iTRAQ Analysis of a Cell Culture Model for Malignant Transformation

Article pubs.acs.org/jpr

iTRAQ Analysis of a Cell Culture Model for Malignant Transformation, Including Comparison with 2D-PAGE and SILAC Stephanie M. Pütz,*,†,‡ Andreas M. Boehm,‡ Thorsten Stiewe,§ and Albert Sickmann∥ †

Institute of Medical Radiation and Cell Research (MSZ), University of Würzburg, D-97078 Würzburg, Germany Rudolf Virchow Center, DFG Research Center for Experimental Biomedicine (Protein Mass Spectrometry and Functional Proteomics), University of Würzburg, D-97078 Würzburg, Germany § Molecular Oncology, Philipps-University Marburg, D-35032 Marburg, Germany ∥ Leibniz-Institut für Analytische Wissenschaften, ISAS - e.V., D-44139 Dortmund, Germany ‡

S Supporting Information *

ABSTRACT: To study human cancer development, cell culture models for malignant transformation can be used. In 1999 Hahn and Coworkers introduced such a model system and established herewith a basis for research on human tumorigenesis. Primary human fibroblasts are sequentially transduced with defined genetic elements (hTERT, SV40 ER, and H-RasV12), resulting in four defined cell lines, whereby the last has a fully transformed phenotype. In order to get a deeper insight into the molecular biology of human tumorigenesis, we compared the proteomes of these four cell lines following a multimethod concept. At the beginning we assumed SILAC and sample fractionation with COFRADIC is the method of choice to analyze the cell culture model for malignant transformation. Here, the compared samples are combined before sample preparation, thus avoiding differences in sample preparation, and using COFRADIC notably reduces sample complexity. Because 2D-PAGE is a standard method for the separation and visualization of closely related proteomes, we decided to analyze and compare the proteomes of these four cell lines in a first approach by differential 2D-PAGE. Surprisingly, we discovered much more unique results with iTRAQ and sample fractionation with SCX than with the combination of 2DPAGE and SILAC-COFRADIC. Moreover, iTRAQ outperforms the other strategies not only in number of yielded results but also in analysis time. Here, we present the iTRAQ quantification results and compare them with the results of 2D-PAGE and SILAC-COFRADIC. We found changes in the protein level at each transition. Thereby, SV40 has the strongest impact on the proteome. In detail we identified 201 regulated proteins. Beside others, these proteins are involved in cytoskeleton, RNA processing, and cell cycle, such as CDC2, hnRNPs, snRNPs, collagens, and MCM proteins. For example, MCM proteins are upregulated and collagens are down-regulated due to SV40 ER expression. Furthermore we made the observation that proteins containing the same domain have analogous regulation profiles during malignant transformation. For instance, several proteins containing a CH or LIM domain are down-regulated. Moreover, by this study and the defined cell culture model, changes could be clearly matched to specific steps during tumorigenesis. KEYWORDS: iTRAQ, SILAC, 2D-PAGE, malignant transformation, cell culture model, hTERT, H-RasV12, SV40 ER

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INTRODUCTION Cancer is caused by genetic alterations that accumulate in a stepwise fashion during the tumorigenesis process1,2 and break down the genetically regulated balance between apoptosis and cell cycle. Complete malignant transformation of human cells requires immortalization as well as increased growth rate and the avoidance of contact inhibition. Frequently these changes are accompanied by the modification of the same molecular pathways in different types of cancer. Often altered are, for example, the Ras-Raf-MEK3,4 and p53 pathways.5,6 Since human and rodent transformation differ, e.g., in telomere biology,7 rodent cancer models cannot explain all the aspects of malignant tumorigenesis in human. The in vivo study of human cancer development is hampered by the genetic © 2012 American Chemical Society

heterogeneity of patients, and a cancer disease is often diagnosed after a tumor is formed. To establish a basis for the research on human tumorigenesis, Hahn and co-workers introduced a cell culture model for malignant transformation;8 they used three genetic elements to transform a primary human fibroblast cell line (Figure 1a). The genetic element hTERT encodes the catalytic subunit of the telomerase holoenzyme and leads to increased telomerase activity. The expression of hTERT is sufficient to immortalize a variety of human primary cell types.9,10 In a second step the simian virus 40 early region (SV40 ER) encoding large and small tumor antigen (LT and ST) increases Received: September 2, 2011 Published: February 7, 2012 2140

dx.doi.org/10.1021/pr200881c | J. Proteome Res. 2012, 11, 2140−2153

Journal of Proteome Research

Article

used for decades is the well established two-dimensional-PAGE (2D-PAGE).19−22 New and faster high-throughput techniques using stable isotope labeling and chromatography followed by mass spectrometry were developed over the turn of the millennium, e.g., isotope coded affinity tag (ICAT),23 stable isotope labeling with amino acids in cell culture (SILAC),24 global internal standard technology (GIST),25,26 and isobaric tag for relative and absolute quantitation (iTRAQ).27 The most prominent of these techniques, SILAC and iTRAQ, have different characteristics concerning workflow and data evaluation. Carrying out SILAC, the proteins are labeled in cell culture with stable isotopes and quantification is done in the MS1 spectrum. In the case of iTRAQ, peptides from previously digested protein samples are chemically labeled with isobaric tags, allowing identification and quantification of up to four or eight samples in the same MS/MS spectrum.27,28 To study cancer development, we used the model previously introduced by Hahn and co-workers to analyze stepwise proteome changes during malignant transformation. A multimethod concept was used to discover regulated proteins. As a first approach, 2D-PAGE (Figure 1b) was applied to visualize and detect changes in the proteome of the four cell lines of the malignant transformation model.29 In order to investigate this model system in further detail using separation techniques other than 2D-PAGE, we used the stable-isotope-based peptide-quantification technique iTRAQ (Figure 1c) in the present study that covers larger proportions of the proteome. Here we present the results from the iTRAQ experiments fractionated with SCX and compare them with the results of a SILAC experiment fractionated with COFRADIC (Figure 1d) and our previously published findings by 2D-PAGE. Moreover, we compare the workflow, handling, and output of the three strategies.

Figure 1. Cell culture model and experimental workflow. (A) Cell culture model for malignant transformation. The primary human fibroblast BJ strain was sequentially transduced with retroviral vectors encoding the genes for hTERT (cell line BJ-T), SV40 early region (ER, cell line BJ-TE), and H-RasV12 (BJ-TER). These cell lines were quantitatively investigated at the proteome level using a multimethod concept. (B) Workflow of the previously published 2D-PAGE analysis.29 (C) Workflow of the iTRAQ experiment. Samples of the four cell lines were lysed, followed by sample preparation and labeling with the four iTRAQ reagents. The biological replicates for the three experiments were labeled alternating. The four labeled peptide samples of the respective experiment were pooled and fractionated using SCX prior to nano-LC−MS/MS analysis. D) Workflow of the SILAC experiments: Lysates from BJ-TER cells, labeled with 13C15N-arginin are mixed with lysates from BJ, BJ-T or BJ-TE cells respectively, all labeled with 12C14N-arginin. The resulting samples are analyzed using COFRADIC followed by nano-LC−MS/MS.

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EXPERIMENTAL PROCEDURES

Cell Culture

Malignant transformation; generation of the cell lines BJ-T, BJTE, and BJ-TER; cell culture model used (Figure 1a); and maintenance of cell lines have been published previously.29,30 Stable Isotope Labeling with Amino Acids in Cell Culture

Cells, labeled using the SILAC strategy, were maintained in arginine- and lysine-free Dulbecco’s Modified Eagle’s Medium (PAA) supplemented with 10% dialyzed fetal bovine serum (PAA), 1% penicillin G/streptomycin (Invitrogen), and 0.4% amphotericin (Sigma). In addition, 500 μL of a pyruvate-lysine stock solution (containing 110 mg pyruvate and 146 mg lysine per mL) and 250 μL of an arginine stock solution (containing 84 mg/mL 13C15N-arginine or 12C14N-arginine, respectively) were added to 500 mL of medium. BJ-TER cells were labeled with 13C15N-arginine, and BJ, BJ-T, and BJ-TE cells were labeled with 12C14N-arginine. Cells were harvested after 4 weeks of SILAC labeling.

the growth rate in monolayer BJ fibroblasts at least in part by LT-mediated inactivation of the two tumor suppressors, p53 and Retinoblastoma protein (pRB).8,11 In the last step, the activation of the Ras-Raf-MEK-pathway by H-RasV12 leads to a fully transformed phenotype.8 This cell culture model offers the opportunity to study malignant transformation in human cells step by step and in turn allows for tracing the changes back to genetically defined steps of tumorigenesis. Furthermore, not only fibroblast cells can be transformed with this set of genetic elements. Other scientists confirmed malignant transformation in human mammary epithelial, airway epithelial, glial, endothelial, and mesothelial cells.8,10,12−16 Thus, this cell culture model can be considered as a more general model for tumorigenesis. In recent years proteomics technology has undergone rapid development; in particular, many quantification strategies have been published.17,18 These strategies provide different prospects and vary in their principal workflows. A common method

Sample Preparation

Confluent cell plates with a diameter of 12 cm were washed twice with PBS (Sigma). Next, cells were scraped from the plate in 2 mL of PBS and centrifuged for 10 min at 400 × g and 4 °C. The resulting cell pellet was lysed with 30 mM Tris-HCl (AppliChem), 1% SDS (AppliChem) at pH 8 supplemented with 1 pill of protease inhibitor Complete Mini (Roche) per 5 mL of buffer. Subsequently, samples were sonicated six times in an ultrasonic bath for 10 s each, with cooling intervals of 1 min. 2141



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Next, the DNA was digested with the addition of 3 μL of Benzonase (Merck) for 200 μL of lysate, and samples were incubated for 30 min at 37 °C. Further sample preparation depended on the analysis strategy, i.e., iTRAQ-labeling or SILACCOFRADIC.

Afterward, the samples were adjusted to pH 3 using 10% TFA, and the peptides were cleaned up with an SPEC C18AR Column (Varian) and were subsequently lyophilized. The peptides were resuspended in 100 μL 0.1% TFA immediately before the first HPLC-run of the COFRADIC procedure used for the isolation of methionine-containing peptides. The peptides were separated using an UltiMate3000 HPLC system (Dionex) and a Zorbax 300SB-C18 column (2.1 mm i.d, 15 cm length, 5 μm particle size, Agilent Technologies) and an 80 min linear binary gradient from 5% to 80% solvent B (solvent A: 0.1% TFA in water; solvent B: 0.1% TFA, 84% ACN) at a flow rate of 80 μL/min and 30 °C column temperature. Thereby, 48 fractions were collected and subsequently pooled into 12 fractions according to Supplemental file 1. The pooled fractions were lyophilized, and the peptides were resuspended immediately before rechromatography in 83 μL of 0.1% TFA plus 17 μL of 3% H2O2 and incubated for 30 min at 30 °C. The 12 rechromatography runs were conducted in the same way and with the same parameters as the first HPLC run of the COFRADIC procedure. The eluates were collected as depicted in Supplemental file 1 and were pooled into nine samples for nano-LC−MS/MS afterward.

iTRAQ Labeling Followed by Strong Cation Exchange Chromatography

The prepared lysates were treated as follows. Disulfide bonds were reduced by the addition of 20 mM dithiothreitol (Roche) for 30 min at 56 °C, and free cysteines were carbamidomethylated with 50 mM iodoacetamide (Sigma) for 30 min at room temperature. Afterward, samples were centrifuged for 10 min at 16,000 × g, and next the proteins of the supernatant were precipitated with TCA (Merck). The precipitated proteins were resuspended in 30 mM Tris-HCl, 0.05% SDS at pH 8. The protein amount was determined with the BCA Protein Assay Kit (Pierce). A 100 μg sample of each protein was dissolved in 100 μL of 30 mM Tris-HCl, 0.05% SDS at pH 8. Tryptic digestion was performed overnight by adding 2 μg of trypsin porcine pancreas (Fluka) to each sample. The next day peptide samples were cleaned up with a SPEC C18AR Pipette Tip (Varian) and eluted with 20%, 40%, and 60% ACN (Merck). Afterward, the peptides were lyophilized using a SpeedVac (Thermo Scientific) and resuspended in 30 μL of iTRAQ dissolution buffer of the iTRAQ Reagent Multiplex Kit (Applied Biosystems). The labeling step with iTRAQ was done as recommended for 1 h at 25 °C. Next, the samples were pooled and lyophilized. The peptides were dissolved in 0.1% TFA (Merck), cleaned up with an SPEC C18AR column, (Varian), and lyophilized again. The peptides were finally dissolved in 80 μL phosphate buffer at pH 2.7. Dissolved iTRAQ labeled peptides were separated by strong cation exchange chromatography (SCX) using an Ultimate HPLC system (Dionex) and a PolySULFOETHYL Aspartamide column (1 mm i.d. × 15 cm length, 5 μm particle size, 200 Å pore size, Chromatographic Technologies) and a 45 min linear binary gradient (solvent A, 10 mM NaH2PO4 in water pH 2.7; solvent B, 10 mM NaH2PO4 and 0.5 M NaCl in water pH 2.7) at a flow rate of 50 μL/min. The eluates were collected in one minute fractions, subsequently lyophilized, and resuspended in 0.1% TFA.

Liquid Chromatography and Mass Spectrometry

Samples of SCX and COFRADIC fractions were preconcentrated using a Synergi Hydro-RP C18 trapping column (100 μm i.d., 2 cm length, 80 Å pore size, 4 μm particle size; Phenomenex) and afterward separated on a Synergi HydroRP C18 main column (75 μm i.d., 150 mm length, 80 Å pore size, 2 μm particle size; Phenomenex) using a 2 h linear binary gradient from 5% to 80% solvent B (solvent A, 0.1% FA in water; solvent B, 0.1% FA, 84% ACN) at a flow rate of 270 nL/min. A reverse phase nano-LC separation was coupled to MS/MS. The peptides were identified by MS/MS, and the analyses were conducted with a QStarElite (Applied Biosystems). Full MS scans from 300 to 1500 m/z were acquired, and the three most intensive peptide ions were subjected to further fragmentation. Duplicate detection of a single m/z within 30s led to dynamic exclusion. Sum Score and Normalized Sum Score Calculation

In detail, the protein sum score (termed sum score in the following sections and tables of results) of the protein identification was calculated by summing up the normalized peptide scores, restricted to the largest one of peptide duplicates (eq 1). The normalized peptide score is calculated by dividing the original peptide score by the identity threshold sp that Mascot provided for that peptide; see eq 2. The calculation of sp is given in eq 3. The threshold sp is calculated using all peptide hits of the complete Mascot run, including the unsignificant ones. In eq 3 we used a significance level of p = 5% = 1/20 for the Mascot searches; other significance levels can be derived from any sp using the procedure described in Zahedi et al.32

SILAC-COFRADIC of Methionine-Containing Peptides

The protein amount of SILAC-labeled protein samples was determined with the BCA Protein Assay Kit (Pierce). The same protein amounts of the BJ-TER sample were mixed 1:1 with samples of BJ, BJ-T, and BJ-TE, respectively. In the pooled samples, the disulfide bonds were reduced by the addition of 20 mM dithiothreitol (Roche) for 30 min at 56 °C, and free cysteines were carbamidomethylated with 50 mM iodoacetamide (Sigma) for 30 min at room temperature. Afterward, the samples were centrifuged for 10 min at 16,000 × g, and next the proteins of the supernatant were precipitated with TCA (Merck). Precipitated proteins were resuspended in one moiety of 6 M GuHCl and afterward two moieties of 75 mM Na2HPO4 were added to adjust the pH to 8.7. Subsequent acetylation was carried out with 25 mM N-acetoxy-d3-succinimide31 for 1 h at 37 °C. Thereafter, hydroxylamine was added, and the sample was incubated for 15 min at room temperature. The reaction was stopped by adding 20 mM glycin for another 10 min at room temperature. The samples were diluted 1:10 with NH4HCO3 before proteolytic digestion. Tryptic digestion was conducted using 100 μg of trypsin per 3 mL sample volume. 2142



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transitions in all three experiments are presented. Moreover, median and mean as well as standard deviation of the medians of the three experiments were calculated for every regulated protein.

We analyzed the procedure of calculating the protein score (termed Mascot score in the following sections and tables of results) as it is done by the original program master_results of Matrix Science: for each protein the set of duplicate-free peptides holding the highest-scored hit per identified peptide sequence is determined. The scores of these peptide hits are summed up after applying eq 4 to each peptide score score, with matchnum representing the rank in the peptide hit list of the underlying MS/MS spectrum, and itol denoting the fragment ion tolerance used for the Mascot search.

SILAC-COFRADIC Data Analysis: Protein Identification and Quantification

The corresponding QStarElite wiff-files were directly loaded into Mascot, Version 2.2 (Matrix Science) using the data import filter Mascot Distiller and parameters as follows: separate search for each sample; peak list format: MGF and PMF; MS/MS readout: m/z. Further Mascot Distiller processing settings are listed in Supplemental file 3. Peptide and protein identification were accomplished using Mascot, Version 2.2 and the SwissProt database (13-11-2007) with 289,473 entries and the following Mascot parameters: protease: Arg-C; taxonomy: Homo sapiens (17,659 entries); missed cleavages: 1; peptide and MS/MS tolerance: ± 0.2; instrument: ESI-QUAD-TOF; significance threshold: p < 0.05. Further settings, especially the parameters concerning modifications, are presented in Supplemental file 4. Afterward, the search results were analyzed using ms_lims version 6.1.1 and the IdentificationGUI. For all unique peptides containing methionine, the corresponding SwissProt accession ID, peptide sequence, and additional modifications were saved as a list. Peptide and protein quantification were accomplished using Mascot Distiller Version 2.2 and the settings for quantification as specified in Supplemental file 4. Thereby the quantification information is exported as an XML file, from which, using the list of identified methionine-containing unique peptides, the corresponding quantitative information was extracted. Subsequently, the identified peptides of all COFRADIC fractions of one SILAC sample were grouped and saved as a table containing information as follows: SwissProt accession ID, number of spectra, number of identified and quantified unique peptides, sum score, normalized sum score, median of peptide ratios and two corresponding quality measures: the maximal standard error (,,max. StdErr.“ taken from Mascot Distiller result file) and the standard deviation of log-transformed peptide ratios.

We simplified the calculation of the normalized score by omitting the penalty of a rank different to the upper-most one. This is legitimate, as far as we only regard significant peptides that are manually validated. In addition, all normalized scores of our experiments were calculated the same way and therefore are comparable across experiments as the absolute scores are not because the identity threshold sp is experiment-dependent. In our case the dependency on sp is normalized out. iTRAQ Data Analysis: Protein Identification and Quantification

The QStarElite wiff-files were converted into mgf-files using mascot.dll with the parameters (a) precursor mass tolerance for grouping 0.05, (b) max number cycles between groups 4, (c) min number cycles per group 1, (d) remove peaks if intensity 1.5-fold and in the 2D-PAGE experiments regulation ratios >2 were examined, Venn diagram (C) displays the number of regulated proteins with a regulation ratio >2-fold. This allows for improved comparability between the different methods.

Table 1. Regulated Proteins Identified and Quantified by Two Methodsa T/BJ Acc ID P53396 Q05682 Q08211 P61978 P07900 P20700 Q03252 Q8WWI1 P33993 P30041 P05120 Q12931

TE/T

iTRAQ PAGE

TER/TE

TE/BJ

protein name

method

ATP-Citrate Synthase Caldesmon ATP-dependent RNA Helikase A Heterogeneous nuclear Ribonukleoprotein K Heat shock Protein HSP 90α (HSP86) Lamin-B1 Lamin-B2 LIM domain only Protein 7 DNA replication licensing factor MCM7 Peroxiredoxin-6 Plasminogen activator inhibitor 2 Heat shock Protein 75 kDa mitochondrial

iTRAQ + SILAC iTRAQ + SILAC iTRAQ + SILAC

1.35 1.43 1.09

1.21 0.44 1.51

0.76 0.83 1.07

0.45

iTRAQ + SILAC

0.67

1.68

1.31

0.83

iTRAQ + PAGE

1.16

1.25

1.11

iTRAQ + SILAC iTRAQ + SILAC iTRAQ + SILAC iTRAQ + SILAC

1.08 1.09 0.96 1.22

1.64 1.37 0.51 2.05

0.99 1.09 0.85 0.86

iTRAQ + PAGE iTRAQ + PAGE

1.13 2.25

0.25 2.18

0.52 0.93

0.87 0.69

0.58 2.35

↓ 1.84

iTRAQ + PAGE

1.04

1.36

1.69

0.90

1.94

3.16

0.99

TER/T

TER/BJ

iTRAQ other iTRAQ SILAC iTRAQ PAGE iTRAQ SILAC iTRAQ 1.62 0.66 1.63

0.91 0.34 1.59

1.12 1.49

0.92 0.58

0.97

1.73 1.46 0.54 2.32

other

1.28 0.50 1.77

2.02

2.11

1.40

1.63

1.45

1.68

2.07

1.71 1.60 0.44 2.35

1.35 0.15

0.44 0.66

0.50 1.62

↓ 1.50

1.79

2.01

2.15

1.61 1.48 0.42 1.77

0.05

2.07 1.85 0.22

a

Listed are the respective SwissProt Acc Id, protein name, detection method, and measured ratio analyzed with iTRAQ and either 2D-PAGE or SILAC. Given that a 2D-PAGE protein spot is not detected in the respective cell line, arrows indicate the direction of regulation.

MS-analysis time each). The SILAC-COFRADIC approach yields 20 regulated proteins in 54 h (breakdown of time calculation: three different analysis BJ/TER, T/TER and TE/ TER; each with nine fractions; per fraction 2 h MS-analysis time). However, only one experiment was made where no replicates were performed, and only protein ratios related to BJTER were analyzed. The iTRAQ study achieved 185 regulated proteins and three replicates required 300 h MS-analysis time (breakdown of time calculation: three biological replicates; each with 50 fractions; per fraction 2 h MS-analysis time). Furthermore, concerning the regulated proteins identified per hour, iTRAQ (0.62) provided the best rate compared to 2D-PAGE (0.08) and SILAC (0.37). Thus, not only 2D-PAGE but also SILAC consume more MS-analysis time compared to iTRAQ. Given that, comparing four samples, SILAC requires six times more experiments to achieve the same information as iTRAQ. Hence, the advantage of high-multiplexing with iTRAQ provides enormous saving of time during LC−MS/MS analysis. As expected, the strategies iTRAQ, 2D-PAGE, and SILACCOFRADIC provide complementary results. The choice of the used methods highly depends on the posed question and moreover on the circumstances and equipment of the laboratory. Nevertheless iTRAQ outperforms the other strategies in number of yielded results and analysis time and therefore in total costs.

regulation ratios detected by SILAC-COFRADIC compared to iTRAQ can be in part ascribed to less peptide identifications per protein. Moreover, due to possible overlapping signals in the MS spectrum, some peptide ratios are maybe defective, and because of few peptides per protein this increases the potential for errors. Comparing the regulation ratios detected by 2DPAGE and iTRAQ, respectively, minor variations may be caused by multi protein spots and because protein isoforms and modifications are separated by 2D-PAGE.20 In contrast, the quantification result of iTRAQ is directly related to the identification result, because both are obtained from the same MS/MS spectrum. All together the regulation results of iTRAQ compared to those of 2D-PAGE and SILAC-COFRADIC are more robust against errors and outliers. The three methods differ also concerning required MS analysis time. In the following paragraph only the MS analysis time is compared, not including the time needed for sample preparation and fractionation. In order to establish a relationship between MS analysis time and identified regulated proteins, subsequent required time and number of identified regulated proteins by the three methods will be compared. Here 66 proteins within 39 regulated spots are identified by 2D-PAGE, and therefore the required MS analysis time was about 800 h (breakdown of time calculation: per spot fife biological replicates of each sample were measured with 1 h 2147



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Phenotypic Observations

sequestered by LT. These expected regulations validate the overall success of the analysis.

Morphological changes often come along with tumorigenesis. Examining the phenotype, the cells of the cell culture model here used differ in morphology; BJ-TER cells are more roundish than BJ cells. It is supposable this change is ascribed to the expression of SV40. Consider that SV40 LT leads to morphological changes and increased proliferation in immortalized fibroblasts.49 Analogous to morphology, the cells at different stages of transformation differ with respect to their proliferation characteristics. For example, BJ and BJ-T cells were regularly split at a 1:2 or 1:4 ratio, whereas BJ-TE and BJ-TER were split 1:8 or 1:10 in the same time intervals. This indicates that BJ-TE and BJ-TER cells proliferate at least twice as fast as BJ and BJ-T cells. Since deregulated proliferation in the absence of growth factors and even in the presence of antiproliferative signals is a criterion of cancer, frequently proliferation markers such as proliferating cell nuclear antigen (PCNA) are verified. An increase of PCNA was detected in the BJ-TE and BJ-TER cell by 2D-PAGE and was verified by Western Blot.29 Thus the upregulation of PCNA supports the findings from cell culture.

Consideration of Biological Aspects Regarding the Regulated Proteins

Since a detailed discussion of each protein would exceed the scope of this study, the following paragraphs will focus on a few biological aspects. Cell Cycle and DNA Replication. The reason for increased proliferation observed at the cellular level is the dysregulation of the cell cycle. Cyclin-dependent kinase 1 (also named CDK1 or CDC2) is an important regulator of mitosis but also has regulatory function in other steps of the cell cycle.53 In cancer CDC2 is often up-regulated.54,55 Within our iTRAQ experiments an up-regulation of CDC2 was discovered after expression of hTERT as well as due to SV40 ER (Figure 5A).

Changes at the Proteome Level

Referring to the proteome level (e.g., Figure 3), the major changes were detected between the cell lines BJ-T and BJ-TE. According to the findings of the previously published 2D-PAGE approach,29 the iTRAQ experiments show most proteins (110) were regulated due to SV40 ER expression, compared to the H-RasV12 (14) and hTERT (16) transition. As commented previously,29 in this step more pathways are altered in a detectable manner than in the other two steps: SV40 LT inactivates pRB and p53 due to binding of pRB via its N-terminal LXCXE motif and p53 via its bipartite C terminal binding domain.50 Unlike SV40 LT the exact contribution of SV40 ST to cellular transformation remains elusive. It is known that SV40 ST binds PP2A, stimulates the phosphorylation of Protein Kinase B (Akt), and affects the c-Myc pathway.51,52 Moreover, it is interesting that the expression of H-RasV12 leads to only a few changes in protein levels, although this step causes the real malignant transformation. The huge alteration in this step is presumably found on the level of posttranslational modifications like phosphorylations, whereas the transcriptional regulators pRB and p53 are considerably more involved in expression control. Thus, it can be hypothesized that the immortalization (up to and including stage BJ-TE) primarily is based on changes of protein amount and the final transformation step instead more on posttranslational modifications.

Figure 5. Hierarchical cluster of regulated proteins involved in cell cycle, DNA replication, and RNA processing. Each row represents one protein, and each column a transition and the respective iTRAQ experiment. Thus, each colored rectangle displays the regulation of a protein within the respective transition and iTRAQ experiment. Upregulation is shown in green and down-regulation in red; nonregulation is colored in black. (A) Depicted are proteins involved in cell cycle, and the proteins of the MCM complex. All displayed proteins are up-regulated due to SV40 ER expression. In addition, CDC2 already shows up-regulation following expression of hTERT. (B) Hierarchical cluster of proteins involved in RNA processing. Several hnRNP and snRNP proteins were found, many of them containing an N-terminal RNA-recognition motif (RRM) domain. Moreover, DEAD-box helicases were identified.

Expressed Genetic Elements and Their Known Downstream Effectors

The cell culture model for malignant transformation is generated by the integration of defined genetic elements. Thus, the upregulation of the thereby encoded proteins hTERT, SV40 LT and ST, and H-Ras could be expected. Moreover, known downstream effectors, e.g., from SV40 LT and ST like p53, pRB, and c-Myc, could be regulated too. These proteins were not in the final result lists of SILAC, 2D-PAGE, and iTRAQ, but when not considering the stringent criteria for the final result list, H-Ras was identified and quantified within two of three iTRAQ experiments. Moreover, p53 was identified in all three iTRAQ experiments but did not meet the criteria of at least two spectra of unique peptides. Both can be ascribed to undersampling effects. Nevertheless, H-Ras is up-regulated 3-fold in the BJ-TER cells within both experiments and p53 gets up-regulated due to SV40 ER expression. The upregulation of p53 can be explained by the fact that p53 is

Certainly, the impact of SV40 ER can be explained by the link via p53 and p21. Although CDC2 is a well studied protein, a causal connection for the influence of hTERT is not known. However, CDC2 is an essential kinase of the cell cycle, and BJT cells proliferate more robust compared to BJ (e.g., no or rather seldom senescent cells). This could be an explanation for the CDC2 up-regulation in the hTERT transition. The DNA-dependent Protein Kinase (DNA-PK), another serin/threonine protein kinase also having functions in the cell cycle, is assembled by the catalytic subunit DNA-PKcs and the DNA-binding subunit Ku70/Ku80.56,57 The dimer Ku70/Ku80 is a regulatory subunit of the DNA-PK complex and enhances the affinity of DNA-PKcs and DNA.57 DNA-PK can be 2148



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activated by DNA damage.58 Moreover, many cancer tissues are DNA-PKcs and Ku80 positive.59 We observed an up-regulation of the whole DNA-PK complex (DNA-PKcs, Ku80, and Ku70) as a result of SV40 expression (Figure 5A). We suppose the DNA-PK complex is up-regulated as a result of increased proliferation and might be involved in coping with proliferation-induced DNA damage. Moreover, proteins involved in DNA replication, for example, the proteins MCM2, MCM3, MCM4, MCM5, MCM6, and MCM7 of the Minichromosome Maintenance complex (MCM complex), are regulated. The MCM complex forms the replicative DNA-helicase,60,61 and the MCM proteins, particularly MCM7, are known as proliferation markers.62,63 Besides, the expression of the MCM genes is repressed by p53, potentially by its target p21.64 The finding that MCM proteins are 2-fold up-regulated by SV40 ER (Figure 5A) may be traced back to the sequestration of p53. RNA Processing. Proteins involved in RNA processing are regulated during malignant transformation. Thereby, the heterogeneous nuclear ribonucleoproteins (hnRNPs) have a wide functional range, varying from transcription, pre-mRNA processing in the nucleus, to cytoplasmic mRNA translation.65 In lung cancer many hnRNPs are up-regulated.66 In addition to the hnRNPs, the small nuclear ribonucleoproteins (snRNPs) are involved in RNA processing. SnRNP are essential components of the spliceosome.67 We found an up-regulation of hnRNPs and snRNPs (Figure 5B). Many among them contain an N-terminal RNA-recognition motif (RRM) domain. Also other proteins having an RRM domain are up-regulated during malignant transformation (Figure 5B). Beside the hnRNPs and snRNPs also the DEAD-box proteins play an important role within RNA metabolism, mainly during reorganization of RNP complexes.68 They are potential RNA helicases, and it has been hypothesized that they are involved in differentiation and carcinogenesis.69 However, the biological function of many human RNA-helicases has to be examined. Regulated expression of human RNA-helicases can be observed in different tumor types.70 A total of eight regulated proteins of the DEAD-Box helicase family were identified within the presented study (Figure 5B). Summing up, homogeneous upregulation of proteins involved in RNA processing suggests that alterations of RNA processing are characteristic for the transformation process. Protein Domains. Protein domains are defined regions in the amino acid sequence of proteins. Proteins sharing the same functional domain often exhibit the same functional characteristics. For example, the calponin homology (CH) domain consists of approximately 110 amino acids and can be found in cytoskeletal and signal-transduction proteins.71 We observed that proteins sharing the same domains show similar regulation profiles during malignant transformation (Figure 6). For instance, several proteins containing a CH or LIM domain are downregulated. We suppose the domains depicted in Figure 6 exhibit special functions during tumorigenesis. We presume that the regulation profile of the corresponding proteins attributes to the characteristics of the respective domain. Cytoskeleton. Alterations of the cytoskeleton are often accompanied with tumorigenesis. Accordingly, regulated cytoskeletal proteins were identified and assigned to different compartments of the cytoskeleton (Table 2). Already in 1980, a decrease in collagen synthesis was observed in human fibroblasts due to SV40 transformation.72 Moreover, within SV40 transformed fibroblasts, collagen VI expression is supposed to

Figure 6. Hierarchical cluster of proteins containing the same domains. Each row represents one protein, and each column a transition. Thus, each colored rectangle displays the median regulation of a protein of all experiments within the respective transition. Upregulation is shown in green, and down-regulation in red; nonregulation is colored in black, and absence of data in white. Regulated proteins containing the denoted domains are either up- or downregulated during malignant transformation correlating with the respective domain. This phenomenon can be found, for example, for the following domains: solcar repeat (solcar), calponin-homology (CH), helicase ATP-binding or helicase C-terminal domain (helicase), LIM zinc-binding (LIM), RNA recognition motif (RRM), VWFA, and SAP. The LIM domain only protein 7 (marked with an arrow) is depicted twice, because it contains both, LIM, and CH domain.

be inhibited by DNA methylation.73 Consistently, in our study all regulated collagens are down-regulated upon SV40 ER expression. Furthermore, cell adhesion proteins, such as Stomatinlike protein 2 (SLP2), were identified. SLP2 is confirmed by many tumor studies to be up-regulated and is not only involved in cell adhesion but also in cell proliferation.74−77 During malignant transformation, we found SLP2 2-fold up-regulated primarily at the T/TE transition. The regulation pattern of the collagens and SLP2 indicates a major influence of SV40 ER on the extracellular matrix. Fibronectin connects the extracellular matrix via integrins with the actin filaments and was also elsewhere found to be upregulated as a result of SV40 expression.78,79 However, it is down-regulated within the hTERT transition. Thus, its regulation is affected in several steps during tumorigenesis, especially since Ras transformation is also known to regulate the interaction of fibronectin with the cell.80 One regulated actin binding protein is filamin-C (Table 2). Filamin links the actin cytoskeleton to the cell membrane and is assumed to be involved in cell motility and its concentration appears to be crucial for the formation of filopodia.81,82 Filamin-C is down-regulated during malignant transformation. The observation that BJ-TER cells are more roundish in contrast to BJ cells might be explained by less formation of filopodia caused by down-regulation of filamin. A regulated nuclear protein is histone H1, a histone protein linking adjacent nucleosomes and hence forming a substantial component of the chromatin. The Ras-MAPK pathway is 2149



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FUTURE PROSPECTS The presented study yields a long list of potential marker proteins for tumorigenesis, and the regulated proteins can be related to defined steps of tumorigenesis. To use the identified regulated proteins as markers for tumorigenesis, the in vivo relevance needs to be investigated. Since the regulation of some proteins is supposed to depend on the origin of the cell type or the genetic background, another interesting question will be the examination of the regulation of the respective proteins in different tumor types and tissues. Such analyses are timeconsuming and expensive. Thus, a meaningful and strong selection of interesting protein candidates is necessary in order to reduce the presented list to a manageable size. For this purpose, proteins, with unknown function and unknown relation to tumorigenesis or cancer, are of special interest. Moreover, subgroups of proteins sharing a common functional domain are also an interesting assortment. Why proteins with a common domain show analogous regulation profiles should be analyzed in the future as this could identify functionally important protein domains as druggable targets for cancer therapy.

Table 2. Cytoskeletal Proteins Identified within the iTRAQ, 2D-PAGE, and SILAC Experiments and Regulated at Least 1.5-Folda Acc ID

protein name

T/BJ

TE/T

TER/ TE

Extracellular Matrix, Cell Junction, Cell Adhesion Stomatin-like protein 2 0.99 1.67 1.23 Septin-11 0.95 0.65 0.80 Zyxin 0.93 0.63 1.05 Catenin β-1 0.56 Fibronectin 0.51 3.15 1.18 Collagen α-1(I) chain 1.25 0.40 1.19 Collagen α-2(I) chain 1.21 0.44 1.21 Collagen α-1(VI) chain 0.73 0.59 1.58 Collagen α-2(VI) chain 0.68 0.64 1.42 Collagen α-1(XII) chain 1.22 0.48 1.39 Actin-Binding O43491 Band 4.1-like protein 2 1.22 1.15 1.05 Q14847 LIM and SH3 domain 0.29 protein 1 Q0ZGT2 Nexilin P06396 Gelsolin 0.75 0.59 0.93 P12814 α-Actinin-1 0.60 Q16658 Fascin 0.88 0.75 0.91 P21333 Filamin-A 0.37 Q14315 Filamin-C 0.87 0.68 0.83 Q05682 Caldesmon 1.43 0.44 0.83 Microtubule-Associated Q15691 Microtubule-associated 1.21 0.35 0.82 protein RP/EB family member 1 P78559 Microtubule-associated 1.02 0.45 0.74 protein 1A P27816 Microtubule-associated 0.91 0.72 0.71 protein 4 P04792 Heat shock protein β-1 0.82 0.48 1.14 Intermediate Filament or Intermediate Filament Associated P08729 Cytokeratin 7 0.96 P20700 Lamin-B1 1.08 1.64 0.99 Q03252 Lamin-B2 1.09 1.37 1.07 Q15149 Plectin 1.20 Nucleosome P07305 Histone H1 1.11 1.03 1.51

Q9UJZ1 Q9NVA2 Q15942 P35222 P02751 P02452 P08123 P12109 P12110 Q99715

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TER/ BJ 1.99 0.48 0.61 0.36 2.49 0.63 0.67 0.72 0.61 0.85 1.75

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ASSOCIATED CONTENT

S Supporting Information *

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Supplemental figures and files. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

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*Phone: +49 (0) 931-201-45806. E-mail: stephanie.puetz@ uni-wuerzburg.de.

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Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors acknowledge the work of Lars Hofmann who generated the cell lines for this study. Thanks go to Steffi Wortelkamp for her support in the COFRADIC analysis. The authors thank Rene Zahedi for help with LC−MS/MS measurements and Patrick Stalph for help with the analysis of SILAC data. S.M.P. wishes to thank Thomas Raabe for his support. A.S. thanks the “Ministerium fü r Innovation, Wissenschaft und Forschung des Landes Nordrhein-Westfalen” and the “Bundesministerium für Bildung und Forschung” for continuous financial support. This work was mainly funded by Deutsche Forschungsgemeinschaft (Forschungszentrum FZT82 and TR17 “Ras-dependent pathways in human cancer” project B2).

↓ 1.71 1.55 0.57 1.69

a

Listed are the protein name according to cytoskeletal function or compartment, the SwissProt Acc Id and the median regulation of the protein of all experiments within the respective transition. Given that a median represents only the 2D-PAGE experiment and the protein spot is not detected in the respective cell line, arrows indicate the direction of regulation.

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known to affect chromatin structure, e.g., by phosphorylation of histone H1 and histone H3.83,84 Moreover, we found an upregulation of histone H1 due to H-RasV12 transformation. Thus, the H-RasV12 transformation has an impact not only on the phosphorylation of histones but also on the protein amount of histone H1. To sum up, many of the cytoskeletal proteins are regulated due to SV40 ER expression, which correlates strikingly with the morphological changes at the BJ-T/TE transition from a mesenchymal to a more roundish shape. Considering these changes in cell morphology, the reorganization of the cytoskeleton appears to be important for this transition.

ABBREVIATIONS 2D-PAGE, two-dimensional PAGE; CDC2, Cyclin-dependent kinase 1; CH, calponin homology; COFRADIC, combined fractional diagonal chromatography; DNA-PK, DNA-dependent Protein Kinase; GIST, global internal standard technology; hnRNPs, heterogeneous nuclear ribonucleoproteins; ICAT, isotope coded affinity tag; iTRAQ, isobaric tag for relative and absolute quantitation; LT, large tumor antigen; MCM, minichromosome maintenance; pRB, retinoblastoma protein; RRM, N-terminal RNA-recognition motif; SCX, strong cation exchange chromatography; SILAC, stable isotope labeling with amino acids in cell culture; SLP2, Stomatin-like protein 2; 2150



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snRNPs, small nuclear ribonucleoproteins; ST, small tumor antigen; SV40 ER, simian virus 40 early region

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iTRAQ Analysis of a Cell Culture Model for Malignant Transformation

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