Probing Latent Biomarker Signatures and in Vivo Pathway Activity in

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Probing Latent Biomarker Signatures and in Vivo Pathway Activity in Experimental Disease States via Statistical Total Correlation Spectroscopy (STOCSY) of Biofluids: Application to HgCl2 Toxicity E. Holmes, O. Cloarec, and J. K. Nicholson* Biological Chemistry, Division of Biomedical Sciences, Sir Alexander Fleming Building, Imperial College, London, SW7 2AZ, United Kingdom Received November 14, 2005

A new multivariate statistical approach, based on the novel combination of projection on latent structure analysis with an inbuilt orthogonal filter (OPLS-DA) coupled with a spectroscopic correlation method statistical total correlation spectroscopy (STOCSY), was used to characterize the in vivo metabolic pathway perturbations of a model renal cortical toxin HgCl2, in the rat, using urine as an indicator of metabolic homeostasis disruption. This method provided an unbiased, sensitive approach to biomarker extraction and identification, and showed potential for generating potential novel pathway connectivities. Keywords: metabonomics • STOCSY • NMR • Hg2+ • renal cortical toxicity • biomarker signature

Introduction There is strong pressure to generate novel biomarker information for probing experimental and human disease states. This has led to the development of genomic, proteomic and metabonomic platforms that can measure multiple parameters simultaneously in order to evaluate the response of an organism to a pathological challenge, and to understand mechanistic and pathway perturbations.1-4 Metabolic profiling tools such as nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), in combination with multivariate mathematical modeling methods, have been applied successfully to the characterization of biochemical perturbations in biofluids and tissues that reflect specific physiological or pathological states. Applications of this technology include the construction of expert systems for the prediction of drug toxicity,5,6 and the characterization of a range of pathologies such as cardiovascular disease, inborn errors of metabolism, renal disease, diabetes and neurodegenerative conditions.7-11 1H NMR-based metabolite profiles of organ specific pathologies are robust and sensitive enough to indicate the site of damage within an organ, for example, glomerular, cortical or papillary toxicity can be differentiated within the kidney.12,13 However, the metabolites that discriminate between these tissue specific toxicities are generally those that are present in urine in relatively high concentrations, and which tend to be global markers of metabolic dysfunction. For example, these ‘usual suspects’ include glucose and a variety of amino acids and organic acids which are elevated in the urine in response to renal cortical toxicity, or creatine, taurine, betaine, and bile acids which are elevated in response to liver toxicity.6,14 These changes are the result of systematic variations in metabolite levels in the plasma, that in turn are dependent on metabolic pathway * To whom correspondence should be addressed. Tel: +44 (0)207 594 3195. Fax: +44 (0)207 594 3221. E-mail [email protected]. 10.1021/pr050399w CCC: $33.50

 2006 American Chemical Society

activities of specific cell types. Other metabolic perturbations are yet more global and representative of generalized tissue dysfunction such as reduction of urinary tricarboxylic acid (TCA) cycle intermediates and hippurate, commonly observed under many types of toxic or pathological challenges and in acidotic states.6,14 More useful as markers of tissue specific damage are compounds that have clear mechanistic significance, and these biomarkers of toxicity can be dominating features of the metabolic signature: examples include 2-aminoadipate in hydrazine treated rats indicative of specific blocks in the lysine catabolism pathway;15 glutaric and adipic aciduria in rats treated with bromoethanamine indicating inhibition of specific mitochondrial fatty acyl CoA dehydrogenases;16 and impairment of fatty acid metabolism by a candidate drug compound MrkA resulting in depletion of TCA cycle intermediates and increased excretion of medium chain dicarboxylic acids.17 Since the kidneys play a major role in homeostasis, urinary metabolite profiles are highly informative about pathway dysregulation in the intact organism. Therefore, urinary metabolic changes are often the first signs of cellular level dysfunctions, which are partly ameliorated by cell-level homeostatic processes (cycles in intracellular fluxes). Thus, analysis of urine can generate system level metabolic regulation/dysregulation information.1 However, for most episodes of toxicity or pathology, the more subtle biochemical changes relating to the mechanisms of pathology can be obscured by the overarching profile changes relating to structural damage at the site of lesion. The way in which spectroscopic data are analyzed in relation to a pathological end point is crucial to biomarker recovery and to discovery of novel pathway interactions.18,19 Preprocessing procedures and subsequent multivariate analysis of spectral data can be optimized to achieve maximum sensitivity in terms of differentiating between individual pathologies. However, although computer-based pattern recognition methods afford Journal of Proteome Research 2006, 5, 1313-1320

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research articles a less biased and more efficient approach to spectral analysis than simple visual comparison, many methods of analysis distort the original spectral structure making interpretation of the loadings (variables contributing to pathological characteristics) more difficult. For example, if unit variance scaling is applied then all spectral regions are equally weighted, but, this produces loadings which are difficult to interpret and can result in artifacts and upweighting of spectral noise. On the other hand, mean centering without further scaling maintains the original spectral structure but results in an emphasis on metabolites that are present in high concentration and tendency to display relatively large variance, which may or may not be related to the toxic effect. Pareto scaling (1/xst.dev) can be used as a compromise, but is still prone to enhancing the contribution from metabolites present in higher concentrations. We have shown that, in principle, using the correlation matrix to establish the significance of differences between two sample classes in combination with backscaling the loading coefficients to reflect the covariance matrix, which more closely resembles the original spectral structure, can vastly improve interpretation of the models.20,21 In addition to implementation of optimal scaling factors, advances in computational and databasing capacities have somewhat negated the requirement for spectral reduction and recent work has shown that positional ‘noise’ such as pH-induced citrate shifts does not hinder analysis but rather can carry complimentary information on differences in physicochemical properties of biological samples.20 Here, we evaluate the practical capacity of this new metabolite profiling approach, combining OPLS-DA of full resolution spectral data (effectively analyzing all computer points in the spectrum) to enhance biomarker detection coupled with a separate statistical correlation analysis which allows direct recovery of proton candidates from the same molecule. Mercury II chloride (HgCl2) was selected as an appropriate exemplar model toxin to illustrate this approach as it has been extensively used as a model of acute renal failure and induces classical renal cortical lesions in rats.22,23 There is an extensive body of literature on both mechanism of toxicity and metabolic response as defined by both conventional toxicological measures and by NMR based metabolic profiling. Uptake and accumulation of Hg2+ occurs in all three portions of the proximal tubule but targets the S2/S3 portion of the proximal convoluted tubule (PCT) in the renal cortex causing overt necrosis in rats within 24 h after a single dose of 0.75 mg/kg i.p.24 Renal organic anion transporters are implicated in the uptake of inorganic mercury. Features of Hg2+ toxicity include a decrease in the capacity for urinary concentration resulting in polyuria, impaired absorption of glucose and other solutes, inhibition of mitochondrial succinate dehydrogenase and increased activity of alkaline phosphatase and other brush border enzymes.25,26 In addition to the information gained from conventional clinical chemistry assays, Hg2+ toxicity has been characterized using 1H NMR spectroscopy. Urine profiles of Hgtreated rats are dominated by glucose, amino acids and organic acids and manifest decreased excretion of several TCA cycle intermediates and hippurate,24,25,27 all of which are also observed after treatment with other toxins that target the PCT, including hexachlorobutadiene and uranyl nitrate.12 The objective of the current study was to evaluate the potential of the OPLS-DA method with statistical correlation-directed metabolite identification to provide a more subtle diagnosis of Hg2+ toxicity that would potentially differentiate this toxin from other S2/S3 toxins. In addition, the general applicability of the 1314

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technology was assessed with respect to generating biomarker signatures for monitoring disease progression and response to therapy.

Experimental Section Animal Husbandry. Male Sprague Dawley rats were obtained from Charles River Ltd., UK (n ) 5), nine weeks old and weighing 260-280 g were given a single intra-peritoneal dose of 0.75 mg/kg HgCl2 in 0.9% saline. A matched group of control rats were given vehicle only. Animals were housed individually in metabolism cages in a well-ventilated room at a temperature of 21 ( 2 °C and a relative humidity of 50 ( 10%, with a 12 h light/12 h dark cycle and allowed to acclimatize for 2 days prior to dosing. Predose urine samples were collected for each animal. Following dosing, urine samples were collected at 8 h, 24 h, 32 h, 48 h, 72 h and 96 h post dose (p.d). For the study duration food [Rat & Mouse No. 1 Diet (Special Diet Services Ltd., Cambridge, U.K.)] and tap water were provided ad libitum. All animal studies were conducted under U.K. Home Office License according to appropriate national legislation. High Resolution 1H NMR Spectroscopy. One-dimensional 1 H NMR spectra of urine were measured at 600.13 MHz on a Bruker Avance NMR spectrometer. A standard presaturation pulse sequence for water suppression28 was employed, with irradiation at the water frequency during the relaxation delay of 3 s and a pulse sequence mixing time of 100 ms. A total of 64 free-induction decays (FID) were collected into 64k data points using a spectral width of 20.0363 ppm, with an acquisition time of 1.7 s and a total pulse recycle time of 3.3 s. The FIDs were multiplied by an exponential weighting function corresponding to a line broadening of 0.3 Hz prior to Fourier transformation. Data Processing and Multivariate Statistical Analysis. NMR processing and pattern recognition were carried out using a power Mac G5 with dual 64-bit 2 GHz processors and 2 GB of Synchronous Dynamic Random Access Memory. NMR and pattern recognition routines were written in-house in the MATLAB 7.1 environment (The Mathworks Inc). Computer based pattern recognition was carried out in order to characterize the effects of Hg2+ toxicity. All data analysis was conducted on normalized full resolution spectral data on meancentered variables autoscaled by dividing each variable by its standard deviation. A supervised pattern recognition algorithm Orthogonal Projection on Latent Structure (O-PLS)29 was used to extract maximum biomarker information from the data. The O-PLS method provides similar prediction capabilities to PLS (Projection on Latent Structure).30 However, in this extension of PLS, the interpretation of the models is improved because the structured noise in the data is modeled separately from the class (or toxicity) related variation. Thus, the O-PLS loading and regression coefficients provide a more straightforward and accurate interpretation of the biological consequences of toxicity than normal PLS analysis. Furthermore, the orthogonal loading matrices provide the opportunity to interpret the structured noise in the data. To test the validity of the model against over-fitting, the cross-validation parameter Q2 was computed.31 Statistical Total Correlation Spectroscopy (STOCSY).21 STOCSY is based on the properties of the correlation matrix calculated for the spectral dataset. The simplest case is autocorrelation analysis where each variable (in this case proton resonances from a single metabolite) will give a correlation value of r ) 1. Theoretically, the different resonance intensities

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Figure 1. Typical 600 MHz 1H NMR spectra of urine from rats dosed with a) HgCl2 24h post dose and b) vehicle only. Key: (1) Glucose, (2) Lysine, (3) Valine, (4) Leucine, (5) Isoleucine, (6) Alanine, (7) Lactate, (8) Citrate, (9) Succinate, (10) 2-oxoglutarate, (11) Creatinine, (12) Dimethylglycine, (13) 2-Hydroxybutyrate, (14) Acetate, (15) Fumarate, (16) Hippurate, (17) N-methylnicotinamide, (18) Nmethylnicotinic acid, and (19) Glutamate.

from the same molecule will always have the same ratio if measurement parameters and spectrometer conditions are identical. However, in reality although a high correlation will generally be achieved for resonances deriving from the same molecule, r will always be 0.6), there are other lower concentration metabolites that are just as robust as predictors of Hg toxicity, such as the decrease in N-methylnicotinic acid (NMNA), N-methylnicotinamide (NMND) and medium chain dicarboxylic acids all with correlation coefficients of r > 0.5 (Figure 2). Decreased fumarate was also found to be influential in discriminating between Hg2+ treatment and control samples (r > 0.45). A small but reproducible change in low concentration metabolites can be more advantageous in defining specific toxicity than changes in the high concentration metabolites, which have a tendency to exhibit a high degree of variation in control populations. For example the cholestatic hepatotoxin R-naphthlyisothiocyanate induces large increases in the urinary excretion of creatine and taurine but these are general biomarkers of liver toxicity and it is the changes in the pattern of urinary bile acids, present in low micromolar concentrations that provide a more specific diagnostic for this type of toxicity.14

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Figure 3. Example statistical correlations for two metabolites: (a-c) driven from the hippurate signal at 3.98 showing strong correlation with resonances from the aromatic spin system; (d and e) driven from the CH3 resonance of NMND at δ 4.48 showing correlation with resonances from NMNA in addition to the slightly stronger correlation with the other resonances from NMND. All signals are numbered according to the proton position on the molecule. Key: NMND, N-methylnicotinamide; NMNA, N-methylnicotinic acid.

This principle is equally applicable to proteomic and transcriptomic biomarker recovery and discovery. In addition to characterizing pathologies through the discriminant analysis framework, the OPLS method can also be used to aid the elucidation of chemical structure of molecules by highlighting the linear relationship between the different peaks of a single molecule spin system. Thus, confident assignment of molecular identification can be made, even when molecules are present in low concentrations. Correlation analysis by O-PLS was applied to selected signals (Statistical TOtal Correlation SpectroscopY; STOCSY)21 and shows, as expected, that the signals exhibiting the highest correlations are derived from proton moieties from the same molecule (autocorrelation). This method mimics the calculation of physical connectivities between different proton environments on the same molecule that are commonly accessed by application of 2-dimensional correlation experiments such as 1H-1H Correlation SpectroscopY (COSY) and 1H-1H Total Correlation SpectroscopY (TOCSY). However, because the correlations are statistical rather than physical, more information is accessible than with NMR experiments. For example the hippurate signal at δ 3.98 is highly correlated with the signals from its aromatic spin system at δ 7.54 (meta-CH), δ 7.63 (para-CH) and δ 7.82 (ortho-CH) (Figure 3a-c). Autocorrelated resonances of hippurate, driven from the δ 3.98 signal, all show a correlation value r > 0.9, whereas all other metabolites in the spectrum

give low correlation values (r < 0.4; Figure 3a-c). Likewise, the correlation matrix calculated for the CH3 resonance of Nmethylnicotinamide (NMND) at δ 4.48 (1) shows strong association with resonances deriving from the aromatic ring at δ 8.19 (4), δ 8.90 and 8.87 (2 and 5) and δ 9.28 (3) (Figure 3d,e). In 2-dimensional NMR experiments the correlation between the aliphatic side chains and the aromatic ring protons of both hippurate and NMND are not observed due to the number of chemical bonds between these protons (4-8 in the case of NMND, depending on the position on the aromatic ring). Typically a standard total correlation spectroscopy (TOCSY) pulse sequence will allow connections of between 4 and 5 bond lengths, usually within the same spin system, to be observed. Thus, with STOCSY, the constraints of ‘through bond’ connections are removed, while the correlation analysis takes only a fraction of the time required to generate 2-dimensional NMR experiments. In addition to correlations observed between signals from NMND, slightly weaker correlations (∼0.65) were observed between the NMND signal at δ 4.48 and resonances from N-methylnicotinic acid (NMNA). Thus, in some instances the STOCSY method can also generate more subtle information regarding related metabolites deriving from common metabolites, pathways and/or mechanisms. For example, from Figure 4a, it can be seen that the triplet assigned to the terminal CH3 group of isoleucine at δ 0.94 has strong correlations with resonances at δ 1.01, also from isoleucine. At slightly lower Journal of Proteome Research • Vol. 5, No. 6, 2006 1317

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Figure 4. Statistical correlations driven from (a) the terminal CH3 resonance of isoleucine at δ0.94 showing correlations with valine and lysine; (b) the fumarate singlet at δ6.54 showing a strong correlation with resonances from 2-oxoglutarate; (c) the CH2 resonance from adipate acid at δ1.56 showing correlations with glutarate.

correlation coefficients, a link between the resonance at δ 0.96 (leucine), 0.99 and 1.04 (valine) and 1.7 and 1.94 (lysine) can be noted. Since all three branched chain amino acids (leucine, isoleucine, valine) and lysine undergo transamination, with 2-oxoglutarate as the amine acceptor, it is perhaps unsurprising that their urinary excretion varies in a similar manner when challenged by a toxic insult that induces decreased excretion of 2-oxoglutarate.32 The singlet corresponding to the fumarate signal in the NMR spectrum at δ 6.54 can barely be observed due to its low concentration (Figure 1), but is shown to be significant in discriminating between control and treated animals. A direct 1-D STOCSY analysis driven from this signal indicated that the variation of fumarate concentration across the study was closely correlated with the concentration variation in 2-oxoglutarate and to a lesser extent with succinate (Figure 4b1 and b2), indicating that the system level regulation of these metabolites is dependent on cellular level pathway activity. Citrate did not correlate highly with fumarate, but this is simply a consequence of the more complex pH-shift dependency and line shape behavior of citrate, which is also influenced by divalent metal ion concentration variations which tend to statistically lower the pathway correlations. 1318

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Metabolic Consequences of HgCl2-Induced Toxicity. The observed glycosuria, amino aciduria and organic aciduria are a consequence of the damage to the renal tubular absorption apparatus and reflect a reduced capacity for tubular reabsorption. This is a feature of many nephrotoxins that target the S2/ S3 portion of the proximal tubules.25 Although general amino aciduria was observed following the administration of HgCl2, the increase in concentration was more apparent in some amino acids than others. For example, isoleucine and lysine show a stronger correlation with treatment than alanine (Figure 2). The decrease in the tricarboxylic acid (TCA) cycle intermediates, citrate, succinate and 2-oxoglutarate were evident from the covariance structure of the data (as represented by downward pointing signals in Figure 1b and c). However, due to their high degree of variation their significance in discriminating between treated and control groups is not so strong. In contrast, decreased urinary excretion of fumarate was found to be one of the most significant features characterizing Hg2+ toxicity, which is a new observation. Modulation of the TCA pathway intermediates is a recurring theme in mammals undergoing many types of pathological or physiological stress such as toxicity, physical restraint or food deprivation12,33 and reflects mitochondrial perturbation. Hg2+ binds to sulfydryl groups and directly inhibits the mitochondrial enzymes succinate and malate dehydrogenase,34 further supporting Hg2+-induced mitochondrial perturbation. Decreased urinary excretion of hippurate concentrations was also shown to be a stable differentiating factor between toxin treated animals and controls. Hippurate is produced by the conjugation of endogenous glycine with benzoic acid derived from gut microfloral activity. Glycine conjugation is acetyl-CoA dependent and occurs in the mitochondria, hence the correlation with TCA cycle intermediates.35 One of the most reproducible changes induced by Hg2+ was the decrease in medium chain fatty acids glutarate and adipate (Figures 2 and 4c). Glutarate and adipate compete with 2-oxoglutarate for the same dicarboxylic acid transporter in the kidney.36 The fact that all three metabolites are decreased and show similar variance in this data set may indicate damage to the transport mechanism. The incubation of both glutarate and adipate with MDCK cells has been shown to protect against the toxic effects of Hg2+ via inhibition of uptake of the N-acetylcysteine conjugate of Hg2+ into the mitochondria.37 Other metabolites changes characteristic of HgCl2 toxicity include NMNA and NMND which decreased in concentration in response to the toxic challenge. Increased urinary excretion of NMND has been associated with certain liver toxins, e.g., R-naphthylisothiocyanate38 and galactosamine39 and, together with elevated NMNA has been documented as a marker of peroxisome proliferation.40 In contrast, the decrease observed here after Hg2+ administration is likely to be indicative of destruction of the organic cation transporters in the proximal tubule since these are responsible for the active part of the secretion of NMND into the lumen of the proximal tubule, and have also been shown to transport nicotinate compounds such as NMNA.41,42

Conclusion The application of the OPLS algorithm, performed on the full spectral range, lends a new dimension to the exploration of spectral fingerprints of toxicity or disease. Combined with the back-scaling post-processing, it confers an intrinsic clarity to spectral interpretation, combining the advantages of both correlation and covariance matrixes, and allows expression of

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the model in a recognizable structure. Biomarker identification is enhanced by the ability to statistically correlate signals from the same molecule and mimics the results achieved by physically acquiring 2-dimensional correlation spectra, which is a time-consuming process and impractical to perform for every sample in a large dataset. An additional feature of the correlation calculation is the inference of pathway responses obtained by establishing correlations between metabolites operating within the same pathway. The statistical strategy outlined here is equally applicable to other ‘omic’ data and is also a resource for obtaining cross-platform correlations between proteins or genes and metabolites. Abbreviations. COSY, correlation spectroscopy; DA, discriminant analysis; NMNA, N-methylnicotinic acid; NMND, N-methylnicotinamide; NMR, nuclear magnetic resonance; OSC, orthogonal signal correction; OPLS-DA, orthogonal projection on latent structure; PCA, principal component analysis; p.d., post dose; PCT, proximal convoluted tubule; SDH, succinate dehydrogenase; STOCSY, statistical total correlation spectroscopy; TCA, tricarboxylic acid; TOCSY, total correlation spectroscopy.

Acknowledgment. We are grateful to the Wellcome Trust for financial support on a related project (O. Cloarec). References (1) Nicholson, J. K.; Connelly, J.; Lindon, J. C.; Holmes, E. Metabonomics: a platform for studying drug toxicity and gene function. Nat. Drug Discov. 2002, 1 (2), 153-161. (2) Fiehn, O. Metabolomics - the link between genotypes and phenotypes. Plant Mol. Biol. 2002, 48, 155-171. (3) Kleno, T. G.; Kiehr, B.; Baunsgaard, D.; Sidelmann, U. G. Combination of ‘omics’ data to investigate the mechanism(s) of hydrazine-induced hepatotoxicity in rats and to identify potential biomarkers. Biomarkers 2004, 9 (2), 116-138. (4) Leighton, J. K. Application of emerging technologies in toxicology and safety assessment: regulatory perspectives. Int. J. Toxicol. 2005, 24 (3), 153-155. (5) Lindon, J. C.; Nicholson, J. K.; Holmes, E.; Antti, H.; Bollard, M. E.; Keun, H.; Beckonert, O.; Ebbels, T. M.; Reily, M. D.; Robertson, D.; Stevens, G. J.; Luke, P.; Breau, A. P.; Cantor, G. H.; Bible, R. H.; Niederhauser, U.; Senn, H.; Schlotterbeck, G.; Sidelmann, U. G.; Laursen, S. M.; Tymiak, A.; Car, B. D.; Lehman-McKeeman, L.; Colet, J. M.; Loukaci, A.; Thomas, C. Contemporary issues in toxicology the role of Metabonomics in toxicology and its evaluation by the COMET project. Toxicol. Appl Pharmacol. 2003, 187 (3), 137-146. (6) Holmes, E.; Nicholls, A. W.; Lindon, J. C.; Connor, S. C.; Connelly, J. C.; Haselden, J. N.; Damment, S. J. P.; Spraul, M.; Neidig P.; Nicholson. J. K. Chemometric Models for Toxicity Classification Based on NMR Spectra of Biofluids. Chem. Res. Toxicol. 2000, 13 (6), 771-778. (7) Brindle, J. T.; Antti, H.; Holmes, E.; Tranter, G.; Nicholson, J. K.; Bethell, H. W. L.; Clarke, S.; Schofield, P. M.; McKilligin, E.; Mosedale, D. E.; Grainger D. J. Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H NMR based metabonomics. Nat. Med. 2002, 8 (12), 1439-1444. (8) Griffin, J. L.; Cemal, C. K.; Pook, M. A. Defining a metabolic phenotype in the brain of a transgenic mouse model of spinocerebellar ataxia 3. Physiol. Genomics 2004, 16 (3), 334-340. (9) Bairaktari, E.; Seferiadis, K.; Liamis, G.; Psihogios, N.; Tsolas, O.; Elisaf, M. Rhabdomyolysis-related renal tubular damage studied by proton nuclear magnetic resonance spectroscopy of urine. Clin. Chem. 2002, 48 (7), 1106-1109. (10) Burns, S. P.; Iles, R. A. An investigation of argininosuccinic acid anhydrides in argininosuccinic acid lyase deficiency by 1H NMR spectroscopy. Clin. Chim. Acta 1993, 30 (1-2), 1-13. (11) Dobbins, R. L.; Malloy, C. R. Measuring in-vivo metabolism using nuclear magnetic resonance. Curr. Opin. Clin. Nutr. Metab. Care 2003, 6 (5), 501-509.

research articles (12) Holmes, E.; Nicholls, A. W.; Lindon, J. C.; Ramos, S.; Spraul M.; Neidig P.; Connor, S. C.; Connelly, J.; Nicholson, J. K. Development of a model for classification of toxin-induced lesions using 1H NMR spectroscopy of urine combined with pattern recognition. NMR Biomed. 1998, 11, 235-244 (13) Gartland, K. P. R.; Bonner, F. W.; Nicholson, J. K. Investigations into the biochemical effects of region-specific nephrotoxins. Mol. Pharmacol. 1989, 35 (2), 242-250. (14) Beckwith-Hall B. M.; Nicholson, J. K.; Nicholls, A. W.; Foxall, P. J. D.; Lindon, J. C.; Connor, S. C.; Abdi, M.; Connelly, J.; Holmes. E. Nuclear magnetic resonance spectroscopic and pattern recognition investigations into biochemical effects of hepatotoxins. Chem. Res. Toxicol. 1998, 11, 260-272. (15) Nicholls, A. W.; Holmes, E.; Lindon, J. C.; Farrant, R. D.; Haselden, J. N.; Damment, S. J. P.; Waterfield, C. J.; Nicholson, J. K. Metabonomic investigations into hydrazine toxicity in the rat: Induction of 2-aminoadipic acid. Chem. Res. Toxicol. 2001, 14 (8), 975-987. (16) Holmes, E.; Caddick, S.; Lindon, J. C.; Wilson, I. D.; Kryvawych, S.; Nicholson, J. K. 1H and 2H NMR spectroscopic studies on the metabolism and biochemical effects of 2-bromoethanamine in the rat: induction of transient glutaric aciduria Biochem. Pharmacol. 1995, 49, 1349-1359. (17) Mortishire-Smith, R. J.; Skiles, G. L.; Lawrence, J. W.; Spence, S.; Nicholls, A. W.; Johnson, B. A.; Nicholson, J. K. Use of metabonomics to identify impaired fatty acid metabolism as the mechanism of a drug-induced toxicity. Chem. Res. Toxicol. 2004, 17 (2), 165-173. (18) Keun, H. C.; Ebbels, T. M. D.; Antti, H.; Bollard, M. E.; Beckonert, O.; Holmes, E.; Lindon, J. C.; Nicholson, J. K. Improved analysis of multivariate data by variable stability scaling: application to NMR-based metabolic profiling, Anal. Chim. Acta 2003, 490 (12), 265-276. (19) Holmes, E.; Antti, H. Chemometric contributions to the evolution of metabonomics: mathematical solutions to characterising and interpreting complex biological NMR spectra. Analyst 2002, 127, 1549-1557 (20) Cloarec, O.; Dumas, M. E.; Trygg, J.; Craig, A.; Barton, R. H.; Lindon, J. C.; Nicholson, J. K.; Holmes, E. Evaluation of the orthogonal projection on latent structure model limitations caused by chemical shift variability and improved visualization of biomarker changes in 1H NMR spectroscopic metabonomic studies. Anal. Chem. 2005, 77 (2), 517-526. (21) Cloarec, O.; Dumas, M. E.; Craig, A.; Barton, R. H.; Trygg, J.; Hudson, J.; Blancher, C.; Gaugier, D.; Lindon, J. C.; Holmes, E.; Nicholson, J. K. Statistical total correlation spectroscopy (STOCSY): a new approach for latent biomarker identification from metabolic 1H NMR datasets Anal. Chem. 2005, 77 (5), 1282-1289. (22) Barnes, J. L.; McDowell, E. M.; McNeil, J. S.; Flamenbaum, W.; Trump, B. F. Studies on the pathophysiology of acute renal failure IV Protective effects of dithiothreitol following administration of mercuric chloride in the rat. Virchows Arch. B. Cell Path. 1980, 32, 201-232. (23) Stein, J. H.; Lifschitz, M. D.; Barnes, L. D. Current concepts on the pathophysiology of acute renal failure. Am. J. Physiol. 1978, 234 (3), F171-181. (24) Holmes, E.; Bonner, F. W.; Sweatman, B. C.; Lindon; J. C.; Beddell, C. R.; Rahr, E.; Nicholson, J. K. Nuclear magnetic resonance spectroscopy and pattern recognition analysis of the biochemical process associated with the progression of and recovery from nephrotoxic lesions in the rat induced by Mercury (II) chloride and 2-bromoethanamine. Mol. Pharmacol. 1992, 42, 922-930. (25) Nicholson, J. K.; Timbrell, J. A.; Sadler, P. J. Proton NMR spectra of urine as indicators of renal damage. Mercury-induced nephrotoxicity in rats. Mol. Pharmacol. 1985, 27 (6), 644-651. (26) Hofmeister, R.; Bhargava, A. S.; Gunzel, P. Value of enzyme determinations in urine for the diagnosis of nephrotoxicity in rats. Clin. Chim. Acta 1986, 160 (2), 163-167. (27) Holmes, E.; Bonner, F. W.; Nicholson J. K. 1H NMR spectroscopic and studies on the biochemical changes associated with the development of mercury II chloride-induced nephrotoxic lesions in the F344 rat and the multimammate mouse (Mastomys natalensis). Comp. Biochem. Pharmacol. (C) 1996, 114 (1), 7-15. (28) Nicholson, J. K.; Foxall, P. J. D.; Spraul, M.; Farrant, R. D.; Lindon, J. C. 750-MHz 1H and 1H-13C NMR spectroscopy of human bloodplasma. Anal. Chem. 1995, 67, 793-811. (29) Trygg, J. O2-PLS for qualitative and quantitative analysis in multivariatecalibration. Chemometrics 2002, 16, 283-293. (30) Martens, H.; Naes T. Multivariate Calibration, 2nd ed.; Wiley: Chichester, 1994.

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