Analytical Strategies in Metabonomics - Journal of Proteome

Analytical Strategies in Metabonomics Eva Maria Lenz and Ian D. Wilson* Department of Drug Metabolism and Pharmacokinetics, AstraZeneca, Mereside, Alderley Park, Cheshire SK10 4TG, United Kingdom Received October 4, 2006

To perform metabonomics investigations, it is necessary to generate comprehensive metabolite profiles for complex samples such as biofluids and tissue/tissue extracts. Analytical technologies that can be used to achieve this aim are constantly evolving, and new developments are changing the way in which such profiles’ metabolite profiles can be generated. Here, the utility of various analytical techniques for global metabolite profiling, such as, e.g., 1H NMR, MS, HPLC-MS, and GC-MS, are explored and compared. Keywords: global metabolite profiling • metabotyping • metabonomics • metabolomics • spectroscopic analysis • separations • hyphenation

Introduction Metabonomics, defined as the “the quantitative measurement of the dynamic multiparametric response of a living system to pathophysiological stimuli or genetic modification”,1,2 depends on the ability of the investigator to determine changes in an organism’s complement of low molecular weight organic metabolites in biofluids and organs. The overriding need is therefore for analytical methods that can produce comprehensive global metabolite profiles from complex biological samples. As such, an ideal technique enabling analysis to be performed directly on the samples, without the need for sample preprocessing should be high throughput, unbiased with respect to particular classes of metabolites, would be both highly sensitive and equally sensitive to all the components in the sample, robust, reproducible, and have a wide dynamic range. In an ideal world, all of these desirable characteristics would also be combined with sufficiently high information content to enable the identification of the key metabolites identified via the post analysis multivariate statistical analysis of the data. A moments thought will be all that is required for the unbiased analyst to realize that there is no technique currently available that can provide all of the desired properties. Consequently, there is a general move toward metabolic profiling via multiple analytical platforms to maximize their coverage of the metabolic profile. At the time of writing, analysis for metabonomics is performed using spectroscopic techniques such as 1H NMR spectroscopy, direct infusion MS, FT-IR, and separations-based techniques such as gas, liquid, and thin-layer chromatography (GC, LC, and TLC), or capillary electrophoresis (CE) often, but not exclusively, with mass spectrometry (MS) as a means of detection and identification. These topics have been described in detail in a variety of reviews, many of which are referenced below and have also recently been the subject of a number of books.3,4 * To whom correspondence should be addressed. Tel: 00 44 1625 513424; Fax: 00 44 1625 516962; E-mail: [email protected]. 10.1021/pr0605217 CCC: $37.00

 2007 American Chemical Society

Here we describe the current state of the art for generating global metabolite profiles, based on the use of information rich spectroscopic techniques, with a commentary on the relative strengths and weaknesses of the individual technologies and strategies for their use.

Spectroscopic Techniques Metabonomics by NMR Spectroscopy. Metabonomics by 1H NMR spectroscopy has evolved from early investigations of biofluid composition using the technique with examples of biofluid NMR dating back to the 1980s.5-10 Since these early investigations, a large number of applications in metabonomics have appeared. Indeed, 1H NMR spectroscopy, in particular of urine, has been the analytical workhorse for metabonomic research. One reason for this is that NMR spectroscopy is an acclaimed technique for providing detailed structural information of small organic molecules and, as such, has enabled a large number of biofluid constituents to be identified and catalogued/listed.11-13 As such, 1H NMR spectroscopy has been employed to investigate changes in biochemical composition prior to and after treatment with a drug or toxin, or to monitor natural processes such as aging and disease progression or recovery in longitudinal studies, as it allows perturbations of the concentrations of endogenous metabolites to be detected. The use of high-resolution NMR spectroscopy (e.g., field strengths of 400 MHz and higher) ensures maximum signal dispersion and sensitivity. When matched against the criteria of an ideal analytical tool for metabonomic investigations, it can be seen that it does have many of the required characteristics. Thus, it requires little or no sample preparation, is costeffective, unbiased, rapid, robust and reproducible, quantitative, nonselective and nondestructive, and although not as sensitive as other techniques, such as MS, useful data can (still) be generated from small samples. Typically, biofluids such as urine, bile, and blood plasma have been investigated, but also tissue extracts (e.g., refs 14 and 15) and studies, often involving small samples, on more obscure biofluids such as cerebrospinal Journal of Proteome Research 2007, 6, 443-458

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Figure 1. 1H NMR spectrum (500 MHz) of a human urine sample freeze-dried and reconstituted in D2O (a) prior to and (b) after storage at room temperature overnight. Loss of creatinine signal was observed due to deuteration on storage.

fluid (CFS), seminal fluid, saliva, and cyst fluid have also been reported.16-31 More recently, intact tissues have been analyzed using magic-angle spinning spectroscopy (MAS) (discussed in more detail later). The following sections describe and discuss the stages of sampling, sample preparation, and data analysis/generation for metabonomic studies by NMR spectroscopy. A number of comprehensive and detailed reviews of applications in the field are available and should be consulted.32-39 1H NMR Spectroscopy. The largest body of work has been produced by 1H NMR spectroscopic investigations of biofluids, such as urine and plasma of rodent and human origin. Highresolution 1H NMR spectroscopy has proven to be a very powerful technology for biofluid investigations, capable of producing comprehensive and diagnostic biofluid profiles without the need for the preselection of analytical parameters or sample derivatization procedures. In rodent toxicity studies, in particular, 1H NMR spectroscopy-based urinalysis has enabled distinct, organ-specific perturbations of metabolic profiles to be identified and defined following the administration of model toxins.1,2,34,35 In humans, characteristic perturbations in the urinary metabolite profile have been identified reflecting inborn errors of metabolism,40 and extensive research is ongoing to identify biomarkers of disease, disease state, and drug efficacy.

The advantages of urinalysis are numerous, especially sample collection is noninvasive, allowing longitudinal studies to be carried out. This has the distinct advantage that, especially in rodent toxicity studies, the animals can provide their own predose control sample and, hence, effects prior to and post dosing can be monitored effectively. Urine samples can be pooled, hence averaging variability (diurnal variation, exercise, etc), which is a further advantage, as spot-samples can be highly variable. Collection of plasma or CSF, or indeed tissue, is invasive and multiple sampling during longitudinal studies may not be possible. However, disease progression may still be monitored as the sample provides a snapshot of the 444

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metabolites at the time of sampling (see biological variability for further discussion). Sample preparation for NMR spectroscopy is generally minimal though a standard universal protocol is not available, and procedures vary depending on the nature of the study and the laboratory. Generally, a small amount of buffer (0.2 M phosphate buffer/ D2O) is added to urine samples to eliminate signal shifts based on differences in sample pH and provide a field frequency lock for the spectrometer to control magnetic shift drifts. Plasma and serum samples are generally diluted with deionized water, saline (0.9% saline), or buffer (0.1 M phosphate) prior to the addition of a small amount of D2O. Some sample preparation of plasma or serum, such as protein precipitation or ultrafiltration,41-43 has also been reported, although usually the protein signals are edited out spectroscopically. Tissues can be extracted into D2O, buffer or deuterated organic solvents, such as methanol or acetonitrile. The samples are generally centrifuged to remove any particulate matter prior to NMR analyses. The samples are then introduced into the NMR spectrometer via tubes (typically 1-5 mm in diameter for sample volumes as small as several µL to 0.5 mL) or via a flow-injection system. As NMR spectroscopic measurements are robust, the measurement of, e.g., the urinary metabolite profile is expected to be reproducible. However, profile changes due to sample aging (queuing in the sample rack/post sample preparation storage etc.) can result in selective deuteration of some signals. This effect is most noticeable with creatinine where the singlet at 4.06 ppm can become greatly attenuated (based on keto-enol tautomerism) Figure 1. Decomposition of the sample due to microbial fermentation can result in increased concentrations of acetate, formate, and in severe cases, ethanol (ref 44, unpublished observations). Even queuing of the samples at refrigerated temperatures can lead to some degradation; hence, it is advisable to use an antibacterial agent, such as sodium azide, randomize or sequentially analyze a number of small batches so that there is a relatively short time between thawing and analysis.

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Figure 2. 1H NMR spectra (800 MHz) displaying the characteristic fingerprints of different biofluids. (Reproduced with permission from ref 40.)

Water Suppression. 1H NMR analyses of biofluids, in particular, urine, requires the attenuation of the dominant signal for the water protons (present in >100 M concentration), which not only obscures a large section of the spectrum (approximately 4.7-4.9 ppm) if unsuppressed, but can also cause dynamic range problems. Generally, water suppression is achieved by the application of a suitable water suppression pulse sequence (such a simple presaturation, NOESYPRESAT or Watergate sequences), which must be kept consistent throughout the studies. As an alternative to water suppression, freeze-drying (lyophilization) of the urine can be performed, followed by reconstitution in a smaller volume can be used to concentrate dilute samples, thereby reducing analysis time. This, however, includes the risk of losing volatile components, such as acetone or ethanol, and can accelerate some deuterium exchange, and hence loss of signal, especially when the sample is reconstituted in a solvent rich in D2O45 Experimental Aspects of the NMR Spectroscopy of Biofluids. 1H is the most commonly used nucleus for NMR spectroscopic studies on biofluids, as it is ubiquitous in organic molecules, has a natural abundance of 99.98% and gives the highest relative sensitivity of all naturally occurring spin-active nuclei. The signals that are typically observed in the 1H NMR spectrum are from the protons of aromatic, methine, methylene, and methyl groups, (whereas exchangeable protons from NH2, NH, COOH, SH, CONH, and OH groups are generally not observed). These signals provide information on the chemical structure, the chemical environment, the dynamic molecular motions and molecular interactions of the molecules to which

they are attached and as such 1H NMR spectroscopy has proved to be a useful method for the structural characterization/ identification of metabolites in clinical and toxicological research since the earliest applications of the technique to biofluids (e.g., see refs 32, 35, 46-49). 1H NMR measurements can be made rapidly (a few minutes per sample for urine) giving it the potential for a moderately high throughput technique and representative 800 MHz 1H NMR spectra of a range of control human biofluid samples are shown in Figure 2. Clearly, once the water signal has been suppressed, a large number of endogenous metabolites are detected in the urine sample and problems of signal overlap are typically encountered in one-dimensional (1-D) spectra, due to the narrow chemical shift range (approximately 10-12 ppm), especially on “low-medium” field spectrometers (e.g., 4.7-9.4 T corresponding to resonance frequencies of 200-400 MHz). Higher field instruments (such as 18.8 T with a NMR frequency of 800 MHz) provide greater signal dispersion but also contribute to an increased spectral-complexity as a consequence of enhanced sensitivity. There are >5000 resolved lines (from metabolites ranging from pM-mM concentrations) in single pulse 750 or 800 MHz 1H NMR spectra of normal human urine, and there is still extensive peak overlap in certain chemical shift ranges reflecting the high biochemical information content of 1H NMR spectra of biofluids (Figure 3). 1H NMR spectral assignments of biological materials can be a complex procedure and are generally based on the chemical shifts of the signals and their relative intensities, the pH dependencies of the chemical shifts, signal multiplicities of the Journal of Proteome Research • Vol. 6, No. 2, 2007 445

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Figure 3. 1H NMR spectrum (800 MHz) of the aliphatic region of a human urine sample. (Reproduced with permission from ref 40.) The broad singlets at δ1H 0.735 and 0.725 represent the axial methyl groups on the cholesterol backbone of bile acids of which ca. 8 µmol/day are excreted into urine by man.11

proton resonances, and coupling constants (usually in comparison to the known chemical shifts and couplings of standards) to provide structure specific information for individual analytes. Alternatively, confirmation of the presence of a suspected metabolite can be achieved by addition of a standard compound and observing an increased signal intensity of the corresponding resonances in the NMR spectrum. Two-dimensional (2-D) NMR experiments (e.g., 1H-1H COSY, TOCSY, JRES) may also prove useful for assignment and spectral simplification purposes. Additionally, as NMR spectroscopy is nondestructive, the sample can be subjected to further experiments in order to identify specific metabolites. Off-line chromatographic procedures, such as solid-phase extraction chromatography(SPEC)oron-linehighperformanceliquidchromatography methods (HPLC-NMR, HPLC-NMR-MS) can be employed to clean up or isolate and identify metabolite structures36,47 as discussed in more detail later. Whereas urine can be analyzed rapidly using single pulse experiments, plasma and serum, on the other hand, contain a 446


combination of both macromolecules (proteins, lipoproteins), lipids and low molecular mass metabolites (such as glucose, lactate, amino acids, etc.). The overall result is an unresolved broad envelope of resonances overlaying the reasonably sharp resonances of the smaller analytes.13 Although all three classes of molecule can be of interest, it is useful to be able to attenuate the broader signals of the macromolecules and lipids to observe the sharper resonances, which are otherwise partially obscured. As a consequence, much effort has been invested in the assessment of different sample preparation versus spectroscopic editing techniques to resolve the different classes.41-43,50 It appears that, ideally, plasma and serum samples require a minimum of four separate experiments, as advised by the COMET Consortium,51 for their characterization. The array of experiments comprises first the simple water suppressed spectrum displaying both the macromolecular resonance envelope and the partially obscured sharp signals from the small molecular weight metabolites. Then, the Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence is employed to attenuate


the resonances resulting from the macromolecules while retaining those from the smaller molecular weight metabolites, based on their different relaxation properties. Following these two experiments, J-resolved spectroscopy can be employed, utilizing the 1-D projection from the generated 2-D-matrix comprising the NMR chemicals shifts versus the homonuclear spin-coupling constants in the orthogonal dimension.13 Last, diffusion-edited experiments (DOSY, diffusion-ordered spectroscopy) have been assessed and applied to selectively show peaks only from slowly moving (i.e., larger) molecules based on differences in diffusion coefficients and spin relaxation times.43,50 1H

NMR spectra with good signal-to-noise ratios can be easily obtained with biofluid samples, such as rat and human urine and plasma, as relatively large sample volumes (generally provided in millilitre quantities) are provided for the analyst. In the case of smaller samples, such as, e.g., mouse urines (often provided in microlitre quantities), it is advisable to reduce the size of the NMR tube (e.g., 2.5-3 mm o.d.) to avoid unnecessary dilution of the sample and to ensure sufficiently good spectral quality. Sensitivity issues are a particular issue with precious samples that can only be obtained in very small volumes, such as rodent CSF, which is usually available in low microlitre quantities. Successful investigation of rat CSF has been achieved utilizing a 1 mm microlitre probe (Bruker Biospin Ltd.) for the analysis of only 2 µL of CSF (diluted to a total volume of 5 µL), a process that would allow multiple sampling and hence longitudinal studies on the same animal.52 Similarly, ultising a nanoprobe (Varian, CA), a CSF sample of 20 µL, generated by in vivo microdialysis, in a study of brain neurochemistry, was investigated.38,53 Recent developments in NMR spectrometer hardware, such as the cryoprobes (Bruker Biospin Ltd) and cold-probes (Varian), can significantly boost sensitivity and reduce analysis time, by eliminating the electronic noise through operating at liquid helium temperatures. A sensitivity gain (S/N gain) of approximately 4-fold is achieved. This methodology is becoming more widely applied to routine 1H NMR investigations in the pharmaceutical industry (in the area of chemical structure elucidation, impurity and metabolite identification) and also metabonomic screening. As well as benefits to small sample analysis this technology has enabled other, less sensitive, nuclei to be investigated, such as 13C. The inherently low sensitivity of 13C NMR is due to its low natural abundance (1.1% that of 1H) and gyromagnetic ratio and as a consequence routine analyses of biological samples has been somewhat curtailed. However, 13C NMR spectroscopy benefits from a larger chemical shift range (approximately 250 ppm) compared to 1H (approximately 10 ppm), resulting in greater signal dispersion and less peak overlap, especially once proton decoupling has been applied, eliminating peak splitting/ multiplicities. Additionally, there is no need for solvent suppression especially when aqueous samples/extracts are being investigated. Despite these advantages, the difficulties of working with such a low abundance nucleus have ensured that there have been few applications in metabonomics. When employing the cryoprobe technology, for a comparable S/N, a significant reduction in acquisition time has been reported in a 13C NMR (metabonomic) study on urine, enabling 2-dimensional experiments (e.g., 1H-13C HSQC) on biological fluids.54 Generally, literature examples employ 13C-enrichment, to either follow the metabolic flux of a molecule, such as 13C enriched glucose, following its incorporation into other endogenous metabolites55

reviews or to study the metabolism of labeled xenobiotics, with known toxicity, in the urine of rodents.56,57 Applications employing 13C NMR are more widely found in the field of magnetic resonance imaging, namely magnetic resonance spectroscopy (MRS), for example, for the investigation of glycogen/glucose metabolism, usually with 13C-enriched glucose. Other nuclei employed in in vivo biological investigations include 31P (usually to study energy metabolism) by monitoring biological phosphates5 and 1H for the investigation of lipid profile differences in diabetes research, but the applications are generally limited due to low sensitivity and poor resolution compared to liquid-state NMR. Tissue Extraction and MAS NMR. An area that has seen a large increase in the number of reported applications is that of tissue analysis. Traditionally, tissue analysis required sample extraction prior to analysis by conventional liquid-state NMR spectroscopy. This has highlighted interesting differences between different types of tissues,14 however, extraction processes can be selective (e.g., in the case of aqueous or perchlorate extracts) leading to loss of certain tissue components such as lipids or proteins. Alternatively, in vivo approaches such as MRS on intact tissues are prone to poor sensitivity and signal dispersion based on the heterogeneity of the sample and, typically, the low magnetic fields employed. Hence, the development of high resolution 1H MAS NMR spectroscopy has had a substantial impact in extending metabonomics profiling, enabling the analysis of ex-vivo intact tissues.58-67 Rapid spinning of the sample (typically at ca. 4-6 kHz) at the magic angle (an angle of 54.78° relative to the applied magnetic field) reduces the line broadening effects due to magnetic field inhomogeneity caused by sample heterogeneity, dipolar couplings, and chemical shift anisotropy36 and gives rise to “solution like” spectra. The sensitivity of the technique is also good such that high quality NMR spectra of whole tissue samples can be obtained with as little as 20 mg of material and virtually no sample pretreatment. The 1H NMR MAS spectra gives rise to distinctly different profiles characteristic of the tissue under investigation (Figure 4). Hence, similar to solution state NMR, differences in tissue profiles are expected and observed following toxic-insult compared to healthy tissue (e.g., refs 58 and 61). Furthermore, MAS NMR spectroscopy has been shown to provide information regarding the compartmentalization of metabolites within cellular environments, shedding light on molecular dynamics (e.g., refs 63 and 67). The obvious disadvantage of tissue analysis by MAS NMR is clearly the limitations in “sampling”, as biopsy samples are not necessarily readily obtained, may be unrepresentative of the tissue as a whole, and only provide a snapshot of the organism/ organ at the time of sampling. Realistically, however, this technology provides the link between the biofluid investigations, i.e., the tissue damage itself and the results from histopathological findings and other omic data (e.g., refs 68 and 69). Sources of Variability. Variability, both instrumental and biological is another major factor that needs to be considered in any metabonomics study. Unlike some of the other analytical techniques used for metabolite profiling it is arguable that the biggest challenge facing the investigator in NMR-based studies is not the analysis itself, which has been shown to be robust and reproducible, but the reduction of the biological variation of the samples themselves. Thus, although differences in “spectrometer output” have been reported in the literature, based on differences in solvent suppression or inter-spectromJournal of Proteome Research • Vol. 6, No. 2, 2007 447

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Figure 4. High-resolution MAS 1H NMR spectra (400 MHz CPMG spin-echo spectra, spun at 4.2 kHz) highlighting the characteristically different profiles obtained from different rat tissues investigated. (Reproduced with permission from ref 36.)

eter variations (refs 70, 71, unpublished observations), these differences are generally minor compared to the effects caused by, e.g., toxicological effects (refs 38, 51, 72, 73, unpublished observations). The largest inherent biological variability is to be expected (and is indeed found) in human studies. Studies concerning/ involving rodents enable the setup/design/protocol to be more tightly controlled. Hence, factors such as strain, gender, diet, and environment (metabowl-housing) can be monitored and controlled, allowing “normal variation” to be established. However, even in such tightly controlled environments, variation in normal rat strains has been observed.74 Much effort has been invested in metabonomic data processing to counteract the variation in differences in sample concentration and chemical shifts (e.g., refs 75 and 76). Thus, in 1H NMR studies, the observation (and classification) of rodent strain differences is well established in metabonomics studies,77 however, sources of further variation (even within a strain) comprise time of sampling (diurnal variation) (ref 78 and Figure 5), gender, oestrus cycle,37,79,80 diet,81,82 metabowl-stress, body weightloss,83,84 and the choice of dosing vehicles.85 Determining, and minimizing, the normal variation is crucial for every study, irrespective of the analytical technique subsequently used for analysis. Hence, for rodent studies, a suitable metabowl acclimatization period must be granted (recommended to be least 3 days,86). Additionally, the importance of differences in urinary metabolic profiles due to changes in gut microflora has also been recognized87-90 (Figure 6). However, above all, especially in longitudinal studies, the effect of aging is very important, as the biofluid profile can change significantly over the duration of a toxicological study (refs 91-93, unpublished observations). This issue is of great importance so as not to confuse biomarkers of aging with those of toxicity or disease. Generally, great care must be taken in the study design for the successful interpretation of biomarkers of the resulting data and rigorous protocols must be employed ensuring reproducible sampling coupled with and a suitable number of replicates per group. In longitudinal studies, in particular, although each experi448


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mental animal will provide its own predose sample, control groups, running in parallel, are essential. In investigations in humans study design is often further complicated by lack of control over diet, lifestyle, age, and time of sampling.94-98 Furthermore additional information on disease state (multiple diseases), information on co-administered medication and the pharmacological effects of medication can further complicate the data. Every effort must be made to exclude signals from excreted medication (drug metabolites) and “food” from the statistical analysis (e.g., the resonances typically due to ethanol, mannitol, paracetamol, etc.). Dietary control should be put in place, where possible, or a detailed account of diet, life-style, and co-administered medication and time of sampling should be made available in clinical studies. Ideally, a suitable matched control-group should be provided in order to allow the metabonomic data to be used for diagnostic purposes. As a simplistic model, to avoid excessive dietary influences, humans could be asked to fast prior to sample collection. However, studies on the same subjects have shown that, despite tight dietary and life-style control, differences in the 1H NMR-derived urinary and plasma profiles can arise.97 Direct Infusion MS. The direct infusion of biofluids or tissue extracts has a particular attraction in terms of the potential for high throughput, and this sort of strategy has been used in, e.g., metabolomic studies on plant and microbial studies (reviewed in ref 38). It is however, difficult to advocate this technique for complex, and highly variable samples such as urine where matrix effects, and differences in salt concentrations etc., have the potential to adversely affect ionization and thus the result. The technique also has obvious disadvantages when isobaric substances are present. FT-IR. There have been limited reports of the use of vibrational spectroscopic techniques in metabonomics, though there are more reports of the use of this sort of technology in metabolomic applications (reviewed in ref 38). The particular advantage of, e.g., FT-IR is the speed with which the speactra can be acquired (5-10 s/sample) and a recent study has applied this methodology to the study of a model of idiosyncratic toxicity in the rat.99 In this study FT-IR combined with genetic programming was able to discriminate between control, ranitidine treated and ranitidine + bacterial lipopolysaccharide treated groups of animals. The nonselective nature of IR spectroscopy is a considerable advantage and is similar to that of NMR spectroscopy in that there is no need to preselect analyte classes prior to analysis. However, although potentially valuable for screening, there is obviously less information content present in the spectrum to aid in biomarker identification, as opposed to showing differences between classes.

MS-Combined With Separations For Metabolic Profiling GC-MS. Gas chromatography (GC) and more recently GC×GC (see later), performed using high-resolution capillary columns when combined with MS detection provides an excellent system for performing global metabolic profiling. To date, GCMS has largely been used to obtain metabolite profile from plants and microorganisms but applications are now emerging to samples obtained from mammals, including man. The high resolution of GC, combined with the MS detection (using either electron impact (EI) or chemical ionization (CI)) provides a good starting point for identification of putative biomarkers, and the availability of commercial structure databases can be helpful in this respect.


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Figure 5. PLSDA scores plot visualizing the gender and diurnal variation in Sprague-Dawley rats, based on 1H NMR 600 MHz spectra of urine samples generated in-house. Black symbols, female day; blue symbols, female night; red symbols, male day; green symbols, male night (unpublished data courtesy of Dr Ina Schuppe and co-workers).

The most obvious practical limitation of GC-based metabolite profiling is that many interesting classes of compound, including sugars, nucleosides, amino acids etc., cannot be analyzed directly due to their polarity and lack of volatility. This means that prior to analysis extraction and chemical derivatization are essential prerequisites, and there is clearly some potential to introduce variability and losses in the process of sample preparation. The most widely used derivatization procedure,100 following extraction, is where the dried down extract is dissolved in pyridine, then reacted first with methoxylamine hydrochloride (28 °C, 90 min) followed by N-methylN(trimethylsilyl)-trifluoracetamide (MSTFA) (37 °C for 30 min). The absolute requirement for fairly extensive sample preparation prior to chromatography, and long chromatographic run times, result in a relatively low throughput technique. However, the advantages of GC-MS, and particularly GC×GC-MS include very high resolution, good sensitivity and robustness. An example of the power of an optimized methodology for the GCMS for metabolite profiling, including human plasma has recently been published.101 An example of the use of GC-ToFMS, applied to the study of a type II diabetes model,102 is shown in Figure 7a-c. These total ion current chromatograms (TICs) show derivatized extracts for 20-week old male Zucker (fa/fa) obese, Zucker lean, and Zucker (fa)/lean crosses obtained using electron impact (EI) ionization. In Figure 8, a 2-D trace (retention time vs mass) obtained for a Zucker (fa/fa) obese animal using chemical ionization (CI) is provided. About 200 peaks can be observed in this sample. A significant enhancement in the resolving power of GC for complex mixtures was the development of multidimensional GC, usually described as GC×GC (or “comprehensive” GC). In this variant of GC two columns, with different selectivities are employed. The first column is generally fairly nonselective,

separating compounds on volatility and is typically 30 M long whereas the second, which separates compound based on polarity, is usually much shorter at only, e.g., 1.5 M. As the analytes elute from the first column they are trapped and cryogenically focused on the second, from which they are rapidly eluted and separated. A recent application in metabolite profiling described the use of GC×GC-ToF-MS to analyze spleen extracts obtained from obese NZO and lean C57BL/6 mice.103,104 Separations were performed on a 30-m capillary GC column coated with dimethyl polysiloxane to separate compounds based on volatility followed by GC in the second dimension on a 1.5 m column coated with 50% polysilphenylene-siloxane to resolve compounds based on relative polarity. This combination resulted in the detection of some 1200 compounds in a 65 min run (ca 2.5 times as many as a 1-D separation). With optimization, it has been shown that many hundreds of compounds can be detected in extracts of human plasma,105 and an example of such an optimized separation is shown in Figure 9. An advantage of GC-based strategies for metabonomics is in the area of identification, where a number of databases exist that can be interrogated in an effort to identify unknowns. While by no means perfect as a source of identification, as many biomolecules are not yet included in the databases, the ready availability of this information represents a valuable resource that will, no doubt, become more comprehensive with time. LC-MS. LC was not been used extensively for metabolic profiling until relatively recently. However, the widespread availability of, relatively, robust LC-MS has resulted in a rapid and continuing increase in the number of publications using the technique for metabonomics (reviewed in ref 106). In many ways, LC-MS is ideal for metabolite profiling as biofluids such Journal of Proteome Research • Vol. 6, No. 2, 2007 449

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Figure 6. 1H NMR (500 MHz) spectra of urine from a control animal collected over 9 days from a representative Han-Wistar derived rat (bred in-house) undergoing clear gut flora-related changes in the metabolism of dietary aromatics with changes in the concentrations of 3-hydroxyphenyl propionic acid and hippuric acid.

as urine can be directly injected whereas samples such as plasma need minimal pretreatment (protein precipitation). LCMS is also capable of moderate to high throughput, has a reasonable dynamic range combined with good potential for biomarker identification (based on the spectral data generated), is not specific to particular classes of compounds and can be extremely sensitive. However, unlike 1H NMR spectroscopy, LCMS is not equally sensitive to all compounds/classes. As well as MS, a variety of other approaches have also been used including UV, NMR, and electrochemical detectors. However, the sensitivity of MS combined with its potential for metabolite identification are likely to mean that it will become the dominant approach for this type of work in the future. In general, separations for LC-MS have been performed using reversed-phase gradient chromatography and electrospray ionization (ESI) in both positive ESI and negative ESI in order to obtain the most comprehensive profile possible. The undoubted benefits of LC-MS have to be balanced against the 450


perennial problems that are provided by the phenomenon known as “ion suppression” (and also enhancement). These effects result when a coeluting compound(s) changes the degree of ionization of a particular analyte causing its signal to be reduced (or increased). This problem is particularly difficult to control in complex multicomponent samples of unknown composition and care has to be taken in interpreting metabolite profiling data. Indeed, it is arguable that biomarkers identified in this way should be treated as provisional until confirmed using a validated, compound specific method. HPLC-MS. Generally metabonomics analyses by HPLC-MS have been performed using solvent gradients, on reversedphase packing materials, using 4.6 or 3.0 mm i.d. columns, of between 5 and 25 cm in length containing 3-5 µm packing materials (reviewed in ref 106). Analysis times have ranged from ca. 10 min per sample up to 1 or 2 h depending upon the application and investigator. A typical HPLC-MS total ion current trace (+ve ESI) for a rat urine sample obtained from a


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Figure 7. Typical TICs obtained from GC-EIMS analysis of plasma obtained from 3 strains of Zucker rat the “wild type lean, the leptin deficient (fa/fa) obese and the fa/lean cross. (A) Lean, (B) lean/(fa) cross, and (C) (fa/fa) obese. Republished from ref 102 with permission.

Figure 8. Two-dimensional mass versus retention “map” obtained from GC-CIMS analysis of plasma from a typical Zucker (fa/fa) obese rat (see Figure 7 for the one-dimensional trace). Republished from ref 102 with permission.

Zucker (fa/fa) obese rat is shown in Figure 10 using this general approach. There are many ions present in these TICs (ca. 1600 in this case) and, by using a variety of statistical approaches (e.g., principal components analysis, PCA) to compare such metabolic profiles, it has proved to be possible to discover differences that enable animals of different strains, gender, ages, or subject to different treatments, to be distinguished from each other. The gradient reversed-phase separations described above are well suited to the analysis of compounds of medium and low polarity but are less well suited to the analysis of polar and polar ionic compounds which elute essentially unretained (e.g., amino acids and some sugars, etc.). One option for the HPLC-MS analysis of such polar compounds using the so-called “HILIC” (hydrophobic interaction chromatography) has been demonstrated for rat urine following solid-phase extraction (SPE) (on Oasis HLB).107 The unretained material was then analyzed on a “ZIC-HILIC” column using a solvent gradient over 15 min (followed by a further 4 min isocratic elution before returning to the starting conditions for re-equilibration). As

described below, other workers have used amino and phenylhexyl columns to increase their coverage of the metabolite profile.108 Rodent toxicology studies have proved to be fertile grounds for the application of HLPC-MS with the first such reported by Plumb et al.109 where urine samples obtained in a long term toxicity study, at several dose levels, were analyzed. The results showed a clear differentiation between dose groups and the controls based on negative ESI. Further applications have included several studies centered on the effects of nephrotoxins on the urinary metabolite profiles of rats that had been administered mercuric chloride,110 cyclosporin,111 gentamicin,112 D-serine113 and heavy metal salts (mercury, uranium and cadmium).114,115 For the mercuric chloride, cyclosporin, and gentamicin studies HPLC-MS analysis was complemented by 1 H NMR spectroscopic studies on the same samples. This approach gave an excellent correlation between the observed metabolic trajectories describing the time course of the toxicity, but based on different metabolic markers detected by the two Journal of Proteome Research • Vol. 6, No. 2, 2007 451

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Figure 9. 3-D surface plot of pooled human serum analyzed with comprehensive GC×GC-TOF-MS employing DB-1 and BPX-50 as column 1 and 2, respectively. The first dimension time depicts separations based on volatility and the second dimension time depicts polarity-based separations. More than 1800 metabolitedefined peaks were detected. These include amino, organic, and fatty acids as well as sugars, amines, amides, and phosphorylated metabolites. Image courtesy of the HUSERMET project and Warwick Dunn and Jason Ashworth.

analytical techniques. In the case of gentamicin,112 it was noted that several of the “biomarkers” were sulfate conjugates of low molecular mass aromatics compounds. This observation resulted in the analysis of the metabolite profiles of test and control animals for differences in sulfate conjugate profiles which were detected via the neutral loss of 80 amu from the parent. In the LC-MS study of the toxicity of uranium and cadmium in the rat, a long reversed phase gradient separation (ca. 2 h) was employed114 with the identification of a range of markers including hippuric acid, 7-methylguanine, phenol, and p-cresol sulfates, among others. A range of mass spectral techniques (MS/MS experiments and manual searches of the CID (collision induced dissociation) spectra for characteristic ions and neutral losses) were used for this and a variety of chemical classes were highlighted. Thus, neutral losses of 44 Da (CO2) obtained in the CID MS data from the negative ESI analysis were used to detect for metabolites containing carboxylic acids, glucuronides were identified by the characteristic ion at m/z 175, and by neutral losses of 176 Da whereas sulfates were found using the radical anion at m/z 80 (SO3-•) and/or by a neutral loss of 80 amu. In a subsequent study,115 these authors concentrated more specifically on sulfates using LC/ ESI-MS to profile urine for a wide range of structurally different sulfoconjugated compounds by monitoring constant losses of 80 u (or 80 Th for doubly charged ions), precursors of m/z 80 (SO3-), and precursors of m/z 97 (HSO4-). In this way, different urinary chromatographic fingerprints were obtained for control, uranium- and cadmium-treated rats with the loss of one of these (4-ethylphenol sulfate, a tryptophan metabolite) apparently specific to uranyl nitrate toxicity. A similar use of HPLCMS combined with constant neutral losses for the detection of specific metabolite classes has been described for the analysis of human urine based on the detection of mercapturates following acetaminophen (paracetamol) administration to volunteers using the constant neutral loss of 129.116 Another example of the use of HPLC-MS and pattern recognition for the study of animal toxicity is the investigation of the phospolipidosis induced by the drug citalopram. A clear 452


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differentiation between control and dosed groups was observed though the markers remain to be identified.117 HPLC-MS has also been used in several studies for examining the urine of normal (Wistar-derived) and Zucker rats, including the Zucker (fa/fa) obese rats,95,118,119 which represent an important model of type II diabetes. The resulting multivariate data provided both age and strain-related changes in the metabolic profiles and, in the case of the (fa/fa) obese strain showed a marked divergence from the more normal strains. As well as studies in disease models there have been a number of applications claimed for investigations of human disease including studies in myocardial ischemia,108 type II diabetes,120 liver cancer, hepatitis, and liver cirrhosis.121 However, by the strict definition of metabonomics, where what is performed is an unbiased study of all metabolites rather than a targeted analysis of particular classes the latter two investigations120,121 may not qualify as metabonomics investigations as they analyzed either phospholipids or cis-diols (followed by multivariate statistical analysis to identify biomarker profiles). The metabolic profiling performed on plasma samples obtained in a myocardial ischemia study (from patients and controls before and after exercise-related stress testing) used 3 chromatographic systems.108 Thus, analysis was performed on a phenylhexyl column for amino acids and amines, an amino column for sugars and ribonucleotides, whereas organic acids were analyzed via a polar RP column. Detection was via a triple quadrupole mass spectrometer using ESI. MSMS with selected reaction monitoring was performed and 477 parent/daughter ions were monitored and the authors claimed to detect diseaserelated changes in metabolites involved in the citric acid pathway in response to exercise. Microbore LC-MS. HPLC-MS has been shown to be capable of reasonable resolution and moderate throughput. However, higher resolution alternatives to conventional HPLC for metabolite would clearly be useful for analyzing the complex mixtures found in metabonomics studies. Alternatives to separations on the 3.0 and 4.6 mm i.d columns, filled with 3-5 um packing materials, used for HPLC separations include narrow bore (ca. 2 mm i.d), micro bore (0.5, 1.0 mm i.d.), and capillary columns. Capillary HPLC can be used to greatly increase the resolving power of the technique and examples of this include the metabolomic analysis of extracts of Aradopsis thaliana on a monolithic C18 bonded silica columns (0.2 mm i.d), of between 30 and 90 cm in length.122 As well as increased resolution capillary LC can be used to reduce the sample size, which may be valuable where sample volumes are limited. A demonstration of this was seen when reversed-phase gradient capillary LC-MS with 10 cm × 320 µm columns, containing a 3.5 µm C18 bonded packing material, was used to analyze the urine of Zucker rats.123 When compared to conventional HPLC-MS on the same samples (on columns of the same length and containing the same packing material) approximately twice as many ions were detected and the system was up to 100-fold more sensitive than HPLC-MS for some metabolites (possibly due to a reduction in ion suppression). Both conventional and capillary LC-MS revealed diurnal variation in rats but, interestingly, there was little overlap between the marker ions detected as responsible for the statistical classification. With very long capillaries much increased resolution can be obtained but at the cost of long analysis times and high operating pressures. In a study of the metabolite profiles of the microorganism Shewanella oneidnedensis, a reversed-phase

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Figure 10. Reversed-phase gradient LC-MS, using +ve ESI, of a sample rat urine from a male Wistar-derived rat.

Figure 11. 3-D maps for the reversed-phase gradient HPLC-MS and UPLC-MS of white male mouse urine from a morning (am) collection, showing retention time, m/z, and intensity. Reproduced from ref 125.

gradient on a 200 cm × 50 µm id fused silica capillary containing a 3 µm porous C-18 bonded stationary phase was undertaken.124 Analysis of the cell lysates in this way enabled the detection of ca. 5000 metabolites but required a solvent delivery system capable of providing 20 K psi and took some 2000 min to perform. Ultra Performance LC. While one method of increasing the separation power of LC is to use capillary systems another way of achieving this is to use smaller particle sizes, maintaining flow rates by using solvent delivery systems capable of operating at high pressures. This is the solution provided by the recently introduced ultra performance (UP) LC where chromatography is performed on columns packed with sub 2 µm particles packed into 2.1 mm i.d columns. The use of pressures of up to 15 kpsi allows flow rates of up to ca. 600 µL min to be used, thereby enabling fast separations to be achieved. An early demonstration of the use of UPLC-MS for metabonomics was provided by the analysis of rat and mouse urine samples on 5 or 10 cm × 2.1 mm ACQUITY columns.125 This application used a linear reversed-phase gradient from 100% 0.1% aqueous

formic acid to 95% acetonitrile 0.1% formic acid over either 2 or 10 min at a flow rate of 800 or 500 µL/min, respectively. The much improved resolution and higher peak count compared to conventional HPLC-MS is shown, for mouse urine, in Figure 11. In a subsequent application the potential high throughput nature of UPLC with a short, 1.5 min reversedphase gradient, was shown for a range of rodent-derived samples, giving similar performance to that that was obtained using conventional HPLC-MS with a 10 min separation.126 As well as urine rat plasma, obtained from 20-week old male rats (Wistar-derived and Zucker (fa/fa) obese) has been analyzed by UPLC-MS in both positive and negative ESI.127 Obvious differences could be seen in the TICs for the two strains, and this was confirmed by PCA. Over 11 000 ions in + ve ESI and 9000 ions in -ve ESI respectively were detected in these plasma samples. One of these, seen in -ve ESI at m/z 514 (3.14 min), elevated in samples from the Zucker (fa/fa) obese animals, was identified as taurocholic acid. Similar results were seen for plasma samples for two other strains of Zucker rat, the Zucker lean, and the lean/(fa) obese cross.128 Journal of Proteome Research • Vol. 6, No. 2, 2007 453

reviews Increasingly performing separations at elevated temperatures is being seen as a means of obtaining more efficient use of chromatographic systems as such conditions result in reduced solvent viscosity, and therefore lower back-pressures. This enables the solvent to be delivered at higher flow rates, thereby reducing analysis times. Recently the use of UPLC at ca. 11 000 psi at a temperature of 90 °C has been described for the analysis of endogenous and drug metabolites in human and animal urine.129 These conditions gave peaks of between 1 and 3 s wide and a peak capacity of ca. 700 in a 10 min separation and perhaps than ca.1000 in 1 h. CE-MS for Metabonomics. Capillary electrophoresis, like LCMS is well suited to the types of samples encountered in metabolic profiling and, within limits, samples such as urine can be analyzed with minimal sample pretreatment. Indeed CE-MS has considerable potential in metabonomics studies. Both CE-UV and CE-MS studies have been reported. Currently the bulk of the applications of CE-MS in this area have been concentrated in the area of bacterial metabolomics, particularly studies on B. subtilis.130,131 In these studies, samples have been investigated using “targeted” analyses with metabolite identification by reference to a panel of ca. 1700 standards. A major advantage of CE-based methods is that they require only very small amounts of sample. In addition, the separation mechanism is different to that employed in both liquid and gas chromatography, and well suited to polar, ionisable, molecules. CE methods are thus likely to prove to be a highly complementary alternative to separation techniques such as HPLC and GC. Strategies for Metabonomic Analysis and Biomarker Identification. The initial aim of any metabonomic study is the generation of comprehensive metabolic profiles from test and control samples that enable the detection of potential biomarkers. From the preceding text, it will be abundantly clear that there are a number of analytical methods that could be used for this purpose, and it would not be unreasonable to pose the question, given that all of these methods have different characteristics, which method from the “metabonomics toolbox” should be used in preference? Unfortunately, there is as yet no simple answer to this question and metabonomics analysis must be performed using, in addition to the available technology, a mixture of pragmatism and scepticism. Thus, in practice none of the analytical techniques described above will yet provide of itself a comprehensive profile because of either a lack of sensitivity or a bias toward particular analytes. Any sensible strategy for global metabolic profiling will therefore use a range of complementary analytical methods in an effort to gain the best coverage possible in a reasonable time. We have performed a number of studies in the areas of toxicology110-112 and disease models95,118,119 where both 1H NMR spectroscopy and HPLCMS have been employed for urine analysis and have been gratified to see that both methods gave similar results but often based on different biomarkers. More recently we have used 1H NMR, HPLC-MS and GC-MS to analyze plasma samples from normal and Zucker (fa/fa) obese rats and seen a similar degree of complementary information.132 Indeed, there seems to be a general trend toward the use of several techniques in combination, with strategies for combining data generated from more than one analytical platform appearing.133 This does not mean that if only one technique is available, for example HPLC-MS, that it is pointless to undertake a metabonomic profiling exercise. On the contrary, we have found HPLC-MS to be a very valuable tool for detecting 454


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potential biomarkers and, once detected, such markers can be used to monitor the condition, and its response to treatment, very successfully. Rather, the mistake would be to assume that, having discovered a selection of markers by LC-MS, that the job is then done and that all the biomarkers that are there to be discovered have been found. This is clearly simplistic and, to gain a better understanding of the condition under study, it may well be necessary to deploy further metabolite profiling techniques with different analytical selectivities. From a purely pragmatic point of view, the widespread availability of LC-MS systems means that they are likely to provide an entry point for many groups into the process of metabonomics profiling. If this is the case, then the potential limitations of the technique must be kept in mind (potential for drift in both chromatographic and mass spectrometer performance) at all times and controlled. This means, at a minimum, to eliminate bias due to a gradual change in the performance of the system, the samples should be analyzed in a random order, and quality control samples (QCs) should be used to rigorously monitor the performance of the method.134 These QC samples can then be assessed against a set of predefined criteria and thus enable the acceptance or rejection of the batch. Our current view is that that a representative “pooled”, or master mix, sample made using aliquots of the whole sample set can be used to provide an appropriate QC sample. This sample is then used, analyzed at the beginning, end, and randomly through the run. In addition to the pooled sample approach, selected standards can be also be spiked in to samples prior to analysis as pseudo internal standards, or run simply alongside the test samples. Post analysis the pool sample QC data and selected standard data can be examined easily for gross changes that would signal a catastrophic change in the system. If the run passes this first test then selected compounds/ions can be monitored against criteria such as peak shape, intensity, mass accuracy and retention time against predetermined acceptance criteria. If the QC samples pass these preliminary screens, then multivariate statistical analysis can be performed to see if the QC data cluster closely together, and show no time related trends. Obviously highly variable QC data would mean that the run had failed while close QC data do not automatically mean that the run was acceptable, but do justify further data analysis. When potential biomarkers have been identified by this further statistical analysis of the data, it is possible to reexamine the QC data specifically to look at the variability of the results obtained for those specific ions. Given our experiences with GC-MS, we would advocate a similar approach here as well. However, clearly the detection of biomarkers (by whatever profiling method) is only the first step and the unequivocal identification of these compounds represents perhaps the most important step because it is only after these compounds have been identified that their biological significance can be determined. The exact strategy employed for biomarker identification will clearly depend upon which technique was used in the initial detection of the compound. Thus, if the marker was first detected using 1H NMR spectroscopy and the metabolite cannot be identified on the basis of the available spectroscopic data (or further 2D-NMR studies) then the missing information required for structure elucidation must be obtained by other means. The required information in this instance is likely to


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Figure 12. Positive ion UPLC-ToF-MS chromatogram obtained from the urine of a 20 week old male Zucker rat using MSE Collision Energy Analysis. The lower trace shows the high-energy and the upper trace shows the low-energy TICs. Reproduced from ref 135 with permission.

relate to the need to identify NMR “invisible” components of the molecule such as, e.g., functional groups such as CN or SO4 etc., as well as heteroatoms such as N or S and to obtain a molecular mass. Much of this information can be obtained via mass spectrometry so that isolation of the target analyte(s) and either direct MS or LC-MS may represent the most appropriate way forward. If on the other hand LC-MS methods were used in the initial identification and identification was not possible by the conventional means based on mass spectral properties (fragmentation, MSMS, atomic composition, etc.) Recently, a technique has been described that allows accurate mass data to be obtained on both parent and fragment ions in HPLC with ToF-MS.13 This, so-called MSe, experiment uses a combination of high- and low-energy ionization to obtain ToF mass spectra with published examples in metabonomics to rat urine. Typical TICs are shown in Figure 12 a,b for both low and high collision energy conditions respectively. Rapid switching between low- and high-energy conditions, with the result that both datasets can be obtained in the same run, allows the fairly rapid investigation of mass spectra for the purposes of attempting identification. If pure mass spectroscopic approaches fail then either isolation followed by NMR spectroscopy or, if available, direct HPLC-NMR (or better, HPLC-MSNMR) should suffice. If LC-MS was used for profiling then the investigator already has a head start for developing an isolation procedure, and the separation can simply be scaled up to provide an appropriate amount of pure material. If on the other hand detection was via NMR spectroscopy then no information is available about the chromatographic properties of the target analyte. In such

circumstances, a stepwise approach to isolation is best implemented. One such relatively undemanding method to achieve the extraction, partial purification and concentration of analytes from the sample is solid-phase extraction (SPE). The SPE approach can be used to rapidly process several mL of sample which can be rapidly extracted, desalted and eluted in a small volume of solvent. This initially provides a concentrate (and can be used to demonstrate that the analyte can indeed be extracted). If NMR analysis of the concentrate demonstrates that the target compound has been successfully isolated then SPE can be repeated using stepwise gradient elution via watersolvent mixtures of different eluotropic strength. This procedure, solid-phase extract-chromatography or SPEC, should effect some fractionation and partial purification and provides a fraction enriched in the target analyte(s) for further chromatographic workup if needed.136 Spectroscopic analysis can be performed (e.g., NMR spectroscopy and MS/GC-MS/HPLCMS) at any stage as the purification proceeds until the identities of the compounds of interest are established. An illustration of the use of this simple SPE methodology is provided by the example of the identification of 3-(3-hydroxyphenyl) propionic acid (3-HPPA), derived from dietary chlorogenic acid via the gut micro flora, in the urine of rats.137 The 3-HPPA was partially characterized from the urinary 1H NMR spectra that revealed the presence of four aromatic multiplets between 7.3 and 6.7 ppm and two triplets at 2.84 and 2.48 ppm integrating to two methylene and four aromatic protons respectively. These data indicated an aromatic system with a 1,3-disubstitution pattern and the presence of a phenolic OH and an aliphatic carboxylic acid. A purified concentrate was Journal of Proteome Research • Vol. 6, No. 2, 2007 455

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obtained by SPE and used for NMR, which enabled the structure to be confirmed as 3-HPPA by comparison to an authentic standard. A similar SPE procedure was used to isolate 5-oxoproline from rat urine, when NMR spectroscopy alone failed to provide enough structural information but combination of NMR and MS on the SPE-purified material rapidly enabled the identification of this marker.138 If the low-resolution SPE technique proves unable to provide a sufficiently pure sample, then a higher resolution technique such as on-line HPLC-NMR can be used. This approach has been demonstrated in the identification of gut-microfloraderived metabolites initially detected in rat urine by 1H NMR spectroscopy. In this application, reversed-phase gradient HPLC-1H NMR spectroscopy was used to identify hippuric acid, 3-(3-hydroxyphenyl) propionic acid, and 3-hydroxycinnamic acid.139 Recently statistically based methods for metabolite identification have been introduced that can be used to obtain structural information from, e.g., NMR spectra. Thus, the use of STOCSY (statistical total correlation spectroscopy) can enable the extraction of biomarker spectra from 1H NMR datasets.140,141 A related technique known as SHY (statistical heterospectroscopy) has been used to integrate data from UPLC and 1H NMR data sets.142 Clearly, once these potential markers have been unequivocally identified the challenge is to convert them into properly validated markers for the system under investigation, and demonstrate that they are indeed directly related to perturbations in the system under investigation. This requires, rather than the global metabolite profiling methodologies used to discover them, the application of specific, robust and validated conventional bioanalytical methods.

Conclusions The application of advanced, information rich, spectroscopic techniques to complex biological samples, either on their own or hyphenated to separation systems, is essential for the generation of global metabolic profiles of the type required for metabonomics/ metabolomic studies and represents an important and growing area of bioanalysis. There is currently no single technique that fulfils all the requirements of an ideal global metabolite profiling tool however, the judicious use of a suite of metabolite profiling techniques from the “metabonomics toolbox” is most likely to result in comprehensive metabolite profiles. Each of the techniques described has advantages and limitations, and their use, and the subsequent interpretation of data generated by them, all require care. As will be clear from the above, this remains a rapidly evolving area and many challenges remain. It should also be remembered that metabonomics does not exist in a vacuum, but should ideally form part of systems biology-based strategy for the study of organisms integrated with other omics technologies such as genomics and proteomics. Although outside the scope of this review, it is noteworthy that studies that combine several different levels of biomolecular organization are now becoming more common in the literature (e.g., see refs 68, 69, 143-148).

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reviews (109) Plumb, R. S.; Stumpf, C. L.; Gorenstein, M. V.; Castro-Perez, J. M.; Dear, G. J.; Anthony, M.; Sweatman, B. C.; Connor, S. C.; Haselden, J. N. Rapid Commun. Mass Spectrom. 2002, 16, 19911996. (110) Lenz, E. M.; Bright, J.; Knight, R.; Wilson, I. D.; Major, H. Analyst 2004, 129, 535-541. (111) Lenz, E. M; Bright, J; Knight, R; Wilson, I. D; Major, H. J. Pharm. Biomed. Anal. 2004, 35, 599-608. (112) Lenz, E. M.; Bright, J.; Knight, R.; Westwood, F. R.; Davies, D.; Major, H.; Wilson, I. D. Biomarkers 2005, 10, 173-187. (113) Williams, R. E.; Major, H.; Lock, E. A.; Lenz, E. M.; Wilson, I. D. Toxicology 2005, 207, 179-190. (114) Lafaye, A.; Junot, C.; Ramounet-le Gall, B.; Fritsch, P.; Tabet, J.C.; Ezan, E. Rapid Commun. Mass Spectrom. 2003, 17, 2541-2549. (115) Lafaye, A.; Junot, C.; Ramounet-Le Gall, B.; Fritsch, P.; Tabet, J.C.; Ezan, E. Rapid. Commun. Mass Spectrom. 2003, 17, 25412549. (116) Wagner, S; Scholz, K; Donegan M.; Burton, L; Wingate, J.; Volkel, W. Anal. Chem. 2006, 78, 1296-1305. (117) Idborg-Bjorkman, H.; Edlund, P.-O.; Kvalheim, O. M.; SchuppeKoistinen, I; Jacobsson S. P. Anal. Chem. 2003, 75, 4784-4792. (118) Williams, R. E.; Lenz, E. M.; Evans, J. A.; Wilson, I. D.; Granger, J. H.; Plumb, R.; Stumpf, C. J. J. Pharm. Biomed. Anal. 2005, 38, 465-471. (119) Williams, R. E.; Lenz, E. M; Lowden, J.; Rantalainen, M.; Wilson, I. D. Mol. Biosystems 2005, 1, 166-180. (120) Wang, C.; Kong, H.; Guan, Y.; Yang, J.; Gu, J.; Yang, S.; Xu, G. Anal. Chem. 2005, 77, 4108-4116. (121) Yang, J.; Xu, G.; Zheng, Y.; Kong, Y.; Wang. C; Zhao, X.; Pang, T. J. Chromatogr. A 2005, 1084, 214-221. (122) Tolstikov, V. T.; Lommen, A.; Nakanishi, K.; Tanaka, A.; Fiehn, O. Anal. Chem. 2003, 75, 6737-6740. (123) Granger, J.; Plumb, R.; Castro-Perez, J.; Wilson, I. D. Chromatographia 2005, 61, 375-380. (124) Shen, Y.; Zhang, Y.; Moore, R. J.; Kim, J.; Metz, T. O.; Hixon, K. K.; Zhao, R.; Livesay, E. A.; Udseth, H. R.; Smith, R. D. Anal. Chem. 2005, 77, 3090-3100. (125) Wilson, I. D.; Nicholson, J. K.; Castro-Perez, J.; Granger, J. H.; Johnson, K.; Smith, B. W.; Plumb, R. S. J. Proteome Res. 2005, 4, 591-598. (126) Plumb, R. S.; Granger, J. H.; Stumpf, C. L.; Johnson, K.; Smith, B. W.; Gaulitz, S.; Wilson, I. D.; Castro-Perez, J. Analyst 2005, 130, 844-849. (127) Williams, R.; Lenz, E.; Wilson, A.; Granger, J.; Wilson, I. D.; Major, H.; Stumpf, C.; Plumb. R. Mol. Biosys. 2006, 2, 174-183. (128) Plumb, R. S.; Johnson, K. A.; Rainville, P.; Shockor, J. P.; Williams, R.; Granger, J. H.; Wilson, I. D. Rapid Commun. Mass Spectrom. 2006, 20, 2800-2806. (129) Plumb, R. S.; Rainville, P.; Smith, B. W.; Johnson, K. A.; CastroPerez, J.; Wilson, I. D.; Nicholson, J. K. Anal. Chem. 2006, 78, 7278-7283.

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Lenz and Wilson (130) Soga, T.; Ohashi, Y.; Ueno, Y.; Naraoka, H.; Tomita, M.; Nishioka, T. J. Proteome Res. 2003, 2, 488-494. (131) Jia, L.; Terabe, S. In Metabolome Analysis; Vaidyanathan, S., Harrigan, G. G., Goodacre, R., Eds.; Springer: New York, 2005; pp 83-101. (132) Williams, R.; Lenz, E. M.; Wilson, A. J.; Granger, J.; Wilson, I. D.; Major, H.; Stumpf, C.; Plumb, R. Mol. Biosyst. 2006, 2, 174-183. (133) Crockford, D. J.; Holmes, E.: Lindon, J. C.; Plumb, R. S.; Zirah, S.; Bruce, S. J.; Rainville, P.; Stumpf, C.; Nicholson, J. K. Anal. Chem. 2006, 78, 363-371. (134) Sangster, T.; Major, H.; Plumb, R.; Wilson, A. J.; Wilson, I. D. Analyst 2006, 131, 1075-1078. (135) Plumb, R. S.; Johnson, K. A.; Rainville, P.; Smith, B. W.; Wilson, I. D.; Castro-Perez, J.; Nicholson, J. K. Rapid Commun. Mass Spectrom. 2006, 20, 1989-1994. (136) Wilson, I. D; Nicholson, J. K. Anal. Chem. 1987, 59, 2830-2832. (137) Phipps, A. N.; Wright, B.; Stewart, J.; Wilson, I. D. Pharm. Sci. 1997, 3, 143-146. (138) Ghauri, F. Y. K.; McLean, A. E. M.; Beales, D.; Wilson, I. D.; Nicholson, J. K. Biochem. Pharmacol. 1993, 46, 953-957. (139) Gavaghan, C. L; Nicholson, J. K; Connor, S. C; Wilson, I. D; Wright, B.; Holmes, E. Anal. Biochem. 2001, 291, 2245-2252. (140) Cloarec, O.; Dumas, M.; Craig, M.; Barton, R. H.; Trygg, A.; Hudson, J.; Blancher, C.; Gaugier, D.; Lindon, J. C.; Holmes, E.; Nicholson, J. K. Anal. Chem. 2005, 77, 1282-1289. (141) Holmes, E.; Cloarec, O.; Nicholson, J. K. J. Proteome Res. 2006, 5, 1313-1320. Crockford, D. J; Holmes, E.; Lindon, J. C.; Plumb, R. S.; Zirah, S.; Bruce, S.; Rainville, P.; Stumpf, C. L.; Nicholson, J. K. Anal. Chem. 2006, 78, 363-371. (142) Craig, A.; Sidaway, J.; Holmes, E.; Orton, T.; Jackson, D.; Rowlinson, R.; Nickson, J.; Tonge, R.; Wilson, I.; Nicholson, J. J. Proteome Res. 2006, 5, 1586-1601. (143) Rantalainen, M.; Cloarec, O.; Beckonert, O.; Wilson, I. D.; Jackson, D.; Tonge, R.; Rowlinson, R.; Rayner, S.; Nickson, J.; Wilkinson, R. W.; Mills, J. D; Trygg, J.; Nicholson, J. K.; Holmes, E. J. Proteome Res. 2006, 5, in press. (144) Hirai, M. Y.; Yano, M.; Goodenowe, D. B.; Kanaya, S.; Kimura, T.; Awazuhara, M.; Arita, M.; Fujiwara, T.; Saito, K. Proc. Natl. Acad. Sci. U.S.A. 2004, 101, 10205-10210. (145) Griffin, J. L.; Bonney, S. A.; Mann, C.; Hebbachi, A. M.; Gibbons, G. F.; Nicholson, J. K.; Shoulders, C. C.; Scott, J. Physiol. Genomics. 2004, 17, 140-149. (146) Kleno, T. G.; Kiehr, B.; Baunsgaard, D.; Sidelmann, U. G. Biomarkers 2004, 9, 116-138. (147) Heijne, W. H.; Lamers, R. J.; van Bladeren, P. J.; Groten, J. P.; van Nesselrooij, J. H. J. Toxicol. Pathol. 2005, 33, 425-433. (148) Thomas, C. E.; Ganji, G. Curr. Opin. Drug Discov. Dev. 2006, 9, 92-100.

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