Research

Rhabdomyosarcoma cells show an energy producing anabolic metabolic phenotype compared with primary myocytes

Teresa WM Fan^1,2,3 , Magda Kucia⁴ , Kacper Jankowski⁴ , Richard M Higashi^1,2 , Janina Ratajczak⁴ , Marius Z Ratajczak⁴ and Andrew N Lane^1,2,3

¹  Department of Chemistry, University of Louisville, KY, USA

²  Center for Regulatory Environmental Analytical Metabolomics, University of Louisville, KY, USA

³  Structural Biology Program, University of Louisville, KY, USA

⁴  Stem Cell Biology Program, J.G. Brown Cancer Center, University of Louisville, 529 S. Jackson St., Louisville, KY 40202, USA

author email corresponding author email

Molecular Cancer 2008, 7:79doi:10.1186/1476-4598-7-79

The electronic version of this article is the complete one and can be found online at: http://www.molecular-cancer.com/content/7/1/79

Received:	11 May 2008
Accepted:	21 October 2008
Published:	21 October 2008

© 2008 Fan et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

The functional status of a cell is expressed in its metabolic activity. We have applied stable isotope tracing methods to determine the differences in metabolic pathways in proliferating Rhabdomysarcoma cells (Rh30) and human primary myocytes in culture. Uniformly ¹³C-labeled glucose was used as a source molecule to follow the incorporation of ¹³C into more than 40 marker metabolites using NMR and GC-MS. These include metabolites that report on the activity of glycolysis, Krebs' cycle, pentose phosphate pathway and pyrimidine biosynthesis.

Results

The Rh30 cells proliferated faster than the myocytes. Major differences in flux through glycolysis were evident from incorporation of label into secreted lactate, which accounts for a substantial fraction of the glucose carbon utilized by the cells. Krebs' cycle activity as determined by ¹³C isotopomer distributions in glutamate, aspartate, malate and pyrimidine rings was considerably higher in the cancer cells than in the primary myocytes. Large differences were also evident in de novo biosynthesis of riboses in the free nucleotide pools, as well as entry of glucose carbon into the pyrimidine rings in the free nucleotide pool. Specific labeling patterns in these metabolites show the increased importance of anaplerotic reactions in the cancer cells to maintain the high demand for anabolic and energy metabolism compared with the slower growing primary myocytes. Serum-stimulated Rh30 cells showed higher degrees of labeling than serum starved cells, but they retained their characteristic anabolic metabolism profile. The myocytes showed evidence of de novo synthesis of glycogen, which was absent in the Rh30 cells.

Conclusion

The specific ¹³C isotopomer patterns showed that the major difference between the transformed and the primary cells is the shift from energy and maintenance metabolism in the myocytes toward increased energy and anabolic metabolism for proliferation in the Rh30 cells. The data further show that the mitochondria remain functional in Krebs' cycle activity and respiratory electron transfer that enables continued accelerated glycolysis. This may be a common adaptive strategy in cancer cells.

Background

Rhabdomyosarcomas are a serious childhood cancer that arise from a primitive muscle cell, the "rhabdomyoblast", and fail to differentiate into striated muscle cells. These tumors account for about 5–8% of all childhood cancers, with a peak incidence in the 1–5 age group. Overall, 50% of the children diagnosed with rhabdomyosarcoma survive 5 years. There are two main forms of the sarcoma, alveolar and the less severe embryonic form. Although there is considerable information about the origin and progression of these tumors [1-5], much less is known about the biochemical phenotype of these sarcomas and how it differs from the primary myocytes of the nearby tissue. Such information would be of great value not only for understanding the basic biochemistry of these transformed cells, but also for developing new therapeutics. Here we report a study of the biochemical phenotype of rhabdomyosarcoma cells of the embryonic variety, Rh30, in comparison with primary myocytes, using a ¹³C-isotopomer-based metabolomic approach.

Rapidly dividing cancer cells in culture show numerous differences from the differentiated primary cells of a comparable type [6-8]. In order to divide, the cell must traverse the cell cycle repeatedly, with a concomitant doubling of macromolecular content during the S and G2 phases. This requires considerable biosynthetic resources and an energy demand far in excess of the basal rate. Biosynthesis of proteins, nucleic acids, lipids and complex carbohydrates involves coupling to the hydrolysis of nucleoside triphosphates (e.g. ATP and GTP in protein biosynthesis, CTP and UTP in lipid and carbohydrate biosynthesis), as well as the incorporation of carbon and nitrogen from metabolic precursors. For cells in culture, many of these precursors are readily available in the medium, including essential amino acids, fatty acids and glucose. In contrast, the precursors of nucleotides, complex carbohydrates and phospholipids are absent from the medium, and so must be synthesized de novo.

There is a hierarchy of ATP-consuming reactions in cellular function [9]. As might be expected, maintenance of ion gradients across membranes for transport and various macromolecular repair processes involved in cell survival are the highest priority reactions and account for a large fraction of the ATP consumed in quiescent (G0/G1) cells [9-11]. Upregulating macromolecule biosynthesis in preparation for cell division requires additional ATP equivalents to be made. Many cancer cells have a tendency to increase glucose uptake [12] and glycolytic flux even under aerobic conditions, while secreting a large fraction of the glucose carbon as lactate [13,14]. This implies a bypass to oxidative phosphorylation, such that glycolysis alone may account for a substantial fraction of the ATP production. This enhanced aerobic glycolysis is known as the Warburg effect [15-17]. A more efficient means for energy production under aerobic conditions is to increase the flux of acetylCoA derived from the oxidation of glucose, amino acids and fatty acids into the Krebs' cycle. Moreover, to sustain the Krebs cycle activity, it is necessary to upregulate anaplerotic reactions that replenish carbon diverted for biosynthesis.

Skeletal muscle uses both glycolysis and oxidative phosphorylation for providing energy for contraction. Under conditions of hard exercise, glycogen stores are rapidly depleted, and oxidative phosphorylation of fuels such as fatty acids cannot keep up with demand. The muscle resorts to a greatly increased rate of glycolysis (> 100 fold [18]) and the production of lactate. Lactate, and to some extent Ala, is exported from the myocytes into the blood, when they are transported the liver for resynthesis into glucose via gluconeogenesis (cf. Figure 1). These comprise the Cori and alanine cycles [19,20]. Thus, myocytes have the capacity to make and secrete large amounts of lactate. The secretion of lactate via the monocarboxylate transporter is often accompanied by a proton symport [21,22]. Moreover, most cells can actively export protons via various exchangers, such as the H⁺/Na⁺ antiporter [23], Many cancer cells pump prodigious quantities of H⁺ into the extracellular environment, which may offer survival advantages, particularly in the light of their accelerated glycolysis [24,25].

However, it has been reported that many cancer cells upregulate the embryonic M2 form of pyruvate kinase (PK) which is defective in catalyzing pyruvate production [26], so alternative substrates must be oxidized to generate ATP [27-29]. One possibility is to oxidize glutamine/glutamate or serine ultimately to produce pyruvate, bypassing the defective PK step, and allowing lactate production to regenerate NAD⁺[26,29,30]. The relative importance of such reactions is likely to be cell dependent [31,32]. Fortunately, such alternative pathways are readily distinguishable using stable isotope tracer approaches in conjunction with NMR and mass spectrometric analysis, which we have been developing [33-37].

Thus questions arise as to the metabolic differences between a transformed cell and a primary (untransformed) cell. Here we have used ¹³C quantitative isotopomer analysis to follow the fate of individual carbon atoms derived from glucose, as outlined in Figure 1, in both rhabdomyosarcoma cells (Rh30) and primary myocytes, grown under the same conditions to probe their biochemical phenotypes. This approach enabled changes in turnover through individual pathways to be followed, which is not generally possible by concentration-based metabolite profiling alone. Furthermore, the use of ¹³C-edited ¹H NMR techniques allowed identification of major phosphorylated sugars that are otherwise difficult to detect in crowded ¹H NMR spectra. Clear differences between the myocytes and the Rh30 cells were evident, consistent with heightened energy and anabolic metabolism in Rh30 cells.

Results and discussion

Cell growth

Under the growth conditions of these experiments, the Rh30 cells double in about 24 h in the presence of serum. The growth rate in the absence of serum (0.5% BSA) was about 30% slower, indicating only a weak dependence on growth factors. In contrast, the primary myocytes grew much more slowly, with a doubling time of approximately 50 h in the presence of serum growth factors. This suggests that the cancer cells have an overall net higher demand for metabolic energy to drive macromolecular biosynthesis as the cells proliferate. This is in addition to the normal basal cell metabolic requirements.

Glucose uptake and lactate secretion

The amount of glucose remaining in the medium and the amounts of lactate and Ala that were secreted by the cells are readily measured by ¹H NMR and/or GC-MS. Furthermore, as NMR can distinguish between ¹³C-labeled and ¹²C lactate and glucose (see below), it was straightforward to determine the fraction of lactate that derived from the supplied ¹³C-glucose, or from other sources, according to Eq. (2). Table 1 shows the data for glucose consumption and secretion of lactate and Ala into the medium from the two cell types.

The consumption of [U-¹³C]-glucose by the Rh30 cells at 24 h was comparable to the consumption by the primary myocytes in 48 h, reflecting the slower growth rate of the latter. The amount and degree of ¹³C enrichment in lactate secreted was very much higher for the Rh30 cells than the primary myocytes (Table 1 and Figure 4). Since the dry weight of the myocytes at 48 h was comparable to that of the Rh30 cells at 24 h, this implies an 8-fold higher lactate production rate in the Rh30 cells than the myocytes. The amount and degree of ¹³C enrichment of Ala in the Rh30 medium was also much higher than measured in the myocyte medium, although Ala secretion constituted a smaller fraction of the labeled glucose consumption (Table 1 and Figure 4). Thus, the Rh30 cells converted a much higher fraction, F (cf. Eq. 2 and Table 1) of the ¹³C glucose consumed into lactate (50%) than the myocytes (10%). We have observed that typically 30–50% of the glucose consumed was converted to lactate by various epithelial derived cancer cell lines, (A.N. Lane, T. WM Fan, unpublished data). This is in contrast to a recent report on glioblastoma cells, in which more than 95% of the glucose consumed was converted to lactate and Ala [38]. The high rate of production of labeled lactate in Rh30 and other cancer cells indicates that the pyruvate kinase (PK) step in glycolysis (Fig. 1) must be active, in contrast with the reports of defective PK in some tumor cells [28].

The high degree of lactate labeling in Rh30 cells indicates that the major source of carbon in lactate and Ala was indeed glucose. However, the fraction of ¹³C Ala labeling in the myocyte was essentially at natural abundance, reflecting its primary origin from the 10% FCS present in the medium. Thus, not only did the Ala labeling pattern in myocytes differ from that in Rh30 cells but also it was distinct from the lactate labeling pattern in both cell lines. As the immediate precursor to Ala and lactate is pyruvate, these differences in labeling patterns could indicate the presence of distinct intracellular pools of pyruvate as observed in other cells [39,40] and see below. Moreover, a significant fraction of glucose taken up by Rh30 (48%) and myocytes (90%) was converted to metabolites other than lactate (cf. Table 1). The fate of some of the remaining glucose carbon was traced in the cellular components is described below

Glucose-dependent cell metabolism

Thirty four metabolites in the TCA extracts of the cell biomass were analyzed using GC-MS (Figures 2, 3), twenty of which were also analyzed by ¹H NMR (Figure 5, 6). An additional eight compounds were determined by ¹H NMR alone for a total of forty two identified metabolites. The profiles of selected metabolites in Rh30 and myocytes determined by GC-MS are compared in Figures 2 and 3. Lactate, Gly, Thr, Glu, glutathione (GSH), Arg, and phosphocholine (P-choline), taurine and myo-inositol were among the more abundant metabolites (cf. Figures 2, 3, 4, 5). There was good agreement between GC-MS and NMR determinations, indicating high analytical reliability. Figs 2, 3, 4, 5 also illustrate that there are significant differences in levels of numerous metabolites between the myocytes and Rh30 cells. Notably there was a much higher content of amino acids, GSH, taurine, P-choline, myo-inositol, adenine nucleotides (5'-AXP), and uracil nucleotides in the Rh30 cells. The high abundance of P-choline in Rh30 compared with the myocytes (Figure 5) appears to be a distinct feature of proliferating cells [35,37,41-46].

Glycolysis

Figure 6 shows an expansion of the lactate region for the Rh30 cells shown in Fig 5. The 3-methyl resonances of both Ala and lactate (doublets at 1.53 and 1.4 ppm, respectively) display pairs of ¹³C satellite peaks displaced 126 Hz apart. These satellite peaks have a complex structure, which can be accounted for by considering additional couplings of these protons to ¹³C2 and ¹³C1 of the compounds (i.e. uniformly labeled) [35,47]. The presence of uniformly ¹³C-labeled lactate and Ala was also evident in the high-resolution 2-D ¹H-¹³C HSQC or the 1-D ¹³C projection spectra of Rh30 and myocyte cell extracts, as illustrated in Figure 7. The respective doublet and triplet patterns of the β-(C3) and α-(C2) carbons of lactate and Ala (due to ¹³C-¹³C coupling) [36] is further consistent with the ¹H TOCSY satellite patterns for these two metabolites (cf. Fig. 8). The NMR data were corroborated by the GC-MS analysis, where the m+3 (lactate+3) mass isotopomers predominated. These data together indicate that the three ¹³C in both lactate and Ala derived directly from the [U-¹³C]-glucose precursor via glycolysis.

Detailed quantification of the ¹³C-isotopomers of intracellular lactate and Ala by NMR and GC-MS is given in Table 2 and Fig. 3. As observed for the medium (see above), the myocytes showed a lower degree of enrichment than the Rh30 cells, especially for Ala, consistent with a substantially lower glycolytic rate in the primary cells. The ¹³C-Ala enrichment in the Rh30 cells grown in the absence of growth factors from FCS was even lower than that in the myocytes. There was no unlabeled Ala present in this medium indicating that the cells under growth factor depleted conditions were not actively transaminating labeled pyruvate (cf. Figure 1). Thus, the unlabeled Ala fraction may reflect contributions from other metabolic processes such as protein turnover or glutaminolysis [30] (cf. Figure 1) and possibly involving a different pool of pyruvate as has been observed in other cells [39,40]. However, the amount of protein turnover or glutaminolysis required to account for the production of unlabeled Ala was small owing to its low concentration (cf. Figures 4, 5).

Krebs' cycle activity

Several other critical metabolites in the cells were also labeled with ¹³C as shown by the 2-D ¹H TOCSY spectrum of an example Rh30 cell extract in Figure 8. The cross-peak patterns are characteristic of particular metabolites, such as the ribose moieties of nucleotides, lactate, Ala and Glu, as annotated. Many of these cross-peaks are surrounded by satellite peaks that denote the presence of ¹³C. These are made clearer in the expansion of the spectral region of Glu, Ala and Lac (Fig. 9). It is clear that Glu was substantially labeled at the C4 (2.65 ppm) and C2 (4.08 ppm) positions and to a lesser extent at the C3 (2.22 ppm) position. Figure 9 also revealed the presence of four different Glu isotopomers, i.e. ¹³C-2,3-Glu, ¹³C-2-Glu, ¹³C-3-Glu, ¹³C-2,4-Glu, ¹³C-4-Glu, and ¹³C-3,4-Glu. Additional isotopomer information was obtained from analysis of the HSQC spectrum (Fig. 7). The doublet pattern of the γ- (C-4) and α- (C-2) carbons of Glu suggests the presence of ¹³C-4,5-Glu and/or ¹³C-3,4-Glu as well as ¹³C-1,2-Glu and/or ¹³C-2,3-Glu isotopomers, respectively. However, the ¹³C satellite pattern for the TOCSY cross-peak of H3 to H2 of Glu (2.22 to 4.08 ppm in Fig. 9) shows a significant amount of ¹³C-2,3-Glu. Similarly, the dominance of ¹³C-3,4-Glu is clear from the ¹³C satellite pattern for the cross peak of H4 to H3 of Glu (2.65 to 4.08 ppm in Fig. 9) but the presence of ¹³C-4,5-Glu or ¹³C-1,2-Glu could not be ruled out with the present data.

The above results are clear evidence of de novo synthesis of Glu from labeled glucose (10 mM), even though unlabeled Gln (2 mM) and Glu (0.13 mM) were both present in the medium. Further examination of the labeling pattern revealed evidence for the route of synthesis for the different Glu isotopomers. Glutamate is derives from 2-oxoglutarate by transamination or reductive amination (Glu dehydrogenase) in mammalian cells. There are two routes of label incorporation from [¹³C-U]-glucose into 2-oxoglutarate via pyruvate (Figures 1, 10). As shown in detail in Figure 10, one route (Fig. 10A) involves the pathway pyruvate → acetylCoA → citrate → 2-oxoglutarate, which leads to the production of ¹³C-4,5-Glu. The other route (Fig. 10B) utilizes the anaplerotic reaction of pyruvate carboxylase to produce OAA directly from pyruvate with the subsequent reaction sequence of citrate → 2-oxoglutarate, which generates ¹³C-2,3-Glu. The presence of ¹³C-2,3-Glu shown in Figures 7 and 9 is consistent with the activity of pyruvate carboxylase. The redistribution of labels into different carbons of Glu is presumably the result of carbon scrambling at the succinate dehydrogenase (SDH) step due to the symmetry of the succinate molecule. Moreover, the presence of multiple ¹³C-Glu isotopomers described above supports multiple turns of the cycle with ¹³C label input (cf. Figure 10).

It is clear from Figure 7 that the Rh30 cells had a much higher buildup of the different ¹³C isotopomers of Glu (than the myocytes. This implicates a higher turnover of the Krebs cycle and specifically a higher capacity for anaplerotic pyruvate carboxylation, as evident by the enhanced buildup of ¹³C-4,5-Glu and ¹³C-2,3-Glu, respectively.

Malate and aspartate also showed substantial ¹³C enrichment (Figure 3B) in both doubly and triply labeled mass isotopomers based on the GC-MS analysis. The positional isotopomer of the triply labeled malate was deduced from the 2-D HSQC analysis, as shown in Figure 7. The doublet of C3 or β-malate and the triplet of C2 or α-malate indicate the prevalence of [¹³C-1,2,3]-malate. The doubly labeled malate isotopomers can be [¹³C-2,3]-malate, [¹³C-1,2]-malate, and/or [¹³C-3,4]-malate, the abundance of which could not be ascertained with the present data. By comparing Rh30 cells with myocytes in terms of the ¹³C-malate isotopomer patterns in Fig. 3B and 7, it is clear that Rh30 cells synthesized a much higher amount of doubly and triply labeled malate, the latter of which was identified as [¹³C-1,2,3]-malate. Similar considerations apply to Asp i.e. the doubly and triply labeled ([¹³C-1,2,3]-Asp) isotopomers were more abundant in Rh30 cells than in myocytes (Figure 3B).

According to the schemes in Figs 1 and 10B, [¹³C-1,2,3]-malate and [¹³C-1,2,3]-Asp can only be synthesized from [U-¹³C]-pyruvate via anaplerotic pyruvate carboxylation (PC). PC can also lead to the synthesis of the doubly labeled [¹³C-2,3]-malate or Asp after one turn of the Krebs cycle, as outlined in Fig. 10B. In contrast, the doubly labeled malate species (as [¹³C-1,2]- and [¹³C-3,4]-malate) or Asp should derive from the normal Krebs' cycle sequence with input of [U-¹³C]-pyruvate, as illustrated in Fig. 10A, or the doubly labeled [¹³C-2,3]-malate from pyruvate carboxylation, as outlined in Fig. 10B. Thus, the combined GC-MS and NMR determination of malate and Asp isotopomers not only revealed an enhanced activity of the Krebs' cycle but also activation of pyruvate carboxylation in the Rh30 cells over the primary myocytes. Such anaplerotic reactions are essential for actively dividing cells, as the production of important metabolites such as pyrimidine nucleobases and reduced glutathione deplete Krebs' cycle intermediates (Fig. 1 and see below), which must be replenished.

Krebs cycle intermediates may also be replenished via glutaminolysis [30,38,48], which has been shown to be activated in glioblastoma cells, based on the production of labeled lactate derived from labeled Gln [38]. The role of glutaminolysis in anaplerosis in Rh30 cells could not be ascertained here for lack of carbon tracing from labeled Gln. However, the comparable production of unlabeled lactate by Rh30 cells and myocytes (Figures 1, 4 and see above) suggests a minor role for glutaminolysis in activating anaplerosis in Rh30 cells.

The sustained activity of the Krebs' cycle in the Rh30 cells requires a functional respiratory chain to regenerate NAD⁺ and FAD in the mitochondria. The same chain also reoxidizes cytosolic NADH produced by glycolysis, and transported into the mitochondria via the malate-aspartate shuttle (cf. Fig. 1), that is not involved in reducing pyruvate to lactate (and see above).

Furthermore, transformed Rh30 cells were much more active in de novo GSH synthesis than the primary myocytes. GSH was labeled in the Glu residue in Rh30 cells and the extent of this labeling was much stronger in Rh30 cells than in the myocytes (cf. Figs. 7 and 8), just as the case for its precursor Glu (Table 4). Although the cysteine supply can be rate limiting in GSH synthesis, in this case the medium contained an adequate supply of cystine (0.2 mM). The incorporation of labeled glutamate indicates a substantial rate of de novo GSH synthesis in the Rh30 cells.

Nucleotide biosynthesis

Asp is a direct crucial precursor to pyrimidine biosynthesis (Fig. 1). We have previously shown that the pyrimidine rings of nucleotides become ¹³C labeled in the Rh30 cells [35]. All possible isotopomers of 5'-UXP C5C6 were observed as shown in the TOCSY spectra (Figure 9B). This ¹³C satellite pattern was similar to that of Asp (Figure 8), which is consistent with Asp being a direct precursor to the de novo synthesis of 5'UXP ring (Figures 1, and 10B). The ¹³C abundance and extent of the labeling in uracil ring of 5'-UXP (Table 3) and its precursor Asp (Figures 3B and 7). was much higher in Rh30 cells than in myocytes. Thus, it is clear that Rh30 cells exhibited a higher capacity for the de novo synthesis of pyrimidine nucleotides than myocytes.

As Asp is derived from the transamination of the Krebs' cycle intermediate, OAA, increased diversion of Asp to pyrimidine ring biosynthesis would ultimately deplete OAA, which must be replenished to sustain the Krebs' cycle activity. As described above, this can occur by enhanced anaplerosis via pyruvate carboxylation in Rh30 cells. Furthermore, labeled Asp synthesized in the mitochondria is presumably transported to the cytoplasm for pyrimidine biosynthesis via the Asp/malate shuttle (Fig. 1). The enhanced rate of pyrimidine synthesis in Rh30 cells may well reflect an increased Asp/malate shuttle activity. This shuttle also facilitates the transfer of NADH produced in the cytoplasm to the mitochondrion for ATP production via oxidative phosphorylation. Although pyrimidine biosynthesis is mainly cytoplasmic, one step occurs within the mitochondrion and involves coupling to the electron transfer chain. Collectively these all point to metabolically functional mitochondria in the cancer cells.

The higher capacity for pyrimidine synthesis in Rh30 cells is corroborated by the¹³C labeling data in the ribose moiety of 5'-UXP, where all five carbons of the ribose residue were uniformly labeled in ¹³C. This was evident from the TOCSY ¹³C satellite patterns of the 5'-UXP cross-peaks (e.g. Fig. 9B for H1' → H2' of 5'-UXP) and ¹³C spin coupling patterns of C1' to C4' of ribose in 5'-UXP (Fig. 8). The abundance of [U-¹³C-ribose]-UXP (Fig. 9B) and % ¹³C enrichment (Table 3) were much higher in Rh30 cells than in myocytes, consistent with an enhanced rate of 5'-UXP synthesis. Also evident in Fig. 8 is the higher rate of synthesis for purine nucleotides (i.e. 5'-AXP and 5'-GXP), as shown by the higher abundance of [U-¹³C-ribose]-5'-AXP and 5'-GXP. This difference in ¹³C labeling of nucleotides is consistent with a higher metabolic demands for macromolecular (e.g. RNA) biosynthesis for the transformed cells compared with slowly dividing primary cells (also see below).

It should also be noted that the ¹H NMR resonances of 5'-AXP and 5'-UXP in Fig. 8 were in fact mixtures of 3 resonances, which correspond to the tri, di and mono phosphates species, with intensities in the order 5'-ATP > 5'-ADP > 5'-AMP and similarly for 5'-UTP, 5'-UDP and 5'-UMP (data not shown).

Intracellular glucose-6-phosphate and glycogen synthesis

Additional 2-D ¹H NMR experiments helped identify ¹³C-labeled glucose-6-phosphate (G6P) and/or glycogen in the Rh30 and myocyte extracts. These included ¹H HCCH-TOCSY and ¹H-¹³C HSQC-TOCSY. The HCCH-TOCSY spectrum of the myocyte extract (Figure 11) displayed covalent connectivities of protons attached to consecutively ¹³C-labeled carbons of various metabolites such as lactate, Ala, Glu, β-glucose-6-phosphate (βG6P), glycogen, and AXP. The Rh30 spectrum (not shown) also exhibited similar covalent networks except for the absence of connectivities for glycogen, lower abundance connectivities for βG6P, and more prominent connectivities for 5'-UXP and 5'-GXP. The connectivity patterns for these metabolites is consistent with the presence of [¹³C-1-5]-βG6P and [U-¹³C-glucose]-glycogen, in addition to [U-¹³C]-lactate, [U-¹³C]-Ala, and [¹³C-2,3,4]-Glu.

To confirm further the assignment for G6P (as opposed to glucose), 2-D high-resolution ¹H-¹³C HSQC-TOCSY spectra were analyzed and that for the myocytes is illustrated in Figure 12. In this spectrum, covalent connectivities from ¹³C1 to H1 then relayed to H2 and H3 as well as ¹³C3 to H3 then to H4 of βG6P were clearly observed. Also evident was the set of ¹³C6 to H6 connectivities of βG6P, thus providing unequivocal identification of βG6P in the crude extract of the myocytes. Moreover, the high-resolution spectrum revealed ¹³C coupling patterns of βG6P carbons, i.e. doublet for C1, triplet for C3-5, as well as doublet for C6, which indicate the presence of uniformly labeled βG6P and thus its origin from [U-¹³C]-glucose.

Free glucose is not expected to be present in cells where the activity of hexokinase exceeds that of glucose transport [49]. The 1D proton spectra (Fig. 5A) show that the concentrations of glucose and G6P were negligible in the Rh30 cells which indicates that the steps downstream of G6P in glycolysis are not flux limiting. The low concentration of free glucose is a feature of cancer cells which together with the low level of G6P reflects the high glycolytic flux, making glucose transport rate limiting [50], despite derepression of GLUT4 expression by mutated p53[12]. In contrast, the concentration of G6P in the myocytes was significant (Fig 7A), which suggests that for these cells, the uptake of glucose or hexokinase was not limiting glycolysis in these cells. Rather, a step downstream of G6P, such as PFK1, limits flux leading to an accumulation of G6P. This is consistent with typical estimates of intracellular G6P in skeletal muscle (ca. 200–400 μM)[51]. The much higher demand from glycolysis and the PPP (see above) in Rh30 cells, compared with the myocytes, may well be limiting glycogen synthesis, which competes for the same glucose precursor. This may underlie the lack of newly synthesized glycogen in Rh30 cells versus its significant presence in the myocytes (cf. Fig. 11).

Finally, the extent of ¹³C labeling in lactate, Ala, and pyrimidine nucleotides was higher in Rh30 cells grown in fetal calf serum (FCS) than cells grown in BSA (Table 3). The low enrichment of the intracellular Ala may be related to differences in pyruvate production from sources other than glucose between the FCS-stimulated and unstimulated Rh30 (BSA) cells (see above). For Glu, the two growth conditions gave a comparable extent of ¹³C labeling, which was significantly higher than that for the myocytes. Thus, even without the influence of the growth factor in FCS, Rh30 cells grown in BSA still exhibited a greater turnover of [U-¹³C]-glucose through glycolysis, Krebs' cycle, and pyrimidine nucleotide biosynthesis than the primary myocytes.

Conclusion

Using [U-¹³C]-glucose as tracer and isotopomer-based metabolomic analysis, we have demonstrated major distinctions in central energy and anabolic metabolism between the primary myocyte and transformed Rh30 cell lines. A major advantage of this approach is the ability to follow the fate of individual atoms simultaneously into many metabolites, and thereby to characterize the interrelationships among multiple biochemical pathways. Our data show that relative to the myocytes, glycolysis, Krebs' cycle, pentose phosphate pathway, and nucleotide biosynthesis (cf. Fig. 1) were coordinately enhanced in Rh30 cells, presumably to meet the demand of accelerated growth. The coordination between energy production (including the Warburg effect [15,17,52]) and generation of biosynthetic precursors in Rh30 cells may well be mediated via an activation of anaplerotic carboxylation and the malate/Asp shuttle which facilitates the transfer of NADH hydride from the cytoplasm to the mitochondrion for oxidative phosphorylation [7,48], thereby sustaining accelerated glycolysis. The role of glutaminolysis as another anaplerotic input to the Krebs cycle is unclear but is likely to be less significant in accounting for the distinction between Rh30 cells and myocytes.

It is therefore evident that mitochondria of transformed Rh30 cells are active, both in citric acid cycling and respiratory electron transport. Since these processes are essential to cell proliferation, it is reasonable to postulate that they may be fundamental in the transformation of primary to malignant cells.

Methods

Materials

Cells

The human RMS cell line Rh30 (established at St. Jude Hospital), and human primary myocytes were cultured in pyruvate and glucose-free RPMI 1640 (Invitrogen, 11879-020), supplemented with 100 IU/ml penicillin, 10 μg/ml streptomycin, and 50 μg/ml neomycin (Life Technologies, Inc., Grand Island, NY) in the presence of 10% heat-inactivated FCS (Life Technologies) and 0.2% glucose. The medium thus contained 0.17 mM ¹²C alanine and 0.5 mM unlabeled lactate and 0.56 mM unlabeled glucose from the FCS. The cells were cultured in a humidified atmosphere at 5% CO₂, 37°C at an initial cell density of 2.5 × 10⁴ cells/flask (Corning) and the media were changed every 48 h. Prior to ¹³C labeling the cells were grown in 0.5% bovine serum albumin (BSA), without FCS to partially synchronize the culture. Control experiments were also run under otherwise identical conditions in which 0.5% BSA was used in place of FCS during the labeling phase. The dry weight of cells was measured for normalizing metabolite concentrations.

Isotopes

[U-¹³C]-glucose (99% ¹³C) was purchased from Cambridge Isotope Laboratories, MA and used without further purification. A stock solution (20%) was prepared in phosphate buffered saline and sterile filtered through a 0.2 μm filter. The appropriate quantity of the stock solution was added to glucose-free RPMI medium to a final concentration of 10.7 mM (excluding the 0.54 mM unlabeled glucose from the FCS).

Methods

Initial experiments were carried out with unlabeled glucose, followed by full metabolite profile experiments using [U-¹³C]-glucose. In one experiment, both the Rh30 and myocytes were cultured for 24 h in labeled glucose. In a second experiment to account for the differences in cell doubling rates, the Rh30 cells were cultured for 24 h, and the myocytes for 48 h in the labeled glucose. In this work, ¹³C atoms in various metabolites deriving exclusively from [U-¹³C]-glucose were monitored by NMR or mass spectrometry, which represented de novo metabolic transformations originating from glucose. Growth rates and viability were measured by direct cell counting on a hemocytometer using 0.4% Trypan Blue.

Metabolite extraction

After growth for 24 or 48 h the cells were harvested by trypsinization, scraping followed by low spin centrifugation at 4°C. The pellet was washed twice with ice-cold PBS to remove contaminating medium, pelleted again, flash frozen in lN₂ and lyophilized. The dry cell mass was recorded for normalizing metabolite concentrations before extractions twice with ice-cold 10% trichloroacetic acid (TCA), followed by lyophilization as previously described[37,53]. The dry mass of the cells was recorded for normalizing metabolite concentrations. The medium was similarly treated with TCA in the same way for measuring secreted metabolites (predominantly lactate and Ala), and to assay the glucose utilization during the experiment. ¹³C-glucose was quantified at t = 0 and at harvest (t) by¹H NMR using the well-resolved ¹³C1 satellite signals of -α glucose centered at 5.22 ppm. This accounts for 36% of the total glucose. The ¹³C and ¹²C lactate and Ala concentrations were determined by integration of the methyl peak and its satellites and calibrated against the concentration of lactate determined independently by GC-MS. From this, the amount of glucose consumed, ΔGlc, and the fraction converted to lactate plus Ala, F, could be estimated, according to Eq. (1) and Eq. (2) [35]:

ΔGlc = n(¹³C-Glc)⁰-n(¹³C-Glc)^t](1)

F = [n(¹³C-lac) + n(¹³CAla)]/2ΔGlc(2)

n is the number of moles of the compound in parentheses.

The factor of 2 accounted for the fact that one molecule of glucose gives rise to two molecules of lactate or Ala.

1-F then represents the glucose carbon that enters other metabolites and macromolecules in the cell mass or otherwise not accounted for. The fractions were not been corrected for the small (5%) contribution from unlabeled glucose present in the medium.

NMR

NMR spectra were recorded at either 14.1 T or 18.8 T on Varian Inova NMR spectrometers at 20°C using a 90° excitation pulse. One-dimensional NMR spectra were recorded with an acquisition time of 2 sec and a relaxation delay of 3 sec. Under these conditions, the protons were essentially full relaxed, as determined by independent measurements of the spin-lattice relaxation time, T₁. For identifying metabolites in the extracts and determining the positional enrichment with ¹³C we used a suite of 2D experiments including TOCSY (or DQ COSY), HSQC, HCCH-TOCSY and HSQC-TOCSY as previously described [35-37,53]. The latter two experiments make use of the isotope editing function. HCCH-TOCSY specifically selects for those molecules in which at least two adjacent carbon atoms are ¹³C whereas HSQC or HSQC-TOCSY detects protons directly attached to ¹³C. The TOCSY experiments were recorded with a spectral width of 6000 Hz in F2, 0.341 s acquisition time in t₂ and 0.05 s in t₁, 1.9 s interpulse delay, 50 ms mixing time, and an 8 kHz B₁ field strength. The HSQC-TOCSY experiments were recorded with an acquisition time of 0.12 s in t₂ and 12 ms in t₁ using a 50 ms proton spin lock at a strength of 8 kHz. For the HCCH-TOCSY experiments, the acquisition times were 0.12 s in t₂, 0.05 s in t₁ using an 8 kHz ¹³C spin lock field centered in the aliphatic region of the spectrum for a duration of 12 ms.

Metabolites were assigned based on their ¹H chemical shift, TOCSY connectivity patterns and correlation with ¹³C, using our in-house database as described previously [36,54]. All metabolites except choline, sugars and the nucleotides were quantified from GC-MS data [55] whereas choline was quantified from the peak at 3.22 ppm in the 1-D NMR spectra, as described previously [56].

NMR analysis of ¹³C enrichment

¹³C enrichment in lactate and Ala was determined from 1D ¹H NMR experiments, as the methyl resonances of these metabolites were well resolved. The peaks were integrated, and the areas of the protons attached to ¹²C (central peak) and to ¹³C (satellite peaks) were recorded. The ¹³C content was then calculated as [35]:

%¹³C = 100*area(¹³C satellites)/[area(¹²C)+area(¹³Csatellites)](3)

For other metabolites, which were not resolved in 1D NMR experiments, we volume-integrated cross-peaks in TOCSY spectra after correcting the base-planes. The various isotopomer enrichments were calculated as for the 1D spectra, i.e. the peak volume of the particular ¹³C isotopomer divided by the total volume of the cross peak including the satellites. In the TOCSY experiments, the protons were partially saturated owing to the shorter recycle time (2 s) of these experiments. Thus, the actual peak volumes were corrected according to the differential T₁ values of protons attached to ¹³C or ¹³C; as:

M(true) = M(obs)/[1-exp-t/T₁](4)

Where M(true) is the corrected area, M(obs) is the observed area, and t is the recycle time. As the relaxation during the spin lock has the opposite effect on peak intensities, this correction was small [35]. Effective T₁ values were determined on standards recorded under the same solvent conditions.

GC-MS

Following NMR analysis, an aliquot (50–100 μl) of the NMR sample was re-equilibrated with H₂O and lyophilized to remove any deuteration, then silylated with 25–50 μl 1:1 (v/v) acetonitrile:MTBSTFA (N-methyl-N-[tert-butyldimethylsilyl]trifluoroacetamide, (Regis Chemical, Morton Grove, IL) by 3 h of sonication followed by standing overnight [57,58]. The solution was directly analyzed on a PolarisQ GC-ion trap MSn (ThermoFinnigan, Austin, TX) using a 0.5 μl injection volume. The column was 0.15 mm i.d. × 50 m fused silica open-tubular, coated with 0.2 μm BPX-5 (5% phenyl-methylsiloxane). The following conditions were employed for GC-MS. Injector at 280°C, column at 60°C for 2 min, followed by a 20°C/min ramp to 150°C, then 6°C/min to 300°C, splitless vent held for 1.5 min, He carrier gas velocity 30 cm/s at 60°C, transfer line = 280°C, electron energy = 70 eV, source heated to 190°C, automatic gain control target value = 50, maximum inject time = 25 ms, He damping gas = 0.3 ml/min, full scan acquisition from 140 to 650 m/z at a rate of five spectra/s which were averaged into one, and mass calibration preformed byperfluorotributylamine.

Metabolites were identified and quantified automatically using Xcalibur software (ThermoFinnigan), based on their GC retention times and mass fragmentation patterns matched against an in-house database and external standards. Identities were also verified by manual inspection. GC-MS quantification of total abundance of metabolites was accomplished by comparing the m/z 147 ion response for each metabolite in the samples with that for the corresponding standard of known concentration. The m/z 147 ion was chosen for quantification of labeled amino acids because this ion fragment did not contain ¹³C as it originated entirely from the silyl moiety. Conversely, for ¹³C isotopomers, the pseudo-molecular ion clusters of authentic standards were used to obtain the empirical ion ratios as the basis of the calculation for label abundance, as described in detail previously [37].

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

MK, KJ, JR, grew cells and determined growth curves; TWMF extracted the cells and recorded and interpreted NMR spectra; RMH acquired and analyzed the mass spectra; MZR directed cell biology and collaboration; ANL recorded and interpreted the spectra. TWMF and ANL drafted the manuscript. All authors have read and approved the contents.

Acknowledgements

This work was supported by the Brown Foundation, Kentucky Challenge for Excellence (to ANL), NIH Grant RR018733 from the National Center for Research Resources, National Cancer Institute Grants 1 R01CA101199-01 and 1R01CA118434-01A2 to TWMF, NIH R01 CA106281-01 to MZR, NSF EPSCoR grant EPS-0132295 for the 18.8 T NMR spectrometer, and EPSCoR grant EPS-0447479 to TWMF for the development of isotopomer-based metabolomic analysis.

References

1. Jankowski K, Kucia M, Wysoczynski M, Reca R, Zhao D, Trzyna E, Trent J, Peiper S, Zembala M, Ratajczak J, et al.: Both hepatocyte growth factor (HGF) and stromal-derived factor-1 regulate the metastatic behavior of human rhabdomyosarcoma cells, but only HGF enhances their resistance to radiochemotherapy.

   Cancer Research 2003, 63:7926-7935. PubMed Abstract | Publisher Full Text

2. Libura J, Drukala J, Majka M, Tomescu O, Navenot JM, Kucia M, Marquez L, Peiper SC, Barr FG, Janowska-Wieczorek A, Ratajczak MZ: CXCR4-SDF-1 signaling is active in rhabdomyosarcoma cells and regulates locomotion, chemotaxis, and adhesion.

   Blood 2002, 100:2597-2606. PubMed Abstract | Publisher Full Text

3. Wysoczynski M, Miekus K, Jankowski K, Wanzeck J, Bertolone S, Janowska-Wieczorek A, Ratajczak J, Ratajczak MZ: Leukemia inhibitory factor: A newly identified metastatic factor in rhabdomyosarcomas.

   Cancer Research 2007, 67:2131-2140. PubMed Abstract | Publisher Full Text

4. Scotlandi K, Picci P: Targeting insulin-like growth factor 1 receptor in sarcomas.

   Current Opinion in Oncology 2008, 20:419-427. PubMed Abstract | Publisher Full Text

5. Baer C, Nees M, Breit S, Selle B, Kulozik AE, Schaefer KL, Braun Y, Wai D, Poremba C: Profiling and functional annotation of MRNA gene expression in pediatric rhabdomyosarcoma and Ewing's sarcoma.

   Int J Cancer 2004, 110(5):687-694. PubMed Abstract | Publisher Full Text

6. Rarnanathan A, Wang C, Schreiber SL: Perturbational profiling of a cell-line model of tumorigenesis by using metabolic measurements.

   Proceedings of the National Academy of Sciences of the United States of America 2005, 102:5992-5997. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

7. Telang S, Lane AN, Nelson KK, Arumugam S, Chesney JA: The oncoprotein H-RasV12 increases mitochondrial metabolism.

   Mol Cancer 2007, 6:77. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

8. Telang S, Yalcin A, Clem AL, Bucala R, Lane AN, Eaton JW, Chesney J: Ras transformation requires metabolic control by 6-phosphofructo-2-kinase.

   Oncogene 2006, 25:7225-7234. PubMed Abstract | Publisher Full Text

9. Buttgereit F, Brand MD: A Hierarchy of ATP-Consuming Processes in Mammalian-Cells.

   Biochem J 1995, 312:163-167. PubMed Abstract | PubMed Central Full Text

10. Schmid D, Burmester GR, Tripmacher R, Kuhnke A, Buttgereit F: Bioenergetics of human peripheral blood mononuclear cell metabolism in quiescent, activated, and glucocorticoid-treated states.

    Bioscience Reports 2000, 20:289-302. PubMed Abstract | Publisher Full Text

11. Buttgereit F, Burmester GR, Brand MD: Therapeutically targeting lymphocyte energy metabolism by high-dose glucocorticoids.

    Biochemical Pharmacology 2000, 59:597-603. PubMed Abstract | Publisher Full Text

12. Schwartzenberg-Bar-Yoseph F, Armoni M, Karnieli E: The tumor suppressor p53 down-regulates glucose transporters GLUT1 and GLUT4 gene expression.

    Cancer Research 2004, 64:2627-2633. PubMed Abstract | Publisher Full Text

13. Wehrle JP, Ng CE, McGovern KA, Aiken NR, Shungu DC, Chance EM, Glickson JD: Metabolism of alternative substrates and the bioenergetic status of EMT6 tumor cell spheroids.

    NMR in Biomedicine 2000, 13:349-360. PubMed Abstract | Publisher Full Text

14. Lanks KW, Li PW: End Products of Glucose and Glutamine Metabolism by Cultured Cell Lines.

    Journal of Cellular Physiology 1988, 135:151-155. PubMed Abstract

15. Garber K: Energy boost: The Warburg effect returns in a new theory of cancer.

    Journal of the National Cancer Institute 2004, 96:1805-1806. PubMed Abstract | Publisher Full Text

16. Werle M, Jahn L, Kreuzer J, Hofele J, Elsasser A, Ackermann C, Katus HA, Vogt AM: Metabolic control analysis of the Warburg-effect in proliferating vascular smooth muscle cells.

    Journal of Biomedical Science 2005, 12:827-834. PubMed Abstract | Publisher Full Text

17. Warburg O: On the origin of cancer cells.

    Science 1956, 123:309-314. PubMed Abstract | Publisher Full Text

18. Fell D: Understanding the Control of Metabolism. London: Portland Press; 1997.

19. Ganong WF: Excitable Tissue: Muscle. In Review of Medical Physiology. New York: McGraw-Hill; 2003.

20. Perriello G, Jorde R, Nurjhan N, Stumvoll M, Dailey G, Jenssen T, Bier DM, Gerich JE: Estimation of Glucose-Alanine-Lactate-Glutamine Cycles in Postabsorptive Humans – Role of Skeletal-Muscle.

    Am J Physiol 1995, 269(3 Pt 1):E443-E450. PubMed Abstract | Publisher Full Text

21. Enerson BE, Drewes LR: Molecular features, regulation, and function of monocarboxylate transporters: Implications for drug delivery.

    Journal of Pharmaceutical Sciences 2003, 92:1531-1544. PubMed Abstract | Publisher Full Text

22. Halestrap AP, Price NT: The proton-linked monocarboxylate transporter (MCT) family: structure, function and regulation.

    Biochemical Journal 1999, 343:281-299. PubMed Abstract | PubMed Central Full Text

23. Moolenaar WH: Regulation of Cytoplasmic Ph by Na+/H+ Exchange.

    Trends in Biochemical Sciences 1986, 11:141-143.

24. Gatenby RA, Gillies RJ: Why do cancers have high aerobic glycolysis?

    Nature Reviews Cancer 2004, 4:891-899. PubMed Abstract | Publisher Full Text

25. Gillies RJ, Gatenby RA: Adaptive landscapes and emergent phenotypes: why do cancers have high glycolysis?

    Journal of Bioenergetics and Biomembranes 2007, 39:251-257.

26. Christofk HR, Heiden MG, Harris MH, Ramanathan A, Gerszten RE, Wei R, Fleming MD, Schreiber SL, Cantley LC: The M2 splice isoform of pyruvate kinase is important for cancer metabolism and tumour growth.

    Nature 2008, 452:230-U274. PubMed Abstract | Publisher Full Text

27. Mazurek S, Boschek CB, Hugo F, Eigenbrodt E: Pyruvate kinase type M2 and its role in tumor growth and spreading.

    Seminars in Cancer Biology 2005, 15:300-308. PubMed Abstract | Publisher Full Text

28. Mazurek S, Grimm H, Boschek CB, Vaupel P, Eigenbrodt E: Pyruvate kinase type M2: A crossroad in the tumor metabolome.

    British Journal of Nutrition 2002, 87:S23-S29. PubMed Abstract | Publisher Full Text

29. Mazurek S, Grimm H, Oehmke M, Weisse G, Teigelkamp S, Eigenbrodt E: Tumor M2-PK and glutaminolytic enzymes in the metabolic shift of tumor cells.

    Anticancer Research 2000, 20:5151-5154. PubMed Abstract

30. Mazurek S, Eigenbrodt E: The tumor metabolome.

    Anticancer Research 2003, 23:1149-1154. PubMed Abstract

31. Moreno-Sanchez R, Rodriguez-Enriquez S, Marin-Hernandez A, Saavedra E: Energy metabolism in tumor cells.

    Febs Journal 2007, 274:1393-1418. PubMed Abstract | Publisher Full Text

32. Elbers JRJ, Vanunnik JAM, Rijksen G, Vanoirschot BA, Roholl PJM, Oosting J, Staal GEJ: Pyruvate-Kinase Activity and Isozyme Composition in Normal Fibrous Tissue and Fibroblastic Proliferations.

    Cancer 1991, 67:2552-2559. PubMed Abstract

33. Lane AN, Fan TW-M, Higashi RM: Stable isotope assisted metabolomics in cancer research.

    IUBMB Life 2008, 60:124-129. PubMed Abstract | Publisher Full Text

34. Lane AN, Fan TW, Higashi RM: Isotopomer-based metabolomic analysis by NMR and mass spectrometry. In Methods Cell Biol. Volume 84. Edited by: John J, Correia HWD. San Diego: Academic Press; 2008:541-588. PubMed Abstract | Publisher Full Text

35. Lane AN, Fan TW: Quantification and identification of isotopomer distributions of metabolites in crude cell extracts using 1H TOCSY.

    Metabolomics 2007, 3:79-86.

36. Fan TW, Lane AN: Structure-based profiling of Metabolites and Isotopomers by NMR.

    Progress in NMR Spectroscopy 2008, 52:69-117.

37. Fan T, Bandura L, Higashi R, Lane A: Metabolomics-edited transcriptomics analysis of Se anticancer action in human lung cancer cells.

    Metabolomics 2005, 1:1-15.

38. DeBerardinis RJ, Mancuso A, Daikhin E, Nissim I, Yudkoff M, Wehrli S, Thompson CB: Beyond aerobic glycolysis: Transformed cells can engage in glutamine metabolism that exceeds the requirement for protein and nucleotide synthesis.

    Proceedings of the National Academy of Sciences of the United States of America 2007, 104:19345-19350. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

39. Zwingmann C, Richter-Landsberg C, Leibfritz D: C-13 isotopomer analysis of glucose and alanine metabolism reveals cytosolic pyruvate compartmentation as part of energy metabolism in astrocytes.

    Glia 2001, 34:200-212. PubMed Abstract | Publisher Full Text

40. Lu D, Mulder H, Zhao P, Burgess SC, Jensen MV, Kamzolova S, Newgard CB, Sherry AD: 13C NMR isotopomer analysis reveals a connection between pyruvate cycling and glucose-stimulated insulin secretion (GSIS).

    Proceedings of the National Academy of Sciences of the United States of America 2002, 99:2708-2713. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

41. Lean CL, Bourne R, Thompson JF, Scolyer RA, Stretch J, Li LXL, Russell P, Mountford C: Rapid detection of metastatic melanoma in lymph nodes using proton magnetic resonance spectroscopy of fine needle aspiration biopsy specimens.

    Melanoma Research 2003, 13:259-261. PubMed Abstract | Publisher Full Text

42. Eliyahu G, Kreizman T, Degani H: Phosphocholine as a biomarker of breast cancer: Molecular and biochemical studies.

    Int J Cancer 2007, 120(8):1721-1730. PubMed Abstract | Publisher Full Text

43. Glunde K, Jie C, Bhujwalla ZM: Molecular causes of the aberrant choline phospholipid metabolism in breast cancer.

    Cancer Research 2004, 64:4270-4276. PubMed Abstract | Publisher Full Text

44. Katz-Brull R, Seger D, Rivenson-Segal D, Rushkin E, Degani H: Metabolic markers of breast cancer: enhanced choline metabolism and reduced choline-ether-phospholipid synthesis.

    Cancer Research 2002, 62:1966-1970. PubMed Abstract | Publisher Full Text

45. Glunde K, Jacobs MA, Bhujwalla ZM: Choline metabolism in cancer: implications for diagnosis and therapy.

    Expert Review of Molecular Diagnostics 2006, 6:821-829. PubMed Abstract | Publisher Full Text

46. Swanson MG, Zektzer AS, Tabatabai ZL, Simko J, Jarso S, Keshari KR, Schmitt L, Carroll PR, Shinohara K, Vigneron DB, Kurhanewicz J: Quantitative analysis of prostate metabolites using H-1 HR-MAS spectroscopy.

    Magnetic Resonance in Medicine 2006, 55:1257-1264. PubMed Abstract | Publisher Full Text

47. Lloyd SG, Zeng HD, Wang PP, Chatham JC: Lactate isotopomer analysis by H-1 NMR spectroscopy: Consideration of long-range nuclear spin-spin interactions.

    Magnetic Resonance in Medicine 2004, 51:1279-1282. PubMed Abstract | Publisher Full Text

48. Mazurek S, Eigenbrodt E, Failing K, Steinberg P: Alterations in the glycolytic and glutaminolytic pathways after malignant transformation of rat liver oval cells.

    Journal of Cellular Physiology 1999, 181:136-146. PubMed Abstract | Publisher Full Text

49. Kong X, Manchester J, Salmons S, Lawrence JCJ: Glucose transporters in single skeletal muscle fibers. Relationship to hexokinase and regulation of contractile activity.

    J Biol Chem 1994, 269:12963-12967. PubMed Abstract | Publisher Full Text

50. Artemov D, Bhujwalla ZM, Pilatus U, Glickson JD: Two-compartment model for determination of glycolytic rates of solid tumors by in vivo C-13 NMR spectroscopy.

    NMR in Biomedicine 1998, 11:395-404. PubMed Abstract | Publisher Full Text

51. Villar-Palasi C, Guinovart JJ: The role of glucose-6-phosphate in the control of glycogen synthase.

    FASEB J 1997, 11:544-558. PubMed Abstract | Publisher Full Text

52. Robey IF, Lien AD, Welsh SJ, Baggett BK, Gillies RJ: Hypoxia-inducible factor-1 alpha and the glycolytic phenotype in tumors.

    Neoplasia 2005, 7:324-330. PubMed Abstract | Publisher Full Text | PubMed Central Full Text

53. Fan TWM, Higashi RM, Lane AN: Integrating metabolomics and transcriptomics for probing Se anticancer mechanisms.

    Drug Metabolism Reviews 2006, 38:707-732. PubMed Abstract | Publisher Full Text

54. Fan TW-M: Metabolite profiling by one- and two-dimensional NMR analysis of complex mixtures.

    Progress in Nuclear Magnetic Resonance Spectroscopy 1996, 28:161-219.

55. Gradwell MJ, Fan TWM, Lane AN: Analysis of phosphorylated metabolites in crayfish extracts by two-dimensional H-1-P-31 NMR heteronuclear total correlation spectroscopy (heteroTOCSY).

    Analytical Biochemistry 1998, 263:139-149. PubMed Abstract | Publisher Full Text

56. Fan TWM, Colmer TD, Lane AN, Higashi RM: Determination of metabolites by proton NMR and GC: Analysis for organic osmolytes in crude tissue extracts.

    Analytical Biochemistry 1993, 214:260-271. PubMed Abstract | Publisher Full Text

57. Fan TWM, Higashi RM, Lane AN, Jardetzky O: Combined Use of H-1-Nmr and Gc-Ms for Metabolite Monitoring and Invivo H-1-Nmr Assignments.

    Biochimica Et Biophysica Acta 1986, 882:154-167. PubMed Abstract

58. Fan TWM, Higashi RM, Frenkiel TA, Lane AN: Anaerobic nitrate and ammonium metabolism in flood-tolerant rice coleoptiles.

    Journal of Experimental Botany 1997, 48:1655-1666.

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