BACKGROUND Significant progress has been made in developing methods to determine and quantify the biochemical processes occurring in living systems. Such methods are valuable in the diagnosis, prognosis and treatment of disease, the development of drugs, as well as for improving therapeutic regimes for current drugs.

Diseases of the human or animal body (such as cancers, degenerative diseases, autoimmune diseases and the like) have an underlying basis in alterations in the expression of certain-genes. The expressed gene products, proteins, mediate effects such as abnormal cell growth, cell death or inflammation. Some of these effects are caused directly by protein-protein interactions; other are caused by proteins acting on small molecules (e. g."second messengers") which trigger effects including further gene expression.

Likewise, disease states caused by external agents such as viruses and bacteria provoke a multitude of complex responses in infected host.

In a similar manner, the treatment of disease through the administration of drugs can result in a wide range of desired effects and unwanted side effects in a patient.

At the genetic level, methods for examining gene expression in response to these types of events are often referred to as"genomic methods,"and are concerned with the detection and quantification of the expression of an organism's genes, collectively referred to as its "genome,"usually by detecting and/or quantifying genetic molecules, such as DNA and RNA. Genomic studies often exploit a new generation of proprietary"gene chips," which are small disposable devices encoded with an array of genes that respond to extracted mRNAs produced by cells (see, for example, Klenk et al., 1997). Many genes can be placed on a chip array and patterns of gene expression, or changes therein, can be monitored rapidly, although at some considerable cost.

However, the biological consequences of gene expression, or altered gene expression following perturbation, are extremely complex. This has led to the development of "proteomic methods"which are concerned with the semi-quantitative measurement of the production of cellular proteins of an organism, collectively referred to as its"proteome" (see, for example, Geisow, 1998).

Proteomic measurements utilise a variety of technologies, but all involve a protein separation method, e. g., 2D gel-electrophoresis, allied to a chemical characterisation method, usually, some form of mass spectrometry.

In recent years, it has been appreciated that the reaction of human and animal subjects to disease and treatments for them can vary according to the genomic makeup of an individual. This has led to the development of the field of"pharmacogenomics."A fuller understanding of how an individual's own genome reacts to a particular disease will allow the development of new therapies, as well as the refinement of existing ones.

At present, genomic and proteomic methods, which are both expensive and labour intensive, have the potential to be powerful tools for studying biological response. The choice of method is still uncertain since careful studies have sometimes shown a low correlation between the pattern of gene expression and the pattern of protein expression, probably due to sampling for the two technologies at inappropriate time points. Even in combination, genomic and proteomic methods still do not provide the range of information needed for understanding integrated cellular function in a living system, since they do not take account of the dynamic metabolic status of the whole organism.

For example, genomic and proteomic studies may implicate a particular gene or protein in a disease or a xenobiotic response because the level of expression is altered, but the change in gene or protein level may be transitory or may be counteracted downstream and as a result there may be no effect at the cellular and/or biochemical level.

Conversely, sampling tissue for genomic and proteomic studies at inappropriate time points may result in a relevant gene or protein being overlooked.

Nonetheless, recent advances in genomics and proteomics now permit the rapid identification of new potential targets for drug development. With a new target in hand, and with the aid of combinatorial chemistry and high throughput screening, the pharmaceutical industry is capable of rapidly generating and screening thousands of new candidate compounds each week.

However, in practice, only a few of these candidate compounds will be taken further, for example, into pre- clinical and clinical development. It is therefore critical to identify those candidate compounds with the most promise, and this is usually judged by efficacy and toxicology, before selection for clinical studies.

However, these selection processes are imperfect and many drugs fail in clinical trials due to lack of efficacy and/or toxicological effects. It is also possible that other drugs may fail overall because they are only effective in a subgroup of patients who have an unrecognised pharmacogenomic response. There is a great need to find new ways of reducing this compound "attrition"or losses of drugs late in the development process, for example, through the development and application of analytical technologies designed to maximise efficiency of compound selection and to minimise attrition rates.

While genomic and proteomic methods may be useful aids in compound selection, they do suffer from substantial limitations. For example, while genomic and proteomic methods may ultimately give profound insights, into

toxicological mechanisms and provide new surrogate biomarkers of disease, at present it is very difficult to relate genomic and proteomic findings to classical cellular or biochemical indices or endpoints. One simple reason for this is that with current technology and approach, the correlation of the time-response to drug exposure is difficult. Further difficulties arise with in vitro cell-based studies. These difficulties are particularly important for the many known cases where the metabolism of the compound is a prerequisite for a toxic effect and especially true where the target organ is not the site of primary metabolism. This is particularly true for pro-drugs, where some aspect of in situ chemical (e. g., enzymatic) modification is required for activity.

A new"metabonomic"approach has been proposed which is aimed at augmenting and complementing the information provided by genomics and proteomics."Metabonomics"is conventionally defined as"the quantitative measurement of the multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification" (see, for example, Nicholson et al., 1999). This concept has arisen primarily from the application of 1H NMR spectroscopy to study the metabolic composition of biofluids, cells, and tissues and from studies utilising pattern recognition (PR), expert systems and other chemoinformatic tools to interpret and classify complex NMR-generated metabolic data sets. Metabonomic methods have the potential, ultimately, to determine the entire dynamic metabolic make-up of an organism.

A pathological condition or a xenobiotic may act at the pharmacological level only and hence may not affect gene regulation or expression directly. Alternatively significant disease or toxicological effects may be completely unrelated to gene switching. For example, exposure to ethanol in vivo may switch on many genes but none of these gene expression events explains drunkenness.

In cases such as these, genomic and proteomic methods are likely to be ineffective. However, all disease or drug-induced pathophysiological perturbations result in disturbances in the ratios and concentrations, binding or fluxes of endogenous biochemicals, either by direct chemical reaction or by binding to key enzymes or nucleic acids that control metabolism. If these disturbances are of sufficient magnitude, effects will result which will affect the efficient functioning of the whole organism.

In body fluids, metabolites are in dynamic equilibrium with those inside cells and tissues and, consequently, abnormal cellular processes in tissues of the whole organism following a toxic insult or as a consequence of disease will be reflected in altered biofluid compositions.

Fluids secreted, excreted, or otherwise derived from an organism ("biofluids") provide a unique window into its biochemical status since the composition of a given biofluid is a consequence of the function of the cells that are intimately concerned with the fluid's manufacture and secretion. For example, the composition of a particular fluid can carry biochemical information on details of organ function (or dysfunction), for example, as a result of xenobiotics, disease, and/or genetic

modification. Similarly, the composition and condition of an organism's tissues are also indicators of the organism's biochemical status. Examples of biofluids include, for example, urine, blood plasma, milk, etc.

Biofluids often exhibit very subtle changes in metabolite profile in response to external stimuli. This is because the body's cellular systems attempt to maintain homeastasis (constancy of internal environment), for example, in the face of cytotoxic challenge. One means of achieving this is to modulate the composition of biofluids. Hence, even when cellular homeostasis is maintained, subtle responses to disease or toxicity are expressed in altered biofluid composition. However, dietary, diurnal and hormonal variations may also influence biofluid compositions, and it is clearly important to differentiate these effects if correct biochemical inferences are to be drawn from their analysis.

One of the most successful approaches to biofluid analysis has been the use of NMR spectroscopy (see, for example, Nicholson et al., 1989); similarly, intact tissues have been successfully analysed using magic-angle-spinning 1H NMR spectroscopy (see, for example, Moka et al., 1998; Tomlins et al., 1998).

The NMR spectrum of a biofluid provides a metabolic fingerprint or profile of the organism from which the biofluid was obtained, and this metabolic fingerprint or profile is characteristically changed by a disease, toxic process, or genetic modification. For example, NMR

spectra may be collected for various states of an organism (e. g., pre-dose and various times post-dose, for one or more xenobiotics, separately or in combination; healthy (control) and diseased animal; unmodified (control) and genetically modified animal).

For example, in the evaluation of undesired toxic side- effects of drugs, each compound or class of compound produces characteristic changes in the concentrations and patterns of endogenous metabolites in biofluids that provide information on the sites and basic mechanisms of the toxic process. 1H NMR analysis of biofluids has successfully uncovered novel metabolic markers of organ-specific toxicity in the laboratory rat, and it is in this"exploratory"role that NMR as an analytical biochemistry technique excels. However, the biomarker information in NMR spectra of biofluids is very subtle, as hundreds of compounds representing many pathways can often be measured simultaneously, and it is this overall metabonomic response to toxic insult that so well characterises the lesion.

All biological fluids and tissues have their own characteristic physico-chemical properties, and these affect the types of NMR experiment that may be usefully employed. One major advantage of using NMR spectroscopy to study complex biomixtures is that measurements can often be made with minimal sample preparation (usually with only the addition of 5-10% D20) and a detailed analytical profile can be obtained on the whole biological sample. Sample volumes are small, typically 0.3 to 0.5 mL for standard probes, and as low as 3 pL for microprobes.

Acquisition of simple NMR spectra is rapid and efficient using flow-injection technology. It is usually necessary to suppress the water NMR resonance.

Many biofluids are not chemically stable and for this reason care should be taken in their collection and storage. For example, cell lysis in erythrocytes can easily occur. If a substantial amount of D20 has been added, then it is possible that certain 1H NMR resonances will be lost by H/D exchange. Freeze-drying of biofluid samples also causes the loss of volatile components such as acetone. Biofluids are also very prone to microbiological contamination, especially fluids, such as urine, which are difficult to collect under sterile conditions. Many biofluids contain significant amounts of active enzymes, either normally or due to a disease state or organ damage, and these may enzymes may alter the composition of the biofluid following sampling. Samples should be stored deep frozen to minimise the effects of such contamination. Sodium azide is usually added to urine at the collection point to act as an antimicrobial agent. Metal ions and or chelating agents (e. g., EDTA) may be added to bind to endogenous metal ions (e. g., Ca Mg2+ and Zon2+) and chelating agents (e. g., free amino acids, especially glutamate, cysteine, histidine and aspartate; citrate) to alter and/or enhance the NMR spectrum.

In all cases the analytical problem usually involves the detection of"trace"amounts of analytes in a very complex matrix of potential interferences. It is, therefore, critical to choose a suitable analytical technique for the particular class of analyte of interest in the particular

biomatrix which could be a biofluid or a tissue. High resolution NMR spectroscopy (in particular 1H NMR) appears to be particularly appropriate. The main advantages of using'H NMR spectroscopy in this area are the speed of the method (with spectra being obtained in 5 to 10 minutes), the requirement for minimal sample preparation, and the fact that it provides a non-selective detector for all the abnormal metabolites in the biofluid regardless of their structural type, providing only that they are present above the detection limit of the NMR experiment and that they contain non-exchangeable hydrogen atoms. The speed advantage is of crucial importance in this area of work as the clinical condition of a patient may require rapid diagnosis, and can change very rapidly and so correspondingly rapid changes must be made to the therapy provided.

NMR studies of body fluids should ideally be performed at the highest magnetic field available to obtain maximal dispersion and sensitivity and most 1H NMR studies have been performed at 400 MHz or greater. With every new increase in available spectrometer frequency the number of resonances that can be resolved in a biofluid increases and although this has the effect of solving some assignment problems, it also poses new ones. Furthermore, there are still important problems of spectral interpretation that arise due to compartmentation and binding of small molecules in the organised macromolecular domains that exist in some biofluids such as blood plasma and bile. All this complexity need not reduce the diagnostic capabilities and potential of the technique,

but demonstrates the problems of biological variation and the influence of variation on diagnostic certainty.

The information content of biofluid spectra is very high and the complete assignment of the 1H NMR spectrum of most biofluids is usually not possible (even using 900 MHz NMR spectroscopy). However, the assignment problems vary considerably between biofluid types. Some fluids have near-constant composition and concentrations and in these the majority of the NMR signals have been assigned. In contrast, urine composition can be very variable and there is enormous variation in the concentration range of NMR-detectable metabolites ; consequently, complete analysis is much more difficult. Those metabolites present close to the limits of detection for 1-dimensional (1D) NMR spectroscopy (ca. 100 nM for many metabolites at 800 MHz) pose severe NMR spectral assignment problems.

(In absolute terms, the detection limit may be ca. 4 nmol, e. g., 1 pg of a 250 g/mol compound in a 0.5 mL sample volume.) Even at the present level of technology in NMR, it is not yet possible to detect many important biochemical substances, e. g. hormones, proteins or nucleic acids in body fluids because of problems with sensitivity, line widths, dispersion and dynamic range and this area of research will continue to be technology-limited. In addition, the collection of NMR spectra of biofluids may be complicated by the relative water intensity, sample viscosity, protein content, lipid content, low molecular weight peak overlap.

Usually in order to assign lu NMR spectra, comparison is made with spectra of authentic materials and/or by

standard addition of an authentic reference standard to the sample. Additional confirmation of assignments is usually sought from the application of other NMR methods, including, for example, 2-dimensional (2D) NMR methods, particularly COSY (correlation spectroscopy), TOCSY (total correlation spectroscopy), inverse-detected heteronuclear correlation methods such as HMBC (heteronuclear multiple bond correlation), HSQC (heteronuclear single quantum coherence), and HMQC (heteronuclear multiple quantum coherence), 2D J-resolved (JRES) methods, spin-echo methods, relaxation editing, diffusion editing (e. g., both 1D NMR and 2D NMR such as diffusion-edited TOCSY), and multiple quantum filtering. Detailed 1H NMR spectroscopic data for a wide range of metabolites and biomolecules found in biofluids have been published (see, for example, Lindon et al., 1999) and supplementary information is available in several literature compilations of data (see, for example, Fan, 1996; Sze et al., 1994).

For example, the successful application of 1H NMR spectroscopy of biofluids to study a variety of metabolic diseases and toxic processes has now been well established and many novel metabolic markers of organ-specific toxicity have been discovered (see, for example, Nicholson et al., 1989; Lindon et al., 1999). For example, NMR spectra of urine is identifiably altered in situations where damage has occurred to the kidney or liver. It has been shown that specific and identifiable changes can be observed which distinguish the organ that is the site of a toxic lesion. Also it is possible to focus in on particular parts of an organ such as the cortex of the kidney and even in favourable cases to very localised

parts of the cortex. Finally it is possible to deduce the biochemical mechanism of the xenobiotic toxicity, based on a biochemical interpretation of the changes in the urine.

A wide range of toxins has now been investigated including mostly kidney toxins and liver toxins, but also testicular toxins, mitochondrial toxins and muscle toxins.

However, a limiting factor in understanding the biochemical information from both 1D and 2D-dimensional NMR spectra of tissues and biofluids is their complexity.

The most efficient way to investigate these complex multiparametric data is employ the 1D and 2D NMR metabonomic approach in combination with computer-based "pattern recognition" (PR) methods and expert systems.

These statistical tools are similar to those currently being explored by workers in the fields of genomics and proteomics.

Pattern recognition (PR) is a general term applied to methods of data analysis which can be used to generate scientific hypotheses as well as testing hypotheses by reducing mathematically the many parameters.

PR methods may be conveniently classified as"supervised" or"unsupervised."Unsupervised methods are used to analyse data without reference to any other independent knowledge, for example, without regard to the identity or nature of a xenobiotic or its mode of action.

Examples of unsupervised pattern recognition methods include principal component analysis (PCA), hierarchical cluster analysis (HCA), and non-linear mapping (NLM).

One of the most useful and easily applied unsupervised PR techniques is principal components analysis (PCA) (see, for example, Sharaf, 1986). Principal components (PCs) are new variables created from linear combinations of the starting variables with appropriate weighting coefficients. The properties of these PCs are such that: (i) each PC is orthogonal to (uncorrelated with) all other PCs,-and (ii) the first PC contains the largest part of the variance of the data set (information content) with subsequent PCs containing correspondingly smaller amounts of variance.

A data matrix, X, can be regarded as composed of a scores matrix, T, and a loadings matrix, L, such that X = TLt, where t denotes the transpose. The covariance matrix, C, is calculated from the data matrix, X. The eigenvalues and eigenvectors of the covariance matrix are determined by diagonalisation. The coordinates in eigenvector plots (the principle components, PCs) are denoted"scores"and comprise the scores matrix T. The eigenvector coefficients are denoted"loadings"and comprise the loadings matrix L, and give the contributions of the descriptors to the PCs.

Thus a plot of the first two or three PC scores gives the "best"representation, in terms of information content, of the data set in two or three dimensions, respectively. A plot of the first two principal component scores, PC1 and PC2, is often called a"scores plot", and provides the maximum information content of the data in two dimensions.

Such PC maps can be used to visualise inherent clustering

behaviour for drugs and toxins acting on each organ according to toxic mechanism. Of course, the clustering information might be in lower PCs and these have also to be examined.

In this simple metabonomic approach, a sample from an animal treated with a compound of unknown toxicity is compared with a database of NMR-generated metabolic data from-control and toxin-treated animals. By observing its position on the PR map relative to samples of known effect, the unknown toxin can often be classified.

However, toxicological data are often more complex, with time-related development of lesions and associated shifts in NMR-detected biochemistry. Also, it is more rigorous to compare effects of xenobiotics in the original n-dimensional NMR metabonomic space.

Hierarchical Cluster Analysis, another unsupervised pattern recognition method, permits the grouping of data points which are similar by virtue of being"near"to one another in some multidimensional space. Individual data points may be, for example, the signal intensities for particular assigned peaks in an NMR spectrum. A "similarity matrix,"S, is constructed with elements sij = 1-rij/rijmax'where rij is the interpoint distance between points i and j (e. g., Euclidean interpoint distance), and rijmax is the largest interpoint distance for all points.

The most distant pair of points will have sij equal to 0, since rij then equals rijmaX. Conversely, the closest pair of points will have the largest sij, approaching 1.

The similarity matrix is scanned for the closest pair of points. The pair of points are reported with their separation distance, and then the two points are deleted and replaced with a single combined point. The process is then repeated iteratively until only one point remains. A number of different methods may be used to determine how two clusters will be joined, including the nearest neighbour method (also known as the single link method), the furthest neighbour method, the centroid method (including centroid link, incremental link, median link, group average link, and flexible link variations).

The reported connectivities are then plotted as a dendrogram (a tree-like chart which allows visualisation of clustering), showing sample-sample connectivities versus increasing separation distance (or equivalently, versus decreasing similarity). The dendrogram has the property in which the branch lengths are proportional to the distances between the various clusters and hence the length of the branches linking one sample to the next is a measure of their similarity. In this way, similar data points may be identified algorithmically.

Non-linear mapping (NLM) is a simple concept which involves calculation of the distances between all of the points in the original d dimensions. This is followed by construction of a map of points in 2 or 3 dimensions where the sample points are placed in random positions or at values determined by a prior principal components analysis. The least squares criterion is used to move the sample points in the lower dimension map to fit the inter- point distances in the lower dimension space to those in

the d dimensional space. Non-linear mapping is therefore an approximation to the true inter-point distances, but points close in d-dimensional space should also be close in 2 or 3 dimensional space (see, for example, Brown et al., 1996; Farrant et al., 1992).

Alternatively, and in order to develop automatic classification methods, it has proved efficient to use a "supervised"approach to NMR data analysis. Here, a "training set"of NMR metabonomic data is used to construct a statistical model that predicts correctly the "class"of each sample. This training set is then tested with independent data ("test set") to determine the robustness of the computer-based model. These models are sometimes termed"Expert Systems,"but may be based on a range of different mathematical procedures. Supervised methods can use a data set with reduced dimensionality (for example, the first few principal components), but typically use unreduced data, with all dimensionality.

In all cases the methods allow the quantitative description of the multivariate boundaries that characterise and separate each class, for example, each class of xenobiotic in terms of its metabolic effects. It is also possible to obtain confidence limits on any predictions, for example, a level of probability to be placed on the goodness of fit (see, for example, Sharaf, 1986). The robustness of the predictive models can also be checked using cross-validation, by leaving out selected samples from the analysis.

Expert systems may operate to generate a variety of useful outputs, for example, (i) classification of the sample as

"normal"or"abnormal" (this is a useful tool in the control of spectrometer automation using sequential flow injection NMR spectroscopy); (ii) classification of the target organ for toxicity and site of action within the tissue where in certain cases, mechanism of toxic action may also be classified; and, (iii) identification of the biomarkers of a pathological disease condition or toxic effect for the particular compound under study. For example, a sample can be classified as belonging to a single class of toxicity, to multiple classes of toxicity (more than one target organ), or to no class. The latter case would indicate deviation from normality (control) based on the training set model but having a dissimilar metabolic effect to any toxicity class modelled in the training set (unknown toxicity type). Under (ii), a system could also be generated to support decisions in clinical medicine (e. g., for efficacy of drugs) rather than toxicity.

Examples of supervised pattern recognition methods include the following, which are-briefly described below: soft independent modelling of class analysis (SIMCA) (see, for example, Wold, 1976); partial least squares analysis (PLS) (see, for example, Wold, 1966; Joreskog, 1982; Frank, 1984); linear descriminant analysis (LDA) (see, for example, Nillson, 1965); K-nearest neighbour analysis (KNN) (see, for example, Brown et al., 1996); artificial neural networks (ANN) (see, for example, Wasserman, 1989; Anker et al., 1992; Hare, 1994); probabilistic neural networks (PNNs) (see, for example, Parzen, 1962; Bishop, 1995; Speckt, 1990; Broomhead et al., 1988; Patterson,

1996); rule induction (RI) (see, for example, Quinlan, 1986); and, Bayesian methods.

As the size of metabonomic databases increases together with improvements in rapid throughput of NMR samples (> 300 samples per day per spectrometer is now possible with the first generation of flow injection systems), more subtle expert systems may be necessary, for example, using techniques such as"fuzzy logic"which permit greater flexibility in decision boundaries.

Pattern recognition methods have been applied to the analysis of metabonomic data, including, for example, complex NMR data, with some success (see, for example, Anthony et al., 1994; Anthony et al., 1995; Beckwith-Hall et al., 1998; Gartland et al., 1990a; Gartland et al., 1990b; Gartland et al., 1991; Holmes et al., 1998a; Holmes et al., 1998b; Holmes et al., 1992; Holmes et al., 1994; Spraul et al., 1994; Tranter et al., 1999).

Although the utility of the metabonomic approach is well established, there remains a great need for improved methods of analysis. The metabolic variation is often subtle, and powerful analysis methods are required for detection of particular analytes, especially when the data (e. g., NMR spectra) are so complex.

It is known to use tracers to obtain information about metabolic pathways both in vivo and in vitro. By labelling an endogenous molecule that is incorporated into a metabolic pathway, it has been possible to trace the movement of the molecule and its metabolites (which will

also contain the label) through various metabolic pathways. Simple kinetics information has also been obtained by the comparison of the integrals of peaks in NMR spectra obtained by analysing biofluids at regular intervals over a period of time.

As far as the applicants are aware, no labelling experiments have been carried out in an attempt to establish the effect of perturbation or an applied stimulus on a metabolic pathway. Furthermore, the basic spectral analysis method involving study of the integrals of spectral peaks is inaccurate and, in most cases, the spectra are very complex and therefore it difficult to locate the peaks of interest.

An applied stimulus may be a drug and therefore, this information is necessary for many aspects of drug design and development.

One aim of the present invention is to provide methods for the detection of metabolic variations arising as a result of a perturbation or applied stimulus, as part of a metabonomic approach to the investigation of metabolic pathways.

SUMMARY OF THE INVENTION One aspect of the present invention pertains to a method for the analysis of metabolic pathways and the effect of perturbation or applied stimuli on metabolic pathways.

One aspect of the invention pertains to a method for analyzing the effect of a perturbation on a metabolic pathway in a system comprising: perturbing the system; labeling at least one endogenous small molecule for incorporation into at least one metabolic pathway in a system; analyzing samples from the perturbed system to determine the time-related incorporation of the at least one label; and comparing the time-related incorporation of the at least one label in the perturbed system with that of an unperturbed said system.

The incorporation data for the unperturbed system may be provided from standard established data for the system.

Alternatively, incorporation data for the unperturbed system may be obtained by: labeling at least one endogenous small molecule for incorporation into at least one metabolic pathway in the unperturbed system: and analyzing samples from the unperturbed system to determine the time-related incorporation of the at least one label.

In preferred embodiments, the perturbing is carried out to have effect simultaneously with the labeling.

In especially preferred embodiments of the present invention, the step of comparing the time-related label

incorporation of the unperturbed and perturbed systems is carried out by pattern recognition (PR) data analysis.

One embodiment of the present invention pertains to a method for analyzing the effect of a perturbation on a metabolic pathway comprising labeling at least one endogenous small molecule for incorporation into at least one metabolic pathway in a system ; analyzing samples from the system, preferably by NMR, to determine the time-related incorporation of the at least one label; perturbing the system; analyzing samples from the perturbed system, preferably by NMR, to determine the time-related incorporation of the at least one label; and comparing the data of the unperturbed and perturbed.

Preferably, the data is compared using pattern recognition data analysis.

In one preferred embodiment, the applied stimulus is a xenobiotic. In one preferred embodiment, the applied stimulus is a disease state. In one preferred embodiment, the applied stimulus is a genetic modification.

DETAILED DESCRIPTION OF THE INVENTION This invention pertains generally to the field of metabonomics, and, more particularly, to methods for the

analysis of metabolic pathways and the effect of perturbation or applied stimuli on metabolic pathways.

Perturbation The term"applied stimulus"or"perturbation"as used herein, pertains to a stimulus under study which is applied to, or is present in, an organism under study, and is not applied to, and is absent in, a control organism.

Examples of applied stimuli include, but are not limited to, a xenobiotic, a disease state, and a genetic modification.

The term"xenobiotic,"as used herein, pertains to a substance (e. g., compound, composition) which is administered to an organism, or to which the organism is exposed. In general, xenobiotics are chemical, biochemical or biological molecules which are not normally present in that organism, or are normally present in that organism, but not at the level obtained following administration. Examples of xenobiotics include drugs, formulated medicines and their components, pesticides, herbicides, substances present in foods (e. g. plant compounds administered to animals), and substances present in the environment.

The term"disease state,"as used herein, pertains to a deviation from the normal healthy state of the organism.

Examples of disease states include bacterial, viral, parasitic infections, cancer in all its forms, degenerative diseases (e. g., arthritis, multiple sclerosis), trauma (e. g., as a result of injury), organ

failure (including diabetes), cardiovascular disease (e. g., atherosclerosis, thrombosis), and inherited diseases caused by genetic composition (e. g., sickle-cell anaemia).

The term"genetic modification,"as used herein, pertains to alteration of the genetic composition of an organism.

Examples of genetic modifications include the incorporation of a gene or genes into an organism from another species, increasing the number of copies of an existing gene or genes in an organism, removal of a gene or genes from an organism, rendering a gene or genes in an organism non-functional.

Labelling The present invention involves the step of labelling at least one endogenous molecule for incorporation into a metabolic pathway.

In a"directed approach", it is possible to identify an appropriate molecule for labelling, for example, using proton NMR studies to investigate a specific metabolic pathway and incorporation of a labelled molecule into that pathway. Then the single identified molecule can be labelled and used in the present invention.

In an"undirected approach"a plurality of different molecules can be uniquely labelled and analysis of samples of the system containing the labelled molecules can be analysed to obtain multiple time-related incorporation data.

The labels used can be any stable isotope. If analysis is to be carried out by NMR, then an NMR active isotope such as 13C or 15N is used. Alternatively, if mass spectrometry is to be used, for example, any stable isotope which affects the mass of the labelled molecule (s).

Analysis Methods Examples of the types of spectroscopy which can be used to analyze the time-related label incorporation include, but are not limited to, the following: magnetic resonance, including nuclear magnetic resonance (NMR), in particular 13C or 15N NMR; and mass spectrometry, including variations of ionization methods, including electron impact, chemical ionisation, thermospray, electrospray, matrix assisted laser desorption ionization (MALDI), inductively coupled plasma, and detection methods, including sector detection, quadrupole detection, ion-trap, time-of-flight, and Fourier transform.

One particularly preferred class of spectroscopy is nuclear magnetic resonance (NMR). Examples of such methods include 1D, 2D, and 3D-NMR, including, for example, 1D spectra, such as single pulse, water-peak saturated, spin-echo such as CPMG (i. e., edited on the basis of relaxation), diffusion-edited; 2D spectra, such as J-resolved (JRES), 1H-lH correlation methods such as NOESY, COSY, TOCSY and variants thereof, heteronuclear correlation including direct detection methods such as HETCOR and inverse-detected methods such as 1H-13C HMQC, HSQC and HMBC; 3D spectra, including many variants, all of

which are combinations of 2D methods, e. g. HMQC-TOCSY, NOESY-TOCSY, etc. All of these NMR spectroscopic techniques can also be combined with magic-angle-spinning (MAS) in order to study samples other than isotropic liquids, such as tissues or foodstuffs, which are characterised by anisotropic composition.

Composite spectra, which are formed from two or more spectra of different types, may also be used.

In especially preferred embodiments 13C is used as the label. Unfortunately, 13C NMR is relatively insensitive using present technology. However, recent developments in cryoprobe and hyper-polarization technology will improve the sensitivity of 13C NMR.

In the meantime, the preferred analytical method for detecting 13C labels is HSQC which allows the determination of 13C shift from correlation with measured proton shift.

This is termed"indirect detection".

System Samples The analysis steps in the methods of the present invention are applied to samples obtained from the systems under study. The system may be any biological or chemical system with a metabolic pathway.

Samples may be in any form which is compatible with the particular type of spectroscopy, and therefore may be, as appropriate, homogeneous or heterogeneous, comprising one or a combination of, a gas, a liquid, a liquid crystal, a

gel, or a solid, and including samples with a biological origin.

Examples of such samples include those originating from an organism, for example, a whole organism (living or dead, e. g., a living human, a culture of bacteria); a part or parts of an organism (e. g., a tissue sample, an organ, a leaf); a pathological tissue such as a tumour; a tissue homogenate (e. g. a liver microsome fraction); an extract prepared from a organism or a part of an organism (e. g., a tissue sample extract, such as perchloric acid extract); an infusion prepared from a organism or a part of an organism (e. g., tea, Chinese traditional herbal medicines); an in vitro tissue such as a spheroid; a suspension of a particular cell type (e. g. hepatocytes); an excretion, secretion, or emission from an organism (especially a fluid); material which is administered and collected (e. g., dialysis fluid); material which develops as a function of pathology (e. g., a cyst, blisters); supernatant from a cell culture.

Examples of fluid samples include, for example, urine, (gall bladder) bile, blood plasma, whole blood, cerebrospinal fluid, milk, saliva, mucus, sweat, gastric juice, pancreatic juice, seminal fluid, prostatic fluid, seminal vesicle fluid, seminal plasma, amniotic fluid, foetal fluid, follicular fluid, synovial fluid, aqueous humour, ascite fluid, cystic fluid, and blister fluid, plus cell suspensions and extracts thereof.

Examples of tissue samples include liver, kidney, prostate, brain, gut, blood, skeletal muscle, heart

muscle, lymphoid, bone, cartilage, and reproductive tissues.

Still other examples of samples include air (e. g., exhaust), water (e. g., seawater, groundwater, wastewater, e. g., from factories), liquids from the food industry (e. g. juices, wines, beers, other alcoholic drinks, tea, milk), solid-like food samples (e. g. chocolate, pastes, fruit peel, fruit and vegetable flesh such as banana, leaves, meats, whether cooked or raw, etc.).

For samples which are, or are drawn from, an organism, the organism, in general, may be a prokaryote (e. g., bacteria) or a eukaryote (e. g., protoctista, fungi, plants, animals).

The organism may be an alga or a protozoan.

The organism may be a plant, an angiosperm, a dicotyledon, a monocotyledon, a gymnosperm, a conifer, a ginkgo, a cycad, a fern, a horsetail, a clubmoss, a liverwort, or a moss.

The organism may be a chordate, an invertebrate, an echinoderm (e. g., starfish, sea urchins, brittlestars), an arthropod, an annelid (segmented worms) (e. g., earthworms, lugworms, leeches), a mollusk (cephalopods (e. g., squids, octopi), pelecypods (e. g., oysters, mussels, clams), gastropods (e. g., snails, slugs)), a nematode (round worms), a platyhelminthes (flatworms) (e. g., planarians, flukes, tapeworms), a cnidaria (e. g., jelly fish, sea anemones, corals), or a porifera (e. g., sponges).

The organism may be an arthropod, an insect (e. g., beetles, butterflies, moths), a chilopoda (centipedes), a diplopoda (millipedes), a crustacean (e. g., shrimps, crabs, lobsters), or an arachnid (e. g., spiders, scorpions, mites).

The organism may be a chordate, a vertebrate, a mammal, a bird a reptile (e. g., snakes, lizards, crocodiles), an amphibian (e. g., frogs, toads), a bony fish (e. g., salmon, plaice, eel, lungfish), a cartilaginous fish (e. g., sharks, rays), or a jawless fish (e. g., lampreys, hagfish).

The organism may be a mammal, a placental mammal, a marsupial (e. g., kangaroo, wombat), a monotreme (e. g., duckbilled platypus), a rodent (e. g., a guinea pig, a hamster, a rat, a mouse), murine (e. g., a mouse), avian (e. g., a bird), canine (e. g., a dog), feline (e. g., a cat), equine (e. g., a horse), porcine (e. g., a pig), ovine (e. g., a sheep), bovine (e. g., a cow), a primate, simian (e. g., a monkey or ape), a monkey (e. g., marmoset, baboon), an ape (e. g., gorilla, chimpanzee, orangutang, gibbon), or a human.

Furthermore, the organism may be any of its forms, for example, a spore, a seed, an egg, a larva, a pupa, or a foetus.

Examples A typical in vitro experiment is described below.

Firstly, liver cells are placed in an incubator in an appropriate incubation medium at 37°C and agitated. Next a solution of glycine labelled with 13C is added to the cells and the mixture is agitated for 15 minutes to allow the labelled glycine to be incorporated into the cells.

Samples of the liver cell tissue are analysed at regular 15 minute time intervals by NMR using the 1H-l3C-HSQC experiment to determine the time-related incorporation of the labelled glycine into the metabolic pathways of the cells.

The liver cells are then perturbed by addition of hydrazine, a known hepatoxin.

Samples of the cell tissue are then reanalysed by lH-l3C- HSQC to determine the effect of the toxin on the incorporation of the labelled glycine.

More preferably, two groups of liver cells are used and analysed. One is labelled and then analysed to provide control data. The other is labelled, perturbed and then analysed. Ideally, studies are carried out to establish the time taken for label incorporation and the time taken for the toxic effect to start such that, in the second group of cells, the two effects are timed to coincide and the sample is first analysed at the time that the two effects commence.

The spectral data derived from the perturbed and unperturbed systems are compared using PR data analysis.

A typical in vivo experiment is described below.

Two groups of five rats are taken and 15N labelled trimethylamine (TMA) is administered orally or by peritoneal injection to all of the animals.

Samples of biofluids from one group of rats are analysed by 15N NMR at regular fifteen minute intervals to observe the incorporation of the labelled TMA and to obtain control data.

2-Bromothallamine is administered to the second group of rats such that time taken for the toxic effect to have effect corresponds with the time of incorporation of the labelled TMA.

Sample of biofluids from the second groups of rats are analysed by NMR using the lH-l5N-HSQC experiment to see the incorporation of the labelled TMA after perturbation.

The spectral data obtained from the two groups of rats is compared using pattern recognition techniques to determine the effect of the toxin on the metabolic pathway of interest.

The method according to the present invention can also be used to compare the perturbation response of wildtype and transgenic organisms.

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