Anal. Chem. 2007, 79, 8956-8966
Heteronuclear 1H-31P Statistical Total Correlation NMR Spectroscopy of Intact Liver for Metabolic Biomarker Assignment: Application to Galactosamine-Induced Hepatotoxicity Muireann Coen,*,† Young-Shick Hong,† Olivier Cloarec,‡ Cindy M. Rhode,§ Michael D. Reily,§ Donald G. Robertson,§ Elaine Holmes,† John C. Lindon,† and Jeremy K. Nicholson*,†
Department of Biomolecular Medicine, Sir Alexander Fleming Building, SORA Division, Faculty of Medicine, Imperial College London, SW7 2AZ, U.K., Technologie Servier, 25-27 Rue Euge` ne Vignat, Orleans 45007, France, and Metabonomics Evaluation Group, Pfizer Global R&D, Ann Arbor, Michigan 48105
As part of our ongoing development of methods for enhanced biomarker information recovery from spectroscopic data we present the first example of a new heteronuclear statistical total correlation spectroscopy (HETSTOCSY) approach applied to intact tissue samples collected as part of a toxicological study. One-dimensional 1H and 31P-{1H} magic angle spinning (MAS) NMR spectra of intact liver samples after galactosamine (galN) treatment to rats and after cotreatment of galN plus uridine were collected at 275 K. Individual samples were also followed by 1H and 31P-{1H} MAS NMR through time generating time dependent modulations in metabolite signatures relating to toxicity. High-resolution 1H NMR spectra of urine and plasma and clinical chemical data were also collected to establish a biological framework in which to place these novel statistical heterospectroscopic data. In HET-STOCSY, calculation of the covariance between the 31P-{1H} and 1H NMR signals of phosphorus containing metabolites allows their molecular connectivities to be established and the construction of virtual twodimensional heteronuclear correlation spectra that connect all protons on the molecule to the heteroatom. We show how HET-STOCSY applied to MAS NMR spectra of liver samples can be used to augment biomarker detection. This approach is generic and can be applied to correlate the covarying signals for any spin-active nuclei where there is parallel or serial collection of data. Metabonomic and metabolomic approaches are now widely used to aid biomarker recovery from tissue and biofluid spectral data in studies of human and animal disease, nutritional interventions, disease, nutritional intervention, and toxin-induced disorders.1-6 The 1H NMR spectrum of a typical biofluid consists of thousands of highly overlapped resonances representing a wide * To whom correspondence should be addressed. † Imperial College London. ‡ Technologie Servier. § Pfizer Global R&D. (1) Nicholson, J. K.; Lindon, J. C.; Holmes, E. Xenobiotica 1999, 29, 1181-89. (2) Nicholson, J. K.; Connelly, J.; Lindon, J. C.; Holmes, E. Nat. Rev. Drug Discovery 2002, 1, 153-61. (3) Holmes, E.; Tsang, T. M.; Tabrizi, S. J. NeuroRx 2006, 3, 358-72.
8956 Analytical Chemistry, Vol. 79, No. 23, December 1, 2007
range of endogenous and exogenous metabolites. The application and utility of the spectroscopic platform can also be greatly enhanced by statistical methods that allow latent spectroscopic information of interest to be extracted from such complex, highly overlapped spectra.26 We developed a new approach for metabolic information recovery from “high-throughput” one-dimensional NMR spectra called statistical total correlation spectroscopy (STOCSY7), and this has proved to be a powerful biomarker identification tool. STOCSY can be combined with supervised chemometric methods to provide linked information on those spectral features that best separate sample classes.8 STOCSY encompasses the computation of correlation statistics between the intensities of all computer points in a set of complex mixture spectra, thus generating connectivities between signals on molecules that vary in concentration between samples. The STOCSY approach is generic and can be applied in both 1D and 2D forms and to homo- or heteronuclear data to aid structural elucidation and determine pathway relationships in a given spectral sample set. The conceptual applications of STOCSY are highlighted in Figure 1 with a homonuclear STOCSY approach involving correlation of data from one experimental type across a series of samples, for example, a 1D 1H diffusion-edited NMR spectroscopic experiment. HET-STOCSY encompasses the statistical correlation of two different types of experimental data, for example, from heteronuclear experiments or any given parallel combination of experimental data. To date, STOCSY has been used to drive assignment of biomarker metabolite NMR resonances in nephrotoxic states9 and (4) Stella, C.; Beckwith-Hall, B.; Cloarec, O.; Holmes, E.; Lindon, J. C.; Powell, J.; van der Ouderaa, F.; Bingham, S.; Cross, A. J.; Nicholson, J. K. J. Proteome Res. 2006, 5, 2780-8. (5) Rezzi, S.; Ramadan, Z.; Fay, L. B.; Kochhar, S. J. Proteome Res. 2007, 6, 513-25. (6) Lindon, J. C.; Holmes, E.; Nicholson, J. K. FEBS J. 2007, 274, 1140-51. (7) Cloarec, O.; Dumas, M. E.; Craig, A.; Barton, R. H.; Trygg, J.; Hudson, J.; Blancher, C.; Gauguier, D.; Lindon, J. C.; Holmes, E.; Nicholson, J. Anal. Chem. 2005, 77, 1282-9. (8) Cloarec, O.; Dumas, M. E.; Trygg, J.; Craig, A.; Barton, R. H.; Lindon, J. C.; Nicholson, J. K.; Holmes, E. Anal. Chem. 2005, 77, 517-26. (9) Holmes, E.; Cloarec, O.; Nicholson, J. K. J. Proteome Res. 2006, 5, 131320. 10.1021/ac0713961 CCC: $37.00
© 2007 American Chemical Society Published on Web 11/01/2007
gies after spectral acquisition. The technique is based on spinning a tissue sample at the magic angle (54.7° relative to the magnetic field) which reduces anisotropic line-broadening effects and hence produces highly resolved spectra. To date, MAS NMR metabolic profiling has been successfully applied to lymph node tissue,16 brain,17 intestinal tissue,18 prostate,19,20 kidney,21-24 tumor tissue,25,26 liver spheroids,27 liver,28-31 whole cell bacteria,32 and parasitic protozoa.33 In addition, the application of 31P MAS NMR to study tissue has been reviewed.34 Recent advances in MAS NMR technologies include the development of techniques such as TOSS, PASS, and PHORMAT that enable high-resolution spectra to be generated at low spin rates so as to minimize sample degradation during spectral acquisition.35-37 In the present study, we wanted to combine the advantages of 1H and 31P MAS NMR spectroscopy with the advantages of HETSTOCSY for information recovery and to apply this to assist with enhancing understanding of a specific biological problem, that of galactosamine (galN) toxicity. GalN is a well-studied, “model” hepatotoxin, but the mechanism whereby it elicits a toxic response that is similar to human hepatitis is controversial and unclear. The accepted wisdom is that galN forms conjugates with uridineFigure 1. Conceptual visualization of the applications of STOCSY to correlate data from one experiment type representing homonuclear STOCSY or from two different experiments representing HETSTOCSY. Key: Carr-Purcell-Meiboom-Gill (CPMG).
to accomplish population-based identification of drug metabolites in human urine samples.10 In addition, it has recently been applied to enhance information recovery from LC-NMR data sets11 and diffusion-edited NMR data sets12 arising from complex biological mixtures. Furthermore, the recent development of statistical heterospectroscopy (SHY) highlights the computation of covariance matrices for successful interrogation of multispectroscopic data such as those from NMR and ultraperformance liquid chromatography-mass spectrometry platforms.13 Related statistical cross-projection methods can also be applied to link proteomic with metabonomic data14 and genome-phenotype data with metabonomic data.15 Magic-angle spinning (MAS) NMR is increasingly being utilized for the determination of metabolic profiles of intact tissue samples and for investigation of the dynamics and physicochemical properties of tissue. It provides a powerful, nondestructive analytical tool that requires small amounts of tissue (approximately 10 mg) that can be further analyzed by complementary technolo(10) Holmes, E.; Loo, R. L.; Cloarec, O.; Coen, M.; Tang, H.; Maibaum, E.; Bruce, S.; Chan, Q.; Elliott, P.; Stamler, J.; Wilson, I. D.; Lindon, J. C.; Nicholson, J. K. Anal. Chem. 2007, 79, 2629-40. (11) Cloarec, O.; Campbell, A.; Tseng, L. H.; Braumann, U.; Spraul, M.; Scarfe, G.; Weaver, R.; Nicholson, J. K. Anal. Chem. 2007, 79, 3304-11. (12) Smith, L. M.; Maher, A. D.; Cloarec, O.; Rantalainen, M.; Tang, H.; Elliott, P.; Stamler, J.; Lindon, J. C.; Holmes, E. C.; Nicholson, J. K. Anal. Chem. 2007, 79, 5682-9. (13) Crockford, D. J.; Holmes, E.; Lindon, J. C.; Plumb, R. S.; Zirah, S.; Bruce, S. J.; Rainville, P.; Stumpf, C. L.; Nicholson, J. K. Anal. Chem. 2006, 78, 36371. (14) Rantalainen, M.; Cloarec, O.; Beckonert, O.; Wilson, I. D.; Jackson, D.; Tonge, R.; Rowlinson, R.; Rayner, S.; Nickson, J.; Wilkinson, R. W.; Mills, J. D.; Trygg, J.; Nicholson, J. K.; Holmes, E. J. Proteome Res. 2006, 5, 2642-55. (15) Dumas, M. E.; Wilder, S. P.; Bihoreau, M. T.; Barton, R. H.; Fearnside, J. F.; Argoud, K.; D’Amato, L.; Wallis, R. H.; Blancher, C.; Keun, H. C.; Baunsgaard, D.; Scott, J.; Sidelmann, U. G.; Nicholson, J. K.; Gauguier, D. Nat. Genet. 2007, 39, 666-72.
(16) Cheng, L. L.; Lean, C. L.; Bogdanova, A.; Wright, S. C., Jr.; Ackerman, J. L.; Brady, T. J.; Garrido, L. Magn. Reson. Med. 1996, 36, 653-8. (17) Holmes, E.; Tsang, T. M.; Huang, J. T.; Leweke, F. M.; Koethe, D.; Gerth, C. W.; Nolden, B. M.; Gross, S.; Schreiber, D.; Nicholson, J. K.; Bahn, S. PLoS Med. 2006, 3, e327. (18) Martin, F. P.; Wang, Y.; Sprenger, N.; Holmes, E.; Lindon, J. C.; Kochhar, S.; Nicholson, J. K. J. Proteome Res. 2007, 6, 1471-81. (19) Cheng, L. L.; Burns, M. A.; Taylor, J. L.; He, W.; Halpern, E. F.; McDougal, W. S.; Wu, C. L. Cancer Res. 2005, 65, 3030-4. (20) Swanson, M. G.; Zektzer, A. S.; Tabatabai, Z. L.; Simko, J.; Jarso, S.; Keshari, K. R.; Schmitt, L.; Carroll, P. R.; Shinohara, K.; Vigneron, D. B.; Kurhanewicz, J. Magn. Reson. Med. 2006, 55, 1257-64. (21) Garrod, S.; Humpfer, E.; Spraul, M.; Connor, S. C.; Polley, S.; Connelly, J.; Lindon, J. C.; Nicholson, J. K.; Holmes, E. Magn. Reson. Med. 1999, 41, 1108-18. (22) Wang, Y.; Bollard, M. E.; Nicholson, J. K.; Holmes, E. J. Pharm. Biomed. Anal. 2006, 40, 375-81. (23) Garrod, S.; Bollard, M. E.; Nicholls, A. W.; Connor, S. C.; Connelly, J.; Nicholson, J. K.; Holmes, E. Chem. Res. Toxicol. 2005, 18, 115-22. (24) Waters, N. J.; Garrod, S.; Farrant, R. D.; Haselden, J. N.; Connor, S. C.; Connelly, J.; Lindon, J. C.; Holmes, E.; Nicholson, J. K. Anal. Biochem. 2000, 282, 16-23. (25) Barton, S. J.; Howe, F. A.; Tomlins, A. M.; Cudlip, S. A.; Nicholson, J. K.; Bell, B. A.; Griffiths, J. R. MAGMA 1999, 8, 121-8. (26) Moka, D.; Vorreuther, R.; Schicha, H.; Spraul, M.; Humpfer, E.; Lipinski, M.; Foxall, P. J.; Nicholson, J. K.; Lindon, J. C. J. Pharm. Biomed. Anal. 1998, 17, 125-32. (27) Bollard, M. E.; Xu, J.; Purcell, W.; Griffin, J. L.; Quirk, C.; Holmes, E.; Nicholson, J. K. Chem. Res. Toxicol. 2002, 15, 1351-59. (28) Bollard, M. E.; Garrod, S.; Holmes, E.; Lindon, J. C.; Humpfer, E.; Spraul, M.; Nicholson, J. K. Magn. Reson. Med. 2000, 44, 201-7. (29) Coen, M.; Lenz, E. M.; Nicholson, J. K.; Wilson, I. D.; Pognan, F.; Lindon, J. C. Chem. Res. Toxicol. 2003, 16, 295-303. (30) Yap, I. K.; Clayton, T. A.; Tang, H.; Everett, J. R.; Hanton, G.; Provost, J. P.; Le Net, J. L.; Charuel, C.; Lindon, J. C.; Nicholson, J. K. J. Proteome Res. 2006, 5, 2675-84. (31) Waters, N. J.; Waterfield, C. J.; Farrant, R. D.; Holmes, E.; Nicholson, J. K. J. Proteome Res. 2006, 5, 1448-59. (32) Li, W.; Lee, R. E.; Lee, R. E.; Li, J. Anal. Chem. 2005, 77, 5785-92. (33) Moreno, B.; Rodrigues, C. O.; Bailey, B. N.; Urbina, J. A.; Moreno, S. N. J.; Docampo, R.; Oldfield, E. FEBS Lett. 2002, 523, 207-12. (34) Payne, G. S.; Troy, H.; Vaidya, S. J.; Griffiths, J. R.; Leach, M. O.; Chung, Y. L. NMR Biomed. 2006, 19, 593-8. (35) Wind, R. A.; Hu, J. Z.; Rommereim, D. N. Magn. Reson. Med. 2001, 46, 213-8. (36) Hu, J. Z.; Rommereim, D. N.; Wind, R. A. Magn. Reson. Med. 2002, 47, 829-36. (37) Taylor, J. L.; Wu, C. L.; Cory, D.; Gonzalez, R. G.; Bielecki, A.; Cheng, L. L. Magn. Reson. Med. 2003, 50, 627-32.
Analytical Chemistry, Vol. 79, No. 23, December 1, 2007
8957
5′-diphosphate sugars which ultimately depletes the hepatic uridine nucleotide pool and inhibits RNA and protein synthesis. This theory has been supported by experiments which have confirmed that supplementation with uridine ameliorates or prevents the toxicity.38-42 We have shown in an earlier metabonomic study that glycine treatment prevents galN toxicity as it leads to increases in the hepatic uridine nucleotide pool and hence counteracts the depletion of UDP-glucose (UDP-glc) by galN.43 In this work, we have used heteronuclear metabonomic methods to profile urine, serum, and liver from rats in order to determine the metabolic response to galN-toxicity together with the protective effects of supplementation with uridine. The analyses of a range of sample matrixes enabled the consequences of galN toxicity to be studied at a systems level. We introduce the application of 1H-31P HET-STOCSY to assign the peaks from phosphorus-containing metabolites in hepatic tissue and also to elucidate relationships between metabolites in related biochemical pathways. The 1H and 31P-{1H} correlated cross-peaks that enable metabolic assignments to be made could not have been obtained from traditional 2D NMR methods due to magnetization transfer and bond-distance constraints. Furthermore the application of HET-STOCSY to a series of 1D NMR spectra is rapid in comparison to acquisition of a host of 2D NMR spectra that would be required for assignment purposes. The application of HET-STOCSY to heteronuclear MAS NMR data has enhanced information recovery and enabled rapid assignment of metabolites of importance in understanding the mechanism of action of galN. This approach will equally be applicable to any heteronuclear or homonuclear NMR data set. MATERIALS AND METHODS Routine Animal Husbandry. Male Sprague-Dawley (Crl: CD(SD) BR) rats (7 weeks old, 200-225 g) were obtained from Charles River Laboratories (Wilmington, MA). Animals were housed in temperature (70-78 °F) and humidity (30-70% RH) controlled rooms with a 12-hour light cycle throughout the study. During urine collection periods, rats were housed in plastic metabolism cages. The collection cone of the cages was rinsed with hot tap water daily. When urine was not collected, animals were housed in individual stainless steel wire hanging cages. Animals were provided with food (powdered Lab Diet 5002, Purina Mills, Richmond, IN) and drinking water ad libitum throughout the study. Test Materials. Galactosamine hydrochloride was purchased from Sigma-Aldrich (St. Louis, MO). The active moiety for this compound is 83.09% and a dosing factor of 1.204 was used to prepare the dosing solution. GalN was dissolved in 0.9% saline at a concentration of 41.5 mg/mL and filter sterilized using a 0.2 µm filter. Uridine was also purchased from Sigma-Aldrich and was considered 100% pure. Three uridine solutions were prepared by (38) Stachlewitz, R. F.; Seabra, V.; Bradford, B.; Bradham, C. A.; Rusyn, I.; Germolec, D.; Thurman, R. G. Hepatology 1999, 29, 737-45. (39) Keppler, D. O.; Pausch, J.; Decker, K. J. Biol. Chem. 1974, 249, 211-6. (40) Keppler, D.; Rudigier, J.; Reutter, W.; Lesch, R.; Decker, K. Hoppe-Seyler’s Z. Physiol. Chem. 1970, 351, 102-4. (41) Decker, K.; Keppler, D. Rev. Physiol. Biochem. Pharmacol. 1974, 77-106. (42) Popov, N.; Schmidt, S.; Matthies, H. Biomed. Biochim. Acta 1984, 43, 1399404. (43) Coen, M.; Hong, Y. S.; Clayton, T. A.; Rohde, C. M.; Pearce, J. T.; Reily, M. D.; Robertson, D. G.; Holmes, E.; Lindon, J. C.; Nicholson, J. K. J. Proteome Res. 2007, 6, 2711-9.
8958
Analytical Chemistry, Vol. 79, No. 23, December 1, 2007
dissolving uridine in 0.9% saline at a concentration of 50, 100, or 200 mg/mL. Each solution was filter sterilized using a 0.2 µm filter. The pH of each uridine solution was adjusted to 7.0. Study Protocol. Thirty-six rats were acclimatized to the animal facility for 6 days prior to study initiation. On day 1, all rats were divided into five groups. Groups 1-4 (n ) 8/group) were given a single intraperitoneal (ip) injection of 415 mg/kg GalN in a dose volume of 10 mL/kg. Rats in Group 5 (n ) 4) received an ip injection of vehicle (0.9% saline) at this time. Two hours after the GalN dose, Groups 1, 2, 3, and 4 were given a single ip injection of 0, 500, 1000, or 2000 mg/kg uridine, respectively, in a dose volume of 10 mL/kg. Group 5 also received an ip injection of 2000 mg/kg uridine in a dose volume of 10 mL/kg at this time. The first 4 rats in Groups 1-4 were euthanized 24 h after the GalN injection. The remaining rats in Groups 1-4 and all rats in Group 5 were euthanized 96 h after the GalN injection. Rats were observed once predose, 2 h after GalN administration, 2 h after uridine dosing, and once daily thereafter for clinical signs. Body weights of all rats were obtained predose on day 1 and daily thereafter until termination. Urine was collected for 24 h prior to galN administration and at 24, 48, and 96 h after galN administration. Urine samples were collected in chilled collection tubes containing sodium azide (1 mL, 1% soln in water). Total urine volume for each collection period was recorded. Serum was isolated from blood samples collected at necropsy (24 and 96 h) from the abdominal vena cava. All samples were stored at -70 °C pending analysis. Clinical Chemistry and Histopathology. Clinical Chemistry Analysis. Serum was analyzed for alanine aminotransferase, aspartate aminotransferase, and total bilirubin levels using a Vitros 950 analyzer (Ortho-Clinical Diagnostics, Rochester, NY). Liver Histology. A portion of the left lateral lobe of the liver from each animal was obtained following euthanasia. Each sample was fixed in 10% buffered formalin, embedded in paraffin, sectioned, and stained with hematoxylin and eosin. Liver sections were then scored according to the following scale: 0 ) no necrosis, 1 ) minimal necrosis, 2 ) mild necrosis, 3 ) moderate necrosis, and 4 ) marked necrosis. NMR Spectroscopy. 1H NMR Spectroscopic Analysis of Urine. Urine samples were thawed, vortexed, and allowed to stand for 10 min prior to mixing aliquots (400 µL) with phosphate buffer (200 µL, 0.2 M containing 10% deuterium oxide (D2O), 3 mM 3-(trimethylsilyl)-[2,2,3,3-2H4]-propionic acid sodium salt (TSP), and 3 mM sodium azide) and centrifuged at 13 000 rpm for 10 min. Supernatants (550 µL) were transferred into 96 well plates (1 mL deep, Lablinks, U.K.). D2O provided a field frequency lock, and TSP provided a chemical shift reference (1H, δ 0). 1H NMR spectra were acquired on a Bruker Avance 600 spectrometer, operating at 600.13 MHz 1H frequency and a temperature of 300 K, using a Bruker flow injection probe (Bruker Biopsin, Rheinstetten, Germany) and automated sample handling unit (BEST, Bruker). For further experimental details please refer to ref 43. 1H NMR Spectroscopic Analysis of Serum. Serum samples were thawed, vortexed, and allowed to stand for 10 min prior to mixing aliquots (200 µL) with saline containing 20% D2O (400 µL). Samples were spun at 10 000 rpm for 10 min. Samples (500 µL) were placed in NMR tubes (Norell, 507-PP), and NMR spectra
were acquired at a 1H observation frequency of 600.13 MHz and temperature of 300 K. For further experimental details please refer to ref 43. MAS NMR Spectroscopic Analysis of Liver. Liver tissue samples (median sample weight of 10 mg) were rinsed with a solution of TSP in D2O (1 mg/mL) and placed in 4 mm zirconium oxide rotors (Bruker Biospin, Rheinstetten, Germany) and spun at 5 kHz. 1H spectra were acquired at a 1H observation frequency of 600.13 MHz and external sample temperature of 275 K. Chemical shifts were referenced to that of TSP (1H, δ 0.0), and D2O provided a field-frequency lock. The Carr-Purcell-Meiboom-Gill (CPMG)44 spin-echo pulse sequence with a fixed spin-spin relaxation delay, 2nτ of 128 ms (n ) 160, τ ) 400 µs), was applied to acquire 1H NMR spectra of all liver samples. The CPMG experiment attenuates broad spectral resonances from high molecular weight compounds with long rotational correlation times and thus enables sharp resonances from low molecular weight compounds to be more clearly identified. For each sample, 64 transients were collected into 32 K data points using a spectral width of 12 ppm with a relaxation delay of 2 s and an acquisition time of 2.28 s. A line-broadening function of 1 Hz was applied to all spectra prior to Fourier transformation. 31P-{1H} spectra were acquired at an observation frequency of 242.93 MHz using a standard Bruker single pulse 1H-decoupled experiment (zgdc, garp decoupling). For each sample, 512 transients were collected into 16 K data points using a spectral width of 35 ppm with a relaxation delay of 1 s and an acquisition time of 0.96 s. A line-broadening function of 5 Hz was applied to all spectra prior to FT. Statistical Analysis of NMR Spectral Data. Full-resolution (each computer point that was generated in the spectrum was used) NMR data were imported into MATLAB (R2006a, Mathworks Inc., 2006).8 The regions corresponding to water/HDO (δ 4.7 to 4.9) and TSP (δ -0.2 to 0.2) were removed from all spectra. In addition, the urea region was removed from urine spectra (δ 5.6-6). The spectral data were then normalized to total spectral area. Statistical Total Correlation Spectroscopy (STOCSY). Standard homonuclear 1H-1H STOCSY analysis was carried out in both one and two dimensions as described by Cloarec et al.7 A correlation map, using the square of the correlation coefficients, was produced, which is similar in spectroscopic structure to a traditional 2D NMR spectroscopic correlation experiment such as correlation spectroscopy (COSY) or total correlation spectroscopy (TOCSY) carried out on a single sample; hence, the highly correlated variables are clearly identified in the plot as those crosspeaks with high correlation value colors, typically red/orange. A correlation cutoff value (r2) of 0.4-0.5 was employed for all 2D 1H-31P HET-STOCSY maps. In one-dimensional 1H-STOCSY, an NMR resonance from a metabolite of interest is chosen. The correlation and covariance between the data point at the apex of this resonance intensity of this resonance and all other data points in the full spectral data set reveal resonance intensities that correlate with this chosen resonance intensity. The covariance and correlation are then represented on the same plot, the shape of the spectrum and the color-code corresponding to the covariance and correlation, respectively. Resonances from the same (44) Meiboom, S.; Gill, D. Rev. Sci. Instrum. 1958, 29, 688-91.
molecule and from metabolites within linked pathways are highlighted, providing information on structural and biochemical relationships. The standard 1D selective pulse TOCSY or 2D TOCSY NMR experiments provide information on all coupled multiplets in a given sample. However, connectivities between noncoupled nuclei in a given molecule or between molecules in the same biochemical pathway cannot be determined. STOCSY relies on calculation of covariance of resonances in spectral data sets and hence is not constrained by spin-spin coupling, bonddistances, spatial proximity, or magnetization transfer. Orthogonal-Projection on Latent Structures-Discriminant Analysis (O-PLS-DA) of NMR Spectral Data. O-PLS-DA45 is a supervised pattern recognition algorithm that prefilters classification-irrelevant variation from data and improves interpretability of spectral variation between classes. O-PLS-DA extends the traditional supervised algorithm of projection on latent structures and enables maximal information to be extracted from complex spectral data. The prefiltered, structured noise in a data set is modeled separately from the class variation and can also be further interpreted via the loading matrices. The loadings coefficients are mean-centered and also “back-scaled” to improve interpretability, as described in ref 7. The O-PLS-DA models give rise to differential loadings plots (in this case we call these differential metabograms) distinguishing the various classes of toxicity. Metabolites that differentiate classes appear in the loadings plots as either positive or negative according to whether they increase or decrease with treatment and the correlation to the model, i.e., how important they are, is encoded in the color scale. Metabolites that we have stated as being significantly changed are those with r2 values of 0.7 and above. To prevent overfitting of spectral data, the 7-fold cross validation method was used and the cross-validation parameter Q2 was calculated.46 RESULTS Clinical Chemistry and Histopathological Data. Administration of a single dose of 415 mg/kg GalN induced clear hepatotoxicity in 3 out of 4 rats at the 24 h time point as characterized by moderate to marked necrosis of hepatocytes (liver histopathology severity scores of 3-4, Figure 2D), infiltration of mixed inflammatory cells, and mild bile duct hyperplasia. Serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), and total bilirubin levels were also significantly increased in these three rats (Figure 2A-C, p-value < 0.05). The remaining rat in the galN-treated class did not exhibit any signs of hepatocyte necrosis but only showed a minimal increase in mitotic hepatocytes (Figure 2D). It also had correspondingly normal levels of serum aminotransferases and total bilirubin (Figure 2A-C). Treatment of rats with uridine at all of the levels tested (500, 1000, or 2000 mg/kg) reduced the severity of the liver histopathology observed at 24 h (Figure 2), with only minimal to mild hepatocyte necrosis detected. There may have been a slight increase in the protective effect with the 2000 mg/kg uridine dose as all the rats in this group displayed only minimal hepatic necrosis at 24 h postGalN dosing (Figure 2D). In addition to the decrease in the severity of damage observed in the liver, serum ALT, AST, and total bilirubin levels were also markedly reduced in animals treated with uridine at 24 h post-GalN injection (Figure 2A-C, p-value < (45) Trygg, J.; Holmes, E.; Lundstedt, T. J. Proteome Res. 2007, 6, 469-79. (46) Trygg, J.; Wold, S. J. Chemom. 2003, 18, 53-64.
Analytical Chemistry, Vol. 79, No. 23, December 1, 2007
8959
Figure 2. Uridine protects against GalN-mediated hepatotoxicity. Results at 24 h after GalN injection: (A) serum ALT; (B) serum AST; (C) serum total bilirubin; (D) liver histopathology severity score. Individual values are identified with black dots and the gray bar represents the mean ( SD of each group of samples. All livers were scored as described in the Materials and Methods section, ranging from 0 ) no necrosis to 4 ) marked necrosis. Values immediately underneath each bar on the x-axis indicate uridine dosage level.
0.05). On the basis of these results, it appears that 500 mg/kg uridine is sufficient to provide the maximal protective effect of uridine against GalN-mediated hepatotoxicity when administered 24 h post-GalN dosing. This suggests the protective effect of uridine is not dose-dependent with respect to the range of concentrations administered in this study The observation that galN-induced toxicity was evident in only three of four animals is consistent with previous metabonomic studies of galN-induced hepatotoxicity that have highlighted extreme interanimal variability in terms of the toxic response.43,47,48 These studies found that rats showing liver damage also showed much urinary galN, while urinary galN was largely absent in rats where liver damage was not evident, with animals being henceforth classified as responder and nonresponder phenotypes. The nonresponder was omitted when calculating the statistical significance of differences in clinical chemistry parameters between classes. 1H NMR Spectroscopic Analyses of Urine. Urinary NMR spectral profiles were generated to determine the metabolic differences that represented the effects of both galN-toxicity and the protective effects of uridine. Figure 3 highlights the 24 h postdose urinary profiles from a representative predose control, a galN-treated animal (predose and postdose urine sample (47) Clayton, T. A.; Lindon, J. C.; Cloarec, O.; Antti, H.; Charuel, C.; Hanton, G.; Provost, J. P.; Le Net, J. L.; Baker, D.; Walley, R. J.; Everett, J. R.; Nicholson, J. K. Nature 2006, 440, 1073-7. (48) Clayton, T. A. Ph.D. Thesis, University of London, U.K., 2001.
8960 Analytical Chemistry, Vol. 79, No. 23, December 1, 2007
acquired from the same animal) with marked hepatic necrosis and a galN plus uridine treated (2000 mg/kg) animal with minimal hepatic necrosis. There were marked differences in the urinary metabolic profile following treatment with galN, which included increased levels of galN, N-acetylglucosamine (glcNAc), and urocanic acid together with decreased levels of 2-oxoglutarate (2OG) and N-methylnicotinamide (NMND). On supplementation with uridine, residual intense levels of uridine and uracil were seen in the spectrum and little glcNAc was seen. The level of glcNAc has previously been shown to correlate with the degree of galN-induced liver damage.43 This observation is further substantiated in this study in that lower levels of glcNAc are present in the urine from animals cotreated with uridine, i.e., those presenting with minimal and mild necrosis. In addition, it is clear that the effects of cotreatment with uridine include prevention of urinary increases in galN, urocanate and reduction of 2-OG and NMND. The use of O-PLS-DA confirmed that the metabolic differences apparent from representative spectra differentiated all predose controls from galN-treated and galN plus uridine supplemented samples at 24 h. O-PLS-DA models of urine collected at 96 h postdosing revealed no metabolic changes for the uridine supplemented or galN-treated classes relative to predose controls. 1H NMR Spectroscopic Analyses of Serum. The corresponding analyses of serum 24 h postdosing further confirmed the protective effects of uridine. O-PLS-DA models that differentiated the galN-treated class from samples cotreated with uridine
Figure 3. Representative 600 MHz 1H NMR urine spectra of samples representing control (predose), galN-treatment, and galN plus 2000 mg/kg uridine treatment (spectra are not normalized to total spectral area).
revealed higher levels of tyrosine, isoleucine, leucine, valine, and lactate and reduced levels of glucose in the galN alone samples. The serum metabolic changes induced by galN administration have previously been shown to include increased levels of tyrosine, phenylalanine, lactate, betaine, and D-3-hydroxybutyrate.43 Hence, it is clear from this work that uridine via the protection it affords is preventing these metabolic outcomes that characterize the toxic response to galN. Serum sampled 96 h postdosing revealed no significant metabolic changes for the uridine supplemented class relative to the galN-treated class. 1H MAS NMR Spectroscopic Analyses of Liver. Representative 1H MAS NMR spectra of liver for each class of sample 24 h postdosing are given in Figure 4A with (i) representing galNtreated samples and (ii), (iii), and (iv) representing cotreatment with 500, 1000, and 2000 mg/kg uridine, respectively. There are clear metabolic differences between galN-treated and uridine supplemented classes as evidenced in the aromatic and aliphatic spectral regions. O-PLS-DA models that discriminated galN-treated samples from those representing cotreatment with galN plus uridine revealed increased levels of glucose, adenosine, uridine 5′-diphosphate-N-acetylglucosamine (UDP-glcNAc), and uridine 5′-diphosphate-N-acetylgalactosamine (UDP-galNAc) and depleted levels of tyrosine in all uridine supplemented samples. The established hepatic metabonomic changes elicited in response to galN treatment include elevated levels of tyrosine and depleted levels of glucose and adenosine,43 and it is apparent in this study that uridine has prevented these toxin-induced changes from occurring. MAS NMR analysis of liver tissue sampled 96 h postdosing revealed no significant metabolic changes between galN-treated and uridine supplemented samples. 31P-{1H} MAS NMR Spectroscopic Analyses of Liver. Representative 31P-{1H} NMR spectra of liver tissue following administration of galN and cotreatment with varying levels of
uridine are given in Figure 4B. The spectra reveal contributions from uridine 5′-diphosphate (UDP)-amino sugars such as UDPglcNAc, UDP-galNAc, UDP-glucose (UDP-glc), and UDP-galactose (UDP-gal) visible in the chemical shift region of δ31P -10 to -13. In addition, resonances are seen for phosphodiesters; glycerophosphocholine and glycerophosphoethanolamine in the chemical shift range of 1 to -1 ppm and for phosphomonoesters such as phosphocholine and phosphoethanolamine at δ31P 3-4. An intense signal is seen for inorganic phosphate at δ31P 1.5. The chemical shift of the resonance that corresponds to inorganic phosphate is shifted in the galN treated samples relative to the cotreated uridine samples. The direction of this shift suggests a decrease in pH for the galN-treated liver samples which may be as a result of partial cellular breakdown and leakage of intracellular metabolites into the extracellular space. 1H and 31P-{1H} MAS NMR Spectral Time Course. The acquisition of interleaved 1H and 31P-{1H} NMR spectra over a time period of 7 h enabled metabolism within the tissue to be followed with acquisition of a 1H spectrum taking approximately 5 min and that of a 31P-{1H} NMR spectrum approximately 17 min. A representative time-course series of 1H and 31P-{1H} NMR spectra for a sample following treatment with galN is given in Figure 5, and a host of changes that occur in the tissue are seen. In the 1H spectra (Figure 5A), the depletion of the UDPaminosugar resonances is apparent together with an increase in the corresponding uridine and sugar resonances. In addition, increased levels of resonances that correspond to amino acids were observed which suggests autolysis is taking place in the tissue over time. The glycerol backbone resonances together with the phosphocholine -N+Me3 resonances were also seen to increase over the spectral time course suggesting the breakdown of phosphocholine and glycerophosphocholine. Analytical Chemistry, Vol. 79, No. 23, December 1, 2007
8961
Figure 4. (A) Representative 1H MAS NMR spectra of rat liver and (B) corresponding 31P-{1H} MAS NMR spectra for (i) galN-treated (ii) galN and 500 mg/kg uridine (iii) galN and 1000 mg/kg uridine (iv) galN and 2000 mg/kg uridine. Key: Ala, alanine; PC, phosphocholine; PE, phosphoethanolamine; Pi, inorganic phosphate; UDP-glcNAc, uridine 5′-diphosphate-N-acetylglucosamine; UDP-galNAc, uridine 5′-diphosphateN-acetylgalactosamine.
Figure 5B shows the corresponding 31P-{1H} NMR spectral time course, where a depletion of UDP-aminosugar resonances correlated with the 1H spectral changes. In addition, the levels of phosphomonoesters were increased and were found to correlate with increases seen in the phosphocholine -N+Me3 resonance of the 1H NMR spectra. The resonances that correspond to the phosphodiesters; glycerophosphorylcholine (GPC) and glycerophosphorylethanolamine (GPE) also increased which suggested cellular membrane breakdown49 has occurred during acquisition of the time course data. This effect was only seen for the galNtreated samples and hence is consistent with the protective effects of uridine in preventing galN-induced hepatic necrosis and cellular (49) Cox, I. J.; Menon, D. K.; Sargentoni, J.; Bryant, D. J.; Collins, A. G.; Coutts, G. A.; Iles, R. A.; Bell, J. D.; Benjamin, I. S.; Gilbey, S. J. Hepatol. 1992, 14, 265-75.
8962
Analytical Chemistry, Vol. 79, No. 23, December 1, 2007
damage. A shift in the inorganic phosphate (Pi) resonance was also observed, and this suggests changes in pH as alluded to in the previous section. It is important to note that the tissue MAS NMR experiments were conducted at 275 K to minimize sample degradation and that the tissue sample when removed from the rotor on completion of the experiment appeared to be physically intact. Statistical Correlation of 1H and 31P-{1H} MAS NMR Spectra. In this study, 16 liver samples from animals euthanized 24 h postdosing of galN and varying levels of uridine were subjected to “through time” 1H and 31P-{1H} MAS NMR analysis. A total of 20 spectra were acquired for each nucleus over a timecourse of 7 h for samples from animals treated with galN alone (n ) 4), and from cotreatment with galN and varying levels of uridine (n ) 4 per class). Hence, the full spectral data set consisted
Figure 5. (A) 1H MAS NMR spectral time-course for a single liver tissue sample (galN-treated class) and (B) 31P {1H} MAS NMR spectra for the same sample. Spectra were interleaved in acquisition over 7 h total acquisition time.
of 320 1H and 320 31P-{1H} spectra which were statistically integrated via computation of a correlation matrix representing covariance of resonance intensities between the 1H and 31P dimensions. The output is given in Figure 6 and is analogous to a 2D NMR correlation experiment; however, it is computed from a series of 1D experiments and results are obtained in a fraction of the time that it would take to generate a 2D NMR spectrum. A series of expansions of the two-dimensional correlation map (boxed region of Figure 6) which represents correlation of all 1H and 31P-{1H} data (n ) 320) is given in Figure 7. Strong correlations from the 1H spectra to the two resonances in the highfield region of the 31P-{1H} spectra at approximately δ31P -11 and δ31P -12 were apparent. Figure 7 concentrates on all detected 1H correlations to the 31P-{1H} resonance at δ 31P -12.77 (identical
results are obtained for the 31P-{1H} resonance at δ31P -11.20). Figure 7A shows correlations to the resonance at δ1H 5.95 which corresponds to the H5 aromatic proton of UDP-galNAc/UDPglcNAc. It is noteworthy that no correlations to the uridine H5 proton resonance were seen, as expected. Correlations to the 1H resonance at δ1H 7.97 correspond to the H6 aromatic proton of UDP-galNAc/UDP-glcNAc (Figure 7B). Figure 7C highlights correlations to the 1H spectrum at δ1H 5.53 and 5.56 which represent the anomeric sugar proton resonances for UDP-glcNAc and UDP-galNAc, respectively. Furthermore, correlation to the N-acetyl moiety at δ1H 2.07 is shown in Figure 7D. Finally, correlations to the 1H region at δ1H 4.37 represents a correlation to protons of the sugar moiety (Figure 7E). Figure 7 also shows the structures of UDP-galNAc and UDP-glcNAc, and the 31PAnalytical Chemistry, Vol. 79, No. 23, December 1, 2007
8963
Figure 6. A two-dimensional 1H-31P HET-STOCSY map representing correlations from all 1H spectra (n ) 320) and 31P-{1H} spectra (n ) 320). A correlation cutoff (r2) of 0.4 was applied. The boxed region corresponds to the expansions presented in Figure 7. The average 1H and 31P spectrum of all acquired spectra is given in the boxes along and above the 2D correlation map.
{1H} signals indicated show correlations to the 1H atoms marked with a red asterisk. The one-dimensional 1H and 31P-{1H} correlation plots constructed from the two-dimensional STOCSY cross-peak at heteronuclear chemical shifts of δ31P -12.77 and δ1H 5.98 are given in Figure 8. The two high-field 31P-{1H} resonances correlate to a host of 1H resonances which have been identified and described above (Figure 7). Figure 8D shows a plot of the spectral intensities for the 31P-{1H} peak at δ31P -12.77 and the 1H peak at δ1H 5.98 for each spectrum in the data set. This validates that the heteronuclear correlation is real and not driven by a number of outlying or spurious spectra. On the basis of the above results, the 31P-{1H} spectral metabolites have been assigned to UDPgalNAc and UDP-glcNAc. These 1H-31P HET-STOCSY-based assignments have been confirmed via spiking of standard compounds into an aqueous extract of liver. The 2D 1H-31P HET-STOCSY map also revealed correlations from a 31P-{1H} resonance at δ31P 0.15 to numerous 1H resonances 8964
Analytical Chemistry, Vol. 79, No. 23, December 1, 2007
which arise from GPC. These included the proton resonances from the -N+Me3 head groups at δ1H 3.24, the glycerol backbone proton resonances in the 1H chemical shift region of δ1H 3.6-4.1 and the RCH2 resonance at δ1H 4.34. Furthermore, the 31P-{1H} resonance for GPE at δ31P 0.68 also revealed correlations to its proton resonances and to those of GPC. In addition, the data can be correlated from different points of the time-course, for instance the first 1H and 31P-{1H} spectrum generated for each sample can be correlated or the spectra in the middle or end of the time-course may be correlated. This provides metabolic information on biochemical relationships unique to these time-points. Furthermore, the 1H-31P HETSTOCSY map for a particular sample class may be investigated as this provides metabolic information unique to the class. For example, a 2D 1H-31P HET-STOCSY correlation map representing spectral data acquired for the galN-treated class alone reveals correlations from the 31P-{1H} resonances of GPC and GPE to phenylalanine, tyrosine, and the branched chain amino acids
Figure 7. 2D 1H-31P HET-STOCSY map expansions representing correlations from the 31P-{1H} peak at δ31P -12.77 to 1H spectral resonances, namely, (A) H5 aromatic resonance of UDP-galNAc/UDP-glcNAc; (B) H6 aromatic resonance of UDP-galNAc/UDP-glcNAc; (C) anomeric sugar proton resonance of UDP-galNAc/UDP-glcNAc; (D) the N-acetyl group of UDP-galNAc/UDP-glcNAc; and (E) the nonoverlapped sugar resonances of UDP-galNAc/UDP-glcNAc. Molecular structures of UDP-galNAc/UDP-glcNAc, the 31P atoms marked with a green asterisk show correlations to the 1H atoms marked with a red asterisk. The average spectral data is displayed (blue) together with the 1H spectral data from the first sample (black).
(valine, leucine, and isoleucine). All of these amino acids are significantly increased in response to galN toxicity. DISCUSSION AND CONCLUSIONS The application of HET-STOCSY to this heteronuclear data has proved a powerful tool with respect to structural assignment in
situations where traditional 2D NMR methods would be constrained by parameters such as lack of J couplings, extended distances, and inefficient magnetization transfer. HET-STOCSY has also provided information on class specific metabolic changes following a toxic insult. Analytical Chemistry, Vol. 79, No. 23, December 1, 2007
8965
Figure 8. 1D 1H-31P HET-STOCSY correlation plots from the 2D 1H-31P HET-STOCSY “cross-peak” at δ1H 5.98-δ31P -12.77. 1D 1H correlation plot from δ31P -12.77 to, part A, aromatic and, part B, aliphatic 1H spectral regions. (C) Corresponding 1D 31P correlation plot from δ1H 5.98. Part D shows the spectral intensities for the 1H and 31P-{1H} resonances for each spectral data set.
The urinary, serum, and liver metabonomic data reflect the protective effects of uridine in preventing toxicity induced by galN. The prevention of the depletion of the uridine nucleotide pool by galN is evident in the urine data as increased levels of uridine and uracil are seen and in the liver as increased levels of UDPaminosugars are observed. Furthermore, increased levels of galN and glcNAc are seen following treatment with galN alone; this suggests that the conjugation of galN to UDP-aminosugars has led to a depletion of UDP-glc which has resulted in the blockage of this pathway. It is probable that the resultant galN is epimerised and N-acetylated to form glcNAc as previously alluded to in our study of the protective effects of glycine on galN-induced toxicity.43 Following cotreatment with uridine, it is hypothesized that UDPglc levels are not depleted and metabolism of galN continues unabated via conjugation to UDP-sugars. The metabonomic serum data also reflect the protective effects of uridine as tyrosine and amino acid levels are not elevated and glucose is not depleted following cotreatment with uridine. Furthermore, the MAS NMR analyses of liver tissue also reveals that adenosine and glucose levels are not depleted following treatment with galN plus uridine which suggests that ATP pools are not affected. The acquisition of 31P-{1H} and 1H spectral time-course data enabled the metabolism within tissue to be followed, and many changes were identified. For example, UDP-amino sugars were metabolized into their constituent parts and autolysis was also evident as increased resolution and intensity of amino acid resonances was observed. An increase in GPE and GPC was seen for the galN-treated class in both 1H and 31P-{1H} spectral timecourse data which suggests that the toxic response involved damage to the cellular membrane. As this increase was not observed for the classes supplemented with uridine, this provides further evidence of the protective properties of uridine, as cellular 8966 Analytical Chemistry, Vol. 79, No. 23, December 1, 2007
membrane damage was not evident following cotreatment with galN and uridine. HET-STOCSY has enabled metabolites that alter simultaneously in response to galN toxicity to be elucidated, for instance HET-STOCSY revealed that the 1H resonances of tyrosine, phenylalanine, leucine, isoleucine, valine, and lipid triglycerides are correlated to the 31P-{1H} resonances of GPE and GPC. All of these metabolites are increased in response to galN-toxicity, and GPE/GPC are markers of galN-induced membrane breakdown and degradation. HET-STOCSY has aided candidate biomarker identification in this toxicological metabonomic study and has identified metabolites of relevance to understanding the mode of action of a hepatotoxin. The HET-STOCSY approach provides a general means of enhancing information recovery from complex multinuclear spectral data sets representing multicompartmental matrices and can be extended to any combination of parallel NMR measurements using multiple probe nuclei. ACKNOWLEDGMENT The authors thank Dr. J. Trygg for use of the O-PLS-DA algorithm. Mr. Jake Pearce is acknowledged for provision of additional MATLAB scripts for data presentation. This work received financial support from Pfizer, Bristol-Myers-Squibb, Sanofi-Aventis, Servier, and Waters as part of COMET 2. The authors acknowledge the Korean Government (MOEHRD) for Korea Research Foundation Grant KRF-2006-214-F00024 for Y. S. Hong. D. F. Wells is acknowledged for conducting the animal work and A. Metz for the histopathological analyses at Pfizer. Received for review July 2, 2007. Accepted August 16, 2007. AC0713961
