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Nontargeted Elucidation of Metabolic Pathways Using Stable-Isotope Tracers and Mass Spectrometry Karsten Hiller, Christian M. Metallo, Joanne K Kelleher, and Gregory Stephanopoulos* Massachusetts Institute of Technology, Department of Chemical Engineering, 77 Massachusetts Ave., 56-439, Cambridge, Massachusetts 02140 Systems level tools for the quantitative analysis of metabolic networks are required to engineer metabolism for biomedical and industrial applications. While current metabolomics techniques enable high-throughput quantification of metabolites, these methods provide minimal information on the rates and connectivity of metabolic pathways. Here we present a new method, nontargeted tracer fate detection (NTFD), that expands upon the concept of metabolomics to solve the above problems. Through the combined use of stable isotope tracers and chromatography coupled to mass spectrometry, our computational analysis enables the quantitative detection of all measurable metabolites derived from a specific labeled compound. Without a priori knowledge of a reaction network or compound library, NTFD provides information about relative flux magnitudes into each metabolite pool by determining the mass isotopomer distribution for all labeled compounds. This novel method adds a new dimension to the metabolomics tool box and provides a framework for global analysis of metabolic fluxes. The metabolism of nutrients from the environment is central to all life. These processes result in the generation of a complex set of metabolites retained within an organism, its metabolome. Changes in the steady state abundance of a metabolite may occur in response to disease, mutation, or other exogenous perturbation. Quantifying these effects provides insight into the mechanism underlying these phenotypes. To measure such changes, various analytical techniques have emerged over the last two decades that efficiently probe the metabolome of biological systems.1-3 For example, chromatography coupled to mass spectrometry enables the simultaneous quantification of several hundred metabolites, and, in combination with computational tools, modern GC/MSbased methods can operate at high throughputs.4-6 * To whom correspondence should be addressed. Phone: 617-253-4583. Fax: 617-258-6876. E-mail:
[email protected]. (1) Fiehn, O.; et al. Nat. Biotechnol. 2000, 18, 1157–61. (2) Dunn, W. B.; Bailey, N. J. C.; Johnson, H. E. Analyst 2005, 130, 606–25. (3) Weckwerth, W.; Morgenthal, K. Drug Discovery Today 2005, 10, 1551– 1558. (4) Bo ¨rner, J.; Buchinger, S.; Schomburg, D. Anal. Biochem. 2007, 367, 143– 51. (5) Stein, S. E. J. Am. Soc. Mass Spectrom. 1999, 10, 770–781. (6) Bunk, B.; et al. Bioinformatics 2006, 22, 2962–5. 10.1021/ac1011574 2010 American Chemical Society Published on Web 07/07/2010
Metabolomics thus generates detailed “snapshots” of biochemical abundances or absolute concentrations of metabolites, providing metabolite profiles useful for biomarker discovery and metabolic engineering applications.7-12 However, the cellular metabolome is not a static entity, and current metabolomics techniques are unable to capture the dynamics of biological processes. For example, concentration changes of a particular metabolite may result from perturbations in several pathways,8 and the causal reaction cannot be identified from abundances alone. Furthermore, an organism may metabolize compounds (e.g., drugs) through endogenous pathways that cannot be identified by changes in metabolite abundances, as the latter often remain unaffected due to the robustness of these reaction networks.13 As a result, pathway-specific information relating to a given compound cannot be ascertained from metabolomics data sets alone. Detection of specific metabolites that are linked to the metabolism of a compound or drug requires that specific labeling techniques be applied. In contrast to proteins, metabolites are very low molecular weight compounds that can not be traced easily through conjugation of a marker or fluorophore. The only way to trace these molecules as they pass through the metabolic reaction network is by exchanging one or all of their atoms with either stable or radioactive isotopes. Isotope-ratio mass spectrometry (IRMS) based methods are able to detect stable-isotope labeled compounds in a nontargeted manner; however, these methods do not provide quantitative information about mass isotopomer distributions (MIDs) nor do they quantify the particular ion fragments present in mass spectra.14-17 This is a major shortcoming as MID quantification is essential for the determination of fluxes in steady state systems.18 Pathway-based compound identification requires two distinct steps: detection of metabolites containing label from the applied (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18)
Hiller, K.; et al. Anal. Chem. 2009, 81, 3429–3439. Sreekumar, A.; et al. Nature 2009, 457, 910–914. Styczynski, M. P.; et al. Anal. Chem. 2007, 79, 966–973. Bennett, B. D.; et al. Nat. Chem. Biol 2009, 5, 593–599. Denkert, C.; et al. Mol. Cancer 2008, 7, 72. Ku ¨ mmel, A.; Panke, S.; Heinemann, M. Mol. Syst. Biol 2006, 2, 2006.0034. Lyon, R. C.; et al. J. Biol. Chem. 2007, 282, 25986–25992. Knapp, D. R.; et al. J. Pharmacol. Exp. Ther. 1972, 180, 784–90. Sano, M.; et al. Biomed Mass Spectrom 1976, 3, 1–3. Abramson, F. P. Mass Spectrom. Rev. 1994, 13, 341–356. Abramson, F. P.; et al. Drug Metab. Dispos. 1996, 24, 697–701. Antoniewicz, M. R.; Kelleher, J. K.; Stephanopoulos, G. Anal. Chem. 2007, 79, 7554–7559.
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substrate and chemical identification of each species in the pathway. The latter step is relatively straightforward using established metabolite libraries or high resolution mass spectrometry.5,19 Presently, detection of stable isotope labeled compounds can only be achieved in targeted systems requiring specific information for each metabolite. Therefore, a nontargeted method of obtaining metabolite and MID data across the metabolome would represent a significant advance in the field of metabolic systems biology. We here introduce the nontargeted tracer fate detection (NTFD) system that expands upon the concept of metabolomics. Using stable isotope tracers, our nontargeted method is intended to shed light on the metabolome, highlighting specific compounds that are linked to a substrate, drug or pathway. Our NTFD strategy allows for the tracing of labeled atoms present in an externally supplied compound as it is metabolized. Thus, in a nontargeted but quantitative way NTFD elucidates metabolic pathways coupled to the applied tracer. The only requirement is the GC/MS measurement of a metabolite extract originating from biological systems treated with a mixture of labeled and unlabeled tracer. In addition, the measurement of the metabolome of the same system but treated with unlabeled tracer alone is necessary. Here, systems may include bacterial cultures, eukaryotic cell cultures, whole animal systems, or nonbiological chemical systems.20 After a deconvolution and compound detection step, the proposed methodology evaluates the labeling of all compounds, thereby detecting all compound fragments labeled by the externally supplied tracer. Moreover, the algorithm determines the MIDs of these fragments and automatically corrects the results for naturally occurring isotopomers, overcoming the limitations of radioactive tracers and IRMS-based techniques. Based on this information (labeled compounds and MIDs), our methodology enables the deduction of metabolic pathways and the determination of relative fluxes associated with the interconversion of the labeled tracer and its metabolic derivatives. Finally, pharmaceutical applications such as the nontargeted tracing of metabolites generated from labeled drugs in humans or animals are conceivable. This methodology will greatly expand the scope of metabolomics research, as we can now (1) detect all observable metabolites derived from a given compound, (2) eliminate “noise” in metabolomics data sets by focusing results on pathways downstream of a tracer molecule, and (3) generate quantitative MID data on all labeled metabolites. These significant advances will improve the ability of researchers to probe metabolic networks using stable isotope tracers and quantify fluxes in specific molecular pathways. To facilitate the application of the methodology we provide a prerelease of our software allowing to perform all described analyses starting with raw GC/MS data and ending with the MID data of all labeled compounds detected in the GC/ MS data. METHODS Cell Culture and Metabolite Extraction. The A549 lung carcinoma cell line was maintained as previously described in DMEM supplemented with 4 mM glutamine, 10% fetal bovine serum (FBS), and 100 U mL-1 penicillin/streptomycin.26 For labeling experiments, semiconfluent cells in 6 well plates were (19) Kind, T.; Fiehn, O. BMC Bioinf. 2007, 8, 105. (20) Nitschke, J. R. Nature 2009, 462, 736–738.
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cultured in DMEM containing 10% FBS and a 1:1 mixture of unlabeled glutamine and either [U-13C] L-glutamine or [R-15N] L-glutamine for 24 h. Cells were quenched with 200 µL -20 °C methanol, an equal volume of water was added, and cells were collected with a cell scraper. Four volumes of chloroform were added, and the cells were vortexed and held on ice for 30 min. After addition of 400 µL water, samples were centrifuged at 13 000 g for 10 min at 4 °C. The aqueous phase was collected in a new tube and evaporated under airflow. Derivatization and GC/MS Measurements. Dried polar metabolites were dissolved and reacted in 30 µL of 2% methoxyamine hydrochloride in pyridine and held at 37 °C for 1.5 h. Subsequent reactions using 30 µL MTBSTFA (N-(butyl-dimethylsilyl)-2,2,2-trifluoro-N-methyl-acetamide) + 1% TBDMCS (chlorodimethyl-tert-butyl-silane) or MSTFA (2,2,2-trifluoro-N-methyl-Ntrimethylsilyl-acetamide) + 1% TMCS (chloro-trimethyl-silane) were completed for 60 min at 55 and 37 °C, respectively. GC/MS analysis was performed using an Agilent 6890 GC equipped with a 30 m DB-35MS capillary column connected to an Agilent 5975B MS operating under electron impact (EI) ionization. The MS source and quadrupole were held at 230 and 150 °C, respectively, and the detector was operated in scan mode. Algorithms and Implementations. The main part of the algorithm comparatively examines mass spectra belonging to the same compound across all chromatograms for potential stable isotope labeled fragments. For this reason, the first part of the introduced technique implies the extraction of pure mass spectra from all chromatograms (Figure 1b) and a subsequent pairing of mass spectra originating from the same compound (Figure 1c). Afterward, the second part of the algorithm detects labeled fragments among these sets of mass spectra (Figure 1d). Finally, a system of linear equations is used to elucidate the composition of mixtures of isotopomers for each detected fragment (Figure 1e). Detection of Stable Isotope Labeled Fragments. Based on the assumptions made by Pickup et al.41 and Lee et al.42 in the context of isotopomer analysis the following rules were derived (proof is described in the Theory section of the Supporting Information (SI)): (1) Relative intensities originating from unlabeled molecule fragments have the same value as in the spectrum measured for the unlabeled compound alone (SI Figure 1c, see eqs 2 and 5 in the Methods section of the SI). (2) The relative signal of the first peak of a fragment recorded for a mixture of labeled and unlabeled isotopomers (fragment 2 in SI Figure 1) is always lower than the signal of the corresponding peak in the spectrum of the unlabeled molecule (eqs 6 and 3 in SI Methods section). (3) The sum of the relative intensities of a fragment is independent from the amount of isotopic enrichment of the isotopomers. This means that the relative intensity of the first peak of a fragment recorded for isotopically enriched isotopomers (e.g., I3l in SI Figure 1b and c) must be decreased by the amount of labeled atoms present in the mixture if compared to the same peak measured for a completely unlabeled molecule (e.g., I3l in SI Figure 1a). The detection of stable isotope labeled fragments present in a mass spectrum mainly relies on these derived rules: Once the pure mass spectra for the same compounds of both GC/MS
Figure 1. General strategy of the proposed method. (a) Prerequisite for the method is the measurement of at least two GC/MS chromatograms. One sample is treated with a mixture of stable isotope labeled and unlabeled tracer. The second sample is treated with only the unlabeled tracer. (b) Both GC/MS chromatograms are deconvoluted to extract pure mass spectra for all compounds. (c) Extracted pure mass spectra originating from the same compound are paired against each other and aligned. (d) The paired mass spectra are applied to compute the difference spectrum d(x) (shown in blue color) for each detected compound. The mass spectrum recorded for the unlabeled sample is shown in positive direction, whereas the spectrum of the labeled sample is plotted in the negative direction. Ion fragments containing stable isotopes are marked in red. Unlabeled ion fragments are eliminated during this step (left fragment), and labeled fragment ions form a shape similar to the first derivative of a peak function (right fragment). (e) The difference spectrum d(x) is integrated over the whole spectrum to obtain the integrated difference spectrum ∫d(x)(shown in green color). Each labeled fragment ion then forms a peak and can be directly used for the detection of labeled fragment ions. (f) For each detected, labeled fragment the mass isotopomer distributions (MID) is determined. If replicates for both samples are provided, 95% confidence intervals are calculated for the relative amount of each mass isotopomer.
chromatograms (unlabeled/labeled + unlabeled) are extracted and assigned to each other, these pairs are analyzed for the presence of stable isotope labels (Figure 1). For this purpose, both the spectrum obtained from the labeled chromatogram as well as the spectrum obtained from the unlabeled chromatogram are normalized by their total signal. Afterward, the element by element difference of the unlabeled (p bul) and labeled (p bl) spectrum is calculated yielding the difference spectrum b pdiff. Since the intensities of unlabeled fragments exhibit exactly the same values in both spectra, they disappear in the difference spectrum (Rule 1). Due to Rule 2 the first peak of a labeled fragment must appear in positive direction within the difference spectrum. Moreover, the remaining peaks of this particular fragment sum to the same value as the first peak of the fragment, however with a negative sign (Rule 3). SI Figure 2a depicts such a difference spectrum. In order to detect labeled fragments the difference spectrum is integrated yielding the integrated difference spectrum b pint (eq 1):
() I0int
b p int )
I1int int , Ii ) ... Inint
∫p i
0
diff
(m)dm∀0 e i e n
(1)
With pdiff(m) as a function of the intensity for ion m in the difference spectrum b pdiff consisting of n intensity values.
Because of Rule 3 each labeled fragment will form a peak in b pint. The peak width is dependent on the isotopomer with the maximal number of labeled atoms, whereas the peak height is dependent on the amount of labeled isotopomers (SI Figure 2b). Finally, labeled fragments are identified by the detection of peaks in the integrated difference spectrum. Determination of Complex Mass Isotopomer Mixtures. In order to further characterize and evaluate potentially labeled fragments, the composition of the different isotopomers is determined by a multi parameter linear regression analysis, see SI Theory for details. For the purpose of analyzing the quality of this calculation we recommend to perform replicates for each measurement (unlabeled sample and unlabeled + labeled sample). This way the linear equation system presented in SI eq 8 is extended to an overdetermined system shown in SI eq 9. Solving this overdetermined system it is possible to calculate the coefficient of determination (R2) for the whole fragment as well as the 95% confidence interval for the relative abundance (fx) for each isotopomer, see SI Theory for details. These values are invaluable to estimate the accuracy and significance for each detected fragment and isotopomer. RESULTS Strategy for Detection of Labeled Compounds. While standard metabolomics techniques detect large numbers of different compounds, methods for the detection and quantification of unknown metabolites downstream from a given tracer are not Analytical Chemistry, Vol. 82, No. 15, August 1, 2010
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yet available. In targeted analyses, the structure of conversion pathways is inferred by tracing the label in the compound that is metabolized among the set of targeted metabolites that are analyzed. Such methods cannot determine metabolites that are not included in the targeted set, which is a major limitation. NTFD solves this problem by including an internal standard generated by unlabeled tracer. GC/MS analysis of metabolite extracts obtained under treatment with unlabeled tracer defines the complete set of all metabolites. By juxtstaposing against this standard the spectra obtained under treatment with a mixture of unlabeled and labeled tracer, labeled compounds are identified. Since their label can only be derived by the label in the supplied tracer, such metabolites must participate in the metabolism of the labeled compound. Upon acquisition of data obtained by treatment with an unlabeled (naturally labeled) compound and another treated with a mixture of unlabeled and stable isotope-labeled compound (Figure 1a) we detect all compounds (labeled and unlabeled) present in the two mixtures. The mass spectra of the same compounds present in the samples with and without tracer are then paired (Figure 1b+c); here, this is accomplished using the deconvolution and compound pairing algorithms of the MetaboliteDetector software.7 The mass spectra originating from the same compounds present in all samples and replicates are normalized by their total signal as described in the Methods section. The main part of label detection is the calculation of the element-wise difference spectrum based on the paired spectra (Figure 1d). As a result of the derived Rules 1-3 (see Figure 1 and the Methods section), each labeled fragment retains a positive intensity in the difference spectrum (second fragment in Figure 1d), while unlabeled fragments are eliminated through subtraction (first fragment in Figure 1d). Since the total fragment intensity is equal in the labeled and unlabeled spectrum the remaining peaks sum to the intensity of the first peak but in the negative direction. Consequently, an integration of this difference spectrum yields significant peaks for each labeled fragment (Figure 1e), and the detection of these peaks enables the identification of all labeled fragments and compounds within the extract. Since the recorded mass fragment intensities represent a mixture of natural and enriched abundances, these data must be corrected for the abundances of naturally occurring stable isotopes. In most cases a fragment consists of several differently enriched mass isotopomers. Mathematical approaches based on linear equation systems are required to determine the relative amount of each isotopomer and to correct for the natural isotope abundances.21-23 In the context of this work, these isotopomers are named according to their content of stable isotopes beginning with the unlabeled molecule (M0) and ending with Mn where n is the number by which the isotopomer’s weight exceeds that of the unlabeled. A corrected MID is generated for each labeled fragment, providing quantitative information on the extent of tracer incorporation. Finally, the algorithm calculates statistical values (e.g., coefficient of determination, 95% confidence intervals) to characterize the significance of the results (Figure 1e). (21) Fernandez, C. A.; et al. J Mass Spectrom 1996, 31, 255–262. (22) Biemann, K. Mass Spectrometry: Organic Chemical Applications; McGrawHill: New York, 1962; p 223. (23) Jennings, M. E., II; Matthews, D. E. Anal. Chem. 2005, 77, 6435-6444.
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To validate our method and determine the limit of detection we analyzed a data set of known metabolites and isotopic enrichment. We generated GC/MS chromatograms from six mixtures of [1-13C] glucose and unlabeled glucose, containing 10%, 5%, 2%, 1%, 0.5%, and 0.25% of labeled compound. Each mixture was measured in triplicate, and its mass spectrum was compared to that of unlabeled glucose alone. After derivatization with trimethylsilyl (TMS) groups we detected two different glucose peaks in the GC/MS chromatograms eluting at retention times of 24.67 and 26.08 min and having identical spectra. We then applied our algorithm to assess the sensitivity and accuracy of isotope detection. The inclusion of replicates enabled calculation of 95% confidence intervals for the determined MIDs of each detected glucose fragment. These calculations are described in the Supplemental Theory and the results of the analysis are depicted in SI Table 1. Depending on the glucose fragment the precision (95% confidence interval) of the algorithm was between 0.39% and 0.59%. Furthermore, the determined MIDs in combination with the calculated confidence intervals are in perfect agreement with the experimentally applied glucose mixtures. Based on the determined MIDs and the corresponding confidence intervals the algorithm detected stable isotopic labels at levels as low as 1%. Nontargeted Detection of Glutamine-Derived Compounds within the Metabolome. To further validate our method we cultured A549 human lung carcinoma cells, using a 1:1 mixture of unlabeled substrate and either [U-13C5] glutamine or aminelabeled [R-15N] glutamine. Cells were also cultivated with unlabeled glutamine, as this is a requirement for the presented method, and extracts were derivatized using tert-butyl dimethyl silylation (TBDMS) prior to GC/MS analysis. Glutamine is a major anaplerotic substrate in tumor cells and will effectively label many metabolites (e.g., organic acids, amino acids).24 In addition, incorporation of intermediates originating from unlabeled glucose with glutamine-derived compounds in the TCA cycle generates a complex distribution of mass isotopomers in each metabolite, providing an excellent test case for our algorithm.25,26 To determine confidence intervals, we performed four replicates for each growth condition. Metabolites were considered labeled if they met the following criteria: the determined coefficient of determination (R2) for each fragment was greater or equal than 0.9, the 95% confidence interval of at least one isotopomer must not include zero, and a minimum of 5% labeling was detected in the analysis (i.e., M0 < 95%). Furthermore, the M0 intensity of a labeled fragment should provide a significant amount of signal to the mass spectrum. Therefore, the signal of the main ion for a valid fragment has to be located inside the 90th percentile of all intensities of the particular unlabeled mass spectrum. Deconvolution and compound detection of the GC/MS data allowed verifiable identification of 241 and 291 compounds in all four replicates in the case of [U-13C] glutamine and [R-15N] glutamine labeled cell extracts, respectively. Of these total compounds, using our algorithm we detected isotopic label in (24) DeBerardinis, R. J.; et al. Proc. Natl. Acad. Sci. U.S.A 2007, 104, 19345– 19350. (25) Yoo, H.; et al. J. Biol. Chem. 2008, 283, 20621–20627. (26) Metallo, C. M.; Walther, J. L.; Stephanopoulos, G. J. Biotechnol. 2009, doi: 10.1016/j.jbiotec.2009.07.010.
Table 1. Summary of All Compounds Which Have Been Detected As Labeled by Either the [U-13C5] or [r-15N] Glutamine Tracera fragments RT 14.23 14.87 15.85 16.41 16.98 19.22 19.37 20.44 21.59 24.36 24.54 24.66 24.99 26.72 26.95 28.66 28.81 30.02 30.46 30.54 31.04 31.39 31.57 32.47 33.00 34.07 34.21 36.64 37.19 38.21 38.39 38.54 38.71 40.47 41.70 42.04 42.83 44.50 49.05 49.80
RI 1482 1501 1530 1546 1563 1631 1636 1669 1705 1792 1798 1802 1813 1872 1880 1939 1944 1985 2000 2003 2022 2035 2042 2076 2096 2136 2141 2235 2257 2299 2306 2312 2320 2390 2444 2459 2494 2567 2780 2817
name
13C 1 1
alanine 2
15N 3 2 1
1 4 4 3
leucine isoleucine 1 succinate fumarate
2 1
serine 2
1 4 1 2 4 2 4 1
1
alpha-ketoglutarate malate aspartate glutamate
6 2 2 8 4 7 7 1 1
2 2 6 6 5 2 12 9 1 1
glutamine citrate
1 6 2 5 1
8 2 5 1
a
Presented are the retention time (RT), the retention index (RI), the compound nameswhenever an identification was possible and the number of fragments that have been detected as labeled either by the [U-13C5] or [R-15N] glutamine tracer.
25 compounds from [U-13C5] glutamine and in 38 compounds from [R-15N] glutamine. Table 1 displays each of these compounds along with its retention time (RT), determined retention index (RI), the name (when possible), and the number of labeled fragments. Compounds that were labeled by both carbon and nitrogen labeled glutamine are highlighted in bold. In the case of 13 C labeled glutamine these compounds include TCA cycle metabolites such as fumarate, glutamate, R-ketoglutarate, malate, aspartate, glutamine, and citrate as well as 18 unidentified compounds. Although proline and succinate were detected during deconvolution, coeluting compounds prevented the algorithm from detecting these metabolites as labeled compounds. Extraction of Relative Flux Information From MID Data. Besides detecting all stable isotope labeled compounds, our method also determines the MID for each fragment (SI eq 9). The MIDs for all stable isotope labeled compounds, including the unknown labeled compounds are presented in SI Table 1. These data provide valuable information about the specific carbon atoms
present in each mass spectral fragment of a compound. For example, one can observe that fragment 244 of aspartate consists of only two carbon atoms since there is no significant amount of M3 or M4 isotopomers for this fragment. Fragments 418 and 460 of aspartate, on the other hand, must contain four carbon atoms (see SI Table 2). Although an identification of the compound was possible in this case, it should be emphasized that our method provides the same information also for unidentified compounds. Fluxes in the Tricarboxylic Acid Cycle. Given the high uptake and turnover rate of glutamine in A549 cells and relatively high concentration in the culture medium, the MID of this substrate should be very close to that of the tracer.26 The NTFD algorithm quantified glutamine labeling at 50.67% M0, 0.15% M1, 0.05% M2, 0.05% M3, 2.73% M4, and 48.55% M5; these values accurately represent the expected MID of the applied 1:1 mixture of unlabeled glutamine and [U-13C] glutamine (99% atom labeled). The MID of an intracellular metabolite pool at steady state provides information on the relative flux of tracer into that pool,27,28 and under nonstationary conditions the dynamic changes of the labeling pattern provide information about the relaxation time of the pool.29 Figure 2 depicts the MIDs of the seven identified compounds in the context of the TCA cycle. At each turn of the TCA cycle unlabeled two-carbon units generated primarily via pyruvate dehydrogenase (PDH) are used to synthesize citrate in mitochondria. The relative level of M2 labeling in each metabolite provides information on the level of oxidative metabolism. During this cycling M4 isotopomers of citrate, fumarate, aspartate, and malate are converted to M2 isotopomers, whereas M5 isotopomers of R-ketoglutarate and glutamate become M3 isotopomers. The abundances of these M2 and M3 species ranged between 5.7% and 6.9%, demonstrating the consistency of our approach in quantifying metabolic rates. Furthermore, the ratio of M2 and M4 (or M3 and M5) isotopomers provides a quantitative readout on the flux through the TCA cycle relative to glutamine oxidation (via R-ketoglutarate). This ratio is relatively constant across the four carbon metabolites fumarate, malate, and aspartate (0.29-0.31) and remains consistent for each metabolite pool across multiple experiments in A549 cells (unpublished observations). The consistency of these data highlight the redundant, quantitative information on the metabolic activity that can be generated using NTFD analysis. Virtually all isotopomers present in the MIDs provide valuable information about specific metabolic reactions. Citrate was reproducibly labeled at M5 in multiple six carbon fragments (3.8% and 4.1%). This isotopomer is primarily generated from [U-13C5] glutamine via reductive carboxylation through isocitrate dehydrogenase,25 a reaction that has been hypothesized to generate NADPH in the cytosol though a substrate cycle involving the transhydrogenase enzyme.30 The presence of this labeling pattern indicates that A549 cells utilize this pathway to metabolize R-ketoglutarate. Glutaminolysis and Malic Enzyme Fluxes. Tumor cells often exhibit increased glutamine utilization through the glutaminolysis pathway,24 which is mediated in part by the MYC oncogene.31 (27) (28) (29) (30) (31)
Sauer, U. Mol. Syst. Biol 2006, 2, 62. Hellerstein, M. K. Metab. Eng 2004, 6, 85–100. Munger, J.; et al. Nat. Biotechnol. 2008, 26, 1179–1186. Sazanov, L. A.; Jackson, J. B. FEBS Lett. 1994, 344, 109–116. Wise, D. R.; et al. Proc. Natl. Acad. Sci. U.S.A 2008, 105, 18782–18787.
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Figure 2. Mass isotopomer distributions (MIDs) for 13C labeled compounds detected in a nontargeted manner. A549 cells were incubated with a 1:1 mixture of uniformly 13C labeled and unlabeled L-glutamine tracer. The initial 50% isotopic enrichment of L-glutamine is converted to glutamate and R-ketoglutarate via glutaminase and transaminase reactions, respectively. Fumarate, malate, and aspartate pools all contain approximately 20-22% M4 label, resulting from glutaminolysis. Unlabeled acetyl-CoA dilutes label via citrate synthase, and upon subsequent cycles generates M2 four carbon and M3 five carbon labeled metabolites. M3 glutamate and R-ketoglutarate peaks and M2 fumarate, malate, aspartate, and citrate comprised 6-7% of each metabolite pool. The M2/M4 and M3/M5 ratios calculated for each pool provide a simplified indicator of the relative flux for that metabolite in the TCA cycle (υTCA/υGLN), where υTCA refers to the mitochondrial turnover of a particular metabolite pool and υGLN is the flux of glutamine carbon atoms to the TCA cycle. Values are consistent across metabolite pools of the same carbon content (four and five carbons) and can be used to quantitatively compare different experimental arms. Significant M3 labeling of fumarate, malate, aspartate, and citrate was also detected, resulting from exchange reactions of malic enzyme (not shown). M5 label detected in the citrate pool indicates reversibility of isocitrate dehydrogenase in A549 cells. All data was generated via nontargeted analysis of GC/MS data. For simplicity, acetyl-CoA is depicted as unlabeled and is colored in gray (can not be measured by GC/MS). Abbreviations: glutaminase (GLS); glutamate dehydrogenase (GLUD); succinate dehydrogenase (SDH); fumarate hydratase (FH); malate dehydrogenase (MDH); citrate synthase (CS); isocitrate dehydrogenase (IDH); aconitase (ACO) R-ketoglutarate dehydrogenase (OGDH)
We quantified M4 labeling of fumarate, malate, and aspartate (derived from oxaloacetate) to be 19.9%, 20.5%, and 22.2%, with 95% confidence intervals no greater than 0.5% in their respective fragments. These data indicate that A549 metabolism involves 6626
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significant flux through the glutaminolysis pathway. Additionally, fumarate, malate, aspartate, and citrate contained M3 label at levels of 5.0%, 5.2%, 5.8%, and 5.2%, respectively. This labeling pattern is generated in exchange reactions of malic enzyme, which generates NADPH in tumor cells for biosynthesis.24 Thus, besides identifying and characterizing labeled compounds within the metabolome, our method generates precise MIDs (corrected for natural isotope abundance) that are particularly useful for the interpretation of fluxes from 13C labeling data. The power of this methodology is demonstrated by its ability to generate these data in a nontargeted manner, and when applied to different experimental arms, this system can precisely indicate relative flux changes in response to pharmacological treatments, mutations, or different disease states. While detailed calculations are required to obtain absolute flux values,27,32 comparative analyses can qualitatively demonstrate the importance of specific metabolic reactions in a given system. Amino Acid Metabolism. Transaminase reactions in the cell are involved in amino acid and nucleotide synthesis, predominantly using nitrogen from glutamate to yield the synthesized amino acid and R-ketoglutarate. Though diluted upon entry into the glutamate pool, the amino-nitrogen of glutamine is most often transferred in these reactions. In cellular A549 extracts labeled with [R-15N] glutamine (1:1 mixture with unlabeled glutamine) we observed labeling throughout the metabolome. As expected, many amino acids were significantly labeled with one 15N isotope (M1) from this tracer, including alanine (22%), valine (6.5%), leucine (16%), isoleucine (18%), serine (21%), aspartate (29%), glutamate (30%), and glutamine (50%). In addition, 20 unknown compounds that contained M1 label were identified in our analysis; the complete results are presented in SI File 2. While significant levels of amino acid biosynthesis in proliferating cells are expected, fluxes from the amino-nitrogen to various amino acids were relatively high. Label in glutamate, the primary donor in most aminotransferase reactions within the cell, was diluted to 30% from the 50% M1 detected in glutamine, presumably from free ammonium ions and essential amino acids in the medium. Indeed, the branched chain amino acids valine, leucine, and isoleucine all contained significant quantities of tracer-derived 15N (6.5%, 16%, and 18%, respectively). These data also indicate that A549 cells exhibit significant, reversible, activity of branched chain aminotransferase (BCAT) enzymes, and, as above for the TCA cycle, these MIDs provide information on the relative exchange flux for each amino acid via BCAT. Although we were able to identify other amino acids in the GC/MS chromatograms, including glycine, proline, threonine, methionine, phenylalanine, cysteine, lysine, arginine, and tyrosine, none were labeled. With the exception of proline and glycine, significant unlabeled sources of these essential amino acids were present in the culture medium. As before with the [U-13C] tracer, proline was not detected as labeled due to the presence of a coeluting compound (though it was in fact labeled). Robustness of the NTFD System. Finally, we evaluated the robustness of our system by comparing [R-15N] glutaminelabeled cellular extracts reacted with two different derivatization agents, MSTFA and MTBSTFA in two separate experiments. The (32) Antoniewicz, M. R.; Kelleher, J. K.; Stephanopoulos, G. Metab. Eng 2007, 9, 68–86.
Figure 3. Comparison of the relative amounts of M1 isotopomer for TMS (colored in red) and TBDMS (colored in blue) derivatized compounds. The A549 human lung cancer cell line was incubated with [R-15N] labeled glutamine for 24 h. Extracted metabolites were derivatized using either MSTFA or MTBSTFA and screened for potential stable isotope labeled compounds. All detected compounds that were identified within the TBDMS derivatized metabolites were also detected as labeled within the TMS derivatized compounds. The largest deviation of the relative labeling amount was 1.9% for gluatamine.
silylation of the metabolites with TMS instead of TBDMS yields completely different chromatographic and mass spectrometric properties, while the corrected MIDs remain unchanged. Although the algorithm operated on distinct GC/MS data sets, we detected a similar number of labeled compounds (27 for TMS versus 28 for TBDMS). Using the NIST reference library we identified 13 of these TMS derivatized compounds, including valine, leucine, isoleucine, proline, alanine, serine, aspartate (two derivatives), glutamate (two derivatives), 5-oxoproline, glutamine, and adenine.5 In addition to all the amino acids that were detected in the case of the TBDMS silylation, the algorithm detected proline, 5-oxoproline, and adenine while screening the TMS derivatized compounds. In this case the proline mass spectrum was not superimposed by a coeluting compound. Although 5-oxoproline was not part of our TBDMS reference library, we presume that the unknown TBDMS-derivatized compound (58% M0, 42% M1) eluting at RT 31.57 min is 5-oxoproline (Table 1) based on the similarity of the MID to the TMS derivative of 5-oxoproline (60% M0, 40% M1). The identification of 15N label in adenine is another exciting finding, as this result provides quantitative information on the relative rate of nucleotide synthesis from glutamine-derived nitrogen. Most importantly, the relative amounts of M1 isotopomers for all compounds detected with both silylation types were virtually identical (Figure 3). The largest deviation between both experiments was determined to 1.9% for glutamine, confirming the robustness and accuracy of this analytical system. DISCUSSION We have introduced NTFD, a method for the nontargeted, quantitative detection of stable isotope-labeled compounds within a highly complex mixture of metabolites. This method requires no a priori knowledge of the pathway, downstream
metabolites, or a reference spectra library. Furthermore, our methodology is easily employed on standard GC/MS systems and can detect labeled compounds present in quantities as low as 1% relative to unlabeled metabolites. Although we have not chemically identified all labeled metabolites arising from glutamine, this procedure is relatively simple using commercially available compound libraries or through targeted MS analysis using accurate mass technology. By directly identifying all compounds within a given pathway beforehand, the NTFD allows researchers to focus their attention on the most relevant metabolites within the chromatogram (or metabolome).19 Additionally, NTFD calculates MIDs for each labeled fragment. These data are critical for the determination of metabolic fluxes, and by generating MIDs for all labeled metabolites, the NTFD system provides a foundation for global flux analysis. While we have documented NTFD here using GC/MS, the technique is readily transferable to LC/MS. We applied stable isotope-labeled glutamine tracers to a cultivated lung carcinoma cell line and measured the polar fraction of cellular extracts via GC/MS. The analysis of our algorithm alone provided quantitative information on the labeling of all metabolites derived from glutamine. This result highlights our ability to focus a metabolomic analysis on particular pathways downstream of a given substrate or drug, eliminating much of the noise present in these data sets. In addition, the MIDs calculated by the algorithm provide information on the relative flux of the tracer into particular metabolite pools compared to other sources. In our nontargeted analysis of A549 cells we characterized the flux through the TCA cycle and identified glutaminolysis and reductive carboxylation as major pathways through which these cells metabolize glutamine. Analysis of different cell types containing specific oncogenic manipulations or tumor phenotypes might enable Analytical Chemistry, Vol. 82, No. 15, August 1, 2010
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identification of enzymes that may be targeted for clinical treatment in cancer.33 The use of [R-15N] glutamine tracer enabled quantification of nitrogen transfer from glutamine to various molecules, ranging from amino acids to nucleotides (adenine). One interesting result was the significant labeling of branched chain amino acids. These compounds cannot be synthesized de novo in human cells, so the presence of label indicates significant and reversible activity in the first step of their degradation, catalyzed by branched chain aminotransferases. While these compounds might not be oxidized within the cell, this finding demonstrates that essential amino acids other than glutamine can supply nitrogen to the glutamate pool. Furthermore, branched chain amino acids are linked to various cellular processes, including signaling through the mammalian target of rapamycin (mTOR) pathway and whole body metabolism (e.g., obesity and insulin resistance) in humans.34,35 Quantitative information on the intracellular metabolism of these molecules may therefore be of great interest to the metabolic research community. Another exciting finding was the fact that no label was present in the glycine pool. Serine, which is considered the primary precursor of glycine, contained a significant fraction of 15N isotope from [R-15N] glutamine (21% M1). These data indicate that glycine may be derived from an alternative source within the culture medium or cell (e.g., essential amino acids, glutathione metabolism). Glycine and serine metabolism is necessary for purine synthesis, which is elevated in tumor cells,36 so differential synthesis of these amino acids is intriguing. Furthermore, glycine lies upstream of sarcosine, a metabolite found at elevated levels in patients with advanced (i.e., metastatic) prostate cancer.37 While traditional metabolomics techniques were effective in identifying this biomarker, the use of our methodology in cellular or animal models would provide information on the surrounding metabolism of sarcosine (and presumably a source pathway). Numerous areas of research will benefit from our methodology. For example, researchers have identified that mutations in IDH1 and IDH2 genes commonly occur in glioblastomas.38 These mutations were heterozygous and found at a specific residue in the active site of these enzymes, indicating a potential gain of function.39 Consistent with this hypothesis, Dang et al. recently demonstrated that these cancer-associated IDH1 mutants catalyzed the reduction of R-ketoglutarate to 2-hydroxyglutarate (2HG).40 Genomewide sequencing was conducted on over 20 tumors to initially identify the IDH1 mutations,39 and the IDH1 and IDH2 genes were sequenced in approximately 1000 tumors (33) Vander Heiden, M. G.; Cantley, L. C.; Thompson, C. B. Science 2009, 324, 1029–1033. (34) She, P.; et al. Cell Metab. 2007, 6, 181–194. (35) Newgard, C. B.; et al. Cell Metab. 2009, 9, 311–326. (36) Tong, X.; Zhao, F.; Thompson, C. B. Curr. Opin. Genet. Dev. 2009, 19, 32–37. (37) Sreekumar, A.; et al. Nature 2009, 457, 910–914. (38) Yan, H.; et al. N. Engl. J. Med. 2009, 360, 765–773. (39) Parsons, D. W.; et al. Science 2008, 321, 1807–1812. (40) Dang, L.; et al. Nature 2009, 462, 739–744. (41) Pickup, J. F.; McPherson, K. Anal. Chem. 1976, 48, 18851890(. (42) Lee, W. N.; et al. Biol Mass Spectrom. 1991, 20, 451–8.
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in one of the follow-up studies38 before the successful metabolomics-based detection of 2HG. Conceivably, the use of NTFD analysis with 13C-labeled glucose or glutamine in a panel of tumors would have identified 2HG as a biomarker and provided information on the source as well. This example highlights the ability of NTFD to not only detect potential biomarkers but also provide information on the mechanism or route of synthesis. CONCLUSIONS The ability to detect labeled downstream metabolites from a given tracer compound in a nontargeted fashion in vivo is particularly useful for the characterization of drugs in clinical and preclinical studies. In addition, the metabolism of xenobiotic compounds from organisms and cellular communities involves many yet to be resolved pathways; investigators of these systems could benefit greatly by using NTFD analysis. Furthermore, accurate calculation of corrected MIDs provides quantitative information on the relative fluxes in each metabolite pool. When applied to biological systems with specific perturbations, one can identify the relative flux changes from a specific substrate to all downstream metabolites. This algorithm is compatible with standard GC/MS (and presumably other MS) technologies and is easily implemented within available GC/MS analysis procedures.7 Although we demonstrated the potential of NTFD by the application of carbon (13C) and nitrogen (15N) stable isotopes, the methodology can also be used with other stable isotopes like 33S or 18O. The only exception is deuterium (2H), since in this case the deuterium effect prevents labeled and unlabeled compounds to elute at the same retention time from the GC. For the calculation of the difference spectrum, the coelution and thus formation of a combined mass spectrum of both labeled and unlabeled compound is essential. In the context of metabolic systems biology, NTFD adds a new dimension of knowledge. Due to its nontargeted nature, NTFD adds information about biochemical reactions and metabolites that were previously unknown. Furthermore, our method can extract substrate-specificity and quantitative flux information from straightforward metabolomics experiments. The NTFD system provides a tool for linking metabolomics to genomics and transcriptomics processes. ACKNOWLEDGMENT We thank Paulo Gameiro for the measurement of several TBDMS derivatized standard compounds. Funding for this work was provided by NIH grant R01 DK075850 and Deutsche Forschungsgemeinschaft (German Research Foundation) Hi1400/11. C.M. is supported by a postdoctoral fellowship from the American Cancer Society. K.H. and C.M. contributed equally to this work. SUPPORTING INFORMATION AVAILABLE Additional discussion, figures, and tables. This material is available free of charge via the Internet at http://pubs.acs.org. Received for review May 3, 2010. Accepted June 23, 2010. AC1011574
