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Metabonomics measures the fingerprint of biochemical perturbations caused by disease, drugs, and toxins.
T
he human genome project has been described as the biggest fishing trip ever, but what did it catch? We now know the gene sequences of numerous organisms and have a large database of the single gene variations among humans, but what does this offer in terms of real biochemistry and physiology or a better understanding of disease? Will it help in the discovery and development of new medicines? Billions of dollars have been pumped into a huge genomics industry using gene-chip technologies and a proteomics effort using MS-based methods for characterizing the changes in protein levels (1, 2). Despite the hype, the money spent, and the new information, genomics and proteomics are only just beginning to fulfill their promise. This could be because proteomics, and genomics in particular, do not provide evidence of real-world endpoints for diagnosing disease or evaluating beneficial or adverse drug effects. Metabonomics, which can provide real biological endpoints, is defined as the quantitative measurement of the time-related multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification (3, 4). Its relationship to the other “omics” is shown in Figure 1. Applying metabonomics involves generating metabolic databases for control animals and humans, diseased patients, and animals used in drug safety testing, and the simultaneous acquisition of multiple biochemical parameters on biological samples. Metabonomics is usually conducted with biofluids, many of which can be obtained noninvasively (urine) or relatively easily (blood), but other more exotic fluids such as cerebrospinal fluid, bile, seminal fluid, cell culture supernatants, tissue extracts, and similar preparations can also be used. Metabonomics is a promising approach because disease, drugs, or toxins cause perturbations in the concentrations and fluxes of endogenous metabolites involved in key cellular pathways. For example, the response of cells to toxic or
John C. Lindon Elaine Holmes Jeremy K. Nicholson IMPERIAL COLLEGE, LONDON (U.K.) S E P T E M B E R 1 , 2 0 0 3 / A N A LY T I C A L C H E M I S T R Y
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over both time and space. Metabolomics, which is the corresponding study in single cells, can be thought of as a subset of the systems covered by metabonomics (6).
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FIGURE 1. The relationships between the genome and the technologies for evaluating changes in gene expression (transcriptomics), protein levels (proteomics), and smallmolecule metabolite effects (metabonomics). Both transcriptomics and proteomics usually require tissue or cell samples and involve a stage of sample extraction. Although metabonomics can be used in this way, it can also provide information non-invasively through biofluids, such as blood plasma, urine, or cerebrospinal fluid.
other stressors generally results in an adjustment of their intraand/or extracellular environment to maintain a constant internal environment (homeostasis). This metabolic adjustment is expressed as a fingerprint of biochemical perturbations, which is characteristic of the nature or site of a toxic insult or disease process. Urine, in particular, often shows changes in metabolic profile in response to toxic or disease-induced stress; one way that the body’s cellular systems attempt to maintain homeostasis in the face of toxic challenge is to modulate the composition of biofluids by eliminating substances from the body. Even when cellular homeostasis is maintained, subtle responses to toxicity or disease are expressed in altered biofluid composition.
Various spectroscopic methods can be used to generate metabonomic data sets for complex biological samples and, as long as the data sets are rich in molecular information, it does not really matter what method is used. Many plant and microbiological investigations have used MS, primarily because its overall sensitivity is greater than that of NMR spectroscopy. However, high-resolution 1H NMR spectroscopy has proved to be one of the most powerful technologies for examining biofluids and is essentially the only one capable of studying intact tissues. The technique produces a comprehensive profile of metabolite signals without separation, derivatization, and preselected measurement parameters (7 ). Furthermore, variable detection responses in MS, such as differential volatilization or ionization, are not an issue for NMR spectroscopy. Typically, 1H NMR spectra of biofluids, such as urine and plasma, contain thousands of signals arising from hundreds of endogenous molecules that represent many biochemical pathways (Figure 2). Conventional measurements of the major NMR signals can be used to detect biochemical changes, but the spectral complexity and the presence of natural biological variation across a set of samples often make it difficult to “see the forest for the trees.” Generally, it is necessary to use data reduction and pattern recognition (PR) techniques to access the latent biochemical information present in the spectra. Robotic sample preparation systems are usually used to prepare 96-well plates containing biofluid samples ready for NMR. Approximately 500 µL of sample are injected into the flow-injection NMR probe. Water is present in high concentration in
Metabonomics vs metabolomics Metabolomics arose from metabolic control theory (5) and was originally based on the metabolome, which is defined as the metabolic composition of a cell and is analogous to the genome or proteome. In metabonomics, static cellular and biofluid concentrations of endogenous metabolites are evaluated, as well as fulltime courses of metabolite fluctuations, exogenous species, and molecules that arise from chemical rather than enzymatic processing (metabonates). In addition to providing molecular concentrations, metabonomics covers the study of molecular dynamic information, such as molecular reorientational correlation times and diffusion coefficients in intact tissues. Metabonomics can be regarded as a full systems biology approach; when a whole organism with separate organs and many cell types is studied, metabonomics can integrate the disparate effects that occur 386 A
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FIGURE 2. The complex 1-D 900-MHz 1H NMR spectrum of human urine. The horizontal axis provides information on chemical identity, and the vertical axis relates to the hydrogen count in functional groups and to metabolite concentration.
biofluids, causing dynamic range problems in the NMR detector and leading to an NMR peak so huge that it can obscure other molecular information. For these reasons, 1H NMR spectra of urine are measured using a standard water-suppression pulse sequence, which results in a total acquisition time of ~4 min/sample. For serum or plasma, a suite of 1H NMR spectra is usually measured, including a spin-echo spectrum (for mainly small molecules) and a diffusion-edited spectrum (for macromolecular profiles). Because of developments in robotic sample preparation and transfer systems and in NMR flow probes, NMR’s analytical capacity has increased enormously, up to 200–300 samples/day. Although 1H NMR spectra of urine and other biofluids are very complex, many resonances can be assigned directly on the basis of their chemical shifts and signal multiplicities and by adding a sample of the putative substance to ascertain whether all of its NMR peaks are exactly co-registered with those in the biofluid NMR spectrum. Further information can be obtained by using spectral editing techniques. Two-dimensional NMR can also be useful for spreading out the signals and for working out the connectivities between signals, thereby enhancing the information content and helping to identify biochemical substances. These include the 2-D coupling-constant (J )-resolved experiment, which reduces the contribution of macromolecules and yields information on the multiplicity and coupling patterns of resonances. Other 2-D experiments, such as correlation spectroscopy and total correlation spectroscopy, provide 1H–1H spin–spin coupling connectivities. Measuring NMR spectra from other nuclei also can help assign NMR peaks. Heteronuclear correlations, usually 1H – 13C, can be obtained by using sequences such as heteronuclear multiple quantum coherence experiments, which gives the chemical shifts of the carbon atoms to which the protons are attached. With the advent of NMR detectors cooled to near-cryogenic temperatures (cryoprobes), a sensitivity gain of ~500% is achievable, making it possible to measure smaller samples or use less time. In addition, natural-abundance 13C NMR spectroscopy is now also feasible. Although identifying molecules is not necessary to classify samples, working out the identity of the molecules that differentiate spectra from different sample classes (biomarker combinations) can provide insights into biochemical mechanisms of disease or drug effects. If all of these methods fail to identify a given set of NMR peaks, then off-line chromatographic procedures, such as solid-phase extraction or HPLC, can simplify or clean up biofluid samples prior to NMR detection. In selected cases, directly coupled HPLC/NMR and HPLC/NMR/MS can be of value in determining endogenous metabolite structures (8). If tissue samples are available, then information complementary to that from biofluids can be obtained. Although researchers have applied in vivo NMR to investigate abnormal tissue biochemistry, the low magnetic fields they used led to poor sensitivity and peak dispersion. Moreover, the heterogeneity of the sample results in magnetic susceptibility differences, which cause magnetic field inhomogeneities that, combined with constrained molecular motions of molecules in some tissue compartments,
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FIGURE 3. Examples of 400-MHz magic angle 1 spinning H NMR spectra from various tissues at a rotation rate of 4.2 kHz.
lead to poor resolutions and loss of signals. Therefore, NMR spectral analysis of tissues has relied largely on tissue-extraction methods. However, tissue components, such as proteins and lipids, are lost in extraction procedures. Within the past few years, the development of high-resolution 1 H magic angle spinning (MAS) NMR has had a substantial impact on the analysis of intact tissues (9). Rapid spinning of the sample (typically ~4–6 kHz) at an angle of 54.7° relative to the applied magnetic field reduces line-broadening effects due to magnetic field inhomogeneity caused by sample heterogeneity, dipolar couplings, and chemical shift anisotropy. Thus, it is possible to obtain very high quality NMR spectra of whole-tissue samples with no sample pretreatment. Typical high-resolution 1 H MAS NMR spectra of a range of tissues are shown in Figure 3. Such experiments indicate that the metabolic profiles of diseased or toxin-affected tissues are substantially different from those of healthy organs (10, 11). In addition, MAS NMR can access information regarding the compartmentalization of metabolites within cellular environments. 1 H MAS NMR spectroscopic analysis of tissues has great potential in the pharmaceutical industry for the toxicological screening of novel compounds. Using this technology, it is posS E P T E M B E R 1 , 2 0 0 3 / A N A LY T I C A L C H E M I S T R Y
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FIGURE 4. The visualization of xenobiotic toxicity using principal components analysis. (a) PC1 versus PC2 scores plot showing sample clustering according to class. The control group of samples based on the 1H NMR spectra of rat urine lies in the center (). A group of samples showing the onset of and recovery from a lesion to the liver moves toward the upper left part of the plot (∆). Samples from animals showing renal toxicity appear in the lower left quadrant (). (b) The corresponding loadings plot shows the regions of the NMR spectra that are responsible for the clustering in the scores plot. Metabolites with significantly altered levels in the various types of toxicity appear at corresponding positions in the scores and loadings plots. For example, those NMR peaks indicative of renal toxicity appear at the chemical shifts shown in the dotted circle.
sible to bridge the gap between biofluid analysis and histopathology and to gain real insight into the mechanisms of toxicity at a molecular level.
Data handling and interpretation The use of NMR spectroscopy for complex molecular systems, whether whole proteins or chemical or biochemical mixtures, has a long history. With the advent of more sensitive NMR spectrometers, the applications are now even more widespread, covering protein structures (12), combinatorial chemistry libraries (13), cell and tissue extracts (14), and drug mixture–proteinbinding studies (15). However, the problems remain of interpreting the data and, for metabonomics in particular, sorting out the “wheat from the chaff ” in a large set of NMR spectra of a biofluid from a cohort of animals or humans and then demonstrating various effects, such as normal physiological variation or drug-induced effects. 388 A
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The answer is PR methods. Chemometrics is generally ascribed to PR and related multivariate statistical approaches applied to chemical numerical data (16). The aim of PR is to classify an object or predict its origin by identifying inherent patterns in a set of experimental measurements or descriptors. PR can be used for reducing the dimensionality of complex data sets, for example, by 2-D or 3-D mapping procedures, thereby facilitating the visualization of inherent patterns in the data set. Alternatively, multiparametric data can be modeled using PR techniques so that the class of a separate sample can be predicted on the basis of a series of mathematical models derived from the original data or “training set”. PR methods can be divided into two categories, “unsupervised” and “supervised”. Unsupervised multivariate techniques are used to establish whether any intrinsic clustering exists within a data set. These methods map samples according to their properties without a priori knowledge of sample class. Examples of unsupervised methods include principal components analysis (PCA) and clustering methods such as hierarchical cluster analysis. Supervised methods of analysis use the class information given for a training set of sample data to optimize the separation between two or more sample classes. These techniques include soft independent modeling of class analogy (SIMCA), K-nearest neighbor analysis, and neural networks. Supervised methods require a second independent data set to test or validate any class predictions made using the training set. Subtle biochemical changes in 1H NMR spectroscopic profiles of biofluids can be obscured by interfering factors such as variations in pH, which can change NMR chemical shifts because of differences in the ionization state of some molecules. In addition, there are NMR artifacts such as errors in peak phase and baseline alignment. In NMR spectroscopy of urine, one means of limiting the effects of pH on the chemical shift of sensitive moieties is to add a standard amount of buffer to the sample before spectroscopic analysis (17 ). Alternatively, mathematical algorithms can be used to realign the chemical shifts of resonances from protons near ionizable groups displaced by pH effects (18). Some spectral regions exhibit a lot of variability caused by water NMR peak suppression effects. In addition, many drugs and their metabolites excreted in biofluids can obscure significant changes in the concentration of endogenous components. Therefore, these redundant spectral regions are removed before PR analysis. In situations in which large numbers of samples need to be processed, automatic data reduction and PR analysis are needed. One example of a widely used, robust automatic data reduction method is the division of the NMR spectrum into regions of equal chemical shift ranges, followed by signal integration within those ranges (19). Automatic data reduction of 2-D NMR spectra can be performed using a procedure similar to that for 1-D spectra, in which the spectrum is divided into a grid containing squares or rectangles of equal size and the spectral integral in each grid box is calculated. This is not a universal solution, and other approaches have been used, including shifting peak positions to take into account small pH-dependent variations in chemical shift so that the full NMR spectrum can be used for
A particular strength of spectroscopy-based metabonomic methods is that they are rapid and not labor-intensive. PR (18). Although PR methods initially might use segmented data to identify regions of interest, it is always possible to return to the real NMR spectra for peak assignment and metabolite identification. PCA is a well-known chemometric approach that expresses most of the variance within a data set as a small number of factors or principal components. Each principal component is a linear combination of the original data parameters whereby each successive principal component explains the maximum amount of variance possible that is not accounted for by the previous components. Each principal component is orthogonal and therefore independent of the others. The output of the method is two matrices known as scores and loadings. Scores are the coordinates for the samples in the established model and may be regarded as the new variables. An example of a principal component scores plot is shown in Figure 4a. Each point represents a single NMR spectrum from a rat urine sample. Control samples cluster near the center. Samples exhibiting biochemical differences caused by various toxins appear in regions away from the controls. The organism’s response to the toxin is to cause a series of biochemical changes that are reflected in altered NMR spectra, which results in points on the plot moving away from the control region as the toxicity develops and moving back toward the control region as the toxicity regresses (indicated by the arrows). The principal component loadings define the way in which the old variables linearly combine to form new variables and the orientation of the computed principal component plane with respect to the original variables. The loadings also indicate which variables carry the greatest weight in transforming the position of the original samples from the data matrix into their new position in the scores matrix. In other words, the loadings unravel the magnitude (large or small correlation) and the manner (positive or negative correlation) in which the measured variables contribute to the formation of the scores. In the loadings plot shown in Figure 4b, each point represents a different NMR spectral region labeled by its chemical shift. For example, those regions that report the biochemical deviation caused by the toxin that moved data to the bottom in Figure 4a are located in the lower left quadrant of Figure 4b. Given these NMR spectral regions, it is then possible to examine the real spectra and identify the biomarkers responsible for the effect. Clustering techniques operate by structuring data into natural groups and hierarchies. In hierarchical cluster analysis, the distance between each pair of samples is calculated and the pair that is most similar is identified. These two samples are then linked, and a new midpoint between the pair is calculated. The distance from the new point to all other samples is then estab-
lished, and the process is repeated until all samples have been linked. The resulting structure is displayed as a tree-like structure called a dendrogram. Unsupervised methods such as PCA are useful for comparing pathological samples with control samples, but supervised analyses, which model each class individually, are preferred when the number of classes is large. SIMCA, a supervised method, operates by establishing the multivariate boundaries for each class in a data set. Models are formed from a training set. A separate PCA is performed for each class of data within the training set, and an independent or test set of samples is then used to assess the models’ predictive abilities. Each sample in the test set is fitted to every class model, and predictions are made on the basis of the goodness of the fit. SIMCA models have advantages over some other supervised techniques. They can assign a sample to one or more categories, or no category, rather than force it into an inappropriate category. Supervised partial least-squares (PLS) relate a data matrix containing independent variables and samples to a matrix containing dependent variables (or measurements of response) for those samples. PLS can be used to examine the influence of time on a data set, which is particularly helpful for biofluid NMR data collected from samples taken over the progression of a disease, therapy, or toxic effect. Discriminant analysis is used to establish the optimal position to place a discriminant surface that best separates classes. Neural networks are a nonlinear method of modeling data. A training set of data is used to develop algorithms, which “learn” the structure of the data and cope with complex functions. The basic network consists of three or more layers, including an input level of neurons (spectral descriptors or other variables), one or more hidden layers of neurons that adjust the weighting functions for each variable, and an output layer that designates the class of the object or sample. Several types of neural networks have been successfully applied to predict toxicity or disease from spectral information (20). A range of different chemometric approaches has been used to evaluate xenobiotic toxicity on the basis of NMR spectroscopy of urine (17, 21).
Normal animals and humans Theoretically, the data from healthy control animals or humans should occupy a defined region of a multidimensional metabolic hyperspace and hence a similar position in a PR map based on 1H NMR biofluid data. However, within a control population, natural variation related to factors such as gender, diet, age, physiological rhythms, and genotype occur. To improve the reliability of detecting specific pathological abnormalities in the profiles of biofluids, the extent of normal physS E P T E M B E R 1 , 2 0 0 3 / A N A LY T I C A L C H E M I S T R Y
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iological variance within the control population must be established and the factors contributing to alterations in normal physiological “mapping space” analyzed. Given the higher variation in biochemical profiles of humans, using supervised PR methods and large data sets is usually necessary to delineate and understand the causes of variation. Another factor that contributes to the overall variation in control populations is the availability of different animal strains. Recent studies have highlighted the sensitivity of chemometric methods at differentiating between the 1H NMR urine profiles obtained from two standard strains of laboratory rats, Sprague Dawley and Han Wistar. Using SIMCA, researchers predicted the strain of rat with 90% accuracy for a set of independent test samples (22). However, although strain-related differences in the biochemical composition of urine could be detected, perturbations in the rats’ urinary profiles caused when toxins were administered were significantly larger than any strain-related urinary variation. The ability of NMR-PR methods to differentiate between laboratory rat strains indicates the potential of this technique to investigate genetic manipulation in experimental animals.
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H NMR spectroscopy has been used to study the composition of biofluids before and after administering a wide range of toxins. Predictive statistical models have been constructed to deal with toxicological profiling on three levels. The first and most basic level is determining if a sample is normal, for example, whether it belongs to a control population. The second level involves fitting abnormal samples to known classes of tissue or mechanism-specific toxicity to predict the toxicity of novel pharmacological compounds. The final level is to identify the spectral regions that are responsible for deviations from normal profiles and to determine toxicity biomarkers within those regions that may help elucidate mechanisms of toxicity. Metabonomics has been used to evaluate many toxins, each producing a distinctive series of metabolic perturbations that are characteristic of the type of tissue damage and/or the mechanism of toxicity. From urine, plasma, and cerebrospinal fluid, the target organ of toxicity (and in some cases, the topographical region of injury within that organ) can be identified. To date, metabonomics has been most widely used to study toxicity of the liver and kidney, but it has also proved helpful for evaluating testicular, cardiac, neurological, and mitochondrial toxicities. Administering a toxin generally induces a series of metabolic changes, and these metabolite levels may not return to their previous homeostatic condition, depending on the severity of the lesion. Because the response to toxic insult is dynamic, biofluid profiles are in a constant state of flux with metabolic response times that are also characteristic of specific toxins. When biofluids are sampled over a series of time intervals, a biochemical trajectory of response can be calculated for individual animals or for groups. The extent and direction of deviation of the trajectory from the coordinate corresponding to the pre-dose time period can yield information concerning the severity and type of 390 A
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biochemical lesion. Toxic effects in kidney and liver tissues can also be studied using 1H MAS NMR (23, 24). The formation of the Consortium on Metabonomic Toxicology (COMET) from six pharmaceutical companies (BristolMeyers-Squibb, Eli Lilly, Hoffmann-La Roche, NovoNordisk, Pfizer, and Pharmacia, the latter two now being one) and Imperial College, London illustrates the interest in using metabonomics to evaluate drug safety. The group has been formed to define and apply metabonomic data generated from 1H NMR spectroscopy of urine, blood serum, and tissues for preclinical toxicological screening of candidate drugs. In its two years of operation, COMET has generated databases of metabonomic results for a wide range of model compounds, toxins, and drugs linked to computer-based expert systems for toxicity prediction (25). The consortium has concentrated its efforts on liver and kidney toxicity in rats and mice and, during the initial phase of the project, a detailed comparison was made of the ability of the six companies to provide consistent urine and serum samples from a study of the toxicity of hydrazine in male rats. An exceptionally high degree of consistency in spectral patterns and biochemical composition was found among samples from the six companies. A detailed statistical model was constructed on the basis of the NMR spectra of urine from control rats, which enabled identification of outlier samples and the metabolic reasons for the deviation. Chemometric models were constructed for the urine samples from rats dosed with hydrazine, allowing dose effects and the biochemical response time course to the toxin to be evaluated. Serum samples were also subjected to multivariate analysis on the basis of four types of NMR spectra, and excellent consistency among all companies was demonstrated. Differences between samples from the various companies were small compared to the biochemical effects of hydrazine. NMR spectra from Imperial College and spectra from two of the companies were also compared; the data were found to have a high degree of robustness and compatibility (26). Metabonomics has already been applied in fields outside human and other mammalian systems. For example, studies in environmental pollution have highlighted the potential benefits in studies of caterpillar hemolymph (27) and earthworm biochemical changes as a result of soil pollution (28). In addition, a study of heavymetal toxicity in wild rodents living on polluted sites has demonstrated specific biochemical effects due to high levels of cadmium and arsenic (29). NMR spectroscopy is a powerful tool for investigating many diseases, such as inherited metabolic disorders, organ failure, and cancers (7 ). PCA has been used to differentiate between tissue extract spectra obtained from normal tissues and to classify tumors by type, such as pituitary tumor, fibrosarcoma, hepatoma, and Walker sarcoma (30). NMR-PR has been used to establish normal physiological variance in a population of human urine samples (31), classify several inborn errors of metabolism using urine spectra (31), and monitor the growth of tumors using serum samples (32). Recently, patients suffering from coronary artery occlusion have been identified on the basis of 1H NMR spectra of their blood serum (33). NMR-PR can also be used to
assess the therapeutic efficacy of treatment. Patient response in end-stage renal failure to hemodialysis was evaluated by using PCA to interpret 750 MHz 1H NMR spectra of plasma. In the PC map, with the exception of ne sample, all patients returned toward the cluster of healthy plasma samples. The patient who failed to return toward the healthy plasma group was later found to be nonresponsive to dialysis (34).
methods, their application in metabonomics, and the interpretation of the biochemical aspects of drug toxicity and disease. Nicholson’s research interests encompass all aspects of metabonomics, including drug metabolism, drug toxicity, and disease diagnosis with emphasis on the underlying mechanisms. Address correspondence about this article to Lindon at Biological Chemistry, Biomedical Sciences Division, Faculty of Medicine, Imperial College, London, Sir Alexander Fleming Bldg., London SW7 2AZ U.K. (
[email protected]).
The overall verdict All of the “omics” approaches, genomics, proteomics, and metabonomics, offer complementary information on physiological function and pathological dysfunction in their respective systems of analysis. These approaches also use the same basic bioinformatic and chemometric tools that are needed for enhanced information recovery. It should therefore be possible to integrate the databases and search for relationships between genomic, proteomic, and metabolic perturbations through the use of appropriate statistical methods, leading to “bionomics”. A particular strength of spectroscopy-based metabonomic methods is that they are rapid and not labor-intensive. Furthermore, the recurrent expenditure is very low in the context of flow-injection, high-throughput experiments. In biological terms, the most important advantage of metabonomics is that individual animals and subjects can be followed noninvasively (especially with urinalysis) through a complete disease-related metabolic trajectory, yielding a holistic picture of integrated biological function over time. This is particularly important when multisystem failure is a possibility or when organ toxicity progresses from one tissue or system to another. In such cases, genomic and proteomic methods are weaker because of the analytical necessity of choosing very limited time points for a study and selected tissue or cell samples. MAS NMR of tissues is also a significant advance in clinical chemistry and experimental toxicology, but it is not yet suited to high-throughput screening because of the technological limitations. Nevertheless, MAS NMR complements biofluid NMR spectroscopic studies because organs can be evaluated for disease and toxicity, and novel tissue-specific biomarkers of damage can be identified. It enables an important new bridge to be constructed between tissue biochemistry studies and conventional histopathology in a way that has not been possible previously. We thank the many colleagues within our research group and among our collaborators, especially Ian Wilson of AstraZeneca Pharmaceuticals and Jeremy Everett of Pfizer R&D, who have helped guide our thinking on the development of metabonomics. John Lindon is professor and senior research investigator, Elaine Holmes is reader, and Jeremy Nicholson is professor and head of biological chemistry at Imperial College, London. Lindon’s research interests include the development of NMR and chemometric methods and their application in metabonomics and drug metabolism studies. Holmes’s research interests include the development of chemometric
References (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) (24) (25) (26) (27) (28) (29) (30) (31) (32) (33) (34)
Sinclair, B. The Scientist 1999, 13, 18–20. Geisow, M. J. Nat. Biotechnol. 1998, 16, 206. Nicholson, J. K.; Lindon, J. C.; Holmes, E. Xenobiotica 1999, 29, 1181–1189. Nicholson, J. K.; Connelly, J.; Lindon, J. C.; Holmes, E. Nat. Rev. Drug Discovery 2002, 1, 153–161. Derr, R. F. Biochem. Archives 1985, 1, 239–247. Raamsdonk, L. M.; et al. Nature Biotech. 2001, 19, 45–50. Nicholson, J. K.; Wilson, I. D. Prog. NMR Spectrosc. 1989, 21, 444–501. Lindon, J. C.; Nicholson, J. K.; Wilson, I. D. J. Chromatogr. B. 2000, 748, 233–258. Garrod, S. L.; et al. Magn. Reson. Med. 1999, 41, 1108–1118. Cheng, L. L.; et al. Magn. Reson. Med. 1996, 36, 653–658. Tomlins, A.; et al. Anal. Comm. 1998, 35, 113–115. Wider, G. Biotechniques 2000, 29, 1278–1294. Jacoby, E.; Davies, J.; Blommers, M. J. J. Curr. Top. Med. Chem. 2003, 3, 11–23. Srivastava, S.; Govil, G. Curr. Org. Chem. 2001, 5, 1039–1057. Moore, J. M. Biopolymers 1999, 51, 221–243. Lindon, J. C.; Holmes, E.; Nicholson, J. K. Prog. NMR Spectrosc. 2001, 39, 1–40. Holmes, E.; et al. NMR Biomed. 1998, 11, 235–244. Brown, T. R.; Stoyanova, R. J. Magn. Reson. 1996, 112, 32–43. Farrant, R. D.; Lindon, J. C.; Rahr, E.; Sweatman, B. C. J. Pharm. Biomed. Anal. 1992, 10, 141–144. Holmes, E.; Nicholson, J. K.; Tranter, G. Chem. Res. Toxicol. 2001, 14, 182–191. Holmes, E.; et al. Chemom. Intell. Lab. Syst. 1998, 44, 245–255. Holmes, E.; et al. Chem. Res. Toxicol. 2000, 13, 471–478. Garrod, S.; et al. Magn. Reson. Med. 2001, 45, 781–790. Waters, N. J.; et al. Chem. Res. Toxicol. 2001, 14, 1401–1412. Lindon, J. C.; et al. Toxicol. Appl. Pharmacol. 2003, 187, 137–146. Keun, H. C.; et al. Chem. Res. Toxicol. 2002, 15, 1380–1386. Phalaraksh, C.; et al. Insect Biochem. Mol. Biol. 1999, 29, 795–805. Bundy, J. G.; et al. Environ. Toxicol. Chem. 2002, 21, 1966–1972. Griffin, J. L.; Walker, L.; Shore, R. F.; Nicholson, J. K. Xenobiotica 2001, 31, 377–385. Howells, S. L.; Maxwell, R. J.; Peet, A. C.; Griffiths, J. R. Magn. Reson. Med. 1992, 28, 214–236. Holmes, E.; et al. Anal. Biochem. 1994, 220, 284–296. Kruse, S.; Kvalheim, O. M.; Gadeholt, G.; Halsteinslid, L.; Sletten, E. Chemom. Intell. Lab. Syst. 1991, 11, 191–196. Brindle, J. T.; et al. Nat. Med. 2002, 8, 1439–1445. Lindon, J. C.; Nicholson, J. K.; Everett, J. R. Ann. Rep. NMR Spectrosc. 1999, 38, 1–88.
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