Metabonomics Evaluation Group, Department of World-Wide Safety Sciences, Pfizer Global Research and Development, Ann Arbor, Michigan 48105
1 To whom correspondence should be addressed at World-Wide Safety Sciences, Pfizer Global Research and Development, 2800 Plymouth Rd., Ann Arbor, MI 48105. Fax: (734) 622-2562. E-mail: donald.robertson{at}pfizer.com.
Received December 2, 2004; accepted January 31, 2005
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ABSTRACT |
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Key Words: metabonomics; metabolomics; biomarkers; mechanisms.
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I. THE NATURE OF THIS REVIEW |
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II. METABONOMICS OR METABOLOMICS? |
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III. WHY OMICS? |
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IV. WHICH PLATFORM? |
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V. MAGIC ANGLE SPINNING (MAS) |
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VI. CHEMOMETRICS |
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A typical NMR metabonomics study can generate hundreds of biofluid samples and, hence, hundreds of NMR spectra. Examining each spectrum individually can be a daunting exercise even for the trained spectroscopist. Tools have been developed or borrowed from other fields for assessing large numbers of NMR spectra in a relatively rapid fashion. PCA is one such tool that has been borrowed for metabonomics to such a point that PCA cluster plots, also known as "scores plots," have become iconic of the metabonomics publication or presentation. Masses of spectral data, such as that generated by NMR, can be thought of in terms of a multivariate statistical problem. The true variables are the metabolite concentrations. In practice, pseudo-variables are generated by integrating the spectral data into discrete regions about the width of spectral peaks associated with metabolites. The integrated area under the curve of each of these regions (referred to as "bins" or "buckets") is calculated, and these values serve as variables. A 0.04 ppm-wide region is a typical bin width which will produce 200 to 250 "buckets" of data from the typical 10 PPM NMR spectrum. Certain regions of the spectrum, such as those containing water and urea resonances (for urine), are typically excluded from the binning process. In the case of NMR spectra of biofluids, it can be expected that subsets of variables will be highly correlated with each other, because molecules may have more than one spectral peak and, hence, contribute to more than one bucket or variable. Principal component analysis (PCA) is a statistical method that reduces a great number of variables (usually correlated) into a smaller number of uncorrelated variables, which are called principal components. In short, PCA is a decomposition of the raw data into "scores" that indicate the relationship between samples and "loadings," which indicates the relationships (correlations) between the variables. The first principal component explains the greatest variability in the data, the second principal component is independent of (orthogonal to) the first component and second best explains the variability of the data and so on. The goal of the exercise is dimensional reduction, while sacrificing as little accuracy as possible. The analysis itself can be conducted using commercial multivariate statistical software available from several vendors. As used in a typical metabonomics study, a 200- to 250-variable set representing one spectrum is reduced to two or three variables, which can be represented as a single point in two- or three-dimensional plots, respectively. It is these plots that one typically sees in publications or presentations of metabonomics data. Whether the preceding paragraph is clear or not, the important point for the toxicologist to remember is that each point on a metabonomics PCA scores plot represents all the data contained in one spectrum. Sample points that cluster together have more similar spectra (and hence more similar biochemical makeup) than things that cluster apart. PCA plots are extremely powerful for rapid identification of inherent clusters in the data (which may be suggestive of a common effect or mechanism), assessment of dose-related and time-related changes, and the identification of individual outliers. However, by themselves, the scores plots add little to biomarker identification, provide little mechanistic insight on a molecular basis, and say nothing about the toxicological significance of the clusters. However, the PCA data can be examined in more detail by examining the loadings to find out which variable relationships are responsible for the loadings. After identifying the variables that are primarily responsible for separation, spectral regions can be identified and specific molecules postulated. PCA is only one of many statistical tools that can be used for metabonomics study. The scope and nature of these tools is beyond the extent of this review, and the reader is referred to any of the above-cited references for guidance on these techniques.
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VII. OTHER APPLICATIONS OF METABONOMICS |
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A. Environmental Applications
Some of the most significant efforts in the area of metabolic profiling have been made in the area of the botanical sciences (Fiehn et al., 2000a,b
, 2001
; Fiehn and Weckwerth, 2003
; Roberts, 2000
; Roberts and Jardetzky, 1981
; Roberts and Xia, 1995
; Trethewey et al., 1999
; Weckwerth et al., 2004
). Though this may seem an odd literature for toxicologists to spend their time perusing, the tools and techniques used by these researchers are quite powerful and applicable to any biological investigation. Of particular note are the efforts of these investigators to bring metabolic control and flux analysis into their experimental design and interpretation. Their experiments serve as a harbinger of where mammalian metabonomics efforts will soon be headed.
Beyond plants, metabonomics technology has made significant inroads into the environmental research community. The environmental applications of metabonomics have been recently briefly reviewed (Viant et al., 2003), and the diversity of work is fascinating. Some of the most interesting work in this area has been conducted in earthworms, where metabonomics has been shown to be useful for speciating worms by phenotype, a typically difficult task (Bundy et al., 2002c
), and for monitoring exposure to environmental chemicals or other physiologic disruption (Bundy et al., 2002a
,b
, 2001
, 2002c
, 2003
; Warne et al., 1999
).
In the marine world, metabonomics has been shown to differentiate between normal, stunted, and diseased abalone (Viant et al., 2003) and the embryonic stages of the Japanese Medaka (Viant, 2003
). Mammals other than laboratory species have not been ignored. Renal MAS and urine biofluid assessment of wood mouse, white-toothed shrew, and bank vole have been recently compared to the laboratory rat and inferences made with regard to the findings and varied metabolic processes between the species (Griffin et al., 2000
).
B. Clinical Applications
For the toxicologist, clinical application of metabonomic technology may be as important as, if not more important than any preclinical work in which he or she may become involved. After all, the human population is the intended target for almost all their efforts (we can't forget about the veterinary market!). Ideally, techniques developed to identify safety concerns preclinically, would be readily transferable to the clinic. One of the great strengths of metabonomics technology is that the use of urine as a primary sample enables noninvasive monitoring of both efficacy and toxicity endpoints.
Clinical application of NMR technology has a long and storied history (Andrew, 1984). Lindon (Lindon et al., 1999
) reviews a series of approximately 40 human inborn errors of metabolism that have been studied using NMR techniques over the past 20 years. To some extent, the questions raised earlier now become apparent. Is there a difference between biofluid NMR and metabonomics, and when does the former graduate to the latter? Lindon et al. (1999)
reports on work conducted by Foxall (Foxall et al., 1993
) and Le Moyec (Le Moyec et al., 1993
) on an interesting case that may serve as a transition between traditional clinical NMR and clearly ascribed clinical metabonomics applications (of course, it is debatable whether any clinical application of NMR can be called traditional, but that is another story). A significant clinical problem with transplant patients is differentiating between patients undergoing graft rejection and those suffering from cyclosporin toxicity. Cyclosporin is an immunosuppressive drug frequently given to transplant patients, and the clinical presentation of the toxicity can look very similar to graft rejection. To address this problem, urine was collected from patients undergoing kidney transplants that were given cyclosporin and monitored by NMR spectroscopy. The use of NMR coupled with pattern recognition techniques and trajectory analysis could clearly differentiate when a patient was undergoing graft rejection versus succumbing to cyclosporin. This may be considered a true clinical metabonomic application, because the spectral pattern and trajectory change were what was used to differentiate the toxicity, not any specific biochemical marker. This is not to say that, had a unique biomolecule been associated with either graft rejection or cyclosporin toxicity, it couldn't be used in isolation, but that isolation and identification of such a unique biomarker was not necessary to gain important and clinically relevant information.
Several recent publications demonstrate quite convincingly the power of clinical metabonomics. While earlier studies on inborn errors of metabolism focused on molecular identification of the relevant metabolic pathways (Holmes et al., 1997), a more recent effort details methods for rapid identification of inborn errors of metabolism using pattern recognition techniques (Constantinou et al., 2004
). Even more compelling was recent work demonstrating that metabonomics could be used to rapidly and noninvasively assess the severity of coronary heart disease in a clinical population (Brindle et al., 2002
). The same group demonstrated a relationship between serum metabolic profiles and hypertension (Brindle et al., 2003
). Metabonomic patterns have also been shown to be useful in the diagnosis of interstitial cystitis (IC), having the ability to differentiate IC from bacterial cystitis in a clinical population with a success rate of approximately 84% (Van et al., 2003
). Beyond disease diagnoses, metabonomics has also been shown to be an effective tool for assessing lifestyle markers of health, particularly related to nutritional variation (German et al., 2003a
,b
; Noguchi et al., 2003
; Teague et al., 2004
).
An oft-raised concern about the use of metabonomics in clinical trials is the inherent variability of the clinical population. Given that metabonomics can identify even the slightest variation in animal studies, what might we expect from the clinical population where genetic and environmental factors (including dietary) can only be minimally controlled? An answer to that question was recently published by Lenz et al. (2003), who demonstrated that both urine and plasma could be reliably collected for metabonomic analyses in well-controlled clinical studies.
It can be anticipated that the rate of expansion of clinical applications of metabonomic technology will probably increase at an even greater pace than preclinical applications. The costs and difficulties associated with conducting clinical studies demand that more efficient, comprehensive tools be made available, so that greater levels of information can be gained from costly clinical trials without increasing the level of discomfort to the patient or decreasing the practicality to the physician. Metabonomics can meet both those requirements.
C. Biomedical Applications
There are a number of biomedical metabonomic applications that fall outside the realm of clinical and toxicology applications. For example, a recent review documents the significance of metabolic profiles of cancer cells as a tool for understanding tumor development and progression (Griffin and Shockcor, 2004). Since metabonomics, by definition, will describe a biochemical phenotype of whatever living system is being evaluated, an obvious application of the technology is generating strain phenotypes from either experimentally altered genotypes (e.g., transgenic models) or those derived by selective breeding (congenic, coisogenic, consomic, etc.). Early work demonstrated the biochemical differences of two albino rat strains commonly used in the pharmaceutical industry, the Han Wistar and SpragueDawley rat (Holmes et al., 2001
). The technology also answered the age-old question of how you tell a white mouse (AlpK:ApfCD) from a black mouse (C57BL/10J). So important was the question that it was answered using both NMR (Gavaghan et al., 2000
) and MS (Plumb et al., 2003
) based platforms. More recent work demonstrated that metabonomic assessment of brain extracts was able to distinguish phenotypic differences in a transgenic mouse model of spinocerebellar ataxia as compared to the background C57BL/6J strain (Griffin et al., 2004
).
The role of the gut microflora in metabolism and toxicity, though well recognized (Boxenbaum et al., 1979; Coates, 1975
; Eyssen, 1973
; Gibson, 1998
; Gonthier et al., 2003
; Rowland, 1981
, 1988
; Upreti et al., 2004
; van der Waaij, 1991
) has not been seriously evaluated at the omic level. Recently Nicholson and Wilson emphasized the role of the gut microflora in understanding systemic response to drug or toxins at both the level of metabolism and pathophysiology (Nicholson and Wilson, 2003
). This is particularly true for anyone trying to understand a metabolic response via metabonomic technology. An interesting amplification of this concept was conducted by Nicholls et al., who followed the urinary metabolic profiles as axenic rats adapted to normal gut microflora in the laboratory environment (Nicholls et al., 2003
).
Other recently reported applications of metabonomics include the identification of a unique biomolecular signature associate with a parasite infection in mice (Wang et al., 2004) and metabonomic assessment of adrenal lipids in the hypoxic neonatal rat (Bruder et al., 2004
).
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VIII. METABONOMICS AND TOXICOLOGY |
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The disadvantage of toxicity models is that toxicities seldom are as "clean" as some literature may suggest. Classical target organ toxins frequently are only classic because most of the literature has focused on one target organ (e.g., CCl4 and hepatic toxicity). While the literature usually correctly identifies the primary target of a toxin (i.e., dose-limiting toxicity), what are frequently missed are all the other systemic effects that are produced by the compound. The omic technologies, and metabonomics is no exception, will not let you forget about other targets. Too often we attempt to associate omic findings with what we know of a compound, which may or may not be indicative of all that is happening. For example if we know a compound is hepatotoxic, we make inferences that the gene, protein or metabolite changes we are seeing must in some way be reflective of the hepatic effect. However, what if the drug is also producing inappetence? As mentioned earlier, that effect may be more profound from an omic standpoint (number of genes, proteins and metabolites affected) than is the hepatic toxicity.
A. Logistical Considerations
A schematic representation of the logistical steps necessary for conducting metabonomics studies is given in Figure 1. It is beyond the scope of this review to detail the physical and logistical requirements for the analytical instrumentation and support necessary for metabonomics technology. It is hoped that the toxicologist will seek out appropriate support and expertise for these functions, because trying to start them from scratch would be a daunting and expensive proposition. A summary of the NMR requirements for metabonomic studies can be found in several places (Lindon et al., 2004a,d
; Robertson et al., 2002
). Regardless of the specific details, three general requirements, common to both the MS and NMR platforms that any toxicologist needs to keep in mind when pursuing metabonomic technology are (1) capital cost (if existing equipment can not be utilized), (2) space (these are not small instruments and require significant space), and (3) trained personnel (the most critical requirement). Fortunately, for most large industrial and academic institutions, appropriate instrumentation, space, and personnel are typically already available. The biggest need then becomes instrument time and the time of the trained personnel.
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For the foreseeable future, in vivo assessment in animal models will remain the primary method for identifying safety issues. With this in mind, one of the early hopes was that metabonomics would provide a generic safety screen for rapid throughput toxicity assessment (Robertson et al., 2000). While that hope has not been abandoned, it has been tempered by the reality of systemic responses to toxins and the complexity of differentiating "off-target" effects from target-organ-specific effects. However, generic screens are not the only type of screening for which metabonomics has application. In some instances, early identification and characterization of a known or presumed toxicity within a pharmacological or chemical class is extremely valuable, particularly when there are no available peripheral biomarkers of that toxicity. Drug-induced vascular injury (vasculitis) is one such application, where there is a lack of definitive peripheral biomarkers, and histopathology of affected tissues is still required to make the diagnosis. Metabonomics has been shown to be quite useful for noninvasive detection of the lesion in rats (Robertson et al., 2001
).
2. Biomarkers.
It has been recognized for some time that metabonomics had enormous potential to identify novel biomarkers of toxicity, with early work focused primarily on biomarkers of renal and hepatic toxicity (Anthony et al., 1994a,b
; Holmes et al., 1992a
,b
, 1995
, 1996
; Nicholls et al., 2001
; Robertson et al., 2000
). This work was eye opening with regard to systemic response to hepatic and renal toxins that has been overlooked for years. However, one need only run a quick comparison of these papers and others like them to recognize one of the most oft-cited criticisms of metabonomics as a tool in toxicology. That is the problem of "usual suspects." Table 2 is a list of urine metabolites that frequently change in response to toxicant administration, regardless of the nature of the toxicant, its mechanism of action, or its target. Importantly, not all these molecules change in response to every toxicant, nor do they necessarily follow the same trajectory (temporal response), but changes in some or most of them frequently drive pattern separation using unsupervised pattern recognition techniques like the now ubiquitous PCA (Beckwith-Hall et al., 1998
; Gartland et al., 1991
; Jansen et al., 2004
; Scholz et al., 2004
; Waters et al., 2001
). This has led to a jaded view of the technology by some observers, with one toxicologist wag calling a high-field magnet nothing more than a big "citrate-ometer," emphasizing the fact that citrate changes are a frequent response to toxicant administration. A comprehensive evaluation of this phenomenon attributed changes to many of the usual suspects to altered diet and/or bodyweight changes which are a frequent consequence of toxicity (Connor et al., 2004
), As the authors observe though, even among the usual suspects the magnitude, direction, and temporal response of the changes may still be useful in providing mechanistic or biomarker data, as long as the changes are evaluated in the context of the systemic effect. It is important to note that metabolites driving pattern separations within PCA does not mean that these are the only metabolites changing within an NMR (or MS) spectrum, nor does it imply that the molecules are necessarily the most interesting, from a biomarker or mechanistic perspective. Weight loss is a quite profound physiological disruption (at least from the animal's perspective), so it should not be surprising that it is responsible for acute biochemical perturbations that dominate the systemic biochemistry. The trick, of course, is separating specific biomarkers of the target of interest from the numerous changes caused by diet and other secondary factors. Rather than fulminate over metabonomics-derived usual suspects, it might be better to ask "where are the usual suspects for the other omic technologies?" After all, no one is suggesting that these components are in any way artifactual; therefore they must be derived from the actions of proteins and the genes that code them.
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Depending on how you define a biomarker, these biomolecular components may or may not fit the bill. Certainly most, if not all, of these biomarkers are unlikely to be specific only to the target of interest. While lack of specificity may hamper use of these markers in the general population, many will probably be adequate for preclinical safety studies or even controlled clinical trials where absence of an effect is of most critical interest.
3. Mechanisms of toxicity.
Arguably, the most important endpoint for an omics investigation would be in elucidating a mechanism of toxicity. An understood mechanism of toxicity will always deliver a biomarker (whether that biomarker is analytically feasible or practical is another story). While biomarkers can be identified without an understood mechanism, there would be little argument that a mechanistically linked biomarker is far more saleable. Metabonomics has proven to be a powerful tool for gaining insights on mechanisms of toxicity. The fact that the usual suspects are usual, by itself, is an interesting mechanistic finding. Most mechanistic metabonomic work has focused on renal and hepatic toxins, associating temporal biofluid biochemical correlations with toxicity endpoints. In most cases, these data were accompanied by speculative inferences of the biological significance of the various metabolic changes (Anthony et al., 1992, 1994b
, 1995b
; Gartland et al., 1989a
,b
; Halligan et al., 1995
; Holmes et al., 1992b
, 1995
, 1996
, 1998
; Lenz et al., 2004a
,b
; Lindon et al., 2004d
; Nicholson et al., 2002
; Robertson et al., 2000
; Shockcor and Holmes, 2002
; Warne et al., 1999
; Waters et al., 2001
).
Beyond these studies, a few notable metabonomic investigations stand out with regard to their mechanistic insights. In 2001 Nicholls et al. published data on hydrazine toxicity that mechanistically linked the neurotoxic effects of hydrazine to markedly increased levels of 2-aminoadipate (2AA), which is known to affect kynurenic acid levels in the brain, thus providing a plausible hypothesis for the heretofore unexplained neurotoxic effects of the compound (Nicholls et al., 2001). Slim et al., demonstrated that the urinary metabolite changes induced by Type 4 phosphodiesterase (PDE4) inhibitors were not the indirect result of concurrent inflammation but were directly associated with vascular pathology (Slim et al., 2002
). Clayton et al., mechanistically linked the "usual suspect" creatine to hepatotoxicity via effects on cysteine synthesis. They later related elevated creatine levels in serum and urine with hepatotoxicity and nutritional effects (Clayton et al., 2003
, 2004
). Mortishire-Smith linked urinary dicarboxylic aciduria to impaired fatty acid metabolism, which may be common to some hepatotoxic mechanisms (Mortishire-Smith et al., 2004
).
While these reports highlight individual mechanisms, novel approaches to metabonomic studies have also served to enhance the utility of the technology for mechanistic purposes. Integrated metabonomics makes use of combined serum and urine biofluid metabonomics coupled with tissue MAS to present a much more comprehensive mechanistic picture of toxicity. These efforts have been successfully employed looking at cadmium, -naphthlyisothiocyanate (ANIT), and acetaminophen toxicity (Coen et al., 2003
; Griffin et al., 2001
; Waters et al., 2001
). Even more exciting are recent efforts to combine metabonomics with other omic technologies, including proteomics and transcriptomics (Coen et al., 2004
; Kleno, 2004
; Verhoeckx et al., 2004
).
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IX. STATE OF THE TECHNOLOGY |
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What to do? While significant challenges face metabonomics, the technology clearly has much promise, and none of the problems are insoluble. Success breeds acceptance. Increased presentation and publication of clear demonstrations of impact on real world toxicology issues (not CCl4 or bromoethylamine examples!) will go a long way in moving the technology forward. The catch, of course, is that the biggest success stories are probably the most valuable from an intellectual property perspective, diminishing the likelihood that they will appear in the literature anytime soon. Still, when possible, the metabonomics community needs to push these examples out to the toxicology community, or the technology may never get the acceptance it deserves.
How will metabonomics be employed in toxicology departments 10 years from now? Who knows? However, if properly researched and developed, metabonomics can take its place as a standard tool in experimental toxicology. The ability to assess samples noninvasively makes it ideal for deployment in early discovery studies at the time sufficient bulk compound becomes available for in vivo studies. Metabonomics can easily be piggybacked on existing in vivo studies, requiring little or no additional technical resources to gather the data. One can envision safety endpoints being moved very early into the discovery process, enabling early attrition of toxicologically problematic compounds. Moreover, the search for biomarkers of efficacy and safety can start with the first dose in whole animals, and derived putative biomarkers will be readily transferable to the clinical setting. The ability to gather comprehensive metabolic profiles will prove invaluable for elucidating mechanisms of action not readily apparent using traditional endpoints such as histology and clinical pathology. Metabonomics clearly has a lot of promise, but just as clearly still a long way to go.
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X. CONCLUSION |
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ACKNOWLEDGMENTS |
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