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NMR-based metabolic profiling of human hepatoma cells in relation to cell growth by culture media analysis
Biochimica et Biophysica Acta 1760 (2006) 1723 – 1731 www.elsevier.com/locate/bbagen
NMR-based metabolic profiling of human hepatoma cells in relation to cell growth by culture media analysis Alberta Tomassini Miccheli a , Alfredo Miccheli a,⁎, Roberta Di Clemente a , Mariacristina Valerio a , Pierpaolo Coluccia b , Mariano Bizzarri c , Filippo Conti a a Department of Chemistry, University “La Sapienza”, P.le Aldo Moro 5-00185 Rome, Italy Department of Surgery “Pietro Valdoni”, University “La Sapienza”, P.le Aldo Moro 5-00185 Rome, Italy Department of Experimental Medicine and Pathology, University “La Sapienza”, V. Scarpa 14/16-00161 Rome, Italy b
c
Received 27 June 2006; received in revised form 12 September 2006; accepted 12 September 2006 Available online 22 September 2006
Abstract Metabolic profiling is a metabolomic approach that allows the characterization of metabolic phenotypes under specific set of conditions. In the present paper we investigated the metabolism of sparse and high density cultures in relation to different cell growth phases. Changes in the metabolome were evaluated by using 1H-NMR spectroscopy, correlation map and Multivariate Data Analysis on the net balances of metabolites in the medium. This approach allowed us to identify two different metabolic profiles in relation to the cell growth phases in subconfluence and confluence cultures. The results have been interpreted on the basis of patterns of correlations obtained in the two physiological cell states. Cells almost arrested in G0/G1 phase by contact dependent growth inhibition underwent changes in the channeling of amino acids utilization from synthetic to energetic purpose and in anaplerosis/cataplerosis regulation of the TCA cycle. © 2006 Elsevier B.V. All rights reserved. Keywords: Metabolomics; 1H NMR; Cancer cell; Amino acid metabolism; HepG2
1. Introduction Many cell types in culture are subjected to contact inhibition of proliferation dependent on cell–cell and cell–matrix interactions, adhesion-dependent signaling, cytoskeletal dynamics and regulated by substrates and growth factor availability. This behaviour, typical of primary cultures, is also common in immortalized cell lines. In this context, the impact of cell density in culture may have important consequences when testing new drugs or biologically active molecules, in particular those believed to influence proliferation and growth. For instance, it has been shown that cell density influences energy metabolism [1], the activation of p53 tumor suppressor [2], the de novo synthesis of sphingolipids [3] and sensitivity to drugs [4], and recently the “critical cell density” of a cultured
⁎ Corresponding author. Tel./fax: +39 06 4455278. E-mail address: [email protected] (A. Miccheli). 0304-4165/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.bbagen.2006.09.009
population was thought to be involved in the adaptive drug resistant mechanism [5]. However, the underlying metabolism that is characteristic of sparse and high density cultures has not been deeply investigated nor the relationship between proliferative rate and metabolism in cells at different phases of cell growth in culture has not been fully clarified. Recently, results obtained by 1HNMR spectroscopy on extracts of a glioma cell line have shown changes in metabolic patterns in relation to log-, confluent- and post-confluent growth stages [6]. Cell growth rate is closely linked to the phases of the cellcycle. A number of different enzyme activities involved in nucleotide synthesis [7], different phospholipid metabolisms [8], differential response to drugs [9], morpho-structural variations and variations in mitochondria intracellular distribution have been associated with cells in different phases of the cycle [10,11]. In a previous work, we demonstrated a different metabolic profile of cell subpopulations in relation to cell cycle phases by 13C-NMR spectroscopy using [1,2-13C2] glucose as a stable-isotope tracer [12].
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Metabolic profiling allows the analysis of the cell metabolome that is defined as the quantitative complement of lowmolecular weight metabolites present in a cell under a given set of physiological activities [13,14]. As changes in medium metabolites, substrate utilization and production and substrate flux distribution reflect the cell physiological state and phenotype and thus the cell metabolome [15], we investigated if the monitoring of metabolites consumed by and released into the medium by cells cultivated at different cell densities provide sufficient information on the metabolic pathways involved. In this respect, NMR spectroscopy, in combination with multivariate data analysis, is a suitable technique allowing the simultaneous quantification of a large number of metabolites without any a priori hypothesis regarding the involved biochemical pathways and leading to the identification of a specific metabolic fingerprint. In this study we utilized a hepatoblastoma cell line (HepG2) which retains the expression of most liver-specific genes [16]. This cell line has been widely used for toxicological and pharmacological studies. In order to obtain the metabolic profiling of cell cultures in relation to the confluence or subconfluence growing state, changes in the exometabolome were evaluated by using 1H-NMR spectroscopy and Multivariate Data Analysis (MVDA) on medium data. This approach allowed us to discriminate two different metabolic profiles in relation to different phases of cell growth in culture and the results were interpreted on the basis of the comparison of the pattern of correlations obtained in the two physiological cell states. The pattern of correlations represents the interconnectivity of the fluxes of metabolites with respect to their pair-wise correlations and can be interpreted as a fingerprint of the underlying system that provides information about the specific physiological state of the cells at a given point in time which can be interpreted as changes in the regulation of metabolic fluxes among the different intracellular pathways [17,18]. 2. Materials and methods 2.1. Materials Minimum Essential Medium (MEM), L-glutamine and fetal bovine serum (FBS) were purchased from GIBCO (Auckland, NZ). Sodium pyruvate, sodium bicarbonate, penicillin and streptomycin, phosphate buffer saline (PBS), Trypan Blue, Trypsin-EDTA 0.25% solution, Propidium Iodide, Triton X100, bovine serum albumin were all from Sigma Aldrich (St. Louis, MO). Methanol, ethanol and chloroform were from Carlo Erba Reagenti (Rodano, Milan, Italy). 3-(trimethylsilyl)-tetradeutero-sodium propionate (TSP) was from Trimital (Milan, Italy).
2.2. Cell culture Human liver tumor cells, HepG2, were obtained from the American Type Culture Collection (Manassas, VA) and were used within ten passages. Cells were grown in 75 cm2 flasks (Falcon, Becton Dickinson Labware) in MEM medium supplemented with 0.3 mg/ml glutamine, 0.11 mg/ml sodium pyruvate, 2 mg/ml sodium bicarbonate, 100 units/ml penicillin and 100 μg/ml streptomycin and 10% FBS. Cells were cultured at 37 °C in a humidified atmosphere of 95% air and 5% CO2. Cell growth and viability were evaluated by the Trypan Blue exclusion test. Briefly, at the end of the experimental time the cells were washed with cold PBS,
detached by trypsinization and 100 μl of the cell suspension were mixed with 2% Trypan Blue and were counted in triplicate in a Burker camera under the optical microscope.
2.3. Experimental protocol and sample preparation for NMR spectroscopy Cells were seeded at concentrations of 4 × 104 and 8 × 104 cells/cm2 in 75 cm2 tissue culture flasks with 10 ml of growth medium. After 24 h of culture for cell attachment, the medium was changed and the cells were cultured for 24 h; in separate experiments the cells were cultured for a total 48 h after the medium change for the evaluation of the proliferation rate. For 1H-NMR spectroscopy the cultures were stopped at 24 h, the medium was collected and extracted and the cells were trypsinized, centrifuged at 300g 4 °C for 5 min and counted. Culture media were extracted using Bligh–Dyer techniques as modified by us [19]. Briefly, 1 ml of medium was sequentially mixed with 3 ml of cold CH3OH:CHCl3 (2:1) and 1 ml of cold CHCl3 and centrifuged at 11,000×g for 20 min. at 4 °C. The aqueous phases were taken up, dried under N2 flow and stored at −80 °C until NMR analysis.
2.4. Flow cytometry An assessment was made of the distribution in conditions of subconfluence and confluence of the cell populations in the various phases of the cell cycle by means of cytofluorimetric analysis. At the end of the experimental 24 h, the cells were detached, washed with phosphate-buffered saline (PBS) (pH 7.4) and stained with aqueous staining solution containing 0.75 μmol/L (0.5 μg/ml) Propidium Iodide (PI), 4 μM sodium citrate, 1% TritonX-100 and 1% bovine serum albumin at 4 °C overnight. PI stained cells were measured using the Coulter Epics XL and analyzed by Modfit LT Software (Veruty software Inc. USA).
2.5. 1H- NMR spectroscopy High-resolution 1H-NMR spectra of medium extracts were obtained on a Bruker Avance 500 spectrometer operating at 500.13 MHz. Medium extracts were redissolved in 600 μl of 1 mM TSP used as reference for chemical shift and concentration determination. 1 H-NMR spectra of medium extracts were acquired using a 6 kHz spectral width and 64 K data point. The acquisition time was 5.44 s, relaxation delay 14.56 s, and 64 scans. The NMR data were processed using ACD/Spec Manager 7.00 software (Advanced Chemistry Development Inc., Toronto, Canada). Peak areas were used to quantify 1H spectra, and resonance assignments were made by comparison with literature data, 2D COSY and TOCSY NMR spectra and by adding reference compounds whenever needed.
2.6. Data analysis 2.6.1. Quantitative evaluation of metabolite balances Results have been expressed as extracellular metabolite balances, that is, as the difference in concentration of metabolites evaluated in the culture medium at time t = 24 h versus the initial experimental time t = 0 h. The data obtained from 12 samples each for confluence and sub-confluence cell cultures were normalized to total cell numbers and reported as means ± SD. Statistical comparisons between the experimental groups were performed by unpaired Student's t test. Differences with a p value of less than 0.05 were considered significant. 2.6.2. Metabolite connectivity evaluation The correlation matrices among metabolite balances were calculated using the Pearson correlation coefficients on normalized data, i.e. mean subtracted and divided for standard deviation, as previously reported [20]. The statistical significance of Pearson correlations was determined using the critical values of Pearson's r. A significance level of p < 0.001 was required to reject the null hypothesis of uncorrelated metabolites according to Morghenthal K. et al. [18].
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2.6.3. Principal component analysis Principal components are linear combinations of the original variables of the system and represent new axes on which the statistical units are projected. In an N-dimensional system the number of Principal Components (PCs) is equal to N. Nevertheless PCs have the crucial property of explaining the variability of the system in a hierarchical way. The factors are extracted by the algorithm in order of percentage of explained variability. The first component represents the axis (direction) along which the ‘cloud of points’ representing the statistical units is maximally extended. Usually a number p < N of extracted components explains a greater percentage of cumulative variance in the system. In these cases the first P components can be considered as retaining the essential information embedded in the system , while the smaller components can be discarded as noise [21]. The matrix of factor loadings (i.e. the correlation coefficients between the original variables of the system and the components) reports the relationships between data analysis results and real metabolic systems. This link is crucial for expressing the results of data analyses in terms endowed with an immediate cognitive significance for the biochemist containing a physico-chemical meaning [22,23,24,25]. The SAS software (Statistical Advanced Software) v.8 (SAS Institute Inc., www.sas.com) was used for all the statistical analysis.
3. Results 3.1. Cell proliferation and phase distribution 4
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Cells, seeded at 4× 10 and 8 × 10 cells/cm , were cultured for 24 h to allow the attachment. Then the medium was changed and the cells were cultured for a further 24 h to reach subconfluence or confluence respectively (<40% and >90% of the culture dishes occupied, respectively). In separate experiments cells showed the same proliferation rate as evaluated by direct cell counts during the first 48 h of culture regardless of the initial seeding density. A decrease in the growth rate was evidenced in the subsequent 72 h only for the high density cultures, reflecting the cell–cell contact growth inhibition. Cell viability as evaluated by the Trypan Blue exclusion test remained greater than 98% both in subconfluence and confluence cultures. After 24 h of culture the phase distribution analysis showed a clearly different pattern; cells in the G0/G1 phase were found to be 48± 4% and 70 ± 4% in S phase 34 ± 6% and 17 ± 5% and in G2/M phase 19± 2% and 13± 3% at subconfluence and confluence respectively (Fig. 1). These results showed that the cells at subconfluence were highly proliferative, while the cells that reached confluence were arrested in the G0/G1 phase. A Proliferation Index (PI= [(S + G2 + M) /total cells counted] × 100%) of 52.6% and 29.5% for low and high cell density cultures respectively was obtained. 3.2. 1H-NMR spectroscopy In Fig. 2 a representative 1 H-NMR spectrum of the deproteinated medium after 24 h of culture is shown. Data are expressed as differences between the levels at the end of the experimental time and at the beginning of the culture (Fig. 3). The values obtained, normalized for cell numbers, are representative of net balances, with positive and negative values being considered an estimate of net fluxes of production and utilization of metabolites, respectively. Both in subconfluence and confluence the utilization of branched amino acids (isoleucine, leucine, valine) and other
Fig. 1. Phase cycle distribution of HepG2 cells at subconfluence and confluence at the end of experimental time (24 h of attachment plus 24 h of culture). Cell cycle analysis was performed by flow cytometry after staining with Propidium Iodide. Results are expressed as percentage from three independent experiments.
amino acids like threonine, methionine, glutamate, glutamine, aspartate, phenylalanine, histidine, tyrosine was determined. Acetate, pyruvate and glucose were also consumed during the experimental time. Conversely, positive delta values were observed for alanine, glycine, formate, lactate as well as for the ketoacids related to the utilized branched amino acids like 2oxo-methyl-isovalerate, 2-oxo-isovalerate and 3-hydroxy-isobutyrate which are released in the culture medium (Fig. 3). Statistically significant variations in metabolite net balances were found only for some compounds; cells at confluence utilized more isoleucine, leucine, glutamine, glutamate, methionine and acetate in respect to cells at subconfluence. Conversely, in low density cultures a greater aspartate utilization was evident. Interestingly, a significantly different behaviour was observed for succinate with production in subconfluence and utilization in confluence cultures. 3.3. Metabolite connectivity evaluation Correlation matrices have been built from the pair-wise linear correlations in each experimental group (Fig. 4a and b). The comparison of correlation maps between confluence and subconfluence cultures showed an invariant group constituted by the branched amino acid utilization and their corresponding ketoacid production, which means that an increase in valine, leucine or isoleucine utilization is correlated with a lower ketoacid release. Relevant variations in connectivity among metabolite net balances were also observed. A negative correlation between glucose and lactate observed in subconfluence cultures, was lost in confluence ones. Conversely, in confluence condition a
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Fig. 2. A representative 1H-NMR spectrum of the medium at 24 h of culture. The following metabolites were identified: (1) isoleucine, (2) leucine, (3) valine, (4) 2oxo-methylisovalerate, (5) 2-oxo-isovalerate, (6) 3-hydroxybutyrate, (7) threonine, (8) lactate, (9) alanine, (10) acetate, (11) methionine, (12) glutamate, (13) pyruvate, (14) succinate, (15) glutamine, (16) aspartate, (17) glycine, (18) α glucose, (19) tyrosine, (20) histidine, (21) phenylalanine, (22) formate.
negative correlation between glucose and alanine was observed. As glucose is utilized and lactate and alanine are produced, an inverse correlation means that when glucose utilization increases an increased production of lactate in subconfluence cultures and of alanine in confluence cultures is observed. Furthermore, while in subconfluence cultures alanine production correlated positively with acetate, glutamate and branched amino acids utilization, in confluence cultures the only alanine correlation was that with glucose. The glucose balance, which showed only one correlation with lactate in subconfluence cultures, correlated with alanine and glycine production and with methionine, glutamine and aspartate consumptions in confluence cultures. Interestingly, no correlations were observed for some metabolites namely glutamine and aspartate in subconfluence conditions and succinate in confluence conditions. In confluent cultures the pyruvate utilization was associated with leucine, valine and glutamate consumption. Conversely, the correlation indicated that the pyruvate consumption was inversely related to the glycine production. Glycine showed significant positive correlations with glucose, glutamate, glutamine and aspartate in confluent cultures which means that when glucose, glutamate, glutamine and aspartate utilization decrease, glycine production increases. Glutamine utilization showed significant positive correlations with glutamate, methionine, aspartate utilization and glucose consumption in confluent cultures.
Aspartate, whose utilization was significantly higher in subconfluent cultures, showed no correlation with any other metabolite balance; conversely, significant positive correlations were found with glutamine, methionine , glucose, glutamate utilization and inverse correlation with the glycine production in confluent cultures. Consequently, an increase in the utilization of these metabolites was related to an increase in aspartate utilization, with the exception of glycine, of which less is produced. Succinate did not show any significant correlation in confluent cultures, where it is utilized, in respect to subconfluent ones, where it is produced; significant positive correlations with acetate, valine, tyrosine, histidine utilization and alanine production were observed in subconfluent cultures, meaning that when succinate production increases, the utilization of all other compounds and the production of alanine increases. Isoleucine, leucine, methionine, glutamate and acetate utilization was significantly higher in confluent cultures. Isoleucine consumption was found to correlate significantly with acetate, glutamate, valine, phenylalanine and to correlate inversely with 2-oxoisovalerate release both in subconfluent and confluent cultures. Methionine utilization showed significant positive correlations with acetate, glutamate, valine, phenylalanine and pyruvate utilization in subconfluent cultures, and with glutamine, aspartate and glucose utilization in confluence.
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Fig. 3. Net balances of extracellular metabolites. Delta values were obtained by subtracting the levels measured in the medium at 24 h of culture from the levels measured at time 0. Positive values represent production, negative values represent utilization. Values represent the mean ± SD of 12 samples in each group. Statistical comparisons were performed by unpaired Student's t test (*p < 0.05, **p < 0.01, ***p < 0.001) (Abbreviation list : ILE (isoleucine), LEU (leucine); VAL (valine), 2OMV (2-oxomethylisovalerate), 2-OIV (2-oxoisovalerate), b-OH (beta-hydroxyisobutyrate), THR (threonine), 3-AIV (3-aminoisovalerate), ALA (alanine), AC (acetate), MET (methionine), GLU (glutamate), PYR (pyruvate), SUCC (succinate), ASP (aspartate), GLY (glycine), PHE (phenylalanine), HIS (histidine), TYR (tyrosine), FOR (formate), LAC (lactate), GLN (glutamine), GLC (glucose).
Glutamate showed significant correlations with acetate, valine, methionine, isoleucine utilization and inverse correlations with alanine and ketoacids production in subconfluent cultures. In confluence conditions, glutamate balance showed significant positive correlations with glutamine and pyruvate which means that an increase in glutamate utilization is related to an increase in the consumption of the other metabolites. 3.4. Principal component analysis In order to distinguish between the two experimental growing conditions, Principal Component Analysis was applied to the data set using metabolite balances obtained both from subconfluence and confluence cultures. The first three components explained more than 70% of the variance of the system,
with PC1, PC2 and PC3 explaining 46.6%, 17.2% and 10.4% of the total variance respectively. The Factor Loadings matrix representing Pearson's correlation coefficients between the PCs and the original variables is reported in Table 1. PC1 was constituted by isoleucine, leucine, valine, 2-oxo-methyl-isovalerate, 2-oxo-isovalerate, 3-hydroxyisobutyrate, acetate, methionine, glutamate, phenylalanine, histidine and tyrosine. Alanine, glycine and glucose represented the PC2, while only aspartate is correlated significantly with PC3. PC scores analysis revealed significant variations (p < 0.05) calculated by unpaired Student's t test only for PC1 and PC3. In Fig. 5 PC1/PC3 scores relative to subconfluence or confluence samples show the discrimination of the samples on the basis of amino acids (PC1) in respect to aspartate utilization (PC3). In particular, the PC1 scores analysis showed that low density and high density cultures are characterized by a different
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Fig. 4. Correlation matrices of medium metabolite balances: (a) subconfluence, (b) confluence. Red and blue represent positive and negative correlations respectively; the colour scale is relative to Pearson's correlation coefficients. Statistically significant correlations (p < 0.001) are considered for coefficients ≥ 0.75. (Abbreviation list: GLC (glucose), TYR (tyrosine), HIS (histidine), FOR (formate), PHE (phenylalanine), GLY (glycine), ASP (aspartate), GLN (glutamine), SUCC (succinate), PYR (pyruvate), GLU (glutamate), MET (methionine), AC (acetate), ALA (alanine), LAC (lactate), THR (threonine), b-OH (beta-hydroxyisobutyrate), 2-OIV (2oxoisovalerate), 2-OMIV (2-oxomethylisovalerate), VAL (valine), LEU (leucine), ILE (isoleucine).
amino acids uptake for energetic purposes. Cells with a higher proliferation index utilized less amino acids for energetic purposes. 4. Discussion Proliferation rate is a widely used quantitative parameter to evaluate in vitro the efficacy of proliferative and antiproliferative drugs in cancer cells. However, culture conditions such as the adhesion matrix, medium composition and supplementation, the presence of growth factors and cell
density in particular may induce variations in the proliferation rate as well as in the distribution within the different phases of the cell cycle. In this context, a particular care in adherent cell cultures has to be paid to cell–cell interactions that are enhanced by increasing cell density and that may activate signal transduction influencing the proliferation and eventually leading to contact inhibition of cell growth. During the exponential phase of growth the metabolic processes are channeled towards de novo protein, RNA and DNA nucleotide, and lipid synthesis by the high structural
A.T. Miccheli et al. / Biochimica et Biophysica Acta 1760 (2006) 1723–1731 Table 1 Principal component loading matrix
ILE LEU VAL 2-OMIV 2-OIV β-OH-IB THR LAC ALA AC MET GLU PYR SUCC GLN ASP GLY PHE FOR HIS TYR GLC
PC1
PC2
PC3
0.906 0.768 0.959 0.921 0.778 0.805 0.329 0.591 0.166 0.899 0.806 0.817 0.592 0.645 0.653 0.037 0.190 0.876 0.251 0.857 0.930 0.226
0.242 0.365 0.054 −0.231 − 0.358 − 0.349 − 0.142 − 0.578 −0.860 0.002 0.325 0.425 0.175 − 0.208 0.555 0.027 0.725 0.052 − 0.586 − 0.168 0.214 0.899
0.020 − 0.008 0.141 − 0.126 0.284 0.149 0.147 0.031 0.181 − 0.291 − 0.011 − 0.171 − 0.169 − 0.443 0.110 0.829 0.320 0.240 0.331 0.129 − 0.067 0.116
Principal component analysis of 1H-NMR derived metabolic profiles. PC loading matrix obtained from the pooled data (confluence and subconfluence samples). PCI, PC2 and PC3 explained more than 70% of the variability of the system with PC1, PC2 and PC3 representing 46.6%, 17,2% and 10.4% respectively. In bold characters are the significant Pearson's correlation coefficients between PCs and the original variables.
demand of dividing cells. In contact inhibited cells, metabolism will switch and will be regulated by structural integrity maintenance and by physiological activities. In this situation a variation in metabolic profile of the cell population characterized by a different substrate utilization is to be expected. In order to determine whether metabolic profiling from medium compounds possibly reflects such an expected metabolic switch, we studied an hepatoma cell line in culture varying only the initial seeding density to reach a confluence or subconfluence state after 24 h of culture, avoiding any other interference. The cells grew at the same proliferation rate with a doubling time of 48 h after seeding, while the effect of high density on the proliferation rate due to contact inhibition became apparent only after 72 h. However, if we compared the cell cycle distribution of cultures seeded at low and high density we found that cells at confluence were arrested in the G0/G1 phase in respect to subconfluence cultures. By calculating the PI we showed that, in our experimental protocol, the cells at subconfluence were highly proliferative (PI = 52.6%) while in the near-plateau cultures the cells that had already cycled were mainly growth arrested (PI = 29.5%). We have calculated the net balances of compounds in the medium by 1H-NMR spectroscopy in order to evaluate the adaptive rearrangement of the cell metabolome in relation to cell growing conditions. Both cells from subconfluence and confluence cultures showed glucose and amino acids utilization with the production of lactate, alanine, glycine and formate in
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agreement to the “tumor metabolome” as described by Mazurek and Eigenbrodt [26]. By applying MVDA to the data as a whole we were able to explain more than 70% of the variance of the system. In both experimental groups the existence in PC1 of an invariant group constituted by the branched amino acids and their respective ketoacids was observed. As the variables considered represent net balances and consequently fluxes, a positive correlation sign means that when branched amino acids utilization increases, the release of their catabolic intermediates decreases. Furthermore, as lactate correlated positively with 3-OH-isobutyrate, an intermediate of valine catabolism and substrate for NAD+dependent activity of 3-hydroxyacyl-CoA dehydrogenase or 3hydroxyisobutyrate dehydrogenase [E.C. 1.1.1.35 or E.C. 1.1.1.31], we suggest that the relation between lactate and 3OH-isobutyrate release in the medium possibly reflects a coupling between the NAD+-dependent dehydrogenases and NADH-dependent Lactate dehydrogenase activity through the oscillations in NAD+/NADH ratio. The PC1 revealed differences between samples from lowand high cell density cultures. We thus inferred a metabolic structure relating acetate and amino acid utilization with ketoacid production, with a quantitatively different behaviour in cells in relation to cell density. In particular, growth arrested cells (confluence cultures) showed an increased amino acids utilization for energetic purposes in respect to cells characterized by a higher proliferation index (subconfluence cultures). Specific amino acids transporters upregulation in transformed cells has been found to be related to cell growth and proliferation [27]. Hepatoma cells transport glutamine 10 to 30 times faster than normal cells, through a high-affinity carrier denominated System ASC, although HepG2 cells express System N too and a higher Na+-dependent glutamine transport activity in more confluent HepG2 cultures was shown [28]. Our results, showing that amino acid utilization was significantly higher at the near-plateau phase of cell growth
Fig. 5. Principal Component analysis of 1H-NMR derived metabolic profiles. Distribution of individual PC scores for PC1 and PC3. The most discriminatory metabolite balances were identified from the loadings scores for PC1 and PC3 components.
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than during rapid growth, are in close agreement with the findings of Sasajima T et al. [29] who demonstrated that the influx of two non-metabolized amino acids was directly related to cell density and inversely related to the proliferation index in glioma cell lines due to a higher rate of influx and not to a reduced efflux. We will outline that a statistical significant variation in the net balances of extracellular metabolites between subconfluence and confluence cultures was observed only for some metabolites with cells at confluence utilizing more glutamine, glutamate, leucine, isoleucine, methionine, acetate and succinate and less aspartate in respect to cells in subconfluence cultures. A high glutamine and amino acid consumption in HepG2 cells is in agreement with the peculiar metabolic pattern of the normal liver, which maintains systemic glutamine levels via the differential consumption and production in the periportal and perivenous regional distribution of the acinus. This architectural and physiological arrangement is subverted in hepatocellular carcinoma which is characterized by decreased glutamine synthetase and increased glutaminase activity [30]. Interestingly, although the subconfluence condition was characterized by a lower utilization of all amino acids, a significantly higher utilization of aspartate was observed as compared to confluence. The absence of correlation of aspartate as well as of glutamine balances with any other metabolite balances, observed in highly proliferating cells, may be interpreted on the basis of a shift of their utilization from energetic towards synthetic pathways, like the purine nucleotide synthesis, through the activity of adenylosuccinate synthetase [E.C. 6.3.4.4] and GMP synthase [E.C. 6.3.5.2] respectively. Indeed, in cells with a lower proliferation index (confluence cultures), which were characterized by an higher glutamate consumption and an higher amino acids utilization, aspartate and glutamine balances showed correlations with glucose and other amino acid utilization, in agreement with a network of substrate utilization inside the TCA cycle for energy production. A difference in TCA cycle regulation was also supported by the different flux direction of succinate that was produced or utilized in highly proliferating or in growth arrested cells, respectively. It is worth noting that comparing the correlation pattern among glucose utilization, lactate and alanine production we were able to evidence a different behaviour in the subconfluence and confluence cultures, even if no significant quantitative changes in these metabolite balances were observed (Fig. 6). In highly proliferating cells the glucose consumption correlated with lactate production according to a glucose utilization for energetic purposes in the so-called anaerobic glycolysis [31]. Conversely, in near-confluent growth arrested cultures the correlation between glucose and lactate balances was lost and glucose utilization correlated with alanine production. These variations in the correlation pattern could be related to a contribution from pools of pyruvate from different sub-cellular compartments, like mitochondria or cytosol, in the production of lactate or alanine depending on the peculiar metabolome of hepatoma cells in different cell growth conditions. The absence
Fig. 6. Correlation pattern among glucose, lactate and alanine balances in subconfluence and confluence cultures. Bold arrows indicate utilization (↓) or production (↑). Thin and dashed arrows represent increased and decreased flux reaction directions respectively. The numerical values are the Pearson's correlation coefficients measured from autoscaling data as reported in Materials and methods.
of correlation between glucose and alanine in subconfluence cultures could be in agreement with the synthesis of alanine in mitochondria through the malic enzyme [E.C.1.1.1.40] and glutamic-pyruvate transaminase activities [E.C.2.6.1.2] [32]. In this condition the variance of the lactate release is closely correlated with the variance of the glucose utilization due to the mainly cytosolic pyruvate production from glycolysis. Conversely, the observed lack of correlation between glucose utilization and lactate release in confluence cultures can be interpreted on the basis of the participation of other substrates besides glucose in the production of cytosolic pyruvate and then of lactate, such as malate deriving from TCA cycle intermediates. Accordingly, an increase in amino acid utilization through TCA cycle for energy purpose in growth arrested HepG2 has been observed. Consequently, the observed correlation between glucose utilization and alanine production starting from pyruvate produced by glycolysis could be explained by a high cytosolic transaminase activity. The suggested cell growth related metabolism regulation seems to be in agreement with the cell-cycle phase related metabolic networks previously shown in other cancer cells by 13C NMRbased metabolic profiling [12]. Finally, our study showed that glucose utilization and the proliferation rate were not necessarily correlated in HepG2 cells growing in culture at a different cell density again in agreement with Sasajima et al. who found that FDG accumulation was unaffected by proliferation rate, growth phase and cell density in two of the studied cell lines. In conclusion, our results show that it is possible to distinguish between different metabolic profiles of tumor cells in culture in relation to different physiological conditions by 1 H-NMR spectroscopy of the culture medium and MVDA analysis. Furthermore, by using metabolite balance quantitative
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analysis and the comparison of correlation pattern of metabolite balances we demonstrated that human hepatoma cells, almost arrested in G0/G1 cycle phase by contact dependent growth inhibition, underwent changes in channeling of amino acid utilization from synthetic to energy purposes and in anaplerosis/ cataplerosis regulation of the TCA cycle. As we shall see, it has recently been pointed out that cell density is a critical factor in the mechanism of resistance to antitumor drugs [33] and our results showed that this could be dependent on a different systems metabolism. The methodological approach presented, associated with transcriptomic data, can be considered a powerful and easy tool for investigating cancer cell metabolic phenotypes and in evaluating their changes in relation to environmental conditions and the effect of biologically active molecules. Acknowledgement This study was supported by a “La Sapienza” University Grant (2005). References [1] J. Bereheiter-Hahn, A. Munnich, P. Woiteneck, Dependence of energy metabolism on the density of cells in culture, Cell Struct. Funct. 23 (1998) 85–93. [2] J. Bar, E. Cohen-Noyman, B. Geiger, M. Oren, Attenuation of the p53 response to DNA damage at high cell density, Oncogene 23 (2004) 2128–2137. [3] Z. Vukelic, S. Kalanj-Bognar, Cell density-dependent changes of glycosphingolipid biosynthesis in cultured human skin fibroblasts, Glycoconj. J. 18 (2001) 429–437. [4] M. Masquelier, S. Vitols, Drastic effect of cell density on the cytotoxicity of daunorubicin and cytosine arabinoside, Biochem. Pharmacol. 67 (2004) 1639–1646. [5] J.M. de Anta, F.X. Real, X. Mayol, Low tumor cell density environment yields survival advantage of tumor cells exposed to MTX in vitro, Biochim. Biophys. Acta 1721 (2005) 98–106. [6] D. Valverde, M.R. Quintero, A.P. Candiota, L. Badiella, M.E. Cabanas, C. Arus, Analysis of the changes in the 1H NMR spectral pattern of perchloric acid extracts of C6 cells with growth, NMR Biomed. 19 (2006) 223–230. [7] W.M. Frederiks, J. Van Marle, C. van Oven, B. Comin-Anduix, M. Cascante, Improved localization of glucose-6-phosphate dehydrogenase activity in cells with 5-cyano-2,3-ditolyl-tetrazolium chloride as fluorescent redox dye reveals its cell cycle-dependent regulation, J. Histochem. Cytochem. 54 (2006) 47–52. [8] S. Jackowski, Coordination of membrane phospholipid synthesis with the cell cycle, J. Biol. Chem. 269 (1994) 3858–3867. [9] A. Coquelle, S. Mouhamad, M.O. Pequignot, T. Braun, G. Carvhalo, S. Vivet, D. Metivier, M. Castedo, G. Kroemer, Cell cycle-dependent cytotoxic and cytostatic effects of bortezomib on colon carcinoma cells, Cell Death Differ. 13 (2006) 873–875. [10] D.H. Margineantu, W.G. Cox, L. Sundell, S.W. Sherwood, J.M. Beechem, R.A. Capaldi, Cell cycle dependent morphology changes and associated mitochondrial DNA redistribution in mitochondria of human cell lines, Mitochondrion 1 (2002) 425–435. [11] P.K. Kennady, M.G. Ormerod, S. Singh, G. Pande, Variation of mitochondrial size during the cell cycle: a multiparameter flow cytometric and microscopic study, Cytometry A 62A (2004) 97–108.
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[12] A. Miccheli, A. Tomassini, C. Puccetti, M. Valerio, G. Peluso, F. Tuccillo, M. Calvani, C. Manetti, F. Conti, Metabolic profiling by 13 C NMR spectroscopy: [1,2-13C2] glucose reveals a heterogeneous metabolism in human leukemia T cells, Biochimie 88 (2006) 437–448. [13] S.G. Oliver, M.K. Winson, D.B. Kell, F. Baganz, Systematic functional analysis of the yeast genome, Trends Biotechnol. 16 (1998) 373–378. [14] J.K. Nicholson, E. Holmes, J.C. Lindon, I.D. Wilson, The challenges of modelling mammalian biocomplexity, Nat. Biotechnol. 22 (2004) 1268–1274. [15] D.B. Kell, M. Brown, H.M. Davey, W.B. Dunn, I. Spasic, S.G. Oliver, Metabolic footprinting and systems biology: the medium is the message, Nat. Rev., Microbiol. 3 (2005) 557–565. [16] D.P. Aden, A. Fogel, S. Plotkin, I. Damjanov, B.B. Knowles, Controlled synthesis of HbsAg in a differentiated human liver carcinoma-derived cell line, Nature 282 (1979) 615–616. [17] W. Weckwerth, K. Morgenthal, Metabolomics: from pattern recognition to biological interpretation, Drug Discov. Today 10 (2005) 1551–1558. [18] K. Morgenthal, W. Weckwerth, R. Steuer, Metabolomic networks in plants: transitions from pattern recognition to biological interpretation, BioSystems 83 (2006) 108–117. [19] A. Miccheli, R. Ricciolini, E. Piccolella, M. Delfini, F. Conti, Modulation of human lymphoblastoid B cell line by phorbol ester and sphingosine. A 31 P-NMR study, Biochim. Biophys. Acta 1093 (1991) 29–35. [20] D. Camacho, A. de la Fuente, P. Mendes, The origin of correlations inmetabolomics data, Metabolomics 1 (2005) 53–63. [21] L. Lebart, A. Morineau, K.M. Warwick, Multivariate descriptive statistical analysis, Wiley, New York, 1984. [22] A. Giuliani, G. Capuani, A. Miccheli, T. Aureli, M.T. Ramacci, F. Conti, Multivariate data analysis in biochemistry: a new integrative approach to metabolic control in brain aging, Cell. Mol. Biol. 37 (1991) 631–638. [23] R. Benigni, A. Giuliani, Quantitative modeling and biology: the multivariate approach, Am. J. Physiol. 266 (1994) R1697–R1704. [24] A. Giuliani, J.P. Zbilut, F. Conti, C. Manetti, A. Miccheli, Invariant features of metabolic networks: a data analysis application on scaling properties of biochemical pathways, Physica A 337 (2004) 157–170. [25] C. Manetti, C. Bianchetti, L. Casciani, C. Castro, M.E. Di Cocco, A. Miccheli, M. Motto, F. Conti, A metabonomic study of transgenic maize (Zea mays) seeds revealed variations in osmolites and branched amino acids, J. Exp. Bot. 57 (2006) 2613–2625. [26] S. Mazurek, E. Eigenbrodt, The tumor metabolome, Anticancer Res. 23 (2003) 1149–1154. [27] H.N. Christensen, Role of amino acid transport and countertransport in nutrition and metabolism, Physiol. Rev. 70 (1990) 43–77. [28] B.P. Bode, W.W. Souba, Modulation of cellular proliferation alters glutamine transport and metabolism in human hepatoma cells, Ann. Surg. 220 (1994) 411–424. [29] T. Sasajima, T. Miyagawa, T. Oku, J.G. Gevolani, R. Finn, R. Blasberg, Proliferation-dependent changes in amino acid transport and glucose metabolism in glioma cell lines, Eur. J. Nucl. Med. Mol. Imaging 31 (2004) 1244–1256. [30] T. Matsuno, I. Goto, Glutaminase and glutamine synthetase activities in human cirrhotic liver and hepatocellular carcinoma, Cancer Res. 52 (1992) 1192–1194. [31] O. Warburg, On respiratory impairment in cancer cells, Science 124 (1956) 269–270. [32] R.W. Moreadith, A.L. Lehninger, The pathways of glutamate and glutamine oxidation by tumor cell mitochondria. Role of mitochondrial NAD(P)+dependent malic enzyme, J. Biol. Chem. 259 (1984) 6215–6221. [33] J.M. de Anta, C. Mayo, F. Sole, M. Salido, B. Espinet, C. Corzo, M. Petzold, O. Villa, S. Serrano, F.X. Real, X. Mayol, Methotrexate resistance in vitro is achieved by a dynamic selection process of tumor cell variants emerging during treatment, Int. J. Cancer 119 (2006) 1607–1615.
NMR-based metabolic profiling of human hepatoma cells in relation to cell growth by culture media analysis Alberta Tomassini Miccheli a , Alfredo Miccheli a,⁎, Roberta Di Clemente a , Mariacristina Valerio a , Pierpaolo Coluccia b , Mariano Bizzarri c , Filippo Conti a a Department of Chemistry, University “La Sapienza”, P.le Aldo Moro 5-00185 Rome, Italy Department of Surgery “Pietro Valdoni”, University “La Sapienza”, P.le Aldo Moro 5-00185 Rome, Italy Department of Experimental Medicine and Pathology, University “La Sapienza”, V. Scarpa 14/16-00161 Rome, Italy b
c
Received 27 June 2006; received in revised form 12 September 2006; accepted 12 September 2006 Available online 22 September 2006
Abstract Metabolic profiling is a metabolomic approach that allows the characterization of metabolic phenotypes under specific set of conditions. In the present paper we investigated the metabolism of sparse and high density cultures in relation to different cell growth phases. Changes in the metabolome were evaluated by using 1H-NMR spectroscopy, correlation map and Multivariate Data Analysis on the net balances of metabolites in the medium. This approach allowed us to identify two different metabolic profiles in relation to the cell growth phases in subconfluence and confluence cultures. The results have been interpreted on the basis of patterns of correlations obtained in the two physiological cell states. Cells almost arrested in G0/G1 phase by contact dependent growth inhibition underwent changes in the channeling of amino acids utilization from synthetic to energetic purpose and in anaplerosis/cataplerosis regulation of the TCA cycle. © 2006 Elsevier B.V. All rights reserved. Keywords: Metabolomics; 1H NMR; Cancer cell; Amino acid metabolism; HepG2
1. Introduction Many cell types in culture are subjected to contact inhibition of proliferation dependent on cell–cell and cell–matrix interactions, adhesion-dependent signaling, cytoskeletal dynamics and regulated by substrates and growth factor availability. This behaviour, typical of primary cultures, is also common in immortalized cell lines. In this context, the impact of cell density in culture may have important consequences when testing new drugs or biologically active molecules, in particular those believed to influence proliferation and growth. For instance, it has been shown that cell density influences energy metabolism [1], the activation of p53 tumor suppressor [2], the de novo synthesis of sphingolipids [3] and sensitivity to drugs [4], and recently the “critical cell density” of a cultured
⁎ Corresponding author. Tel./fax: +39 06 4455278. E-mail address: [email protected] (A. Miccheli). 0304-4165/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.bbagen.2006.09.009
population was thought to be involved in the adaptive drug resistant mechanism [5]. However, the underlying metabolism that is characteristic of sparse and high density cultures has not been deeply investigated nor the relationship between proliferative rate and metabolism in cells at different phases of cell growth in culture has not been fully clarified. Recently, results obtained by 1HNMR spectroscopy on extracts of a glioma cell line have shown changes in metabolic patterns in relation to log-, confluent- and post-confluent growth stages [6]. Cell growth rate is closely linked to the phases of the cellcycle. A number of different enzyme activities involved in nucleotide synthesis [7], different phospholipid metabolisms [8], differential response to drugs [9], morpho-structural variations and variations in mitochondria intracellular distribution have been associated with cells in different phases of the cycle [10,11]. In a previous work, we demonstrated a different metabolic profile of cell subpopulations in relation to cell cycle phases by 13C-NMR spectroscopy using [1,2-13C2] glucose as a stable-isotope tracer [12].
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Metabolic profiling allows the analysis of the cell metabolome that is defined as the quantitative complement of lowmolecular weight metabolites present in a cell under a given set of physiological activities [13,14]. As changes in medium metabolites, substrate utilization and production and substrate flux distribution reflect the cell physiological state and phenotype and thus the cell metabolome [15], we investigated if the monitoring of metabolites consumed by and released into the medium by cells cultivated at different cell densities provide sufficient information on the metabolic pathways involved. In this respect, NMR spectroscopy, in combination with multivariate data analysis, is a suitable technique allowing the simultaneous quantification of a large number of metabolites without any a priori hypothesis regarding the involved biochemical pathways and leading to the identification of a specific metabolic fingerprint. In this study we utilized a hepatoblastoma cell line (HepG2) which retains the expression of most liver-specific genes [16]. This cell line has been widely used for toxicological and pharmacological studies. In order to obtain the metabolic profiling of cell cultures in relation to the confluence or subconfluence growing state, changes in the exometabolome were evaluated by using 1H-NMR spectroscopy and Multivariate Data Analysis (MVDA) on medium data. This approach allowed us to discriminate two different metabolic profiles in relation to different phases of cell growth in culture and the results were interpreted on the basis of the comparison of the pattern of correlations obtained in the two physiological cell states. The pattern of correlations represents the interconnectivity of the fluxes of metabolites with respect to their pair-wise correlations and can be interpreted as a fingerprint of the underlying system that provides information about the specific physiological state of the cells at a given point in time which can be interpreted as changes in the regulation of metabolic fluxes among the different intracellular pathways [17,18]. 2. Materials and methods 2.1. Materials Minimum Essential Medium (MEM), L-glutamine and fetal bovine serum (FBS) were purchased from GIBCO (Auckland, NZ). Sodium pyruvate, sodium bicarbonate, penicillin and streptomycin, phosphate buffer saline (PBS), Trypan Blue, Trypsin-EDTA 0.25% solution, Propidium Iodide, Triton X100, bovine serum albumin were all from Sigma Aldrich (St. Louis, MO). Methanol, ethanol and chloroform were from Carlo Erba Reagenti (Rodano, Milan, Italy). 3-(trimethylsilyl)-tetradeutero-sodium propionate (TSP) was from Trimital (Milan, Italy).
2.2. Cell culture Human liver tumor cells, HepG2, were obtained from the American Type Culture Collection (Manassas, VA) and were used within ten passages. Cells were grown in 75 cm2 flasks (Falcon, Becton Dickinson Labware) in MEM medium supplemented with 0.3 mg/ml glutamine, 0.11 mg/ml sodium pyruvate, 2 mg/ml sodium bicarbonate, 100 units/ml penicillin and 100 μg/ml streptomycin and 10% FBS. Cells were cultured at 37 °C in a humidified atmosphere of 95% air and 5% CO2. Cell growth and viability were evaluated by the Trypan Blue exclusion test. Briefly, at the end of the experimental time the cells were washed with cold PBS,
detached by trypsinization and 100 μl of the cell suspension were mixed with 2% Trypan Blue and were counted in triplicate in a Burker camera under the optical microscope.
2.3. Experimental protocol and sample preparation for NMR spectroscopy Cells were seeded at concentrations of 4 × 104 and 8 × 104 cells/cm2 in 75 cm2 tissue culture flasks with 10 ml of growth medium. After 24 h of culture for cell attachment, the medium was changed and the cells were cultured for 24 h; in separate experiments the cells were cultured for a total 48 h after the medium change for the evaluation of the proliferation rate. For 1H-NMR spectroscopy the cultures were stopped at 24 h, the medium was collected and extracted and the cells were trypsinized, centrifuged at 300g 4 °C for 5 min and counted. Culture media were extracted using Bligh–Dyer techniques as modified by us [19]. Briefly, 1 ml of medium was sequentially mixed with 3 ml of cold CH3OH:CHCl3 (2:1) and 1 ml of cold CHCl3 and centrifuged at 11,000×g for 20 min. at 4 °C. The aqueous phases were taken up, dried under N2 flow and stored at −80 °C until NMR analysis.
2.4. Flow cytometry An assessment was made of the distribution in conditions of subconfluence and confluence of the cell populations in the various phases of the cell cycle by means of cytofluorimetric analysis. At the end of the experimental 24 h, the cells were detached, washed with phosphate-buffered saline (PBS) (pH 7.4) and stained with aqueous staining solution containing 0.75 μmol/L (0.5 μg/ml) Propidium Iodide (PI), 4 μM sodium citrate, 1% TritonX-100 and 1% bovine serum albumin at 4 °C overnight. PI stained cells were measured using the Coulter Epics XL and analyzed by Modfit LT Software (Veruty software Inc. USA).
2.5. 1H- NMR spectroscopy High-resolution 1H-NMR spectra of medium extracts were obtained on a Bruker Avance 500 spectrometer operating at 500.13 MHz. Medium extracts were redissolved in 600 μl of 1 mM TSP used as reference for chemical shift and concentration determination. 1 H-NMR spectra of medium extracts were acquired using a 6 kHz spectral width and 64 K data point. The acquisition time was 5.44 s, relaxation delay 14.56 s, and 64 scans. The NMR data were processed using ACD/Spec Manager 7.00 software (Advanced Chemistry Development Inc., Toronto, Canada). Peak areas were used to quantify 1H spectra, and resonance assignments were made by comparison with literature data, 2D COSY and TOCSY NMR spectra and by adding reference compounds whenever needed.
2.6. Data analysis 2.6.1. Quantitative evaluation of metabolite balances Results have been expressed as extracellular metabolite balances, that is, as the difference in concentration of metabolites evaluated in the culture medium at time t = 24 h versus the initial experimental time t = 0 h. The data obtained from 12 samples each for confluence and sub-confluence cell cultures were normalized to total cell numbers and reported as means ± SD. Statistical comparisons between the experimental groups were performed by unpaired Student's t test. Differences with a p value of less than 0.05 were considered significant. 2.6.2. Metabolite connectivity evaluation The correlation matrices among metabolite balances were calculated using the Pearson correlation coefficients on normalized data, i.e. mean subtracted and divided for standard deviation, as previously reported [20]. The statistical significance of Pearson correlations was determined using the critical values of Pearson's r. A significance level of p < 0.001 was required to reject the null hypothesis of uncorrelated metabolites according to Morghenthal K. et al. [18].
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2.6.3. Principal component analysis Principal components are linear combinations of the original variables of the system and represent new axes on which the statistical units are projected. In an N-dimensional system the number of Principal Components (PCs) is equal to N. Nevertheless PCs have the crucial property of explaining the variability of the system in a hierarchical way. The factors are extracted by the algorithm in order of percentage of explained variability. The first component represents the axis (direction) along which the ‘cloud of points’ representing the statistical units is maximally extended. Usually a number p < N of extracted components explains a greater percentage of cumulative variance in the system. In these cases the first P components can be considered as retaining the essential information embedded in the system , while the smaller components can be discarded as noise [21]. The matrix of factor loadings (i.e. the correlation coefficients between the original variables of the system and the components) reports the relationships between data analysis results and real metabolic systems. This link is crucial for expressing the results of data analyses in terms endowed with an immediate cognitive significance for the biochemist containing a physico-chemical meaning [22,23,24,25]. The SAS software (Statistical Advanced Software) v.8 (SAS Institute Inc., www.sas.com) was used for all the statistical analysis.
3. Results 3.1. Cell proliferation and phase distribution 4
4
2
Cells, seeded at 4× 10 and 8 × 10 cells/cm , were cultured for 24 h to allow the attachment. Then the medium was changed and the cells were cultured for a further 24 h to reach subconfluence or confluence respectively (<40% and >90% of the culture dishes occupied, respectively). In separate experiments cells showed the same proliferation rate as evaluated by direct cell counts during the first 48 h of culture regardless of the initial seeding density. A decrease in the growth rate was evidenced in the subsequent 72 h only for the high density cultures, reflecting the cell–cell contact growth inhibition. Cell viability as evaluated by the Trypan Blue exclusion test remained greater than 98% both in subconfluence and confluence cultures. After 24 h of culture the phase distribution analysis showed a clearly different pattern; cells in the G0/G1 phase were found to be 48± 4% and 70 ± 4% in S phase 34 ± 6% and 17 ± 5% and in G2/M phase 19± 2% and 13± 3% at subconfluence and confluence respectively (Fig. 1). These results showed that the cells at subconfluence were highly proliferative, while the cells that reached confluence were arrested in the G0/G1 phase. A Proliferation Index (PI= [(S + G2 + M) /total cells counted] × 100%) of 52.6% and 29.5% for low and high cell density cultures respectively was obtained. 3.2. 1H-NMR spectroscopy In Fig. 2 a representative 1 H-NMR spectrum of the deproteinated medium after 24 h of culture is shown. Data are expressed as differences between the levels at the end of the experimental time and at the beginning of the culture (Fig. 3). The values obtained, normalized for cell numbers, are representative of net balances, with positive and negative values being considered an estimate of net fluxes of production and utilization of metabolites, respectively. Both in subconfluence and confluence the utilization of branched amino acids (isoleucine, leucine, valine) and other
Fig. 1. Phase cycle distribution of HepG2 cells at subconfluence and confluence at the end of experimental time (24 h of attachment plus 24 h of culture). Cell cycle analysis was performed by flow cytometry after staining with Propidium Iodide. Results are expressed as percentage from three independent experiments.
amino acids like threonine, methionine, glutamate, glutamine, aspartate, phenylalanine, histidine, tyrosine was determined. Acetate, pyruvate and glucose were also consumed during the experimental time. Conversely, positive delta values were observed for alanine, glycine, formate, lactate as well as for the ketoacids related to the utilized branched amino acids like 2oxo-methyl-isovalerate, 2-oxo-isovalerate and 3-hydroxy-isobutyrate which are released in the culture medium (Fig. 3). Statistically significant variations in metabolite net balances were found only for some compounds; cells at confluence utilized more isoleucine, leucine, glutamine, glutamate, methionine and acetate in respect to cells at subconfluence. Conversely, in low density cultures a greater aspartate utilization was evident. Interestingly, a significantly different behaviour was observed for succinate with production in subconfluence and utilization in confluence cultures. 3.3. Metabolite connectivity evaluation Correlation matrices have been built from the pair-wise linear correlations in each experimental group (Fig. 4a and b). The comparison of correlation maps between confluence and subconfluence cultures showed an invariant group constituted by the branched amino acid utilization and their corresponding ketoacid production, which means that an increase in valine, leucine or isoleucine utilization is correlated with a lower ketoacid release. Relevant variations in connectivity among metabolite net balances were also observed. A negative correlation between glucose and lactate observed in subconfluence cultures, was lost in confluence ones. Conversely, in confluence condition a
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Fig. 2. A representative 1H-NMR spectrum of the medium at 24 h of culture. The following metabolites were identified: (1) isoleucine, (2) leucine, (3) valine, (4) 2oxo-methylisovalerate, (5) 2-oxo-isovalerate, (6) 3-hydroxybutyrate, (7) threonine, (8) lactate, (9) alanine, (10) acetate, (11) methionine, (12) glutamate, (13) pyruvate, (14) succinate, (15) glutamine, (16) aspartate, (17) glycine, (18) α glucose, (19) tyrosine, (20) histidine, (21) phenylalanine, (22) formate.
negative correlation between glucose and alanine was observed. As glucose is utilized and lactate and alanine are produced, an inverse correlation means that when glucose utilization increases an increased production of lactate in subconfluence cultures and of alanine in confluence cultures is observed. Furthermore, while in subconfluence cultures alanine production correlated positively with acetate, glutamate and branched amino acids utilization, in confluence cultures the only alanine correlation was that with glucose. The glucose balance, which showed only one correlation with lactate in subconfluence cultures, correlated with alanine and glycine production and with methionine, glutamine and aspartate consumptions in confluence cultures. Interestingly, no correlations were observed for some metabolites namely glutamine and aspartate in subconfluence conditions and succinate in confluence conditions. In confluent cultures the pyruvate utilization was associated with leucine, valine and glutamate consumption. Conversely, the correlation indicated that the pyruvate consumption was inversely related to the glycine production. Glycine showed significant positive correlations with glucose, glutamate, glutamine and aspartate in confluent cultures which means that when glucose, glutamate, glutamine and aspartate utilization decrease, glycine production increases. Glutamine utilization showed significant positive correlations with glutamate, methionine, aspartate utilization and glucose consumption in confluent cultures.
Aspartate, whose utilization was significantly higher in subconfluent cultures, showed no correlation with any other metabolite balance; conversely, significant positive correlations were found with glutamine, methionine , glucose, glutamate utilization and inverse correlation with the glycine production in confluent cultures. Consequently, an increase in the utilization of these metabolites was related to an increase in aspartate utilization, with the exception of glycine, of which less is produced. Succinate did not show any significant correlation in confluent cultures, where it is utilized, in respect to subconfluent ones, where it is produced; significant positive correlations with acetate, valine, tyrosine, histidine utilization and alanine production were observed in subconfluent cultures, meaning that when succinate production increases, the utilization of all other compounds and the production of alanine increases. Isoleucine, leucine, methionine, glutamate and acetate utilization was significantly higher in confluent cultures. Isoleucine consumption was found to correlate significantly with acetate, glutamate, valine, phenylalanine and to correlate inversely with 2-oxoisovalerate release both in subconfluent and confluent cultures. Methionine utilization showed significant positive correlations with acetate, glutamate, valine, phenylalanine and pyruvate utilization in subconfluent cultures, and with glutamine, aspartate and glucose utilization in confluence.
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Fig. 3. Net balances of extracellular metabolites. Delta values were obtained by subtracting the levels measured in the medium at 24 h of culture from the levels measured at time 0. Positive values represent production, negative values represent utilization. Values represent the mean ± SD of 12 samples in each group. Statistical comparisons were performed by unpaired Student's t test (*p < 0.05, **p < 0.01, ***p < 0.001) (Abbreviation list : ILE (isoleucine), LEU (leucine); VAL (valine), 2OMV (2-oxomethylisovalerate), 2-OIV (2-oxoisovalerate), b-OH (beta-hydroxyisobutyrate), THR (threonine), 3-AIV (3-aminoisovalerate), ALA (alanine), AC (acetate), MET (methionine), GLU (glutamate), PYR (pyruvate), SUCC (succinate), ASP (aspartate), GLY (glycine), PHE (phenylalanine), HIS (histidine), TYR (tyrosine), FOR (formate), LAC (lactate), GLN (glutamine), GLC (glucose).
Glutamate showed significant correlations with acetate, valine, methionine, isoleucine utilization and inverse correlations with alanine and ketoacids production in subconfluent cultures. In confluence conditions, glutamate balance showed significant positive correlations with glutamine and pyruvate which means that an increase in glutamate utilization is related to an increase in the consumption of the other metabolites. 3.4. Principal component analysis In order to distinguish between the two experimental growing conditions, Principal Component Analysis was applied to the data set using metabolite balances obtained both from subconfluence and confluence cultures. The first three components explained more than 70% of the variance of the system,
with PC1, PC2 and PC3 explaining 46.6%, 17.2% and 10.4% of the total variance respectively. The Factor Loadings matrix representing Pearson's correlation coefficients between the PCs and the original variables is reported in Table 1. PC1 was constituted by isoleucine, leucine, valine, 2-oxo-methyl-isovalerate, 2-oxo-isovalerate, 3-hydroxyisobutyrate, acetate, methionine, glutamate, phenylalanine, histidine and tyrosine. Alanine, glycine and glucose represented the PC2, while only aspartate is correlated significantly with PC3. PC scores analysis revealed significant variations (p < 0.05) calculated by unpaired Student's t test only for PC1 and PC3. In Fig. 5 PC1/PC3 scores relative to subconfluence or confluence samples show the discrimination of the samples on the basis of amino acids (PC1) in respect to aspartate utilization (PC3). In particular, the PC1 scores analysis showed that low density and high density cultures are characterized by a different
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Fig. 4. Correlation matrices of medium metabolite balances: (a) subconfluence, (b) confluence. Red and blue represent positive and negative correlations respectively; the colour scale is relative to Pearson's correlation coefficients. Statistically significant correlations (p < 0.001) are considered for coefficients ≥ 0.75. (Abbreviation list: GLC (glucose), TYR (tyrosine), HIS (histidine), FOR (formate), PHE (phenylalanine), GLY (glycine), ASP (aspartate), GLN (glutamine), SUCC (succinate), PYR (pyruvate), GLU (glutamate), MET (methionine), AC (acetate), ALA (alanine), LAC (lactate), THR (threonine), b-OH (beta-hydroxyisobutyrate), 2-OIV (2oxoisovalerate), 2-OMIV (2-oxomethylisovalerate), VAL (valine), LEU (leucine), ILE (isoleucine).
amino acids uptake for energetic purposes. Cells with a higher proliferation index utilized less amino acids for energetic purposes. 4. Discussion Proliferation rate is a widely used quantitative parameter to evaluate in vitro the efficacy of proliferative and antiproliferative drugs in cancer cells. However, culture conditions such as the adhesion matrix, medium composition and supplementation, the presence of growth factors and cell
density in particular may induce variations in the proliferation rate as well as in the distribution within the different phases of the cell cycle. In this context, a particular care in adherent cell cultures has to be paid to cell–cell interactions that are enhanced by increasing cell density and that may activate signal transduction influencing the proliferation and eventually leading to contact inhibition of cell growth. During the exponential phase of growth the metabolic processes are channeled towards de novo protein, RNA and DNA nucleotide, and lipid synthesis by the high structural
A.T. Miccheli et al. / Biochimica et Biophysica Acta 1760 (2006) 1723–1731 Table 1 Principal component loading matrix
ILE LEU VAL 2-OMIV 2-OIV β-OH-IB THR LAC ALA AC MET GLU PYR SUCC GLN ASP GLY PHE FOR HIS TYR GLC
PC1
PC2
PC3
0.906 0.768 0.959 0.921 0.778 0.805 0.329 0.591 0.166 0.899 0.806 0.817 0.592 0.645 0.653 0.037 0.190 0.876 0.251 0.857 0.930 0.226
0.242 0.365 0.054 −0.231 − 0.358 − 0.349 − 0.142 − 0.578 −0.860 0.002 0.325 0.425 0.175 − 0.208 0.555 0.027 0.725 0.052 − 0.586 − 0.168 0.214 0.899
0.020 − 0.008 0.141 − 0.126 0.284 0.149 0.147 0.031 0.181 − 0.291 − 0.011 − 0.171 − 0.169 − 0.443 0.110 0.829 0.320 0.240 0.331 0.129 − 0.067 0.116
Principal component analysis of 1H-NMR derived metabolic profiles. PC loading matrix obtained from the pooled data (confluence and subconfluence samples). PCI, PC2 and PC3 explained more than 70% of the variability of the system with PC1, PC2 and PC3 representing 46.6%, 17,2% and 10.4% respectively. In bold characters are the significant Pearson's correlation coefficients between PCs and the original variables.
demand of dividing cells. In contact inhibited cells, metabolism will switch and will be regulated by structural integrity maintenance and by physiological activities. In this situation a variation in metabolic profile of the cell population characterized by a different substrate utilization is to be expected. In order to determine whether metabolic profiling from medium compounds possibly reflects such an expected metabolic switch, we studied an hepatoma cell line in culture varying only the initial seeding density to reach a confluence or subconfluence state after 24 h of culture, avoiding any other interference. The cells grew at the same proliferation rate with a doubling time of 48 h after seeding, while the effect of high density on the proliferation rate due to contact inhibition became apparent only after 72 h. However, if we compared the cell cycle distribution of cultures seeded at low and high density we found that cells at confluence were arrested in the G0/G1 phase in respect to subconfluence cultures. By calculating the PI we showed that, in our experimental protocol, the cells at subconfluence were highly proliferative (PI = 52.6%) while in the near-plateau cultures the cells that had already cycled were mainly growth arrested (PI = 29.5%). We have calculated the net balances of compounds in the medium by 1H-NMR spectroscopy in order to evaluate the adaptive rearrangement of the cell metabolome in relation to cell growing conditions. Both cells from subconfluence and confluence cultures showed glucose and amino acids utilization with the production of lactate, alanine, glycine and formate in
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agreement to the “tumor metabolome” as described by Mazurek and Eigenbrodt [26]. By applying MVDA to the data as a whole we were able to explain more than 70% of the variance of the system. In both experimental groups the existence in PC1 of an invariant group constituted by the branched amino acids and their respective ketoacids was observed. As the variables considered represent net balances and consequently fluxes, a positive correlation sign means that when branched amino acids utilization increases, the release of their catabolic intermediates decreases. Furthermore, as lactate correlated positively with 3-OH-isobutyrate, an intermediate of valine catabolism and substrate for NAD+dependent activity of 3-hydroxyacyl-CoA dehydrogenase or 3hydroxyisobutyrate dehydrogenase [E.C. 1.1.1.35 or E.C. 1.1.1.31], we suggest that the relation between lactate and 3OH-isobutyrate release in the medium possibly reflects a coupling between the NAD+-dependent dehydrogenases and NADH-dependent Lactate dehydrogenase activity through the oscillations in NAD+/NADH ratio. The PC1 revealed differences between samples from lowand high cell density cultures. We thus inferred a metabolic structure relating acetate and amino acid utilization with ketoacid production, with a quantitatively different behaviour in cells in relation to cell density. In particular, growth arrested cells (confluence cultures) showed an increased amino acids utilization for energetic purposes in respect to cells characterized by a higher proliferation index (subconfluence cultures). Specific amino acids transporters upregulation in transformed cells has been found to be related to cell growth and proliferation [27]. Hepatoma cells transport glutamine 10 to 30 times faster than normal cells, through a high-affinity carrier denominated System ASC, although HepG2 cells express System N too and a higher Na+-dependent glutamine transport activity in more confluent HepG2 cultures was shown [28]. Our results, showing that amino acid utilization was significantly higher at the near-plateau phase of cell growth
Fig. 5. Principal Component analysis of 1H-NMR derived metabolic profiles. Distribution of individual PC scores for PC1 and PC3. The most discriminatory metabolite balances were identified from the loadings scores for PC1 and PC3 components.
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than during rapid growth, are in close agreement with the findings of Sasajima T et al. [29] who demonstrated that the influx of two non-metabolized amino acids was directly related to cell density and inversely related to the proliferation index in glioma cell lines due to a higher rate of influx and not to a reduced efflux. We will outline that a statistical significant variation in the net balances of extracellular metabolites between subconfluence and confluence cultures was observed only for some metabolites with cells at confluence utilizing more glutamine, glutamate, leucine, isoleucine, methionine, acetate and succinate and less aspartate in respect to cells in subconfluence cultures. A high glutamine and amino acid consumption in HepG2 cells is in agreement with the peculiar metabolic pattern of the normal liver, which maintains systemic glutamine levels via the differential consumption and production in the periportal and perivenous regional distribution of the acinus. This architectural and physiological arrangement is subverted in hepatocellular carcinoma which is characterized by decreased glutamine synthetase and increased glutaminase activity [30]. Interestingly, although the subconfluence condition was characterized by a lower utilization of all amino acids, a significantly higher utilization of aspartate was observed as compared to confluence. The absence of correlation of aspartate as well as of glutamine balances with any other metabolite balances, observed in highly proliferating cells, may be interpreted on the basis of a shift of their utilization from energetic towards synthetic pathways, like the purine nucleotide synthesis, through the activity of adenylosuccinate synthetase [E.C. 6.3.4.4] and GMP synthase [E.C. 6.3.5.2] respectively. Indeed, in cells with a lower proliferation index (confluence cultures), which were characterized by an higher glutamate consumption and an higher amino acids utilization, aspartate and glutamine balances showed correlations with glucose and other amino acid utilization, in agreement with a network of substrate utilization inside the TCA cycle for energy production. A difference in TCA cycle regulation was also supported by the different flux direction of succinate that was produced or utilized in highly proliferating or in growth arrested cells, respectively. It is worth noting that comparing the correlation pattern among glucose utilization, lactate and alanine production we were able to evidence a different behaviour in the subconfluence and confluence cultures, even if no significant quantitative changes in these metabolite balances were observed (Fig. 6). In highly proliferating cells the glucose consumption correlated with lactate production according to a glucose utilization for energetic purposes in the so-called anaerobic glycolysis [31]. Conversely, in near-confluent growth arrested cultures the correlation between glucose and lactate balances was lost and glucose utilization correlated with alanine production. These variations in the correlation pattern could be related to a contribution from pools of pyruvate from different sub-cellular compartments, like mitochondria or cytosol, in the production of lactate or alanine depending on the peculiar metabolome of hepatoma cells in different cell growth conditions. The absence
Fig. 6. Correlation pattern among glucose, lactate and alanine balances in subconfluence and confluence cultures. Bold arrows indicate utilization (↓) or production (↑). Thin and dashed arrows represent increased and decreased flux reaction directions respectively. The numerical values are the Pearson's correlation coefficients measured from autoscaling data as reported in Materials and methods.
of correlation between glucose and alanine in subconfluence cultures could be in agreement with the synthesis of alanine in mitochondria through the malic enzyme [E.C.1.1.1.40] and glutamic-pyruvate transaminase activities [E.C.2.6.1.2] [32]. In this condition the variance of the lactate release is closely correlated with the variance of the glucose utilization due to the mainly cytosolic pyruvate production from glycolysis. Conversely, the observed lack of correlation between glucose utilization and lactate release in confluence cultures can be interpreted on the basis of the participation of other substrates besides glucose in the production of cytosolic pyruvate and then of lactate, such as malate deriving from TCA cycle intermediates. Accordingly, an increase in amino acid utilization through TCA cycle for energy purpose in growth arrested HepG2 has been observed. Consequently, the observed correlation between glucose utilization and alanine production starting from pyruvate produced by glycolysis could be explained by a high cytosolic transaminase activity. The suggested cell growth related metabolism regulation seems to be in agreement with the cell-cycle phase related metabolic networks previously shown in other cancer cells by 13C NMRbased metabolic profiling [12]. Finally, our study showed that glucose utilization and the proliferation rate were not necessarily correlated in HepG2 cells growing in culture at a different cell density again in agreement with Sasajima et al. who found that FDG accumulation was unaffected by proliferation rate, growth phase and cell density in two of the studied cell lines. In conclusion, our results show that it is possible to distinguish between different metabolic profiles of tumor cells in culture in relation to different physiological conditions by 1 H-NMR spectroscopy of the culture medium and MVDA analysis. Furthermore, by using metabolite balance quantitative
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analysis and the comparison of correlation pattern of metabolite balances we demonstrated that human hepatoma cells, almost arrested in G0/G1 cycle phase by contact dependent growth inhibition, underwent changes in channeling of amino acid utilization from synthetic to energy purposes and in anaplerosis/ cataplerosis regulation of the TCA cycle. As we shall see, it has recently been pointed out that cell density is a critical factor in the mechanism of resistance to antitumor drugs [33] and our results showed that this could be dependent on a different systems metabolism. The methodological approach presented, associated with transcriptomic data, can be considered a powerful and easy tool for investigating cancer cell metabolic phenotypes and in evaluating their changes in relation to environmental conditions and the effect of biologically active molecules. Acknowledgement This study was supported by a “La Sapienza” University Grant (2005). References [1] J. Bereheiter-Hahn, A. Munnich, P. Woiteneck, Dependence of energy metabolism on the density of cells in culture, Cell Struct. Funct. 23 (1998) 85–93. [2] J. Bar, E. Cohen-Noyman, B. Geiger, M. Oren, Attenuation of the p53 response to DNA damage at high cell density, Oncogene 23 (2004) 2128–2137. [3] Z. Vukelic, S. Kalanj-Bognar, Cell density-dependent changes of glycosphingolipid biosynthesis in cultured human skin fibroblasts, Glycoconj. J. 18 (2001) 429–437. [4] M. Masquelier, S. Vitols, Drastic effect of cell density on the cytotoxicity of daunorubicin and cytosine arabinoside, Biochem. Pharmacol. 67 (2004) 1639–1646. [5] J.M. de Anta, F.X. Real, X. Mayol, Low tumor cell density environment yields survival advantage of tumor cells exposed to MTX in vitro, Biochim. Biophys. Acta 1721 (2005) 98–106. [6] D. Valverde, M.R. Quintero, A.P. Candiota, L. Badiella, M.E. Cabanas, C. Arus, Analysis of the changes in the 1H NMR spectral pattern of perchloric acid extracts of C6 cells with growth, NMR Biomed. 19 (2006) 223–230. [7] W.M. Frederiks, J. Van Marle, C. van Oven, B. Comin-Anduix, M. Cascante, Improved localization of glucose-6-phosphate dehydrogenase activity in cells with 5-cyano-2,3-ditolyl-tetrazolium chloride as fluorescent redox dye reveals its cell cycle-dependent regulation, J. Histochem. Cytochem. 54 (2006) 47–52. [8] S. Jackowski, Coordination of membrane phospholipid synthesis with the cell cycle, J. Biol. Chem. 269 (1994) 3858–3867. [9] A. Coquelle, S. Mouhamad, M.O. Pequignot, T. Braun, G. Carvhalo, S. Vivet, D. Metivier, M. Castedo, G. Kroemer, Cell cycle-dependent cytotoxic and cytostatic effects of bortezomib on colon carcinoma cells, Cell Death Differ. 13 (2006) 873–875. [10] D.H. Margineantu, W.G. Cox, L. Sundell, S.W. Sherwood, J.M. Beechem, R.A. Capaldi, Cell cycle dependent morphology changes and associated mitochondrial DNA redistribution in mitochondria of human cell lines, Mitochondrion 1 (2002) 425–435. [11] P.K. Kennady, M.G. Ormerod, S. Singh, G. Pande, Variation of mitochondrial size during the cell cycle: a multiparameter flow cytometric and microscopic study, Cytometry A 62A (2004) 97–108.
1731
[12] A. Miccheli, A. Tomassini, C. Puccetti, M. Valerio, G. Peluso, F. Tuccillo, M. Calvani, C. Manetti, F. Conti, Metabolic profiling by 13 C NMR spectroscopy: [1,2-13C2] glucose reveals a heterogeneous metabolism in human leukemia T cells, Biochimie 88 (2006) 437–448. [13] S.G. Oliver, M.K. Winson, D.B. Kell, F. Baganz, Systematic functional analysis of the yeast genome, Trends Biotechnol. 16 (1998) 373–378. [14] J.K. Nicholson, E. Holmes, J.C. Lindon, I.D. Wilson, The challenges of modelling mammalian biocomplexity, Nat. Biotechnol. 22 (2004) 1268–1274. [15] D.B. Kell, M. Brown, H.M. Davey, W.B. Dunn, I. Spasic, S.G. Oliver, Metabolic footprinting and systems biology: the medium is the message, Nat. Rev., Microbiol. 3 (2005) 557–565. [16] D.P. Aden, A. Fogel, S. Plotkin, I. Damjanov, B.B. Knowles, Controlled synthesis of HbsAg in a differentiated human liver carcinoma-derived cell line, Nature 282 (1979) 615–616. [17] W. Weckwerth, K. Morgenthal, Metabolomics: from pattern recognition to biological interpretation, Drug Discov. Today 10 (2005) 1551–1558. [18] K. Morgenthal, W. Weckwerth, R. Steuer, Metabolomic networks in plants: transitions from pattern recognition to biological interpretation, BioSystems 83 (2006) 108–117. [19] A. Miccheli, R. Ricciolini, E. Piccolella, M. Delfini, F. Conti, Modulation of human lymphoblastoid B cell line by phorbol ester and sphingosine. A 31 P-NMR study, Biochim. Biophys. Acta 1093 (1991) 29–35. [20] D. Camacho, A. de la Fuente, P. Mendes, The origin of correlations inmetabolomics data, Metabolomics 1 (2005) 53–63. [21] L. Lebart, A. Morineau, K.M. Warwick, Multivariate descriptive statistical analysis, Wiley, New York, 1984. [22] A. Giuliani, G. Capuani, A. Miccheli, T. Aureli, M.T. Ramacci, F. Conti, Multivariate data analysis in biochemistry: a new integrative approach to metabolic control in brain aging, Cell. Mol. Biol. 37 (1991) 631–638. [23] R. Benigni, A. Giuliani, Quantitative modeling and biology: the multivariate approach, Am. J. Physiol. 266 (1994) R1697–R1704. [24] A. Giuliani, J.P. Zbilut, F. Conti, C. Manetti, A. Miccheli, Invariant features of metabolic networks: a data analysis application on scaling properties of biochemical pathways, Physica A 337 (2004) 157–170. [25] C. Manetti, C. Bianchetti, L. Casciani, C. Castro, M.E. Di Cocco, A. Miccheli, M. Motto, F. Conti, A metabonomic study of transgenic maize (Zea mays) seeds revealed variations in osmolites and branched amino acids, J. Exp. Bot. 57 (2006) 2613–2625. [26] S. Mazurek, E. Eigenbrodt, The tumor metabolome, Anticancer Res. 23 (2003) 1149–1154. [27] H.N. Christensen, Role of amino acid transport and countertransport in nutrition and metabolism, Physiol. Rev. 70 (1990) 43–77. [28] B.P. Bode, W.W. Souba, Modulation of cellular proliferation alters glutamine transport and metabolism in human hepatoma cells, Ann. Surg. 220 (1994) 411–424. [29] T. Sasajima, T. Miyagawa, T. Oku, J.G. Gevolani, R. Finn, R. Blasberg, Proliferation-dependent changes in amino acid transport and glucose metabolism in glioma cell lines, Eur. J. Nucl. Med. Mol. Imaging 31 (2004) 1244–1256. [30] T. Matsuno, I. Goto, Glutaminase and glutamine synthetase activities in human cirrhotic liver and hepatocellular carcinoma, Cancer Res. 52 (1992) 1192–1194. [31] O. Warburg, On respiratory impairment in cancer cells, Science 124 (1956) 269–270. [32] R.W. Moreadith, A.L. Lehninger, The pathways of glutamate and glutamine oxidation by tumor cell mitochondria. Role of mitochondrial NAD(P)+dependent malic enzyme, J. Biol. Chem. 259 (1984) 6215–6221. [33] J.M. de Anta, C. Mayo, F. Sole, M. Salido, B. Espinet, C. Corzo, M. Petzold, O. Villa, S. Serrano, F.X. Real, X. Mayol, Methotrexate resistance in vitro is achieved by a dynamic selection process of tumor cell variants emerging during treatment, Int. J. Cancer 119 (2006) 1607–1615.