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The potential of metabonomics in drug safety and toxicology
Vol. 1, No. 3 2004
Drug Discovery Today: Technologies Editors-in-Chief Kelvin Lam – Pfizer, Inc., USA Henk Timmerman – Vrije Universiteit, The Netherlands DRUG DISCOVERY
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The potential of metabonomics in drug safety and toxicology Julian L. Griffin Department of Biochemistry, Tennis Court Road, University of Cambridge, Cambridge, UK CB2 1GA
Metabonomics (or metabolomics) defines a global profile of the metabolites in a cell, tissue or organism using either 1H NMR spectroscopy or mass spectrometry in conjunction with statistical pattern recognition. Unlike other functional genomic tools, it is high-throughput and cheap on a per sample basis, providing a rapid screen for large scale drug toxicity testing and screening of large populations. However, for its widespread use in toxicology there is an urgent need for biomarker databases.
Section Editor: Oliver Zerbe – Institute of Organic Chemistry, University of Zurich, Switzerland The main purpose of metabonomics is to quickly establish some basic toxicology and pharmacokinetics of compound candidates. It thereby assists in selecting those lead candidates that are worth further optimising. An important aspect of metabonomics is that it allows the connection of data from proteomics and transcriptomics, and thereby enables coordinating the timing of the analysis to physiologically important windows. The article by Griffin describes modern analytical methods for measuring the content of metabolites and mathematical methods to deconvolute them into their components. Julian Griffin from the University of Cambridge is an expert in the field who has written several reviews and made substantial contributions to the development of methods.
Introduction With the rise of systems biology, several approaches have been developed to globally profile a tier of organisation in a cell, tissue or organism. In this article, the key technologies involved in metabolic profiling of tissues and biofluids will be discussed, in particular with reference to applications in the drug safety assessment process. The terms metabolomics and metabonomics have widely been used to describe ‘the quantitative measurement of metabolic responses to pathophysiological stimuli or genetic modification’ [1,2]. Some researchers have distinguished these two terms, suggesting that metabolomics deals with metabolism at the cellular level, whereas metabonomics addresses the complete system. Other researchers have distinguished the terms by the technology used to generate a ‘metabolic profile’ (largely used for mass spectrometry-based approaches) or metabolic fingerprint (NMR spectroscopy and other approaches that only detect the high-concentration E-mail address: (J.L. Griffin) [email protected] 1740-6749/$ ß 2004 Elsevier Ltd. All rights reserved.
DOI: 10.1016/j.ddtec.2004.10.011
metabolites) [3]. In this article, the term metabonomics will be used as this is currently the most widely used term in toxicology [1,4]. The concept is to measure all the small molecule concentrations through a global analytical approach, and then to apply pattern recognition techniques to define a metabolic phenotype or ‘metabotype’ ([5]; Fig. 1).
Key technologies and approaches The most extensively used analytical approach for metabonomics in toxicology is NMR spectroscopy, largely as a result of pioneering work in this area by Professor Jeremy Nicholson of Imperial College London (http://www.imperial.ac.uk/; http:// www.metabometrix.com/; [1,4]; Table 1). This approach has analysed biofluids and tissue extracts using solution state NMR [6,7], intact tissues using high-resolution magic angle spinning (HRMAS) 1H NMR spectroscopy [8,9] and even tissues within living organisms using magnetic resonance spectroscopy [10]. Solution state NMR is also amenable to high-throughput and www.drugdiscoverytoday.com
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Glossary Fourier transform-infrared spectroscopy (FT-IR): spectroscopic technique based on examining the stretching frequencies of given molecules. In general, poor at discriminating metabolites from a similar class of compounds. High-resolution magic-angle-spinning 1H NMR spectroscopy: the NMR spectra of intact tissues can be significantly be improved by spinning the sample at an angle (the so-called magic angle) to a magnetic field. This approach is particularly useful for studying metabolic compartmentation within tissues and cells. Metabolic profiling: the total complement of individual metabolites of a given biological sample. Metabolite arrays: these devices use a 96-well plate assay system to phenotype organisms via an ‘assay-on-a-chip’ system. Metabolomics and metabolome: Oliver [2] defines the metabolome ‘as the complete set of metabolites/low molecular weight intermediates which is context dependent, varying according to the physiology, developmental or pathological state of the cell, tissue, organ or organism’. Metabonomics: in contrast to Oliver’s definition of metabolomics, Nicholson et al. [1] coined the term metabonomics defining this as ‘the quantitative measurement of the multivariate metabolic responses of multicellular systems to pathophysiological stimuli or genetic modification’. This definition reflects the extra tier of complexity by moving from unicellular to multicellular organisms.
relatively robust, making it ideal as a large scale profiling tool for metabolites. However, NMR spectroscopy has relatively low sensitivity and can only detect the high-concentration metabolites. The major competition to NMR spectroscopy for analytical approaches involves a range of mass spectrometry-based approaches, and in particular gas chromatography (GC) and liquid chromatography (LC) mass spectrometry (MS). These approaches are more sensitive, and hence potentially truly global in terms of metabolic profiling. However, both GC– MS and LC–MS depend critically on the reproducibility of the chromatography, and in the case of LC–MS results might be impaired by ion suppression. In addition to these approaches, others have employed FT-IR spectroscopy, thin layer chromatography, metabolite arrays (analogous to lab-on-a-chip arrangements) and even automated biochemical assays to provide a global description of metabolism. An integral part to the metabonomic approach is the application of pattern recognition techniques [11,12] to identify the metabolites most correlated with a pathology or toxic insult. Unsupervised techniques require no prior information about class membership and use the innate variation in a data set to map samples measured into multidimensional space. Examples of unsupervised techniques include principal components analysis (PCA) and hierarchical cluster analysis (HCA). Supervised techniques correlate variation in the data set with an external variable such as disease status, age or drug response. Examples of supervised techniques include partial least squares (PLS), orthogonal signal correction (OSC), neural networks and genetic algorithms. PCA is one of the most widely used approaches and this approach is illustrated in Fig. 2. 286
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Figure 1. A potential strategy for metabonomic analysis of biofluids and tissue samples in drug toxicity studies.
Metabonomics and genomics Metabolic profiling techniques have been used to phenotype a wide range of animals, plants and microbes (for this area of application the term metabolomics has been most widely used). One of the first applications of the approach was to genotype Arabidopsis thaliana leaf extracts [13]. Plant metabolomics is a huge analytical challenge as despite typical plant genomes containing 20,000–50,000 genes there is currently estimated 50,000 identified metabolites with this number set to rise to 200,000. The current preferred technique for metabolic profiling of plants uses GC–MS and in the seminal manuscript in this area Fiehn et al. [13] quantified 326 distinct compounds in A. thaliana leaf extracts, further elucidating the chemical structure of half of these compounds. As well as Arabidopsis, yeast, the other work horse of functional genomics, has been examined by metabolomics [14]. Yeast was the first eukaryote to be sequenced and mutant strains for the 6000 genes in yeast can be examined from cell banks such as EUROFAN (http://www.unifrankfurt.de/fb15/mikro/euroscarf/). This suggests that researchers could potentially phenotype all the genes in yeast, having a significant impact on human disease through comparison of gene sequence similarities in terms of the metabolic consequences produced in the mutants. The standard method to phenotype yeast strains is to see how rapidly a strain grows on a given substrate mixture. If the mutation does not alter the rate of growth it is said to be a silent mutation, and thus no function can be deduced from this gene deletion. However, Raamsdonk et al. [14]
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Table 1. Technology comparison summary table for metabonomics GC–MS
LC–MS
FT-IR spectroscopy
Pros
Cheap after initial magnet purchase, robust and reproducible on a per sample basis, minimal sample preparation, high-throughput.
Good sensitivity. Cheap to purchase. Good chromatography by comparison with LC–MS. Good identification software.
Excellent sensitivity. No need to derivatise samples prior to analysis. More truly global than NMR and GC–MS (?). Can be used to analyse specific metabolites as well as produce global profiles.
Cheap and easy to use.
Cons
Only detects the highest concentration of metabolites. Significant metabolite overlap in simple 1H NMR spectra. Large initial purchase of spectrometer and superconducting magnet.
Need to derivatise the metabolites to ensure they are volatile.
Ion suppression can prevent quantitation or even detection of certain metabolites. LC reproducibility is lower than GC.
Very poor distinction between classes of metabolites.
Companies and related websites
Bruker BioSpin (http://www.brukerbiospin.com); Varian Inc (http://www.varianinc.com/cgi-bin/nav? products/nmr/index&cid=NOIPKOHFIH); Jeol (http://www.jeoleuro.com/)
Agilent (http://www.chem.agilent.com/ Scripts/PCol.asp?lPage=180); Thermo Electron (http://www.thermo.com/com/ CDA/Category/CategoryFrames/ 1,2213,184,00.html); Shimadzu (http://www1.shimadzu.com/products/ lab/ms.html); Varian (http://www.varianinc.com)
Agilent (http://www.chem.agilent.com/ Scripts/PCol.asp?lPage=181); Thermo Electron (http://www.thermo.com/com/CDA/ Category/CategoryFrames/1,2213,264,00.html); Shimadzu (http://www1.shimadzu.com/products/ lab/ms.html); Varian (http://www.varianinc.com); Waters (http://www.waters.com/watersdivision/ Contentd.asp?ref=CEAN-5KUSS8); Bruker Daltonics (http://www.bdal.com/)
Varian (http://www.varianinc.com); Shimadzu (http://www1.shimadzu. com/products/lab/ms.html); Perkin Elmer Inc (http://www.perkinelmer.com/peaitran.nsf/pkihome)
References
[1,4,14]
[3,13]
[26]
A brief summary of some of the technologies currently being used in metabonomics. The list of manufacturers is not a definitive list, but a guide for those interested in pursuing the area further. www.drugdiscoverytoday.com
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NMR spectroscopy
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Figure 2. Multivariate statistics: pattern recognition tools are a vital part of the process of metabolomics, and are increasingly being used to fully analyse the large multivariate data sets that are produced by other ‘-omic’ technologies. To investigate the innate variation in a data set in an unsupervised manner, techniques such as principal components analysis (PCA; see figure) or hierarchical cluster analysis (HCA) have been applied. Multivariate statistics such as PCA allows the interrogation of large data sets with both many variables and multiple samples (a) PCA can be used to investigate the variation across the variables to produce loading scores and variation across samples to produce a scores plot (b) The process involves the mapping of a given sample according to the values of the variables measured. (c) Shows the mapping of a sample representing three variables. If this is repeated for all the variables, correlates can be investigated. (d) The most amount of correlated variation is found along Principal Component (PC) 1 and the second most amount of variation is found along PC2. This is repeated until the variation in the data set is described by new latent variables represented by PCs. (e) Shows an example of PCA plot that results from such a process; (figure supplied by Dr Henrik Antii, Umea University).
have used 1H NMR-based metabonomics to distinguish these silent phenotypes. Applying a combination of PCA and discriminate function analysis (DFA) they were able to co-clutser strains with deletions of similar genes together. This included one cluster consisting of mutants related to oxidative phosphorylation and another 288
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cluster involving 6-phosphofructo-2-kinase. Since this original paper a range of analytical techniques have been used to further characterise yeast mutants including LC–MS of the cell extracts and MS analysis of the yeast media, with this latter approach being referred to as ‘metabolic footprinting’ [15].
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cing the need for angiography, currently the gold standard for CAD diagnosis (Fig. 3). This approach has also been used to correlate blood pressure with the 1H NMR-derived metabolic profile of blood plasma [20]. This study has since been extended to include results from transcriptional analysis using DNA microarrays, in the hope that a joint transcriptional and metabonomic study of blood sera could compete with the 99% correct prediction of angiography.
Metabonomics and transcriptomics
Figure 3. Applications of metabonomics to human disease. Both NMR and mass spectrometry-based metabolic profiling allows the highthroughput acquisition of large data sets. This figure shows an application to human disease where the NMR spectral profiles of blood plasma were used to distinguish both the occurrence and severity of coronary artery disease. The supervised pattern recognition technique Partial least squares discriminate analysis (PLS-DA) successfully separated patients without coronary artery disease (NCA) from those with triple vessel disease (TVD). Key: NCA, red triangles; TVD, blue squares. Adapted from [16] with permission.
Metabonomics and disease A key advantage of NMR spectroscopy-based metabonomics is that the approach is high-throughput, allowing the rapid acquisition of large data sets. This makes it ideal as a screening tool, particularly for human populations where there can be significant environmental and dietary influences on tissue and biofluid ‘metabolomes’. A range of diseases have been investigated including Duchenne muscular dystrophy, multiple sclerosis, cancer and schizophrenia (e.g. [10,16–18]). As metabonomics makes no prior assumption as to the metabolic events that accompany a disease, it is particularly appropriate for diseases where conventional approaches have to date drawn a blank. NMR-based metabonomics has also been used for screening human populations for both the presence and severity of coronary artery disease (CAD) using blood plasma [19]. To reduce the variation in the data set not correlated with disease presence or severity OSC was used as a data filter to subtract variation orthogonal to that associated with disease severity. The resultant pre-processed data was then analysed using PLS-discriminate analysis to produce a pattern recognition model that was greater than 90% accurate in predicting disease severity. Such an intelligent pattern recognition model could produce significant financial savings by redu-
There is currently a great deal of interest in toxicology studies in combining the high-throughput screening approach of metabonomics, with the potentially more truly global profiling capability of DNA microarrays. This has led to several studies where the two approaches have been used to build up both mRNA and metabolite descriptions of drug induced pathology simultaneously. One such study has examined orotic acid induced fatty liver in the rat [21]. Supplementation of orotic acid to normal food intake is known to induce fatty liver in the rat, producing symptoms very similar to alcohol induced fatty liver disease. Although it has been known since the 1950s that disruption of the various Apo proteins occurs during orotic acid exposure it is still not known how the disruption of nucleotide metabolism, the primary effect of orotic acid, results in the impaired production of the various Apo proteins important for the transport of lipids around the body. To investigate this, Griffin et al. [21] applied a transcriptomic and metabonomic analysis to the Kyoto and Wistar strains of rat. The Wistar rat is an out-bred strain of rat and has been classically used to follow orotic acid induced fatty liver disease. However, the Kyoto rat, an in-bred strain, is particularly susceptible to fatty liver disease. These two strains provided a pharmacogenomic model of the drug insult, and illustrate a common problem with the development of many drugs that the induced response can vary depending on the population it is administered to. To fully characterise the systemic changes, as well as analysing the metabolite and mRNA composition of the liver, blood and urine was also analysed using NMR spectroscopy. Analysis of blood plasma demonstrated the expected decrease in circulating low-density lipids (LDL) and very low-density lipids (VLDL) lipids, demonstrating the disruption of ApoB and ApoC production, but also identified a blood plasma increase in bhydroxybutyrate, suggesting diabetes-like response to the lesion. The use of NMR-based metabonomics to follow systemic metabolism was possible because of the relative cheapness of the approach on a per sample basis. This ensured that some information was obtained outside the liver, despite transcriptional analysis being confined to the liver because of cost (Fig. 4). Using PLS to cross correlate transcriptional and metabonomic data in the liver, both data sets identified pathways concerning uridine production, choline turnover, and stress www.drugdiscoverytoday.com
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Figure 4. An application of metabonomics and transcriptomics to understanding fatty liver disease in the rat. In this study, the metabolic consequences of exposing two strains of rats to orotic acid were investigated using 1H NMR spectroscopy and DNA microarray technology. Using high-resolution magic angle spinning (HRMAS) 1H NMR spectroscopy (a), the liver tissue from Kyoto rats were found to be distinct from that obtained from Wistar rats using Principal components analysis to investigate the spectra (b). This was caused in part by increased lipid content in the liver tissue from Kyoto rats. Orotic acid induced fatty liver in both strains of rats. (c) Shows an electron micrograph of a section of fatty liver with clear droplets of fat visible in the tissue (right) compared with a piece of control tissue. This effect was far more pronounced in the Kyoto rat demonstrated the different pharmacokinetics of the drug in the two strains. Key: PC, principal component; PtdChol, Phosphatidyl Choline.
responses as being perturbed by the drug. By careful analysis of these pathways it was possible to trace the metabolic perturbations from the initial exposure to orotic acid to disruption of fatty acid metabolism in the liver (Fig. 3). Furthermore, the approach modelled the pharmacogenomics of the drug, showing that the metabolome of the Kyoto rat was more profoundly influenced by orotic acid. Transcriptional analysis, using real time (RT)-PCR, has also been used to assist in unravelling the metabolic changes detected by a urinary metabonomic study of peroxisome proliferation in the rat [22]. In this study, Ringeissen et al. found that peroxisome proliferator-activated receptor (PPAR) ligands induced large increases in urinary N-methylnicotinamide (NMN) and N-methyl-4-pyridone-3-carboxamide (4PY) concentrations, intermediates in the Tryptophan-NAD+ 290
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pathway. Furthermore, these biomarkers were shown to correlate with peroxisome proliferation as measured by electron microscopy, suggesting that NMN and 4-PY could be used as biomarkers for peroxisome proliferation in cases where the biopsy of liver tissue was not possible. This has great relevance to the drug safety and assessment as the PPAR ligands are currently being investigated as drugs for treating dyslipidaemia and type 2 diabetes, but there is no current clinical method for assessing peroxisome proliferation, a potential side effect in rodents, in humans. RT-PCR of key enzymes also identified transcriptional changes involved in TryptophanNAD+ pathway, indicating that both transcriptional and metabonomic analysis of the tissue agreed in terms of the major metabolic pathway targeted. The two biomarkers were then measured by a HPLC assay as part of a high-throughput-
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specific screen for peroxisome proliferation in the rat. This study demonstrates how metabonomics can be used to identify biomarkers, which can be validated through transcriptomics, and these biomarkers can ultimately be used to assess drug toxicity in situations relevant to drug safety and assessment.
Metabonomics and pharmacokinetics Metabonomics also has tremendous promise in determining the pharmacokinetics of a toxic insult or drug response [1]. By using biofluid analysis to derive metabolic profiles a large number of time points can be analysed in a noninvasive manner. This allows the deconvolution of metabolic events which occur on a different temporal scale, providing some insight into the different tissues affected by the toxin. In this manner, Waters and colleagues have used NMR-based metabonomics to monitor metabolite changes in urine and blood plasma, and then cross correlate these changes with specific tissue damage during alphanaphthylisothiocyanate toxicity [23]. In a similar manner, Mortishire-Smith et al. [24] have used HRMAS 1H NMR spectroscopy to analyse metabolite changes in the liver to confirm biofluid metabolite perturbations that accompany impaired fatty acid metabolism in the liver. Furthermore, these researchers were able to further substantiate these correlations using in vitro enzymatic assays. Although there are still relatively few studies involving combined tissue and biofluid analysis, given the potential of metabonomics for understanding phamacodynamics this area of research is set to increase in the future.
Future developments Cryoprobe NMR spectroscopy The primary drive in metabonomics is to improve analytical techniques to provide an ever increasing coverage of the complete metabolome of an organism. To date NMR-based techniques have focused on using 1H NMR spectroscopy, but this approach suffers from a small chemical shift range, producing significant overlap of the resonances of several different metabolites. Although 13C NMR spectroscopy has a much larger chemical shift range, allowing the resolution of a wider range of metabolites the approach is intrinsically less sensitive compared with 1H NMR spectroscopy, as a result of the lower gyromagnetic ratio of the 13C nucleus compared with the 1H. However, in cryoprobes, a NMR probe where the receiver and transmitter coil is cooled using liquid helium, a significant improvement in sensitivity can be achieved by cooling the coil of a NMR probe to 4 K, allowing the rapid acquisition of 13C NMR spectra. Keun et al. [25] have already applied this approach to studying hydrazine toxicity through biofluid 13C NMR spectroscopy. Although in this particular study the biomarkers of hydrazine toxicity were already largely known, the use of 13C spectroscopy did allow
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the identification of these metabolites largely from 1-dimensional spectroscopy, without the need to identify the metabolites responsible for key resonances from a series of two dimensional approaches. Furthermore, this approach can be particularly good for quantifying concentrations of metabolites which only produce singlets in 1H NMR spectra. Although 1H NMR might not conclusively identify the metabolite, the extra chemical shift range for 13C NMR is usually enough to allow unambiguous assignment of a given singlet.
Liquid chromatography NMR spectroscopy Another approach to improve the sensitivity of the NMR experiment is to hyphenate the NMR spectroscopy with liquid chromatography. This improves sensitivity by two mechanisms. Firstly, high and low concentration metabolites are separated by the liquid chromatography, reducing the likelihood of co-resonant peaks and also improving the dynamic range of the NMR experiment for the low concentration metabolites. Secondly, metabolites are concentrated by the chromatography, further aiding the detection of low concentration metabolites. Bailey and co-workers have used this approach of LC-NMR spectroscopy to metabolically profile several plants [26,27]. This can be a particularly useful approach if hyphenated further with cryoprobe technology to allow cryo-LC-NMR.
Mass spectrometry advances LC–MS and GC–MS are increasingly being used as profiling tools for diseases and toxicology studies [28,29]. This is set to increase as the chromatography becomes more reliable, and the software for matching mass fragmentation patterns is improved. Indeed several manufacturers are developing systems which are designed to work in tandem with NMR to provide LC–NMR/MS analysis of biofluids, thus, reaping the benefits of these two technologies while avoiding many of the pitfalls.
High-resolution magic-angle-spinning 1H NMR spectroscopy With all the techniques discussed above there is a need to form tissue extracts if the toxicologist is to examine the metabolomic changes in a tissue directly. However, if highresolution MAS 1H NMR spectroscopy could be automated this would provide a viable alternative to laborious tissue extraction procedures. Finally, there is an urgent need for improved pattern recognition processes for integrating the information produced by a variety of analytical approaches to provide a fuller coverage of the metabolome as well as a means to disseminate this information. Currently, there is no consensus as to what information should be reported alongside a metabonomic study so that the data can be interpreted by other researchers. Such metadata (i.e. data about the data) will be vital if researchers are to generate www.drugdiscoverytoday.com
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Links
Outstanding issues
http://www.metabometrix.com/glossary1.htm (A useful glossary of terms written from a metabolic point of view of functional genomics.) http://www.metabolomics.nl/ (A resource site for plant metabolomics. This site also contains lots of useful links to the wider metabolomic community.) http://www.spectroscopynow.com (A central resource for all types of spectroscopy including NMR and mass spectrometry-based approaches.) http://www.waters.com/WatersDivision/ContentD.asp?ref=PSTD5RQBCK; http://www.corporate-ir.net/ireye/ir_site.zhtml?ticker=brkr& script=412&layout=-6&item_id=502276 (Manufacturer websites for mass spectrometry devoted to high-throughput metabolic profiling and metabolomics.)
How many metabolites must be measured to generate a representative metabolic profile of a tissue, biofluid or cell? How can one reconstruct cellular and organ specific metabolism from the systemic metabolism reported by biofluids such as urine and blood plasma? How can databases be created that are fully inclusive for the range of different analytical techniques and approaches currently being used in metabonomics? What is the minimum information required to report results from a metabonomic experiment?
References databases akin to those being produced by the microarray and proteomic communities.
Conclusions Metabonomics is being performed using a variety of analytical approaches and pattern recognition techniques to answer the questions to a diverse range of problems relevant to the pathologist and toxicologist. At its most immediate level it provides a rapid screen for large scale drug toxicity testing and screening of large human populations for common diseases such as coronary artery disease. These metabolic profiles are also ideal for following changes in transcriptional and proteomic profiles, and there are already several mathematical tools for this sort of data fusion. The high-throughput nature of NMR spectroscopy suggests that it will increasingly be used as a first line screening tool for toxicology. However, this is likely to be assisted by more comprehensive metabolic profiling through mass spectrometrybased approaches. This rapid increase in interest in metabonomics, as well as a need to disseminate information about drug safety or toxicity issues necessitates the development of metabonomic databases and ultimately a standardisation of approaches to provide an equivalent of the transcriptomic MIAME protocol for metabonomics (http://www.mged.org/ Workgroups/MIAME/miame.html).
Related articles Griffin, J.L. (2003) Metabonomics: NMR spectroscopy and pattern recognition analysis of body fluids and tissues for characterisation of xenobiotic toxicity and disease diagnosis. Curr. Opin. Chem. Biol. 7, 648– 654 Kell, D.B. (2004) Metabolomics and systems biology: making sense of the soup. Curr. Opin. Microbiol. 7, 296–307 Lindon, J.C. et al. (2004) Metabonomics technologies and their applications in physiological monitoring, drug safety assessment and disease diagnosis. Biomarkers 9, 1–31 Lindon, J.C. et al. (2003) So what’s the deal with metabonomics? Anal. Chem. 75, 384A–391A Steuer, R. et al. (2003) Interpreting correlations in metabolomic networks. Biochem. Soc. Trans. 31(Pt 6), 1476–1478
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1 Nicholson, J.K. et al. (2002) Metabonomics: a platform for studying drug toxicity and gene function. Nat. Rev. Drug Discov. 1, 153–161 2 Oliver, S.G. (2002) Functional genomics: lessons from yeast. Phil. Trans. R. Soc. Lond. B 357, 17–23 3 Fiehn, O. (2002) Metabolomics – the link between genotypes and phenotypes. Plant Mol. Biol. 48, 155–171 4 Nicholson, J.K. and Wilson, I. (2003) Understanding ‘Global’ systems biology: metabonomics and the continuum of metabolism. Nat. Rev. Drug Discov. 2, 668–676 5 Gavaghan, C.L. et al. (2000) An NMR-based metabonomic approach to investigate the biochemical consequences of genetic strain differences: application to the C57BL10J and Alpk:ApfCD mouse. FEBS Lett. 484, 169–174 6 Beckwith-Hall, B.M. et al. (1998) Nuclear magnetic resonance spectroscopic and principal components analysis investigations into biochemical effects of three model hepatotoxins. Chem. Res. Toxicol. 11, 260–272 7 Holmes, E. et al. (1998) Development of a model for classification of toxininduced lesions using 1H NMR spectroscopy of urine combined with pattern recognition. NMR Biomed. 11, 235–244 8 Garrod, S. et al. (1999) High resolution magic angle spinning 1H NMR spectroscopic studies on intact rat renal cortex and medulla. Magn. Reson. Med. 41, 1108–1118 9 Griffin, J.L. et al. (2000) The biochemical profile of rat testicular tissue as measured by magic angle spinning 1H NMR spectroscopy. FEBS Lett. 478, 147–150 10 Griffin, J.L. et al. (2003) Assignment of 1H nuclear magnetic resonance visible polyunsaturated fatty acids in BT4C gliomas undergoing ganciclovir-thymidine kinase gene therapy-induced programmed cell death. Cancer Res. 63, 3195–3201 11 Lindon, J.C. et al. (2001) Pattern recognition methods and applications in biomedical magnetic resonance. Prog. Nucl. Magn. Reson. 39, 1–40 12 Valafar, F. (2002) Pattern recognition techniques in microarray data analysis. Ann. N.Y. Acad. Sci. 980, 41–64 13 Fiehn, O. et al. (2000) Metabolite profiling for plant functional genomics. Nat. Biotechnol. 18, 1157–1161 14 Raamsdonk, L.M. et al. (2001) A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat. Biotechnol. 19, 45–50 15 Allen, J. et al. (2003) High-throughput classification of yeast mutants for functional genomics using metabolic footprinting. Nat. Biotechnol. 21, 692–696 16 Griffin, J.L. et al. (2002) Metabolic profiles of dystrophin and utrophin expression in mouse models of duchenne muscular dystrophy. FEBS Lett. 530, 109–116 17 Griffin, J.L. and Shockcor, J.P. (2004) Metabolic profiles of cancer cells. Nat. Rev. Cancer 4, 551–561 18 Prabakaran, S. et al. (2004) Mitochondrial dysfunction in Schizophrenia: evidence for compromised brain metabolism and oxidative stress. Mol. Psychiatry 9, 684–697 19 Brindle, J.T. et al. (2002) Rapid and non-invasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics. Nat. Med. 8, 1439–1444
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20 Brindle, J.T. et al. (2003) Application of chemometrics to 1H NMR spectroscopic data to investigate a relationship between human serum metabolic profiles and hypertension. Analyst 128, 32–36 21 Griffin, J.L. et al. (2004) An integrated reverse functional genomic and metabolic approach to understanding orotic acid-induced fatty liver. Physiol. Genomics 17, 140–149 22 Ringeissen, S. et al. (2004) Potential urinary and plasma biomarkers of peroxisome proliferation in the rat. Biomarkers 8, 240–271 23 Waters, N.J. et al. (2001) NMR and pattern recognition studies on the timerelated metabolic effects of alpha-naphthylisothiocyanate on liver, urine, and plasma in the rat: an integrative metabonomic approach. Chem. Res. Toxicol. 14, 1401–1412 24 Mortishire-Smith, R.J. et al. (2004) Use of metabonomics to identify impaired fatty acid metabolism as the mechanism of a drug-induced toxicity. Chem. Res. Toxicol. 17, 165–173
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25 Keun, H.C. et al. (2002) Crogenic probe 13C NMR spectroscopy of urine for metabonomic studies. Anal. Chem. 74, 4588–4593 26 Bailey, N.J. et al. (2003) Metabolomic analysis of the consequences of cadmium exposure in Silene cucubalus cell cultures via 1H NMR spectroscopy and chemometrics. Phytochemistry 62, 851–858 27 Bailey, N.J. et al. (2002) Multi-component metabolic classification of commercial feverfew preparations via high-field 1H-NMR spectroscopy and chemometrics. Planta Med. 68, 734–738 28 Plumb, R.S. et al. (2002) Metabonomics: the use of electrospray mass spectrometry coupled to reversed-phase liquid chromatography shows potential for the screening of rat urine in drug development. Rapid Commun. Mass Spectrom. 16, 1991–1996 29 Plumb, R.S. et al. (2003) Use of LC/TOF mass spectrometry and multivariate statistical analysis shows promise for the detection of drug metabolites in biological fluids. Rapid Commun. Mass Spectrom. 17, 2632–2638
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The potential of metabonomics in drug safety and toxicology Julian L. Griffin Department of Biochemistry, Tennis Court Road, University of Cambridge, Cambridge, UK CB2 1GA
Metabonomics (or metabolomics) defines a global profile of the metabolites in a cell, tissue or organism using either 1H NMR spectroscopy or mass spectrometry in conjunction with statistical pattern recognition. Unlike other functional genomic tools, it is high-throughput and cheap on a per sample basis, providing a rapid screen for large scale drug toxicity testing and screening of large populations. However, for its widespread use in toxicology there is an urgent need for biomarker databases.
Section Editor: Oliver Zerbe – Institute of Organic Chemistry, University of Zurich, Switzerland The main purpose of metabonomics is to quickly establish some basic toxicology and pharmacokinetics of compound candidates. It thereby assists in selecting those lead candidates that are worth further optimising. An important aspect of metabonomics is that it allows the connection of data from proteomics and transcriptomics, and thereby enables coordinating the timing of the analysis to physiologically important windows. The article by Griffin describes modern analytical methods for measuring the content of metabolites and mathematical methods to deconvolute them into their components. Julian Griffin from the University of Cambridge is an expert in the field who has written several reviews and made substantial contributions to the development of methods.
Introduction With the rise of systems biology, several approaches have been developed to globally profile a tier of organisation in a cell, tissue or organism. In this article, the key technologies involved in metabolic profiling of tissues and biofluids will be discussed, in particular with reference to applications in the drug safety assessment process. The terms metabolomics and metabonomics have widely been used to describe ‘the quantitative measurement of metabolic responses to pathophysiological stimuli or genetic modification’ [1,2]. Some researchers have distinguished these two terms, suggesting that metabolomics deals with metabolism at the cellular level, whereas metabonomics addresses the complete system. Other researchers have distinguished the terms by the technology used to generate a ‘metabolic profile’ (largely used for mass spectrometry-based approaches) or metabolic fingerprint (NMR spectroscopy and other approaches that only detect the high-concentration E-mail address: (J.L. Griffin) [email protected] 1740-6749/$ ß 2004 Elsevier Ltd. All rights reserved.
DOI: 10.1016/j.ddtec.2004.10.011
metabolites) [3]. In this article, the term metabonomics will be used as this is currently the most widely used term in toxicology [1,4]. The concept is to measure all the small molecule concentrations through a global analytical approach, and then to apply pattern recognition techniques to define a metabolic phenotype or ‘metabotype’ ([5]; Fig. 1).
Key technologies and approaches The most extensively used analytical approach for metabonomics in toxicology is NMR spectroscopy, largely as a result of pioneering work in this area by Professor Jeremy Nicholson of Imperial College London (http://www.imperial.ac.uk/; http:// www.metabometrix.com/; [1,4]; Table 1). This approach has analysed biofluids and tissue extracts using solution state NMR [6,7], intact tissues using high-resolution magic angle spinning (HRMAS) 1H NMR spectroscopy [8,9] and even tissues within living organisms using magnetic resonance spectroscopy [10]. Solution state NMR is also amenable to high-throughput and www.drugdiscoverytoday.com
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Glossary Fourier transform-infrared spectroscopy (FT-IR): spectroscopic technique based on examining the stretching frequencies of given molecules. In general, poor at discriminating metabolites from a similar class of compounds. High-resolution magic-angle-spinning 1H NMR spectroscopy: the NMR spectra of intact tissues can be significantly be improved by spinning the sample at an angle (the so-called magic angle) to a magnetic field. This approach is particularly useful for studying metabolic compartmentation within tissues and cells. Metabolic profiling: the total complement of individual metabolites of a given biological sample. Metabolite arrays: these devices use a 96-well plate assay system to phenotype organisms via an ‘assay-on-a-chip’ system. Metabolomics and metabolome: Oliver [2] defines the metabolome ‘as the complete set of metabolites/low molecular weight intermediates which is context dependent, varying according to the physiology, developmental or pathological state of the cell, tissue, organ or organism’. Metabonomics: in contrast to Oliver’s definition of metabolomics, Nicholson et al. [1] coined the term metabonomics defining this as ‘the quantitative measurement of the multivariate metabolic responses of multicellular systems to pathophysiological stimuli or genetic modification’. This definition reflects the extra tier of complexity by moving from unicellular to multicellular organisms.
relatively robust, making it ideal as a large scale profiling tool for metabolites. However, NMR spectroscopy has relatively low sensitivity and can only detect the high-concentration metabolites. The major competition to NMR spectroscopy for analytical approaches involves a range of mass spectrometry-based approaches, and in particular gas chromatography (GC) and liquid chromatography (LC) mass spectrometry (MS). These approaches are more sensitive, and hence potentially truly global in terms of metabolic profiling. However, both GC– MS and LC–MS depend critically on the reproducibility of the chromatography, and in the case of LC–MS results might be impaired by ion suppression. In addition to these approaches, others have employed FT-IR spectroscopy, thin layer chromatography, metabolite arrays (analogous to lab-on-a-chip arrangements) and even automated biochemical assays to provide a global description of metabolism. An integral part to the metabonomic approach is the application of pattern recognition techniques [11,12] to identify the metabolites most correlated with a pathology or toxic insult. Unsupervised techniques require no prior information about class membership and use the innate variation in a data set to map samples measured into multidimensional space. Examples of unsupervised techniques include principal components analysis (PCA) and hierarchical cluster analysis (HCA). Supervised techniques correlate variation in the data set with an external variable such as disease status, age or drug response. Examples of supervised techniques include partial least squares (PLS), orthogonal signal correction (OSC), neural networks and genetic algorithms. PCA is one of the most widely used approaches and this approach is illustrated in Fig. 2. 286
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Figure 1. A potential strategy for metabonomic analysis of biofluids and tissue samples in drug toxicity studies.
Metabonomics and genomics Metabolic profiling techniques have been used to phenotype a wide range of animals, plants and microbes (for this area of application the term metabolomics has been most widely used). One of the first applications of the approach was to genotype Arabidopsis thaliana leaf extracts [13]. Plant metabolomics is a huge analytical challenge as despite typical plant genomes containing 20,000–50,000 genes there is currently estimated 50,000 identified metabolites with this number set to rise to 200,000. The current preferred technique for metabolic profiling of plants uses GC–MS and in the seminal manuscript in this area Fiehn et al. [13] quantified 326 distinct compounds in A. thaliana leaf extracts, further elucidating the chemical structure of half of these compounds. As well as Arabidopsis, yeast, the other work horse of functional genomics, has been examined by metabolomics [14]. Yeast was the first eukaryote to be sequenced and mutant strains for the 6000 genes in yeast can be examined from cell banks such as EUROFAN (http://www.unifrankfurt.de/fb15/mikro/euroscarf/). This suggests that researchers could potentially phenotype all the genes in yeast, having a significant impact on human disease through comparison of gene sequence similarities in terms of the metabolic consequences produced in the mutants. The standard method to phenotype yeast strains is to see how rapidly a strain grows on a given substrate mixture. If the mutation does not alter the rate of growth it is said to be a silent mutation, and thus no function can be deduced from this gene deletion. However, Raamsdonk et al. [14]
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Table 1. Technology comparison summary table for metabonomics GC–MS
LC–MS
FT-IR spectroscopy
Pros
Cheap after initial magnet purchase, robust and reproducible on a per sample basis, minimal sample preparation, high-throughput.
Good sensitivity. Cheap to purchase. Good chromatography by comparison with LC–MS. Good identification software.
Excellent sensitivity. No need to derivatise samples prior to analysis. More truly global than NMR and GC–MS (?). Can be used to analyse specific metabolites as well as produce global profiles.
Cheap and easy to use.
Cons
Only detects the highest concentration of metabolites. Significant metabolite overlap in simple 1H NMR spectra. Large initial purchase of spectrometer and superconducting magnet.
Need to derivatise the metabolites to ensure they are volatile.
Ion suppression can prevent quantitation or even detection of certain metabolites. LC reproducibility is lower than GC.
Very poor distinction between classes of metabolites.
Companies and related websites
Bruker BioSpin (http://www.brukerbiospin.com); Varian Inc (http://www.varianinc.com/cgi-bin/nav? products/nmr/index&cid=NOIPKOHFIH); Jeol (http://www.jeoleuro.com/)
Agilent (http://www.chem.agilent.com/ Scripts/PCol.asp?lPage=180); Thermo Electron (http://www.thermo.com/com/ CDA/Category/CategoryFrames/ 1,2213,184,00.html); Shimadzu (http://www1.shimadzu.com/products/ lab/ms.html); Varian (http://www.varianinc.com)
Agilent (http://www.chem.agilent.com/ Scripts/PCol.asp?lPage=181); Thermo Electron (http://www.thermo.com/com/CDA/ Category/CategoryFrames/1,2213,264,00.html); Shimadzu (http://www1.shimadzu.com/products/ lab/ms.html); Varian (http://www.varianinc.com); Waters (http://www.waters.com/watersdivision/ Contentd.asp?ref=CEAN-5KUSS8); Bruker Daltonics (http://www.bdal.com/)
Varian (http://www.varianinc.com); Shimadzu (http://www1.shimadzu. com/products/lab/ms.html); Perkin Elmer Inc (http://www.perkinelmer.com/peaitran.nsf/pkihome)
References
[1,4,14]
[3,13]
[26]
A brief summary of some of the technologies currently being used in metabonomics. The list of manufacturers is not a definitive list, but a guide for those interested in pursuing the area further. www.drugdiscoverytoday.com
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NMR spectroscopy
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Figure 2. Multivariate statistics: pattern recognition tools are a vital part of the process of metabolomics, and are increasingly being used to fully analyse the large multivariate data sets that are produced by other ‘-omic’ technologies. To investigate the innate variation in a data set in an unsupervised manner, techniques such as principal components analysis (PCA; see figure) or hierarchical cluster analysis (HCA) have been applied. Multivariate statistics such as PCA allows the interrogation of large data sets with both many variables and multiple samples (a) PCA can be used to investigate the variation across the variables to produce loading scores and variation across samples to produce a scores plot (b) The process involves the mapping of a given sample according to the values of the variables measured. (c) Shows the mapping of a sample representing three variables. If this is repeated for all the variables, correlates can be investigated. (d) The most amount of correlated variation is found along Principal Component (PC) 1 and the second most amount of variation is found along PC2. This is repeated until the variation in the data set is described by new latent variables represented by PCs. (e) Shows an example of PCA plot that results from such a process; (figure supplied by Dr Henrik Antii, Umea University).
have used 1H NMR-based metabonomics to distinguish these silent phenotypes. Applying a combination of PCA and discriminate function analysis (DFA) they were able to co-clutser strains with deletions of similar genes together. This included one cluster consisting of mutants related to oxidative phosphorylation and another 288
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cluster involving 6-phosphofructo-2-kinase. Since this original paper a range of analytical techniques have been used to further characterise yeast mutants including LC–MS of the cell extracts and MS analysis of the yeast media, with this latter approach being referred to as ‘metabolic footprinting’ [15].
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cing the need for angiography, currently the gold standard for CAD diagnosis (Fig. 3). This approach has also been used to correlate blood pressure with the 1H NMR-derived metabolic profile of blood plasma [20]. This study has since been extended to include results from transcriptional analysis using DNA microarrays, in the hope that a joint transcriptional and metabonomic study of blood sera could compete with the 99% correct prediction of angiography.
Metabonomics and transcriptomics
Figure 3. Applications of metabonomics to human disease. Both NMR and mass spectrometry-based metabolic profiling allows the highthroughput acquisition of large data sets. This figure shows an application to human disease where the NMR spectral profiles of blood plasma were used to distinguish both the occurrence and severity of coronary artery disease. The supervised pattern recognition technique Partial least squares discriminate analysis (PLS-DA) successfully separated patients without coronary artery disease (NCA) from those with triple vessel disease (TVD). Key: NCA, red triangles; TVD, blue squares. Adapted from [16] with permission.
Metabonomics and disease A key advantage of NMR spectroscopy-based metabonomics is that the approach is high-throughput, allowing the rapid acquisition of large data sets. This makes it ideal as a screening tool, particularly for human populations where there can be significant environmental and dietary influences on tissue and biofluid ‘metabolomes’. A range of diseases have been investigated including Duchenne muscular dystrophy, multiple sclerosis, cancer and schizophrenia (e.g. [10,16–18]). As metabonomics makes no prior assumption as to the metabolic events that accompany a disease, it is particularly appropriate for diseases where conventional approaches have to date drawn a blank. NMR-based metabonomics has also been used for screening human populations for both the presence and severity of coronary artery disease (CAD) using blood plasma [19]. To reduce the variation in the data set not correlated with disease presence or severity OSC was used as a data filter to subtract variation orthogonal to that associated with disease severity. The resultant pre-processed data was then analysed using PLS-discriminate analysis to produce a pattern recognition model that was greater than 90% accurate in predicting disease severity. Such an intelligent pattern recognition model could produce significant financial savings by redu-
There is currently a great deal of interest in toxicology studies in combining the high-throughput screening approach of metabonomics, with the potentially more truly global profiling capability of DNA microarrays. This has led to several studies where the two approaches have been used to build up both mRNA and metabolite descriptions of drug induced pathology simultaneously. One such study has examined orotic acid induced fatty liver in the rat [21]. Supplementation of orotic acid to normal food intake is known to induce fatty liver in the rat, producing symptoms very similar to alcohol induced fatty liver disease. Although it has been known since the 1950s that disruption of the various Apo proteins occurs during orotic acid exposure it is still not known how the disruption of nucleotide metabolism, the primary effect of orotic acid, results in the impaired production of the various Apo proteins important for the transport of lipids around the body. To investigate this, Griffin et al. [21] applied a transcriptomic and metabonomic analysis to the Kyoto and Wistar strains of rat. The Wistar rat is an out-bred strain of rat and has been classically used to follow orotic acid induced fatty liver disease. However, the Kyoto rat, an in-bred strain, is particularly susceptible to fatty liver disease. These two strains provided a pharmacogenomic model of the drug insult, and illustrate a common problem with the development of many drugs that the induced response can vary depending on the population it is administered to. To fully characterise the systemic changes, as well as analysing the metabolite and mRNA composition of the liver, blood and urine was also analysed using NMR spectroscopy. Analysis of blood plasma demonstrated the expected decrease in circulating low-density lipids (LDL) and very low-density lipids (VLDL) lipids, demonstrating the disruption of ApoB and ApoC production, but also identified a blood plasma increase in bhydroxybutyrate, suggesting diabetes-like response to the lesion. The use of NMR-based metabonomics to follow systemic metabolism was possible because of the relative cheapness of the approach on a per sample basis. This ensured that some information was obtained outside the liver, despite transcriptional analysis being confined to the liver because of cost (Fig. 4). Using PLS to cross correlate transcriptional and metabonomic data in the liver, both data sets identified pathways concerning uridine production, choline turnover, and stress www.drugdiscoverytoday.com
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Figure 4. An application of metabonomics and transcriptomics to understanding fatty liver disease in the rat. In this study, the metabolic consequences of exposing two strains of rats to orotic acid were investigated using 1H NMR spectroscopy and DNA microarray technology. Using high-resolution magic angle spinning (HRMAS) 1H NMR spectroscopy (a), the liver tissue from Kyoto rats were found to be distinct from that obtained from Wistar rats using Principal components analysis to investigate the spectra (b). This was caused in part by increased lipid content in the liver tissue from Kyoto rats. Orotic acid induced fatty liver in both strains of rats. (c) Shows an electron micrograph of a section of fatty liver with clear droplets of fat visible in the tissue (right) compared with a piece of control tissue. This effect was far more pronounced in the Kyoto rat demonstrated the different pharmacokinetics of the drug in the two strains. Key: PC, principal component; PtdChol, Phosphatidyl Choline.
responses as being perturbed by the drug. By careful analysis of these pathways it was possible to trace the metabolic perturbations from the initial exposure to orotic acid to disruption of fatty acid metabolism in the liver (Fig. 3). Furthermore, the approach modelled the pharmacogenomics of the drug, showing that the metabolome of the Kyoto rat was more profoundly influenced by orotic acid. Transcriptional analysis, using real time (RT)-PCR, has also been used to assist in unravelling the metabolic changes detected by a urinary metabonomic study of peroxisome proliferation in the rat [22]. In this study, Ringeissen et al. found that peroxisome proliferator-activated receptor (PPAR) ligands induced large increases in urinary N-methylnicotinamide (NMN) and N-methyl-4-pyridone-3-carboxamide (4PY) concentrations, intermediates in the Tryptophan-NAD+ 290
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pathway. Furthermore, these biomarkers were shown to correlate with peroxisome proliferation as measured by electron microscopy, suggesting that NMN and 4-PY could be used as biomarkers for peroxisome proliferation in cases where the biopsy of liver tissue was not possible. This has great relevance to the drug safety and assessment as the PPAR ligands are currently being investigated as drugs for treating dyslipidaemia and type 2 diabetes, but there is no current clinical method for assessing peroxisome proliferation, a potential side effect in rodents, in humans. RT-PCR of key enzymes also identified transcriptional changes involved in TryptophanNAD+ pathway, indicating that both transcriptional and metabonomic analysis of the tissue agreed in terms of the major metabolic pathway targeted. The two biomarkers were then measured by a HPLC assay as part of a high-throughput-
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specific screen for peroxisome proliferation in the rat. This study demonstrates how metabonomics can be used to identify biomarkers, which can be validated through transcriptomics, and these biomarkers can ultimately be used to assess drug toxicity in situations relevant to drug safety and assessment.
Metabonomics and pharmacokinetics Metabonomics also has tremendous promise in determining the pharmacokinetics of a toxic insult or drug response [1]. By using biofluid analysis to derive metabolic profiles a large number of time points can be analysed in a noninvasive manner. This allows the deconvolution of metabolic events which occur on a different temporal scale, providing some insight into the different tissues affected by the toxin. In this manner, Waters and colleagues have used NMR-based metabonomics to monitor metabolite changes in urine and blood plasma, and then cross correlate these changes with specific tissue damage during alphanaphthylisothiocyanate toxicity [23]. In a similar manner, Mortishire-Smith et al. [24] have used HRMAS 1H NMR spectroscopy to analyse metabolite changes in the liver to confirm biofluid metabolite perturbations that accompany impaired fatty acid metabolism in the liver. Furthermore, these researchers were able to further substantiate these correlations using in vitro enzymatic assays. Although there are still relatively few studies involving combined tissue and biofluid analysis, given the potential of metabonomics for understanding phamacodynamics this area of research is set to increase in the future.
Future developments Cryoprobe NMR spectroscopy The primary drive in metabonomics is to improve analytical techniques to provide an ever increasing coverage of the complete metabolome of an organism. To date NMR-based techniques have focused on using 1H NMR spectroscopy, but this approach suffers from a small chemical shift range, producing significant overlap of the resonances of several different metabolites. Although 13C NMR spectroscopy has a much larger chemical shift range, allowing the resolution of a wider range of metabolites the approach is intrinsically less sensitive compared with 1H NMR spectroscopy, as a result of the lower gyromagnetic ratio of the 13C nucleus compared with the 1H. However, in cryoprobes, a NMR probe where the receiver and transmitter coil is cooled using liquid helium, a significant improvement in sensitivity can be achieved by cooling the coil of a NMR probe to 4 K, allowing the rapid acquisition of 13C NMR spectra. Keun et al. [25] have already applied this approach to studying hydrazine toxicity through biofluid 13C NMR spectroscopy. Although in this particular study the biomarkers of hydrazine toxicity were already largely known, the use of 13C spectroscopy did allow
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the identification of these metabolites largely from 1-dimensional spectroscopy, without the need to identify the metabolites responsible for key resonances from a series of two dimensional approaches. Furthermore, this approach can be particularly good for quantifying concentrations of metabolites which only produce singlets in 1H NMR spectra. Although 1H NMR might not conclusively identify the metabolite, the extra chemical shift range for 13C NMR is usually enough to allow unambiguous assignment of a given singlet.
Liquid chromatography NMR spectroscopy Another approach to improve the sensitivity of the NMR experiment is to hyphenate the NMR spectroscopy with liquid chromatography. This improves sensitivity by two mechanisms. Firstly, high and low concentration metabolites are separated by the liquid chromatography, reducing the likelihood of co-resonant peaks and also improving the dynamic range of the NMR experiment for the low concentration metabolites. Secondly, metabolites are concentrated by the chromatography, further aiding the detection of low concentration metabolites. Bailey and co-workers have used this approach of LC-NMR spectroscopy to metabolically profile several plants [26,27]. This can be a particularly useful approach if hyphenated further with cryoprobe technology to allow cryo-LC-NMR.
Mass spectrometry advances LC–MS and GC–MS are increasingly being used as profiling tools for diseases and toxicology studies [28,29]. This is set to increase as the chromatography becomes more reliable, and the software for matching mass fragmentation patterns is improved. Indeed several manufacturers are developing systems which are designed to work in tandem with NMR to provide LC–NMR/MS analysis of biofluids, thus, reaping the benefits of these two technologies while avoiding many of the pitfalls.
High-resolution magic-angle-spinning 1H NMR spectroscopy With all the techniques discussed above there is a need to form tissue extracts if the toxicologist is to examine the metabolomic changes in a tissue directly. However, if highresolution MAS 1H NMR spectroscopy could be automated this would provide a viable alternative to laborious tissue extraction procedures. Finally, there is an urgent need for improved pattern recognition processes for integrating the information produced by a variety of analytical approaches to provide a fuller coverage of the metabolome as well as a means to disseminate this information. Currently, there is no consensus as to what information should be reported alongside a metabonomic study so that the data can be interpreted by other researchers. Such metadata (i.e. data about the data) will be vital if researchers are to generate www.drugdiscoverytoday.com
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Links
Outstanding issues
http://www.metabometrix.com/glossary1.htm (A useful glossary of terms written from a metabolic point of view of functional genomics.) http://www.metabolomics.nl/ (A resource site for plant metabolomics. This site also contains lots of useful links to the wider metabolomic community.) http://www.spectroscopynow.com (A central resource for all types of spectroscopy including NMR and mass spectrometry-based approaches.) http://www.waters.com/WatersDivision/ContentD.asp?ref=PSTD5RQBCK; http://www.corporate-ir.net/ireye/ir_site.zhtml?ticker=brkr& script=412&layout=-6&item_id=502276 (Manufacturer websites for mass spectrometry devoted to high-throughput metabolic profiling and metabolomics.)
How many metabolites must be measured to generate a representative metabolic profile of a tissue, biofluid or cell? How can one reconstruct cellular and organ specific metabolism from the systemic metabolism reported by biofluids such as urine and blood plasma? How can databases be created that are fully inclusive for the range of different analytical techniques and approaches currently being used in metabonomics? What is the minimum information required to report results from a metabonomic experiment?
References databases akin to those being produced by the microarray and proteomic communities.
Conclusions Metabonomics is being performed using a variety of analytical approaches and pattern recognition techniques to answer the questions to a diverse range of problems relevant to the pathologist and toxicologist. At its most immediate level it provides a rapid screen for large scale drug toxicity testing and screening of large human populations for common diseases such as coronary artery disease. These metabolic profiles are also ideal for following changes in transcriptional and proteomic profiles, and there are already several mathematical tools for this sort of data fusion. The high-throughput nature of NMR spectroscopy suggests that it will increasingly be used as a first line screening tool for toxicology. However, this is likely to be assisted by more comprehensive metabolic profiling through mass spectrometrybased approaches. This rapid increase in interest in metabonomics, as well as a need to disseminate information about drug safety or toxicity issues necessitates the development of metabonomic databases and ultimately a standardisation of approaches to provide an equivalent of the transcriptomic MIAME protocol for metabonomics (http://www.mged.org/ Workgroups/MIAME/miame.html).
Related articles Griffin, J.L. (2003) Metabonomics: NMR spectroscopy and pattern recognition analysis of body fluids and tissues for characterisation of xenobiotic toxicity and disease diagnosis. Curr. Opin. Chem. Biol. 7, 648– 654 Kell, D.B. (2004) Metabolomics and systems biology: making sense of the soup. Curr. Opin. Microbiol. 7, 296–307 Lindon, J.C. et al. (2004) Metabonomics technologies and their applications in physiological monitoring, drug safety assessment and disease diagnosis. Biomarkers 9, 1–31 Lindon, J.C. et al. (2003) So what’s the deal with metabonomics? Anal. Chem. 75, 384A–391A Steuer, R. et al. (2003) Interpreting correlations in metabolomic networks. Biochem. Soc. Trans. 31(Pt 6), 1476–1478
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1 Nicholson, J.K. et al. (2002) Metabonomics: a platform for studying drug toxicity and gene function. Nat. Rev. Drug Discov. 1, 153–161 2 Oliver, S.G. (2002) Functional genomics: lessons from yeast. Phil. Trans. R. Soc. Lond. B 357, 17–23 3 Fiehn, O. (2002) Metabolomics – the link between genotypes and phenotypes. Plant Mol. Biol. 48, 155–171 4 Nicholson, J.K. and Wilson, I. (2003) Understanding ‘Global’ systems biology: metabonomics and the continuum of metabolism. Nat. Rev. Drug Discov. 2, 668–676 5 Gavaghan, C.L. et al. (2000) An NMR-based metabonomic approach to investigate the biochemical consequences of genetic strain differences: application to the C57BL10J and Alpk:ApfCD mouse. FEBS Lett. 484, 169–174 6 Beckwith-Hall, B.M. et al. (1998) Nuclear magnetic resonance spectroscopic and principal components analysis investigations into biochemical effects of three model hepatotoxins. Chem. Res. Toxicol. 11, 260–272 7 Holmes, E. et al. (1998) Development of a model for classification of toxininduced lesions using 1H NMR spectroscopy of urine combined with pattern recognition. NMR Biomed. 11, 235–244 8 Garrod, S. et al. (1999) High resolution magic angle spinning 1H NMR spectroscopic studies on intact rat renal cortex and medulla. Magn. Reson. Med. 41, 1108–1118 9 Griffin, J.L. et al. (2000) The biochemical profile of rat testicular tissue as measured by magic angle spinning 1H NMR spectroscopy. FEBS Lett. 478, 147–150 10 Griffin, J.L. et al. (2003) Assignment of 1H nuclear magnetic resonance visible polyunsaturated fatty acids in BT4C gliomas undergoing ganciclovir-thymidine kinase gene therapy-induced programmed cell death. Cancer Res. 63, 3195–3201 11 Lindon, J.C. et al. (2001) Pattern recognition methods and applications in biomedical magnetic resonance. Prog. Nucl. Magn. Reson. 39, 1–40 12 Valafar, F. (2002) Pattern recognition techniques in microarray data analysis. Ann. N.Y. Acad. Sci. 980, 41–64 13 Fiehn, O. et al. (2000) Metabolite profiling for plant functional genomics. Nat. Biotechnol. 18, 1157–1161 14 Raamsdonk, L.M. et al. (2001) A functional genomics strategy that uses metabolome data to reveal the phenotype of silent mutations. Nat. Biotechnol. 19, 45–50 15 Allen, J. et al. (2003) High-throughput classification of yeast mutants for functional genomics using metabolic footprinting. Nat. Biotechnol. 21, 692–696 16 Griffin, J.L. et al. (2002) Metabolic profiles of dystrophin and utrophin expression in mouse models of duchenne muscular dystrophy. FEBS Lett. 530, 109–116 17 Griffin, J.L. and Shockcor, J.P. (2004) Metabolic profiles of cancer cells. Nat. Rev. Cancer 4, 551–561 18 Prabakaran, S. et al. (2004) Mitochondrial dysfunction in Schizophrenia: evidence for compromised brain metabolism and oxidative stress. Mol. Psychiatry 9, 684–697 19 Brindle, J.T. et al. (2002) Rapid and non-invasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics. Nat. Med. 8, 1439–1444
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20 Brindle, J.T. et al. (2003) Application of chemometrics to 1H NMR spectroscopic data to investigate a relationship between human serum metabolic profiles and hypertension. Analyst 128, 32–36 21 Griffin, J.L. et al. (2004) An integrated reverse functional genomic and metabolic approach to understanding orotic acid-induced fatty liver. Physiol. Genomics 17, 140–149 22 Ringeissen, S. et al. (2004) Potential urinary and plasma biomarkers of peroxisome proliferation in the rat. Biomarkers 8, 240–271 23 Waters, N.J. et al. (2001) NMR and pattern recognition studies on the timerelated metabolic effects of alpha-naphthylisothiocyanate on liver, urine, and plasma in the rat: an integrative metabonomic approach. Chem. Res. Toxicol. 14, 1401–1412 24 Mortishire-Smith, R.J. et al. (2004) Use of metabonomics to identify impaired fatty acid metabolism as the mechanism of a drug-induced toxicity. Chem. Res. Toxicol. 17, 165–173
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25 Keun, H.C. et al. (2002) Crogenic probe 13C NMR spectroscopy of urine for metabonomic studies. Anal. Chem. 74, 4588–4593 26 Bailey, N.J. et al. (2003) Metabolomic analysis of the consequences of cadmium exposure in Silene cucubalus cell cultures via 1H NMR spectroscopy and chemometrics. Phytochemistry 62, 851–858 27 Bailey, N.J. et al. (2002) Multi-component metabolic classification of commercial feverfew preparations via high-field 1H-NMR spectroscopy and chemometrics. Planta Med. 68, 734–738 28 Plumb, R.S. et al. (2002) Metabonomics: the use of electrospray mass spectrometry coupled to reversed-phase liquid chromatography shows potential for the screening of rat urine in drug development. Rapid Commun. Mass Spectrom. 16, 1991–1996 29 Plumb, R.S. et al. (2003) Use of LC/TOF mass spectrometry and multivariate statistical analysis shows promise for the detection of drug metabolites in biological fluids. Rapid Commun. Mass Spectrom. 17, 2632–2638
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