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WO2001092880A2 - Methode d'analyse de voies metaboliques - Google Patents

Methode d'analyse de voies metaboliques Download PDF

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Publication number
WO2001092880A2
WO2001092880A2 PCT/GB2001/002388 GB0102388W WO0192880A2 WO 2001092880 A2 WO2001092880 A2 WO 2001092880A2 GB 0102388 W GB0102388 W GB 0102388W WO 0192880 A2 WO0192880 A2 WO 0192880A2
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nmr
incorporation
data
analysis
samples
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PCT/GB2001/002388
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WO2001092880A3 (fr
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Jeremy Nicholson
John Lindon
Elaine Holmes
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Metabometrix Limited
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Priority to AU2001260463A priority Critical patent/AU2001260463A1/en
Publication of WO2001092880A2 publication Critical patent/WO2001092880A2/fr
Publication of WO2001092880A3 publication Critical patent/WO2001092880A3/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • G01N33/5008—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells for testing or evaluating the effect of chemical or biological compounds, e.g. drugs, cosmetics
    • G01N33/5082—Supracellular entities, e.g. tissue, organisms
    • G01N33/5088—Supracellular entities, e.g. tissue, organisms of vertebrates

Definitions

  • 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.
  • Diseases of the human or animal body 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.
  • genomic methods 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
  • Genomic 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.
  • 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.
  • a protein separation method e.g., 2D gel-electrophoresis
  • chemical characterisation method usually, some form of mass spectrometry.
  • 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.
  • 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.
  • 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.
  • 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 .
  • sampling tissue for genomic and proteomic studies at inappropriate time points may result in a relevant gene or protein being overlooked.
  • 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.
  • 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 vi tro 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.
  • in situ chemical e.g., enzymatic
  • 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 X E ⁇ MR 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 ⁇ MR-generated metabolic data sets. Metabonomic methods have the potential, ultimately, to determine the entire dynamic metabolic make-up of an organism.
  • PR pattern recognition
  • a pathological condition or a xenobiotic may act at the pharmacological level only and hence may not affect gene regulation or expression directly.
  • significant disease or toxicological effects may be completely unrelated to gene switching.
  • exposure to ethanol in vivo may switch on many genes but none of these gene expression events explains drunkenness .
  • genomic and proteomic methods are likely to be ineffective.
  • 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.
  • 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 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.
  • 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.
  • 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 home&stasis (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 .
  • NMR spectroscopy see, for example, Nicholson et al . , 1989
  • intact tissues have been successfully analysed using magic-angle-spinning X H 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.
  • 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) .
  • each compound or class of compound prodiices 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.
  • 1 H 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.
  • 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.
  • 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 D 2 0 has been added, then it is possible that certain X H 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
  • the main advantages of using X 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 X H NMR studies have been performed at 400 MHz or greater.
  • 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.
  • 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 detection limit may be ca. 4 nmol , e.g., 1 ⁇ g of a 250 g/mol compound in a 0.5 mL sample volume.
  • 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.
  • Pattern recognition 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.
  • PCA principal component analysis
  • HCA hierarchical cluster analysis
  • NLM non-linear mapping
  • PCA principal components analysis
  • PCs Principal components
  • 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 .
  • 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 .
  • 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, PCI 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.
  • 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.
  • the closest pair of points will have the largest s i:j , 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 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 -dimensional space should also be close in 2 or 3 dimensional space (see, for example, Brown et al., 1996; Farrant et al . , 1992).
  • 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 examp-le, 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.
  • 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, ⁇ illson, 1965) ; K-nearest neighbour analysis (K ⁇ ) (see, for example, Brown et al . , 1996); artificial neural networks (ANN) (see, for example, Wasserman, 1989; Anker et al .
  • SIMCA soft independent modelling of class analysis
  • PLS partial least squares analysis
  • LDA linear descriminant analysis
  • K ⁇ K-nearest neighbour analysis
  • ANN artificial neural networks
  • PNNs probabilistic neural networks
  • RI rule induction
  • 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).
  • 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 .
  • 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 deterjnine 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.
  • 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.
  • the perturbing is carried out to have effect simultaneously with the labeling.
  • the step of comparing the time-related label incorporation of the unperturbed and perturbed systems is carried out by pattern recognition (PR) data analysis.
  • PR pattern recognition
  • 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 syst m; 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.
  • the data is compared using pattern recognition data analysis .
  • 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.
  • 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.
  • 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.
  • applied stimuli include, but are not limited to, a xenobiotic, a disease state, and a genetic modification.
  • xenobiotic refers to a substance (e.g., compound, composition) which is administered to an organism, or to which the organism is exposed.
  • 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.
  • disease state refers to a deviation from the normal healthy state of the organism.
  • diseases 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) .
  • genetic modification 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 .
  • the present invention involves the step of labelling at least one endogenous molecule for incorporation into a metabolic pathway.
  • 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 13 C or 15 N is used. Alternatively, if mass spectrometry is to be used, for example, any stable isotope which affects the mass of the labelled molecule (s) .
  • 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 13 C or 15 N 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.
  • NMR nuclear magnetic resonance
  • MALDI matrix assisted laser desorption ionization
  • detection methods including sector detection, quadrupole detection, ion-trap, time-of-flight , and Fourier transform.
  • NMR nuclear magnetic resonance
  • ID 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)
  • JRES J-resolved
  • NOESY NOESY
  • COSY COSY
  • TOCSY nuclear magnetic resonance
  • heteronuclear correlation including direct detection methods such as
  • HETCOR and inverse-detected methods such as ⁇ -"C 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.
  • MAS magic-angle-spinning
  • Composite spectra which are formed from two or more spectra of different types, may also be used.
  • 13 C is used as the label.
  • 13 C NMR is relatively insensitive using present technology.
  • recent developments in cryoprobe and hyper-polarization technology will improve the sensitivity of 13 C NMR.
  • the preferred analytical method for detecting 13 C labels is HSQC which allows the determination of 13 C shift from correlation with measured proton shift. This is termed "indirect detection” .
  • 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.
  • 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., dialysisizid); material which develops as a function of pathology (e.g., a cyst, blisters); supernatant from a cell culture.
  • 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
  • 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.
  • urine 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 synovial fluid
  • ascite fluid cystic fluid
  • blister fluid plus cell suspensions and extracts thereof.
  • tissue samples include liver, kidney, prostate, brain, gut, blood, skeletal muscle, heart uscle, lymphoid, bone, cartilage, and reproductive tissues .
  • 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.) .
  • 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.
  • the organism in general, may be a prokaryote (e.g., bacteria) or a eukaryote (e.g., protoctista, fungi, plants, animals) .
  • a prokaryote e.g., bacteria
  • 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 platyhelminth.es (flatworms) (e.g., planarians, flukes, tapeworms), a cnidaria (e.g., jelly fish, sea anemones, corals), or a porifera (e.g., sponges).
  • a nematode round worms
  • 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) .
  • an insect e.g., beetles, butterflies, moths
  • a chilopoda centipedes
  • a diplopoda millipedes
  • crustacean e.g., shrimps, crabs, lobsters
  • 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) .
  • a reptile e.g., snakes, lizards, crocodiles
  • an amphibian e.g., frogs, toads
  • a bony fish e.g., salmon, plaice, eel, lungfish
  • cartilaginous fish e.g., sharks, rays
  • 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,
  • the organism may be any of its forms, for example, a spore, a seed, an egg, a larva, a pupa, or a foetus . Examples
  • liver cells are placed in an incubator in an appropriate incubation medium at 37°C and agitated.
  • a solution of glycine labelled with 13 C 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 ⁇ -"C-HSQC experiment to determine the time-related incorporation of the labelled glycine into the metabolic pathways of the cells.
  • liver cells are then perturbed by addition of hydrazine, a known hepatoxin.
  • 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 .
  • TMA trimethylamine
  • Samples of biofluids from one group of rats are analysed by 15 N 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.
  • 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.
  • Nicholson J.K et al . , 1989 “High Resolution proton NMR spectroscopy of biological fluids” , Progress in NMR Spectroscopy, Vol. 21, pp449-501

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Abstract

La présente invention concerne des méthodes d'analyse de voies métaboliques et de l'effet de perturbations ou d'applications de stimuli sur des voies métaboliques. Cette méthode consiste à perturber un système, par exemple au moyen d'un xénobiotique, d'un état de maladie ou d'une modification génétique, à marquer au moins une petite molécule endogène devant être incorporée à une voie métabolique, dans le système, à analyser des échantillons issus du système perturbé, afin de déterminer l'incorporation dans le temps de la molécule marquée, puis à comparer les données ainsi obtenues avec des données obtenues à partir d'un système correspondant, non perturbé, par utilisation de techniques de reconnaissance de formes, par exemple.
PCT/GB2001/002388 2000-05-30 2001-05-30 Methode d'analyse de voies metaboliques WO2001092880A2 (fr)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2004040300A3 (fr) * 2002-10-29 2005-08-25 Pharmacia & Upjohn Co Llc Procede d'utilisation de modeles animaux pour prevoir les effets indesirables d'un medicament
US8068987B2 (en) 2001-08-13 2011-11-29 Bg Medicine, Inc. Method and system for profiling biological systems
CN110632113A (zh) * 2019-07-04 2019-12-31 北京大学 一种原子分子水平的原位细胞分析方法

Non-Patent Citations (8)

* Cited by examiner, † Cited by third party
Title
BECKWITH-HALL B M ET AL: "Nuclear magnetic resonance spectroscopic and principal components analysis investigations into biochemical effects of three model hepatotoxins." CHEMICAL RESEARCH IN TOXICOLOGY, vol. 11, no. 4, April 1998 (1998-04), pages 260-272, XP001024928 ISSN: 0893-228X cited in the application *
GARBOW J R ET AL: "Milacemide metabolism in rat liver and brain slices by solids NMR." DRUG METABOLISM AND DISPOSITION, vol. 22, no. 2, 1994, pages 298-303, XP001024922 ISSN: 0090-9556 *
GRUETTER ROLF ET AL: "Localized in vivo 13C-NMR of glutamate metabolism in the human brain: Initial results at 4 Tesla." DEVELOPMENTAL NEUROSCIENCE, vol. 20, no. 4-5, July 1998 (1998-07), pages 380-388, XP001024919 ISSN: 0378-5866 *
HOLMES E ET AL: "The identification of novel biomarkers of renal toxicity using automatic data reduction techniques and PCA of proton NMR spectra of urine" CHEMOMETRICS AND INTELLIGEMT LABORATORY SYSTEMS, ELSEVIER SCIENCE PUBLISHERS, AMSTERDAM, NL, vol. 44, no. 1-2, 14 December 1998 (1998-12-14), pages 245-255, XP004152698 ISSN: 0169-7439 cited in the application *
HOLMES ELAINE ET AL: "1H and 2H NMR spectroscopic studies on the metabolism and biochemical effects of 2-bromoethanamine in the rat." BIOCHEMICAL PHARMACOLOGY, vol. 49, no. 10, 1995, pages 1349-1359, XP001024920 ISSN: 0006-2952 *
NICHOLSON J K ET AL: "'METABONOMICS':UNDERSTANDING THE METABOLIC RESPONSES OF LIVING SYSTEMS TO PATHOPHYSIOLOGICAL STIMULI VIA MULTIVARIATE STATISTICAL ANALYSIS OF BIOLOGICAL NMR SPECTROSCOPIC DATA" XENOBIOTICA, TAYLOR AND FRANCIS, LONDON,, GB, vol. 29, no. 11, November 1999 (1999-11), pages 1181-1189, XP001021360 ISSN: 0049-8254 cited in the application *
SONNEWALD U ET AL: "NMR Spectroscopic Studies of 13C Acetate and 13C Glucose Metabolism in Neocortical Astrocytes: Evidence for Mitochondrial Heterogeneity." DEVELOPMENTAL NEUROSCIENCE, vol. 15, no. 3-5, 1993, pages 351-358, XP001024914 ISSN: 0378-5866 *
WALKER T E ET AL: "CARBON-13 NMR STUDIES OF THE BIOSYNTHESIS BY MICROBACTERIUM-AMMONIAPHILUM OF L GLUTAMATE SELECTIVELY ENRICHED WITH CARBON-13" JOURNAL OF BIOLOGICAL CHEMISTRY, vol. 257, no. 3, 1982, pages 1189-1195, XP001024870 ISSN: 0021-9258 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8068987B2 (en) 2001-08-13 2011-11-29 Bg Medicine, Inc. Method and system for profiling biological systems
WO2004040300A3 (fr) * 2002-10-29 2005-08-25 Pharmacia & Upjohn Co Llc Procede d'utilisation de modeles animaux pour prevoir les effets indesirables d'un medicament
CN110632113A (zh) * 2019-07-04 2019-12-31 北京大学 一种原子分子水平的原位细胞分析方法

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