Metabolic signatures of malignant progression in prostate epithelial cells

The International Journal of Biochemistry & Cell Biology 43 (2011) 1002–1009

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Metabolic signatures of malignant progression in prostate epithelial cells Orla Teahan a , Charlotte L. Bevan b , Jonathan Waxman b , Hector C. Keun a,∗ a Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Biomolecular Medicine, Sir Alexander Fleming Building, South Kensington Campus, Exhibition Rd., London SW7 2AZ, United Kingdom b Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, Molecular and Cellular Biology Unit, Section of Oncology, Hammersmith Hospital Campus, Du Cane Rd., London W12 ONN, United Kingdom

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Article history: Available online 13 July 2010 Keywords: Metabolomics NMR spectroscopy Prostate cancer Biomarkers Branched chain amino acids Metabonomics

a b s t r a c t Prognostic markers that can distinguish indolent from aggressive prostate cancer could have substantial patient benefit, helping to target patients most in need of radical intervention, while avoiding overtreatment of a highly prevalent condition. The search for novel cancer biomarkers has been facilitated by the development of technologies for “global” biomolecular profiling, used in the sciences of transcriptomics, proteomics and metabolic profiling (metabonomics/metabolomics). Using an NMRbased approach we compared intracellular and extracellular metabolic profiles from the immortalised, non-tumourigenic prostate epithelial cell line, RWPE-1 and two tumourigenic sublines with increasing malignant phenotypes, WPE1-NB14 and WPE1-NB11, generated by N-methyl-N-nitrosourea (MNU) mutagenesis. Collectively, these cell lines present an in vitro model of prostate cancer progression and disease aggression. We observed progressive alterations to intracellular levels of multiple metabolites from choline and branched chain amino acid metabolic pathways from RWPE-1 to WPE1-NB14 to WPE1NB11 cells. In addition specific perturbations to intracellular glycine and lactate and extracellular lactate and alanine were observed relative to the parent line. The pathways implicated by comparative metabolic profiling in this model are known to be altered in human prostate cancer, and potentially represent a source of biomarkers for prostate cancer aggression. © 2010 Elsevier Ltd. All rights reserved.

1. Introduction Metabonomics is defined as “the quantitative measurement of the multiparametric metabolic response of living systems to a pathophysiological stimuli or genetic modification” (Nicholson et al., 1999), i.e. the conducting of multivariate analyses of metabolic dysfunction in a biological system (Nicholson et al., 2004). To achieve this analytical methods, such as NMR, LC–MS or GC–MS, are often combined with chemometric data analysis (‘pattern recognition’) for respectively generating and interpreting profiles of metabolism in complex biological systems. The complete analysis of the spectral features gives a wide snapshot of the metabolism at that point. In recent years metabonomics has been successfully applied to the field of cancer research. Studies have been conducted on detection of ovarian cancer (Odunsi et al., 2005), diagnosis and staging of oral (Tiziani et al., 2009), prostate (Cheng et al., 2005; Sreekumar et al., 2009) and breast cancer (Sitter et al., 2006); response to therapy in breast cancer (Keun et al., 2009); response of tumour cells to perturbations with specific metabolic inhibitors (Ramanathan et al., 2005; McFate et al., 2008); characterisation of

∗ Corresponding author. Tel.: +44 0 207 59 43161; fax: +44 0 207 5943226. E-mail address: [email protected] (H.C. Keun). 1357-2725/$ – see front matter © 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.biocel.2010.07.003

tumour metabolic phenotypes in cell lines (Ackerstaff et al., 2001; DeBerardinis et al., 2007); and animal models of human cancer (Griffiths et al., 2002; Teichert et al., 2008; Backshall et al., 2009; Rantalainen et al., 2006). Early detection is paramount for reducing cancer morbidity and mortality and the high incidence of prostate cancer has led to calls for a national screening programme to be established to improve early detection. Currently available biomarkers for CaP do not have sufficient predictive power (i.e. sensitivity and specificity) to justify population screening. In addition, current biomarkers do not have good prognostic value. The best available test for prostate cancer is the PSA test, which identifies only around 70% of prostate cancers, does not distinguish sufficiently between malignant and benign disease, which includes prostatitis and benign prostatic hypertrophy (BPH) (Stenman et al., 1999). Hence biopsy is the only definitive method for CaP diagnosis. Prostate cancer can be a slow-growing malignancy, hence determining which tumours are indolent and which are aggressive is also paramount for management of CaP, allowing the accurate identification of tumours requiring radical treatment interventions and preventing unnecessary treatment in patients for whom the disease is not life threatening. There is substantial evidence to suggest that metabolism is an important source of new CaP biomarkers. Prostate cells possess a unique metabolism that is specialised for the production, storage

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and secretion of PSA, polyamines and citrate (Takyi et al., 1977). CaP is characterised by high levels of lactate and low citrate levels compared to normal prostate and BPH tissue (Schick et al., 1993; Kurhanewicz et al., 1996a,b; Costello et al., 1999; Cheng et al., 2005; Glunde and Serkova, 2006). While PSA can be measured in the plasma, citrate and polyamines can be assessed in vivo through magnetic resonance spectroscopy (MRS) of the prostate (Costello et al., 1999); MRS measurement of the alterations in prostate cell metabolism that occur with malignancy hence provides a method of diagnosing and monitoring CaP. In general a pattern of high choline/creatine with low citrate and polyamines is associated with CaP (Schick et al., 1993; Kurhanewicz et al., 1996a,b; Swanson et al., 2003; Cheng et al., 2005; Glunde and Serkova, 2006). Some studies have described changes in taurine, myo-inositol and scyllo-inositol (Heerschap et al., 1997; Lynch and Nicholson, 1997; Swanson et al., 2003; Serkova et al., 2008); glutamine (Menard et al., 2001), glutamate (Hahn et al., 1997), and lactate and alanine (Swanson et al., 2006; Tessem et al., 2008) associated with prostate malignancy, however theses metabolic changes have not been associated with tumour grade. Loss of citrate is characteristic of prostate cancer and occurs due to the lowered levels of intracellular zinc (Costello et al., 2005). Citrate levels are low in early stage CaP and effectively absent in poorly differentiated CaP tumours (Kurhanewicz et al., 1993). Increased choline and decreased citrate prostatic levels are correlated to the Gleason score (Kurhanewicz et al., 2000; Swanson et al., 2006). Additionally, it has been found that polyamines are absent in 80% of high grade tumours suggesting that reduction in polyamines is a marker of CaP progression, as well as an indication of tumour aggression (Swanson et al., 2003). More recently a metabolomic study revealed sarcosine as a possible marker of prostate cancer progression (Sreekumar et al., 2009). Thus, measurement of in vivo metabolite patterns in CaP may aid in identifying potentially lethal tumours. In vitro studies targeted towards the metabolic changes identified by in vivo studies can provide insight into tumour biology, and metabolic profiling experiments in cellular systems have been shown to recapitulate many of the metabolic features seen in tumours, such as altered choline or glutamine metabolism (Aboagye and Bhujwalla, 1999; Ackerstaff et al., 2001; DeBerardinis et al., 2007). The aims of this metabonomic study were to identify metabolic changes associated with malignant transformation, and to identify biomarkers for aggressive prostate cancer disease through metabolic profiling of a panel of transformed prostate cell lines. The RWPE-1 cell line is a normal epithelial prostate cell line that has been isolated from the peripheral zone of a normal adult human prostate and immortalised by transfection with a plasmid containing one copy of the HPV-18 (Rhim et al., 1994). This parental cell line exhibits many of the characteristics of a normal prostatic epithelial cell, including expression of AR, and full genomic response to androgen stimulation including PSA response (Webber et al., 1996, 1997a,b, 2001; Bello et al., 1997). Mutagenesis of the RWPE-1 cell line using the carcinogen N-methyl-N-nitrosourea (MNU) produced a panel of cell lines with increasing malignant potential that serves as a model system for investigating prostate cancer progression and essentially prostate cancer aggression (Webber et al., 2001). Like RWPE-1, the WPE1-NB11 and WPE1-NB14 cell lines express AR and PSA but when injected into nude mice the MNUtransformed cells form tumours whereas the RWPE-1 cells do not. Furthermore, while RWPE-1 cells do not proliferate in agar but arrange themselves into glandular acini of polarised cells, similar to normal glands in vivo, WPE1-NB11 cells form poorly differentiated invasive tumours while WPE1-NB14 cells form well-differentiated non-invasive tumours, i.e. are of intermediate phenotype (Webber et al., 2001). The cells have a common genetic background that makes them an ideal model for tracking the molecular changes

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that are associated with the progression of CaP from normal to invasive. To investigate metabolic changes that are associated with increased malignant potential in these models, the baseline intracellular and extracellular metabolic profiles from RWPE-1, WPE1-NB14 and WPE1-NB11 cells were examined by 1 H NMR spectroscopy. Pattern recognition was used to identify variation in metabolite levels in the data that coincided with malignant progression.

2. Materials and methods WPE1-NB11, WPE1-NB14 and RWPE-1 cell lines (purchased from American Type Culture Collection) were subcultured in Keratinocyte-Serum Free medium supplemented with 5 ng/ml human recombinant EGF and 0.05 mg/ml bovine pituitary extract (GIBCO). All cells were maintained in an incubator at 37 ◦ C, 5% CO2 . Each cell line was cultured and profiled in triplicate. On reaching the desired confluency of 70%, the media was replenished with fresh media for 24 h prior to cell harvesting. This allowed for standardisation of the time between equilibration of extra- and intracellular metabolites across the cell lines and reduced effects of substrate limitation on the intracellular metabolism.

2.1. Extraction and preparation for NMR spectroscopy For NMR analysis each replicate was harvested from 1 T75 cm3 culture flask (∼4 million cells). Spent media in a tissue culture flask was transferred into a sterile tube and centrifuged (4 ◦ C, 125 × g, 5 min) to pellet dead cells. The media was then transferred into a sterile tube and frozen at −40 ◦ C until required for NMR analysis. The cells were then washed twice in cold PBS and the PBS discarded. 5 ml of cold PBS was added to the flask and the cells detached from the surface with a cell scrapper. The cell suspension was transferred into a sterile tube and centrifuged at 125 × g for 5 min. The PBS was discarded and the cell pellet resuspended in 1 ml of cold PBS. 20 ␮l of cell suspension was removed and cell counts taken using a haemocytometer. The remaining suspension was centrifuged at 16 000 × g for 5 min before the PBS was removed and the cell pellets stored at −40 ◦ C until required for extraction. Cells were extracted using methanol–chloroform–water extraction method similar to that previously evaluated and recommended for 1 H NMR spectroscopy of cells (Le Belle et al., 2002). Cells were stored in eppendorfs on ice while 200 ␮l CHCl3 and 100 ␮l of methanol were added. Eppendorfs were vortexed before a further 300 ␮l H2 O was added, then vortexed again and the resulting suspension centrifuged at 16 000 × g for 10 min. The upper aqueous layer was carefully separated from the lower organic layer and both aliquoted in clean glass vials. Extraction was repeated on remaining pellet to maximize metabolite recovery. The aqueous fraction was allowed to stand overnight to allow methanol to evaporate before being freeze-dried. The aqueous cell extract was reconstituted for NMR in 600 ␮l of deuterated 0.1% sodium phosphate buffer containing 0.002% 3,3,3trimethylsilylpropionate (TSP) and 0.02% azide in an eppendorf tube. The eppendorf was vortexed and 550 ␮l of the aqueous extract was pipetted into an NMR tube. The media samples were defrosted at room temperature and pipetted into NMR tubes. The total volume added to each tube was 600 ␮l: 550 ␮l of media and 50 ␮l of 0.2% TSP in D2 O. Prior to NMR analysis, tubes were inverted twice to ensure samples were mixed.

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2.2. Total protein quantification with BCA assay The Pearce MicroBCA protein assay was used to quantify total protein in the extraction interface pellet generated from chloroform/methanol extraction as described previously (Smith et al., 1985). 2.3. NMR spectroscopy NMR spectra were acquired on a Bruker DRX600 spectrometer operating at 600. 13 MHz 1 H NMR frequency and 300 K. Gradient shimming was used immediately prior to spectral acquisition. 1 H NMR spectra of the samples were acquired using a 1D Carr–Purcell–Meiboom–Gill (CPMG) pulse sequence (RD−90◦ − { − 180◦ − }n -acquire). The CPMG sequence generates spectra edited by T2 relaxation times, i.e. with reduced signals from high molecular weight species or systems in intermediate chemical exchange. For all spectra 128 free induction decays (FIDs) were collected into 32 K complex data points, using a spectral width of 12 019 Hz (20 ppm), with a 2 s relaxation delay between pulses. A water presaturation pulse was applied throughout the relaxation delay. n = 80 and  = 400 ␮s, for a total T2 relaxation time of 64 ms. The CPMG spectral editing method was chosen to decrease high molecular weight content in the spectra. 2.4. Spectral processing Data were zero-filled by a factor of 2 and the FIDs were multiplied by an exponential weighting function equivalent to a line broadening of 1 Hz prior to Fourier transformation (FT) using XWINNMR software. All subsequent data processing and analysis, unless specifically stated otherwise, was conducted using in house software NMRProc (for baseline corrections and referencing spectra) and MetaSpectra (for import of NMR data into MATLAB) which were written and compiled in MATLAB by Dr. T.M.D. Ebbels, Dr. H.C. Keun, Dr. J.T. Pearce, Dr. Rachel Cavill and Dr. Olivier Cloarec (Cloarec et al., 2005; Pearce et al., 2008). The acquired NMR spectra were corrected for phase and baseline distortions and referenced internally to TSP ␦0.0. Spectra were interpolated from 32 K to ∼42 K data points to regularise the abisscae prior to pattern recognition analysis. Metabolite peaks were integrated for comparative quantitative evaluation; i.e. the area under the resonance peak of interest was computed for each spectrum with a local linear baseline correction, using MATLAB code written by Dr. Rachel Cavill and Dr. Hector Keun. This data was imported into SPSS or SIMCA-P for comparative analysis. Integral intensities were divided by the protein concentration and the cell number for normalisation to each of these measures respectively. 2.5. Statistical tests One-way analysis of variance (ANOVA) was used to test for significance between integral intensity metabolite means for different experimental groups or conditions. The Dunnett T3 post hoc test was chosen as it was assumed that there was non-homogeneity of variance in the data. 3. Results The non-tumourigenic RWPE-1 prostate epithelial cell lines and the two MNU-transformed sublines WPE1-NB11 and WPE1-NB14 were cultured under standard conditions and both intracellular extracts and culture media collected for spectroscopic analysis. An initial pilot study was conducted which indicated significant alterations to the metabolism with transformation relative to the

Fig. 1. Intracellular metabolic profiles from transformed prostate cell lines with progressive malignant phenotypes. Mean spectra are shown from 1 H NMR analysis of intracellular extracts from RWPE-1 (red), WPE1-NB14 (blue) and WPE1-NB11 (green) cells (aliphatic spectral region, ␦0–4.1, only). Spectra were referenced to TSP and normalised to the median fold change. Each spectrum is the mean of three replicates.

parent line, specifically increases to intracellular phosphocholine (up to +148%) and decreases to branched chain amino acids (up to −41%) and ␤-hydroxyisovalerate (up to −58%) (normalised by median fold change, Supplementary Data). However, this initial study did not control for depletion of substrates in the media, which can have a profound effect on intracellular metabolic profiles. For this reason a nutrient-controlled experiment was performed on the same cell lines with a more defined protocol used to measure baseline metabolism of the different cell types. Specifically, on reaching the desired confluency the media was replenished with fresh media for 24 h prior to cell harvesting. This allowed for standardisation of the time between equilibration of extra- and intracellular metabolites across the cell lines and reduced effects of substrate limitation on intracellular metabolism. Cell counts were taken and the total protein concentration was determined for normalisation. The resulting 1 H NMR spectra were visually inspected for changes in intracellular metabolite intensity that correlated with disease progression across the cell lines (Fig. 1). An O-PLS-DA model (Cloarec et al., 2005) was built using intracellular profiles from the parent RWPE-1 cell line and the most transformed cell line WPE1-NB11 to identify metabolites that varied with malignant potential which had not been detected by visual inspection (2 latent variables, predicted variance explained with 7-fold cross validation Q2 = 0.9; Fig. 2). A panel of resonances were identified that changed in response to malignant transformation with MNU. Integral intensities for this select panel were calculated and tested for significant differences between the lines. The amino acids leucine, valine and isoleucine exhibited highly correlated responses and so methyl group resonances from these metabolites were integrated together presenting a single response value. Table 1 reports the percentage change in metabolite intensity from the parent cell line for each resonance identified (columns 3–6). In a further two separate analyses, the spectral data were normalised to (1) cell number (columns 7–10) and (2) total protein concentration (columns 11–14) and the panel of resonances identified from the initial screen again probed for changes relating to tumour progression (Table 1). The majority of the resonances exhibited statistically significant differences between the cell lines and could be assigned to specific metabolites from several pathways, including choline metabolism (choline, phosphocholine and glycerophosphocholine), methylation (glycine),

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Fig. 2. O-PLS-DA model loadings highlighting differences between intracellular metabolite levels in RWPE-1 and WPE1-NB11 cells. Metabolites projecting up are increased after malignant transformation; conversely metabolites projecting down are decreased in WPE1-NB11 cells. The colour code corresponds to the correlation coefficients of the variables. (a–d) Represent different regions of the spectra: (a) 2.8–1.6 ppm, (b) 0.9–1.6 ppm, (c) 4.2–3.3 ppm and (d) 3.35–2.95 ppm. 1 H NMR spectra were calibrated to TSP and normalised to the median fold change. Replicate numbers: RWPE-1 n = 3, WPE1-NB11 n = 3.

amino acid metabolism and transport (leucine, isoleucine, valine, hydroxyisovalerate, glycine, glutamine, glutamate, alanine) and glycolysis/pyruvate metabolism (lactate, alanine). In general different normalisation techniques produced quantitatively similar differences in metabolite abundance between the cell lines. However normalising by cell count typically gave lower levels of statistical significance than the median fold change normalised data and total protein normalised data. Although the protein concentrations and cell counts were not significantly different between the cell lines (ANOVA), a poor linear correlation was observed between the total protein concentration and the cell numbers for the WPE1-NB14 and RWPE-1 lines. This suggested a high analytical variability in either the cell count or protein quantification measurements. We concluded that the median fold change normalisation was the most sensitive method for initial detection of metabolites of interest, but have considered the reproducibility of

the magnitude of metabolic difference across normalisation methods for subsequent interpretation. Two pathways in particular, choline metabolism and branched chain amino acid metabolism, appeared to be systematically perturbed by MNU transformation, exhibiting differences in multiple members of the pathway and largely irrespective of normalisation method. These effects were also observed in our pilot study indicating that these were robust to the extracellular milieu and nutrient availability (Supplementary Data). 3.1. Choline metabolism is altered with CaP progression Fig. 3 illustrates the variation in intracellular levels of choline metabolites across the cell lines. Intracellular phosphocholine (PC) levels broadly increased with increasing malignancy and glycerophosphocholine (GPC) levels were higher in both transformed

Table 1 Relative (%) change from parent RWPE-1 cell line in intracellular metabolite intensity with increasing malignant phenotype. Metabolite

Branched chain amino acids ␤-Hydroxy isovalerate Lactate Alanine Lac/ala Unassigned Glutamine + glutamate Glutamate Glutathione Unassigned Unassigned Aspartate Choline PC GPC PC/GPC Glycine Unassigned Unassigned Unassigned Unassigned a b

␦ 1 H (ppm)

0.9–1.1 1.27 1.32 1.48 1.7 2.14 2.35 2.55 2.7 2.78 2.79–2.84 3.2 3.22 3.23 3.22–3.23 3.565 4 7.89 8.275 8.83

WPE1-NB14 Normalised by median fold changea

WPE1-NB11 Normalised by median fold changea

WPE1-NB14 Normalised by cell count

WPE1-NB11 Normalised by cell count

WPE1-NB14 Normalised by total protein

WPE1-NB11 Normalised by total protein

% change

pb

% change

pb

% change

pb

% change

pb

% change

pb

% change

pb

−24 −36 −8 10 −16 −9 −15 −3 8 −30 −59 −27 87 103 155 −22 −41 19 −23 32 −34

0.02 0.01 0.8 0.4 0.1 0.2 0.2 0.9 0.9 0.1 0.03 0.08 0.03 0.003 0.002 0.07 0.005 0.3 0.2 0.007 0.03

−28 −33 139 17 105 −12 −26 −12 −14 −59 −35 −49 146 130 63 39 −36 22 −37 40 −32

0.2 0.1 0.6 0.2 1 0.4 0.1 0.4 0.7 0.02 0.9 0.9 0.07 0.009 0.005 0.02 1 0.9 0.4 0.5 0.5

−23 −35 −7 12

0.1 0.1 0.9 0.4

−39 −44 100 −1

0.02 0.05 0.003 1

−24 −35 −7 11

0.3 0.08 1 0.9

−40 −45 97 −3

0.07 0.007 0.1 1

−8 −12 −1 10 −30 −59 −26 90 106 156

−0.8 0.5 1 0.9 0.3 0.05 0.2 0.03 0.002 0.006

−26 −38 −26 −28 −66 −46 −58 106 93 36

0.06 0.003 0.05 0.3 0.04 0.1 0.03 0.02 0.003 0.3

−9 −15 −3 8 −28 −57 −25 88 105 158

0.9 0.6 1 1 0.5 0.2 0.4 0.2 0.1 0.1

−27 −37 −26 −28 −66 −48 −57 103 93 35

0.2 0.2 0.4 0.5 0.03 0.2 0.02 0.07 0.2 0.5

−39 20 −20 34 −33

0.01 0.4 0.3 0.7 0.03

−46 2 −47 18 −43

0.001 1 0.003 0.6 0.09

−41 22 −25 33 −35

0.04 0.8 0.4 1 0.4

−46 22 −48 16 −44

0.08 1 0.1 1 0.2

See Dieterle et al. (2006). ANOVA/Dunnett’s T3, bold indicates p < 0.05.

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Fig. 3. Intracellular choline metabolism is altered with progression in malignant phenotype (a) mean 1 H NMR spectral overlay including the choline metabolites in the intracellular baseline metabolic profiles from RWPE-1 (red), WPE1-NB14 (blue) and WPE1-NB11 (green) cell lines. (b–d) Mean values for integral intensity calculated for intracellular choline metabolites in RWPE-1, WPE1-NB14 and WPE1-NB11 cells at baseline metabolism. Each spectrum was normalised to the median fold change and referenced to TSP prior to integral intensity calculations. Figures (b–d) are intracellular PC, GPC and PC/GPC ratios. Significance was tested using ANOVA followed by Dunnett’s T3 test (* = p < 0.05 relative to RWPE-1). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)

lines (Fig. 3, Table 1). In the non-invasive, MNU-transformed WPE1NB14 cells there was a 103% (p < 0.001) increase in PC levels compared to the parental cell line observed. The highest levels of PC were found in the more progressive WPE1-NB11 tumour cells with a 130% increase observed (p < 0.01). This was consistent with what has been previously reported for CaP tumour progression (Ackerstaff et al., 2001). When the data was normalised to cell number, intracellular PC levels increased in both the WPE1-NB14 and WPE1-NB11 cells by 106% (p < 0.005) and 93% (p < 0.005) respectively compared to the parental cell line (Table 1). A similar but non-significant trend in PC levels was observed for protein normalised data. A 155% increase in GPC levels from the parent line (p < 0.005) was observed in WPE1-NB14 cells, however only 63% (p < 0.01) rise in GPC was detected in the more malignant WPE1NB11 cells (Table 1). In the cell number and protein normalised data again a quantitatively similar trend was observed with a lower level of significance (Table 1). Calculating the PC/GPC ratio allows variation in choline metabolites to be observed in the different cell lines independent of cell number or biomass (i.e. independent of normalisation) (Table 1). In the median fold change normalised data the PC/GPC ratio was decreased by 22% (p < 0.1) in the WPE1-NB14 cells but increased by 39% (p < 0.05) in the most invasive WPE1-NB11 cells (Table 1). These results highlight the importance of PC in tumour cell progression. PC increased with increasing malignancy with highest levels of PC detected in more progressed tumour cells.

3.2. Branched chain amino acid metabolism is affected by malignant progression The intracellular levels of branched chain amino acids (leucine, valine and isoleucine) decreased on malignant transformation (Fig. 1), i.e. the highest levels were observed in the parental RWPE1 cell line. As levels of these amino acids appeared to vary in a coordinated manner, the methyl resonances from these were integrated together for quantitative analysis (Table 1). The branched

chain amino acid levels decreased by similar levels in the transformed cell lines compared to the parental line, with a 24% decrease (p < 0.05) found in WPE1-NB14 cells and a 28% reduction (p < 0.2) in WPE1-NB11 cells. When the data was normalised by either the cell number or the total protein concentration the pattern of depletion in branched chain amino acids was similar, with a larger reduction in the more malignantly transformed WPE1-NB14 line (∼40%, p < 0.1). These results suggest that the metabolism of the branched chain amino acids is altered on malignant transformation of RWPE1 cell lines, and this was further supported by the observation of alterations to ␤-hydroxyisovalerate. This metabolite is a hydrolysis by-product of an intermediate in leucine degradation, and levels were depleted in malignantly transformed cells (by 36% in WPE1NB14 cells and by 33% in WPE1-NB11).

3.3. Other intracellular metabolite differences between the cell lines Lactate levels were significantly perturbed only when the profiles were normalised to cell count and then only in the most invasive WPE1-NB11 line (+100%, p = 0.003). However other normalisations suggested similar increases in lactate for this cell line (97 and 137%). No differences were observed in levels of alanine across any of the cell lines, a metabolite that, like lactate, can be derived directly from pyruvate. Glycine levels were substantially and significantly reduced in WPE1-NB14 line compared the parent line with a high degree of consistency across normalisations (39–41%, 0.001 < p < 0.04). The most aggressive WPE1-NB11 line exhibited an approximately similar depletion (36–46%) but this was only clearly significant when normalised by cell count. Sarcosine is a metabolite associated with CaP progression (Sreekumar et al., 2009) and is a methylation product of glycine. We observed quantitatively similar effects (i.e. reduction in the cell with tumourigenic phenotype) in a singlet resonance at 2.7 ppm that was likely to originate from sarcosine, but due to the low abundance of the resonance could not confirm the

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4. Discussion

Fig. 4. Extracellular metabolic profiles from metabolism of prostate cell lines with progressive malignant phenotypes. Mean 1 H NMR spectral overlay of baseline extracellular metabolic profiles: RWPE-1 (red), WPE1-NB14 (blue) and WPE1-NB11 (green). (a) The aliphatic spectral region (␦0.5–␦4.3). All spectra were referenced and normalised to TSP. Each spectrum is the mean of four replicates. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)

assignment. Lower levels of aspartate were also typically observed in both cell lines, but only reaching significance in the most aggressive WPE1-NB11 line (57–58% reduction when normalised to cell count or protein, p < 0.05). 3.4. Metabolic footprint analysis Metabolic footprinting is defined as ‘a strategy for analyzing the properties of cells or tissues by looking in a high-throughout manner at the metabolites that they excrete or fail to take up from their surroundings’ (Kell et al., 2005), i.e. investigation of the extracellular metabolome by profiling conditioned media (culture supernatant). In our study the extracellular metabolic profile was examined by 1 H NMR spectroscopy of media from the same cultures of the cell lines used to generate intracellular profiles. The spectral overlay of the mean spectra from media for each cell line (Fig. 4) demonstrates that only subtle variations in the media composition are observed after 24 h in culture across the different cell lines, and not substantial nutrient depletion. Resonances from two metabolites typically accumulating in the culture media, lactate and alanine, exhibited clear visual differences in intensity. The highest levels of extracellular lactate were found in the media from the parental RWPE-1 cell line. With transformation, there was a decrease in extracellular lactate levels with lowest levels found in the less differentiated WPE1-NB14 cells (26% reduction, p < 0.05) and a non-significant reduction of 11% (p < 0.5) in invasive WPE1NB11 cells. Thus with increasing malignant transformation there appears to be an initial decrease then a subsequent partial recovery in the rate of accumulation of extracellular lactate. Interestingly, extracellular alanine appeared to be negatively correlated with the extracellular lactate, with an increase in alanine accumulation observed in the less differentiated WPE1-NB14 cells (+47%, p < 0.05) and in WPE1-NB11 cells (15%, p < 0.1). As the effects on extracellular lactate and alanine were inversely correlated we investigated whether the extracellular lactate/alanine ratio (representing the ratio of the rate of accumulation in the media) was more sensitive to the transformation process than the individual metabolites. When considering this ratio both WPE1-NB14 and WPE1-NB11 exhibited significant differences from RWPE-1 of 49% (p < 0.05) and 23% (p < 0.01) respectively, confirming this hypothesis.

Effective biomarkers for staging and monitoring CaP are not currently available in the clinic and the standard staging methods have a number of associated limitations. The discovery of a biomarker for tumour staging, and also one that distinguishes aggressive disease from indolent, would benefit patients greatly in terms of facilitating personalised therapy. In this in vitro study of CaP progression, we have conducted metabolic profiling of the HPV-18 immortalised normal prostate cell line RWPE-1 and its MNU malignantly transformed sublines WPE1-NB14 and WPE1-NB11 (Bello et al., 1997; Webber et al., 2001). WPE1-NB11 has a malignantly progressed, poorly differentiated and invasive phenotype while WPE1-NB14 is intermediate between the benign RWPE-1 and tumourigenic WPE1-NB11 cell lines, characterised by a well-differentiated, and non-invasive phenotype. With the acquisition of with malignant phenotype we observed significant, progressive alterations to multiple metabolic pathways with malignant phenotype, namely choline and branched chain amino metabolism, with further perturbations to levels of intracellular glycine and lactate and extracellular lactate and alanine relative to the benign parent line. Several of the metabolic pathways and metabolites implicated in these experiments as associated with malignant transformation in vitro have been previously identified in multiple independent studies as differentiating between benign and malignant prostate disease in vivo. MRS/NMR studies in particular have reported increases in total choline/phosphocholine (Schick et al., 1993; Kurhanewicz et al., 1996a,b; Cheng et al., 2005; Glunde and Serkova, 2006), lactate and alanine (Tessem et al., 2008) in prostate tissue and lactate enrichment by hyperpolarised 13 C-pyruvate has been observed in the TRAMP mouse model (Albers et al., 2008). In prostate tissues appearance of PC resonances after non-radical therapy, radiotherapy or hormonal, indicates tumour recurrence (Mueller-Lisse et al., 2001; Schilling et al., 2008). More recently glycine metabolism, in particular the metabolite sarcosine, has been highlighted as playing a role in prostate cancer aggression using MS-based metabolomics (Sreekumar et al., 2009). Increased glucose consumption and lactate production are classical hallmarks of tumour cell metabolism (Griffin and Shockcor, 2004). Increased lactate has been observed in a variety of metastatic cancers including cervical (Walenta et al., 2000) and squamous head and neck (Wallace, 2007; McFate et al., 2008). In tumour cells, glycolysis is upregulated and glucose is converted to lactate in an energyinefficient manner. The TCA cycle is impaired, however it remains functional as demonstrated by McFate et al. (2008). When the inhibition of pyruvate dehydrogenase kinase (PDK) was lifted through the use of a PDK inhibitor, the TCA cycle was restored and tumour cells reverted to a normal metabolic phenotype with low levels of lactate observed intracellularly by 1 H NMR spectroscopy. The alterations to choline metabolism can be exploited by imaging modalities more sensitive than magnetic resonance in order to probe for tumour aggression. Based on the findings of cancerassociated increased choline levels, 11 C- and 18 F-choline tracers are being developed for use in PET imaging for the localisation and post-therapy staging of CaP. 11 C-choline uptake is high in primary and metastatic CaP and imaging this has proved to be more sensitive than bone scintigraphy in the detection of bone metastases (Hara et al., 1998). In patients with biochemically relapsed CaP, 11 C-choline PET was able to correctly identify tumours with detection rate of 73% in patients with PSA above 3 ng/ml (Krause et al., 2008). The uptake of a 18 F-labelled choline analogue (FCH) in CaP patients has been used for PET imaging of CaP and shown to be detect more lesions than 18 F-fluorodeoxyglucose (FDG) (Price et al., 2002). Hence, development of enhanced choline analogues for use as PET tracers may provide new and robust detection of CaP tumours and improve disease detection and management.

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Effects on metabolism of branched chain amino acids are less frequently reported in the context of prostate cancer. A recent study of a xenograft mouse model of CaP reported decreased branched chain amino acids in the plasma of tumour-bearing mice (15). Cancer cells generally have a high avidity for leucine and valine as proven by early PET studies using 11 C-leucine or 14 C-valine (Kirikae et al., 1989; Ishiwata et al., 1993). In the current study, the intracellular and extracellular levels of branched chain amino acids were decreased in the MNU-transformed cells compared to the parental RWPE-1 cell line. This may be due to greater metabolic flux through the anabolic pathway, whereby the amino acids are being incorporated into proteins as soon as they are formed. However the catabolic pathways may also play a role since intracellular levels of ␤-hydroxyisovalerate, a metabolite involved the degradation pathway of branched chain amino acids, was depleted in MNUtransformed cells but present in the non-malignant parent line. This is suggestive of an increase of amino acid catabolism (probably via the TCA cycle) in tumourigenic cells. Branched chain amino acids have roles beyond energy or synthetic substrates: these metabolites are regulators of protein metabolism and excess amounts of leucine and glutamine can stimulate cell growth and cell cycle progression through activation of mTOR cell signalling cascades (Dann and Thomas, 2006; Wang and Proud, 2006; Cook and Morley, 2007). It has been shown that increasing branched chain amino acids in the culture media of canine neoplastic cells results in diminished cell proliferation (Kirikae et al., 1989). Wakshlag et al. (2006) found that increasing the concentration of leucine to supra-physiological levels caused apoptosis of tumour cells. Thus, a mechanism that reduces the amount of cellular branched chain amino acids may assist malignant cells in avoiding apoptosis. It is important to note that no citrate was observed in either the intracellular extracts or culture medium from the parent or MNU-transformed cell lines. In normal prostate tissue and prostatic secretions citrate concentrations are unusually high (∼100 mM) and this is believed to be due to a high import of zinc in prostate glandular epithelial cells that inhibits mitochondrial aconitase, leading to a truncated TCA cycle (reviewed by Mycielska et al., 2009). It has been shown that during malignancy prostate cells lose the ability to produce large amounts of citrate due to the depletion of intracellular zinc (Costello et al., 2005). This results in reactivation of m-aconitase enzyme leading to the full oxidation of citrate by the TCA cycle (Costello and Franklin, 1991, 2005). This increase in oxidative phosphorylation is thought to increase mitochondrial DNA damage from reactive oxygen species, accelerating disease progression (Dakubo et al., 2006). A low citrate/choline ratio, detected by MRS, is indicative of an aggressive tumour (Garcia-Segura et al., 1999; Swanson et al., 2003). RWPE-1 clearly does not retain the normal prostate function of high citrate production. It is possible that HPV-18 transformation of the RWPE-1 cells causes suppression of citrate metabolism and secretion in these cells, similar to what would be associated with tumour metabolism. It has been observed that radiotherapy and hormone deprivation therapy causes citrate production to cease in prostate cells (Menard et al., 2001; Mueller-Lisse et al., 2001), thus this particular metabolic process is susceptible to downregulation in response to wider molecular alterations. Furthermore, metabolic effects often associated with tumours have previously been shown to appear in target tissues prior to tumourigenic transformation as a result of the loss of tumour suppressor function in mouse models (Teichert et al., 2008; Backshall et al., 2009). This observation highlights the difficulties of generating immortalised cell models with ‘normal’ physiological phenotypes. The artificial nature of typical culture conditions also places limitations on the interpretation of in vitro metabolic profiling studies. While the metabolites exhibiting altered levels with malignant progression

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