.236

 

<not measured>

  

Ifna5 - 8

cyTokine (n=4)

  

<not measured>

  

Ifna10, 13, 14, 16, 17, 21

Cytokine (n=6)

  

<not measured>

  

Ifnk

Cytokine

  

<not measured>

  

Ifnw1

Cytokine

  

<not measured>

  

Ifnz

Cytokine

  

<not measured>

  

Ifn beta:

Group of cytokines (n=2)

2.767

     

Ifnb1

Cytokine

2.591

0.079

2.953

−1.949

−3.511

 

Il6

Cytokine

2.443

0.014

1.481

−0.730

−1.296

 

Ifnar:

Group of transmembrane receptors

2.749

  

<group>

  

Ifnar1

Transmembrane receptor

  

<not measured>

  

Ifnar2

Transmembrane receptor

  

<not measured>

  

Ifne

Cytokine

2.219

 

 <not measured>

  

Ifng

Cytokine

2.811

 

<not measured>

  

Ifnl1

Cytokine

2.764

 

<not measured>

  

Igf2

Growth factor

2.213

0.001

−9.285

1.909

5.124

[Down L1]

Ikbke

Kinase

2.090

0.013

−2.034

−1.262

−0.238

[Down L3]

Il1: group of

Cytokines (n=11)

2.207

     

Il1b

Cytokine

  

<not measured>

  

Il18

Cytokine

2.372

0.300

−1.056

0.531

0.610

 

Il1f10

Cytokine

 

0.689

1.102

−2.284

−2.424

 

Il1rn

Cytokine

 

0.009

1.812

0.062

−0.796

[Up L2]

Il33

Cytokine

 

0.001

10.690

1.998

−1.420

[Up L1]

Il17a

Cytokine

  

<not measured>

  

Il36a

Cytokine

  

<not measured>

  

Il36b

Cytokine

  

<not measured>

  

Il36g

Cytokine

  

<not measured>

  

Il36rn

Cytokine

 

0.019

1.393

2.076

1.598

 

Il37

Cytokine

  

<not measured>

  

Irf3

Transcription regulator

3.157

0.520

1.336

3.450

3.033

 

Irf5

Transcription regulator

2.934

0.113

1.096

1.321

1.188

 

Irf7

Transcription regulator

3.901

0.003

5.540

5.574

3.104

[Up L1]

Kras

Enzyme

2.616

0.191

−1.097

3.119

3.253

 

Lh [Cga, Lhb]

Complex

2.012

     

Lhb

Other

 

0.480

1.044

2.593

2.530

 

Cga

Other

 

0.251

1.273

−0.193

−0.541

 

Map3k7

Kinase

2.375

0.352

−1.067

4.577

4.671

 

Mavs

Other

2.630

0.021

−1.231

2.569

2.868

 

Pdgf bb

Complex

3.491

  

<group>

  

Pdgfb

Growth factor

  

1.156

2.037

1.828

 

Pdlim2

Other

2.324

0.003

1.346

3.226

2.798

 

Samsn1

Other

2.309

 

<not measured>

  

Sash1

Other

2.530

 

<not measured>

  

Sphk1

Kinase

2.172

0.611

1.237

−0.033

−0.341

 

Src

Kinase

2.158

0.033

1.348

3.704

3.273

 

Stat1

Transcription regulator

2.194

0.013

1.375

3.163

2.703

 

Stat2

Transcription regulator

2.173

0.535

1.067

3.865

3.772

 

Tac1

Other

2.153

0.910

1.055

−2.385

−2.462

 

Tgfa

Growth factor

2.165

0.586

1.088

2.152

2.031

 

Ticam1

Other

2.702

0.574

−1.035

3.646

3.696

 

Tlr3

Transmembrane receptor

3.633

0.049

−1.414

−0.244

0.256

 

Tlr4

Transmembrane receptor

3.175

 

<not measured>

  

Tlr9

Transmembrane receptor

3.645

0.249

1.134

2.534

2.353

 

Tnfsf11

Cytokine

2.168

0.539

1.115

−0.643

−0.800

 

Z-score < 2.0

Based on gene expression patterns, predictions are made on activation or inactivation of known upstream regulators. Absolute activation z-scores of higher than 2.0 are considered to be highly significant. We found more than twice as much regulators predicted to be activated as compared to inhibited. These tables show the predicted upstream regulators with an absolute z-score above 2.0 in detail—some are in fact complexes or groups. The prediction is opposed to the real measurement on the micro array (rightmost columns), as far as the respective genes have passed QC and is otherwise marked as “not measured”. Mean expression per group is given as logarithm of the intensity to base 2. Reasonably high expression values are in bold face. The column “regulation AR17” denotes if the respective gene is contained in the set of regulated genes (level = L1) or at least close to significance (level = L2/L3) which holds true for the minority of genes. Activation or inhibition is not necessarily reflected by significant change of gene expression, since processes not measurable on a micro array, like for example phosphorylation, are more likely to be responsible for that

Table 6

Upstream regulator analysis with IPA: Predicted inactivated regulators

IPA–prediction

Micro array analysis

Upstream regulator

Molecule type

z-score

FDR

FC

Mean AR17

Mean noAR

Regulation AR17 [level]

Ackr2

G-protein coupled receptor

−3.162

0.061

1.308

0.389

0.001

 

Bcl6

Transcription regulator

−2.353

0.233

1.041

1.031

0.973

 

Fbxo32

Enzyme

−2.213

0.797

1.048

−0.588

−0.655

 

Gata2

Transcription regulator

−2.965

0.061

−3.356

−1.682

0.065

 

Gdf2

Growth factor

−2.400

 

<not measured>

  

Hmox1

Enzyme

−2.425

0.011

1.631

3.108

2.402

[Up L3]

Htt

Transcription regulator

−2.828

0.560

1.033

2.380

2.334

 

Il10

Cytokine

−2.394

 

<not measured>

  

Il1rn

Cytokine

−3.108

0.009

1.812

0.062

−0.796

[Up L2]

Irgm1

Other

−2.236

 

<not measured>

  

Mitf

Transcription regulator

−2.535

0.081

−1.456

2.487

3.029

 

Nkx2-3

Transcription regulator

−2.183

0.168

1.119

−1.622

−1.785

 

Pparg

Ligand-dependent nuclear receptor

−2.353

0.009

−1.950

−0.203

0.761

[Down L2]

Runx2

Transcription regulator

−2.137

0.021

1.358

4.291

3.850

 

Sftpa1

Transporter

−2.111

0.752

−1.087

−2.019

−1.899

 

Shh

Peptidase

−2.168

 

<not measured>

  

Socs1

Other

−3.084

 

<not measured>

  

Socs3

Phosphatase

−2.216

0.591

1.111

−1.131

−1.283

 

Sox9

Transcription regulator

−2.219

 

<not measured>

  

Trim24

Transcription regulator

−2.331

0.119

−1.166

2.191

2.413

 

Z-score < −2.0

Based on gene expression patterns, predictions are made on activation or inactivation of known upstream regulators. Absolute activation z-scores of higher than 2.0 are considered to be highly significant. We found more than twice as much regulators predicted to be activated as compared to inhibited. These tables show the predicted upstream regulators with an absolute z-score above 2.0 in detail—some are in fact complexes or groups. The prediction is opposed to the real measurement on the micro array (rightmost columns), as far as the respective genes have passed QC and is otherwise marked as “not measured”. Mean expression per group is given as logarithm of the intensity to base 2. Reasonably high expression values are in bold face. The column “regulation AR17” denotes if the respective gene is contained in the set of regulated genes (level = L1) or at least close to significance (level = L2/L3) which holds true for the minority of genes. Activation or inhibition is not necessarily reflected by significant change of gene expression, since processes not measurable on a micro array, like for example phosphorylation, are more likely to be responsible for that

Validation of microarray data by RT-qPCR

For validation of microarray results we performed RT-qPCR for 22 candidate genes, showing different ranges of regulation (up, down). Among the chosen genes, some are mainly associated with development and are known Sertoli cell markers, such as Dhh [20], Gja1 [21], Inhbb [22], and Tf [23]. Other genes are markers for differentiation and proliferation (e.g. Bambi and Tgfb1i1 [24]) and some are involved in apoptosis, such as Myc and Tnfrsf1a [25]. Results from RT-qPCR were mostly consistent with data from microarray analysis (Fig. 4). Relative gene expression was lower in transfected compared to non-transfected Sertoli cells in 13 of 22 cases. Gene expression of Cdkn1a, Egr1, Fst, Gja1, Myc, Pmepa1, Ptsg2, Rarg and Tnfrsf1a was higher in 93RShAR17 cells compared to Ar-deficient 93RS2 cells. In the latter case, it has to be mentioned, that differences of the means did not reach significance in four genes, due to high variability of Cq.
Fig. 4

Quantitative RT-PCR was performed to validate microarray analysis results. Gene expression analysis for 22 genes that showed deviant gene expression in microarray analysis has been performed using 2−ΔΔCq method. RT-qPCR has been performed using three technical replicates in a double determination. Gene expression in non-transfected 93RS2 cells was used as calibrator and therefore set as “1”. Data are presented as mean ± SEM. (standard error of the mean) and differences in mean values have been assessed with SPSS software; *p ≤ 0.05, n.d. not detectable

Discussion

To study the effects of androgens and AR/Ar on diverse cell culture systems and the relevance for cell biology, cell culture experiments were conducted in different human cell lines (e.g. breast cancer cells, adrenocortical carcinoma cells, murine skeletal muscle cells or liver carcinoma cells [912]). Also AR-deficient cell lines have been used, either transfected with AR [14, 15] or without [13]. Both groups working with transfected cell lines performed their experiments using either not stimulated [14] or mock-transfected cells [15] as negative controls. Moreover, Jacobsen et al. [26] showed, that transfection of MCF-7 breast cancer cells lead to severe differences in gene expression levels in distinct genes, depending on the transfection reagent used. Interestingly, transfection with a vector encoding for a reporter gene and a vector without insert, respectively, revealed no differences in gene expression. This implies, that the transfection procedure itself might alter gene expression in these cells. Therefore, we performed gene expression analysis with AR-transfected rat Sertoli cells using non-transfected cells as controls to show “intrinsic” gene expression alterations due to the transfection procedure. As electroporation has been shown to be superior with respect to cell viability and also transfection efficiency compared to chemical transfection using lipofectamine [27], we applied this technique to introduce the AR. Cell viability was not influenced by electroporation, but whole genome microarray analysis showed an altered gene expression. Surprisingly, more genes have been down-regulated than up-regulated comparing transfected and non-transfected cells. We selected 22 genes showing an altered expression pattern and confirmed microarray results with RT-qPCR analysis. In the following, we will discuss in more depth interesting genes and pathways, respectively.

Among the down-regulated genes, many are involved in metabolic processes, as for example in iron transport and metabolism (cytochrome b reductase 1 (Cybrd1), FC = −107; transferrin (Tf), FC = −6,898; six-transmembrane epithelial antigen of the prostate 2 (Steap2), FC = −2.3). Iron is relevant for Sertoli cells in two different aspects: as supervisors of germ cell development, Sertoli cells provide iron which is needed for DNA synthesis and cell growth of germ cells that undergo multiple mitotic divisions [28]. On the other hand, Sertoli cells avoid toxic environmental conditions that might be given at elevated concentrations of insoluble ferric iron (Fe3+). Therefore, Sertoli cells secrete transferrin, the product of the Tf gene [29], which may be used as a marker for Sertoli cell function and differentiation [23] as it creates an environment low in free iron that impedes bacterial survival in a process called iron withholding. The protein level of Tf decreases in inflammation. The lower expression of Tf gene, which was confirmed in RT-qPCR (Fig. 4), could be interpreted as a sign of severe disturbance and inflammation of cells.

The latter is reflected by the high proportion of upstream regulators related to immune response (= cytokines and members of the MAP kinase signalling pathway) that are predicted to be activated (Table 3) and the presence of multiple immune response-related genes on top of the list in up-regulation (Table 1). “Immune response” is the second huge cluster of altered genes in our study, represented by e.g. prostaglandin-endoperoxidase synthase 2 (Ptgs2, FC = 3.558) also known as cyclooxygenase 2 (Cox2). An increase in Cox2 expression was observed by Matzkin et al. [30] in Leydig cells of infertile men showing either hypospermatogenesis, Sertoli cell only syndrome or maturational arrest. By increased numbers of testicular macrophages, levels of interleukin 1β (Il-) are increased and activates Ptgs2, the key enzyme in prostaglandin synthesis culminating in inflammation. The expression of Tf, Ptgs2 and interleukins is coupled in Sertoli cells; as shown by Yamaguchi et al. [31], incubation with cisplatin lead to an increase in Ptgs2 and a decrease in Tf expression in Sertoli cell cultures, similar to our study. Additionally, an analysis of upstream regulation using IPA revealed a high number of key players in inflammation to be activated showing congruently high FCs for Ccl5, Irf7, and Ifnb1. This might on the one side be due to the transfection procedure itself and/or reflect inflammatory processes in the cells due to increased cell damage. Remarkably, an influence of molecular biological techniques on gene expression and immune response has been observed also in regard to short-interfering RNAs (siRNAs). Sledz et al. reported an induction of interferon β levels in a human glioblastoma cell line which was transfected with siRNAs as a non-specific side effect additionally to the silencing of the target gene lamin [32] .

Not only metabolism and immune response gene expression seem to be altered in transfected Sertoli cells, but also cell cycle and development genes (desert hedge hog (Dhh) FC = −2.032; fibroblast growth factor receptor 2 (Fgfr2) FC = −8.239; follistatin (Fst) FC = 2.162; inhibin beta b (Inhbb) FC = −3.126). Dhh is involved in various areas of embryonic development, including testicular cord formation. It is expressed in mouse Sertoli cell precursors during mid- to late gestation [33] and also is important for germ cell development after puberty in mouse [34] and rat testis [35]. In the mouse, a lack of Dhh results in a severe impairment of spermatogenesis due to a lack of spermatogonial development beyond primary spermatocytes [34]. Fgfr2 is a known differentiation factor in prenatal Sertoli cells as it is concomitantly expressed with Sry and is essential for subsequent expression of anti-muellerian hormone (Amh) and Sox9 [36]. IPA analysis of upstream regulation predicted an inhibition of transcription factor Sox9 with a z-score of −2.2 (Table 6). Moreover, lack of Fgfr2 might cause a partial XY sex reversal, as loss of Fgfr2 leads to an up-regulation of Follistatin (Fst), a female somatic cell marker [37], which was confirmed by microarray and RT-qPCR. A down-regulation of the Sertoli cell marker Inhbb (for review see [38]) also points to a decreased Sertoli cell function and a severe disturbance of spermatogenesis in the rat [39]. Figure 5 shows the association of Inhbb, Fst, Dhh, Pmepa1, Fgfr2, Ptgs2, Tf and Myc as especially interesting genes on known pathways as predicted by IPA.
Fig. 5

Illustration of eight genes and their association to known pathways in IPA. Green color denotes down-regulation, whereas red color denotes up-regulation

A disturbance of Sertoli cell function is also visible in gene expression alteration concerning the functional cluster “Cell adherence” or “Cell adhesion” (Fig. 6, e.g. collagen type IV alpha (Col4a1) FC = −12.503; gap junction protein 1 (Gja1) FC = −1.188). Cell adhesion and formation of tight junctions between Sertoli cells generating the blood-testis barrier is one of the most important features of Sertoli cell maturation and function (for review see [22]) as it is a prerequisite for intact spermatogenesis. Also cell-to-cell contact and communication seem to be disturbed in transfected cells as indicated by the down-regulation of Gja1, also known as connexin 43 (for review see [20]).
Fig. 6

Hierarchical clustering of significantly regulated genes involved in cell adhesion. Clustering was done using “Pearson correlation” and “complete linkage”. The tree on the left reflects the distances between gene profiles based on this algorithm. AJ actin/intermediate = adherents junctions based on actin or intermediate filaments, TJ = tight junctions. Low significance: 1.5 < FC < 2.0 and/or FDR 0.01–0.05 High significance: FC > 2.0 and FDR < 0.01

Conclusion

Our results indicate a severe disturbance of Sertoli cell metabolism, function and cell biology concerning immune status and generation of blood-testis barrier, caused by the transfection procedure even without androgen stimulation of cells. The alterations in gene expression levels might either be related to the transfection procedure itself and/or to the insertion of human AR into Ar-free rat Sertoli cells. A microarray analysis with mock-transfected Sertoli cell line would be needed to distinguish both possibilities. We consider the altered gene expression to be caused by AR insertion, as many of the altered genes were identified as AR and Sertoli cell specific. In either case, incubation of transfected cell lines with testosterone or dihydrotestosterone might lead to false-positive or false-negative results and additionally, also non-genomic pathways including AR/Ar action may be altered by transfection procedures. Therefore, suitable negative controls are needed for stimulation experiments with T or DHT, i.e. non-transfected cells as appropriate negative controls. Gene expression has to be normalized by these non-transfected cells to avoid false-positive or false-negative results regarding gene regulation.

Methods

Cell lines and culture conditions, human positive control tissue

We determined the expression of Ar in different Sertoli cell lines by RT-PCR. For this study, we used four existing immortalized Sertoli cell lines from either mouse (WL3, SK-11) [40, 41] or rat testis (93RS2) [18] kindly provided by our collaborators. Additionally, SCIT-C8 cells were generated from immortalized Sertoli cells from rat testis as described by Konrad et al. [42]. We did not conduct any animal research in our study and therefore ethics approval was not required. Total RNA of these cells was isolated by peqGold Total RNA Kit (Peqlab, Erlangen, Germany), set to a concentration of 200 ng/µl and genomic DNA was digested by RNase-Free DNase Set (Qiagen, Hilden, Germany). Reverse transcription was performed with Omniscript RT Kit (Qiagen). The mastermix was prepared as follows: 2 µl Buffer RT (10×), 2 µl dNTP mix (5 mM each), 0,7 µl RNAse inhibitor (20 units/µl, Invitrogen via LifeTechnologies, Carlsbad, CA, USA), 0,2 µl Oligo-dT primer (10 µM, Qiagen) and 1 µl Omniscript Reverse Transcriptase were mixed and RNase free water was added to a final volume of 10 µl. To test genomic DNA digestion success, we omitted reverse transcriptase and replaced it by RNase free water for one reaction. After addition of 1 µl RNA (200 µg/µl), we incubated the reaction mix for 1 h at 37 °C. cDNA not directly used for further experiments was stored at −20 °C. Amplification of Ar was achieved with a matching primer pair for murine and rat Ar obtained from Eurofins MWG Operon (Huntsville, AL, USA) as can be seen in Table 7 and Taq PCR Master Mix Kit (Qiagen). Mastermix was prepared as follows: 34 µl Taq PCR Master Mix, forward and reverse primer (2,5 µl each) and RNase free water as well as 1 µl cDNA were mixed to a final volume of 50 µl. Amplification was performed with 1× 94 °C for 4 min, 35× (94 °C for 40 s, 60 °C for 45 s, 72 °C for 90 s) and 1× 72 °C for 5 min.

As the prepubertal rat Sertoli cell line 93RS2 proved to be devoid of Ar, we chose this cell line for further experiments. The cells were maintained in a 5 % CO2 atmosphere at 34 °C. The standard culture media consists of DMEM high glucose mixed 1:1 with Ham’s F-12 media plus 100 units/ml penicillin, 0,1 mg/ml streptomycin, 10 % FBS-Gold (total protein 3.0–4.5 g/dl), and 1 % ITS (1000 mg/l Insulin, 550 mg/l Transferrin, 0.68 mg/l Selenin). Unless otherwise stated, cell culture media were purchased from Invitrogen (via Life Technologies, Carlsbad, CA, USA).

Ethics, consent and permissions

For positive control used in RT-PCR and Western Blotting, we used testis homogenate from a patient showing normal spermatogenesis attending the andological clinic in Münster for re-fertilization surgery. After written informed consent, biopsies were taken under general anesthesia. The reported study has been approved by the Ethics committee of the Medical Faculty of the Justus Liebig University Giessen (decision 75/00 and 56/05).

AR transfection in 93RS2 Sertoli cells

We introduced a commercial available full length human AR (OriGene, Rockville, MD, USA), containing 17 CAG triplets, into the expression vector pcDNA 6.2 C-EmGFP (Invitrogen) after amplification of AR using GC-Rich PCR System (Roche, Basel, Switzerland), according to manufacturer’s instructions. Transfection of 93RS2 cells was performed using the microporation system MP-100 (Peqlab). Cells were detached by Trypsin (PAA, Piscataway, NY, USA) and subsequently adjusted to 200,000 cells per well in a 6-well-plate. After re-suspending the cells in the provided buffer, plasmid DNA was added. We used a current strength of 1150 V for 20 ms with two pulses.

Validation of transfection success in 93RS2 by immunofluorescence, RT-PCR and Western Blotting

24 h after transfection, transfected cells (93RShAR17) were fixed in 6-well-plates with 4 % paraformaldehyde for 20 min at room temperature, washed three times with PBS and permeabilized with 0.1 % Triton x-100. After transferring the cells to a 12-well-plate and washing with PBS, unspecific binding sites were blocked with 3 % BSA (bovine serum albumin, Carl Roth GmbH + Co.KG, Karlsruhe, Germany) in TBST (Tris-Buffered Saline and Tween 20, Carl Roth) and washed again with PBS. The rabbit anti-GFP antibody (ab290, Abcam, Cambrigde, UK) was added in a dilution of 1:200. After incubation for 3 h and washing with PBS, goat anti-rabbit Alexa 488 antibody (Invitrogen) was added in a dilution of 1:200. After a final incubation for 1 h in the dark, cells were washed and embedded with Vectashield mounting medium with DAPI (H-1200, Vector Laboratories, Dossenheim, Germany). Transfection efficiency was evaluated using a fluorescence microscope (AxioPhot, Zeiss, Oberkochen, Germany). Western Blot analysis to prove antibody specificity and AR protein expression in transfected Sertoli cells was performed as described elsewhere [43]. Shortly, proteins extracted from cell lysates of transfected 93RS2 cells and human testis tissue were submitted to protein extraction using TRI Reagent® RNA Isolation Reagent (Sigma-Aldrich, St. Louis, MO, USA) according to Chomczynski [44]. Proteins were run on a 3–8 % Tris–acetate gel (Life Technologies, Carlsbad, CA, USA) for 75 min at 150 V and blotted on nitrocellulose membrane for 75 min at 30 V. A polyclonal rabbit anti-human AR antibody (sc-816, Santa Cruz Biotechnology Inc., Dallas, TX, USA) in a 1:500 dilution and a biotinylated goat anti-rabbit antibody (E0432, Dako, Glostrup, Denmark) in a 1:1000 dilution were used. As weight marker, we used HiMark™ Pre-Stained Protein Standard (Life Technologies). Signal detection was performed by incubating the membrane with Vectastain Elite ABC Standard Kit (Vector Laboratories, Inc., Burlingame, CA, USA) and TrueBlue™ Peroxidase Substrate (KPL, Gaithersburg, MD, USA). A negative control was performed by omitting the primary antibody.

To detect AR mRNA in transfected cells, we performed RT-PCR (primers may be seen in Table 7) as described earlier with minor changes concerning the cycling conditions: 1× 94 °C for 4 min, 35× (94 °C for 45 s, 55 °C for 45 s, 72 °C for 90 s) and 72 °C for 5 min resulting in a 591 bp amplicon. The CAG repeat length was confirmed using RT-PCR with subsequent high resolution polyacrylamide gel electrophoresis (PAGE) as described recently [45].
Table 7

Primer sequences

Primer name

GenBank accession no.

Sequence (5′ ≥ 3′)

Amplicon length (bp)

RT-qPCR efficiency (%)

Ar

NM_013476

For

CACATCCTGCTCAAGGCGCTT

181

n.a.

 

(mouse)

Rev

CCCAGAAAGGATCTTGGGCAC

  
 

NM_012502

  

181

n.a.

 

(rat)

    

AR

NM_000044

For

TATCCCAGTCCCACTTGTG

592

n.a.

  

Rev

TCTCTCCCAGTTCATTGAGG

  

Aldh1a2

NM_053896

For

TCAGACTTCGGGCTTGTAGC

125

94.3

  

Rev

GGGCTCTGAGCATTTAAGGC

  

Apoe

NM_001270681

For

TGATGGAGGACACTATGACG

188

105.8

  

Rev

CATGGTGTTTACCTCGTTGC

  

Bambi

NM_139082

For

CCATGCCCACTTTGGAATGC

126

128.0

  

Rev

TTCTGCTGCTGTCATGCTGG

  

Cdkn1a

NM_080782

For

CACAGGAGCAAAGTATGCCG

125

135.1

  

Rev

GCGAAGTCAAAGTTCCACCG

  

Col4a1

NM_0011350009

For

GGAGAACCTGGCAGTGATG

118

99.9

  

Rev

CACCCTTGGAACCTTTGTC

  

Dhh

NM_053367

For

TTGGCACTCCTGGCACTATC

124

102.2

  

Rev

CGGGCATACTAGGCACAAAC

  

Egr1

NM_012551

For

GTGGGAGAAAGTTTGCCAGG

125

111.3

  

Rev

GTAGGAAGAGAGGGAAGAGG

  

Fgfr2

NM_012712

For

CAGCTTCCCCAGATTACCTG

92

94.4

  

Rev

CATTCGGCAAAAGATGACTG

  

Fst

NM_012561

For

TCCAGTACCAGGGCAAATG

78

96.2

  

Rev

TCTGATCCACCACACAAGTG

  

Gja1

NM_012567

For

GTACGGGATTGAAGAGCACG

119

105.5

  

Rev

TGTACCACTGGATGAGCAGG

  

Hsd17b10

NM_031682

For

GAGGAAACTGCATATTTGCC

106

110.5

  

Rev

TTGACAGCCACATCTATACG

  

Inhbb

NM_080771

Rev

ACGGGTCAAGGTGTACTTCC

96

100.3

  

For

AAGGTATGCCAGCCACTACG

  

Myc

NM_0123603

Rev

TACATCCTGTCCGTTCAAGC

67

108.0

  

For

GCCGTTTCCTCAGTAAGTCC

  

Ncam1

NM_031521

Rev

ACGATGATGACTCCTCTACC

150

94.1

  

For

GCGCATTCTTGAACATGAGC

  

Pmepa1

NM_001107807

Rev

TGGTGATGGTGGTGATGATC

76

134.2

  

For

CTGTGTCGGCTGATGAAGG

  

Ptsg2

NM_017232

Rev

ACCGTGGTGAATGTATGAGC

104

98.4

  

For

TCTTGTCAGAAACTCAGGCG

  

Rarg

NM_001135249

Rev

TCACCAAGGTCAGCAAAGCC

125

141.9

  

For

ACTGAACTTGTCCCACAGCC

  

Rbp1

NM_012733

Rev

CTTCAGTGTGTTCAGAAGGG

117

87.9

  

For

CTTGAACACTTGCTTGCAGG

  

Rplp2

NM_001030021

Rev

TTGCCTCTTATCTGCTGGCC

110

103.4

  

For

GTTGAGTCGTTCATCGTCCG

  

Sulf2

NM_001034927

Rev

TTCCTGCCCAAGTATCAGC

108

111.5

  

For

CCCAGAAGCGTCCTCTACAC

  

Tf

NM_001013110

Rev

TGAGGTCTTGCCACAGAAGG

125

102.4

  

For

CCACAACAGCATGAGAAGGG

  

Tgfb1i1

NM_001191840

Rev

ACTACATCTCGGCACTCAGC

101

106.5

  

For

ACCCTCGTGCTCAAAGAAGC

  

Tnfrsf1a

NM_013091

Rev

AAAGAGGTGGAGGGTGAAGG

128

101.7

  

For

ACAGGATGACTGAAGCGTGG

  

Ubc

NM_017314

Rev

GGCAAAGATCCAGGACAAGG

100

99.4

  

For

TTGTAGTCTGACAGGGTGCG

  

Sequence and RT-qPCR efficiency of primers used for the study

n.a. not applied

RNA isolation for microarray analysis

Total RNA of transfected 93RShAR17 cells as well as of non-transfected 93RS2 cells (using three technical replicates (N1-N3) each) was extracted using the peqGold total RNA kit (Peqlab) following manufacturer’s instructions. The amount of RNA was measured on a BioPhotometer (Eppendorf, Hamburg, Germany) as follows: 93RS2 N1 2200 ng/µl, N2 2130 ng/µl and N3 1920 ng/µl and 93RS2hAR17 N1 990 ng/µl, N2 1150 ng/µl and N3 1065 ng/µl (each replicate with a total volume of 15 µl). RNA was stored after extraction until use at −80 °C and transported in liquid nitrogen. The quality of total RNA was checked on a 1 % agarose gel stained with ethidium bromide (Sigma-Aldrich) as well as on Agilent 2100 Bioanalyzer using Eukaryote Total RNA Nano Assay (Agilent Technologies, Santa Clara, CA, USA). For this purpose, RNA was diluted to a concentration of 300 ng/µl. Only high quality RNA samples were used for microarray analysis.

Microarray analysis

cRNA synthesis and hybridization

Extracted RNA was transcribed into biotinylated cRNA using MessageAmp™ II-Biotin Enhanced Kit (LifeTechnologies). Biotinylated cRNA again was quality checked on Agilent 2100 Bioanalyzer as stated above followed by cRNA fragmentation and finally hybridization on CodeLink Rat Whole Genome using the CodeLink Expression Assay Kit (GE Healthcare, Chalfont St. Giles, Buckinghamshire, UK). For this, 10 µg cRNA was diluted with nuclease-free water to final volume of 20 µl and mixed with 5 µl fragmentation buffer (taken from CodeLink iExpress iAmplify cRNA Prep & Hyb Kit, GE Healthcare) and fragmented at 94 °C for 20 min and subsequent cooling to 0 °C on ice. Hybridization solution was prepared by mixing hybridization buffer component A and B (taken from CodeLink iExpress iAmplify cRNA Prep & Hyb Kit), nuclease-free water and 25 µl fragmented cRNA. Denaturation of cRNA was performed at 90 °C for 5 min with subsequent cooling on ice. Hybridization reaction was carried out at 37 °C for 18 h. Subsequent washing was performed with 0.75 × TNT (1 M Tris–HCl, 5 M NaCl and 20 % Tween 20) buffer. Bioarrays were stained with Cy5™-streptavadin (GE Healthcare) and scanned using the GenePix® 4000 B scanner and the GenePix Pro 4.0 Software (Axon Instruments, Arlington, USA). Scan resolution was set to 5 microns. A total of 2 × 3 = 6 array images were subjected to data analysis. Spot signals of CodeLink bioarrays were quantified using the CodeLink System Software 5.0.0.31312 which generated local background corrected raw as well as median centred intra-slide normalized data.

Quality control of microarray data

The genes represented by probe sets were annotated using the biocLite package (BioConductor) with the library “rwgcod.db” for CodeLink Rat Whole Genome arrays. The intra-slide normalized data containing 35129 rows and 6 columns (200 k values) were processed by an automated workflow that includes omission of control genes (n = 1280), removal of genes with poor QC (n = 1300 values, 0.6 %) or negative sign (n = 1603 values, 0.8 %), removal of probe sets with too high proportion (≥50 %) of missing values per group (n = 203 probe sets, 0.5 %) or with not any group having at least 50 % of values flagged as “G = good” and 50 % values above threshold (n = 7177 probe sets, 21.2 %), removal of outliers (expression values deviating more than fourfold from the group median, n = 427 values, 0.3 %). A total of 26452 probe sets remained after quality control with 1257 probe sets (=4.7 %) containing 1235 missing values (=0.8 %).

Remaining missing values were imputed by probabilistic principal component analysis (PPCA) using the R-package pca Methods. Imputed dataset was quantile normalized using the R-package limma [46], and logarithm for the base 2 was calculated.

Differential gene expression

Students t test was applied and a false discovery rate (FDR) ≤0.01 was set for the significance level with an absolute fold change (FC) ≥2 between transfected and non-transfected cells.

Functional gene analysis: overrepresentation analysis

Enriched functional gene ontology (GO) categories within the differentially regulated genes were determined using DAVID version 6.7 [47, 48]. Functional annotation clustering as well as an enrichment score was calculated for each cluster.

Upstream regulation analysis

To identify the regulators responsible for the observed gene expression profiles, we performed prediction analysis for activation or inhibition of upstream regulators using the Ingenuity® Pathway Analyzer and the Ingenuity®Knowledge Base (IPA, Qiagen). Prediction is given as a z-score with >2 for activated and <2 for inactivated upstream regulators.

Validation of microarray results by quantitative RT-PCR (RT-qPCR)

For validation of microarray data, we performed RT-qPCR with 93RShAR17 and non-transfected 93RS2 cells for 22 genes (Table 7) that have been shown to be significantly altered in microarray analysis. All primer pairs obtained from MWG Operon have been validated in standard RT-PCR using rat testis as positive control. For this pupose, total RNA from rat testis was extracted using TRI Reagent® RNA Isolation Reagent (Sigma-Aldrich) according to Chomczynski [44]. Genomic DNA was digested by using DNase I (Roche). For this, 6,65 µl RNA (200 ng/µl) were incubated with 1 µl MgCl2 (25 mM, Thermo Fisher Scientific), 1 µl DNase Buffer (Roche), 0,25 µl RNase inhibitor (40 units/µl, Thermo Fisher Scientific) and 1 µl DNase I for 25 min at 37 °C in a thermocycler. After a enzyme heat inactivation for 5 min at 75 °C, RNA was immediately reversely transcribed into cDNA. For this, 1,5 µl DNase-treated RNA was mixed with 1 µl 10x PCR Gold Buffer, 2 µl MgCl2 (25 mM), 1 µl dNTP mix (each 2,5 mM), 0,5 µl random hexamer primer (50 mM), 0,5 µl RNase inhibitor (20 units/µl), 0,5 µl MultiScribe® Reverse Transcriptase (50 units/µl) and RNase free water to a final volume of 9 µl. All reagents were obtained from Thermo Fisher Scientific. For -RT control, reverse transcriptase was replaced by the same amount of RNase free water. Incubation was performed as follows: 8 min at 21 °C, 15 min at 42 °C and 5 min at 99 °C. cDNA was stored at −20 °C until use. For primer validation in standard RT-PCR, 1 µl cDNA was mixed with 2,5 µl 10× PCR Gold Buffer, 2 µl MgCl2 (25 mM), dNTP mix (each 2.5 mM), 1 µl forward and reverse primer, respectively (each 10 pM), 0.125 µl AmpliTaq Gold® DNA Polymerase (5 units/µl) and RNase free water to a final volume of 25 µl. Cycling conditions were: 1× 94 °C for 9 min, 35× (94 °C for 45 s, 60 °C for 45 s, 72 °C for 45 s) and 72 °C for 5 min. Length of the resulting amplicons was checked in an agarose gel electrophoresis as described earlier. For RT-qPCR dilution series we used rat Rplp and Ubc as internal reference genes and performed triple determination in a decreasing 10- fold dilution series (undil., 1:10, 1:100). RT-qPCR efficiency (E) has been calculated using Bio-Rad CFX Manager version 3.1 (Bio-Rad) from the standard curve’s slope and may be seen in Table 7. Reference genes have been determined by using a TaqMan® Array Rat Endogenous Control Plate (96-well, 32 reference genes pre-plated, Applied Biosystems via Thermo Fisher Scientific, Waltham, MA, USA).

For RT-qPCR, total RNA from transfected and non-transfected cells was extracted using peqGold Total RNA Kit (PEQlab) and reversely transcribed into cDNA as described above. As technical replicates we used cell pellets from three independent passages and for each specimen, double determination was performed using 1 µl of cDNA, 4 µl EvaGreen mastermix (no Rox) (Bio&Sell, Feucht, Germany), 0.6 µl forward and reverse primer each and 12.8 µl sterile aqua bidest to a final volume of 20 µl. RT-qPCR conditions were 1× 95 °C for 15 min, 40× (95 °C for 15 s, 60 °C for 30 s, 72 °C for 20 s) followed by melt curve analysis (1× 95 °C for 10 s, 65 °C to 95 °C, increment 0.5 °C for 5 s) on a CFX96 RealTime cycler (Bio-Rad Laboratories, Hercules, CA, USA). Relative gene expression was calculated by the 2−ΔΔCq method, using Rplp and Ubc as internal reference genes. Expression levels represent x fold higher expression in the transfected than in the non-transfected cells (set as “1”). For statistical analysis, differences of the mean were assessed by ANOVA analysis. P-values of p ≤ 0.05 are set as statistically significant. The Cq values for all transcripts may be seen in Additional file 1: Table S1.

Availability of supporting data

Complete microarray data may be found on GEO Accession Viewer database [19] with accession number GSE57653. Single Sertoli cell line data may be found under accession numbers GSM1385418 (Sertoli Cell Line noAR_1), GSM1385419 (Sertoli Cell Line noAR_2), GSM1386001 (Sertoli Cell Line noAR_3), GSM1385420 (Sertoli Cell Line AR17_1), GSM1385421 (Sertoli Cell Line AR17_2), GSM1385422 (Sertoli Cell Line AR17_3). Raw data of RT-qPCR experiments can be seen in Additional file 1: Table S1.

Notes

Abbreviations

93RS2, SCIT-C8: 

rat Sertoli cell lines

93RShAR17: 

with human AR transfected 93RS cells

AIS: 

androgen insensitivity syndrome

Amh

anti-muellerian hormone

AR/Ar

androgen receptor

ARE: 

androgen responsive element

Bambi

BMP and activin membrane-bound inhibitor

Ccl5

chemokine (C–C motif) ligand 5

Ckdn1a

cyclin-dependent kinase inhibitor 1a

CDS: 

coding DNA sequence

Col4a1

collagen type IV alpha 1

Cox2

cyclooxygenase 2

Cybrd1

cytochrome b reductase 1

DBD: 

DNA binding domain

Dhh

desert hedgehog

DHT: 

dihydrotestosterone

Egr1

early growth response 1

FBS: 

fetal bovine serum

FC: 

fold change

FDR: 

false discovery rate

Fgfr2

fibroblast growth factor receptor 2

Fst

follistatin

Gja1

gap junction protein alpha 1

GO: 

gene ontology

IF: 

immunofluorescence

Ifnb1

interferon beta 1, fibroblast

Ifr7

interferon regulatory factor 7

Il-

interleukin 1β

Inhbb

inhibin beta B

IPA: 

Ingenuity® Pathway Analyzer

ITS: 

insulin-transferrin-selenin

TJ: 

tight junctions

LBD: 

ligand binding domain

Myc

myelocytomastosis oncogene

NTC: 

no template control

PAGE: 

polyacrylamide gel electrophoresis

PBS: 

phosphate buffered saline

Pmepa1

prostate transmembrane protein, androgen induced 1

PPCA: 

probabilistic principal component analysis

Ptsg2

prostaglandin-endoperoxide synthase 2

Rarg

retinoid acid receptor, gamma

Rplp

ribosomal protein l 16

RT-PCR: 

reverse transcription polymerase chain reaction

RT-qPCR: 

quantitative RT-PCR

SK-11: 

WL3 mouse Sertoli cell lines

Smad 1

SMAD familiy member 1

Sox9

Sry-box 9

Sry

sex determining reagion on Y chromosome

Steap2

six-transmembrane epithelial antigen of the prostate 2

T: 

testosterone

TBST: 

Tris-Buffered Saline and Tween 20

Tf

transferrin

Tgfb1i1

transforming growth factor beta 1 induced transcript 1

Tnfrsf1a

tumor necrosis factor receptor superfamily, member 1a

Ubc

ubiquitin c

Declarations

Authors’ contributions

DF and MM drafted the manuscript and performed quantitative RT-PCR, microarray analysis and statistical analysis, respectively. DL performed screening for cell lines, transfection, and immunofluorescence staining. KL was responsible for study design and supervision of the transfection procedure; furthermore he was involved in cell line acquisition and cell culture experiments. GJ supervised transfection procedure as well and generously provided the AR-GFP construct. KS provided human testis material for positive control purposes. Both GJ and KS critically revised the manuscript. CT and HH participated in the design of microarray experiments and HH also helped to draft the manuscript. MB was responsible for study design, supervision and critically revised the manuscript. All authors read and approved the final manuscript.

Acknowledgements

The authors want to thank J. Vogelsberg and D. Zoltan for their skilful technical assistance. Cell lines were generously provided by Profs. Korach, Boekelheide and Gromoll. The presented study was funded by German Research Foundation (DFG KFO181, BE1061/7-1).

Competing interests

The authors declare that they have no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Institute of Veterinary Anatomy, Histology and Embryology, Justus Liebig University
(2)
Institute of Medical Microbiology, Justus Liebig University
(3)
Department of Gynecology and Obstetrics, Justus Liebig University
(4)
Department of Clinical Andrology, Centre for Reproductive Medicine and Andrology, University Clinic Münster

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Copyright

© Fietz et al. 2015