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代谢组学技术 代谢组学是继基因组学和蛋白质组学之后新近发展起来的一门学科，是系统生物学的重要组成部分。基因组学和蛋白质组学分别从 和蛋白质层面探寻生命的活动，而实际上细胞内许多生命活动是与代谢物相关的，如细胞信号（cell signaling），能量传递等都是受代谢物调控的。代谢组学正是研究代谢组（metabolome）——在某一时刻细胞内所有代谢物的集合——的一门学科。基因与蛋白质的表达紧密相连，而代谢物则更多地反映了细胞所处的环境，这又与细胞的营养状态，药物和环境污染物的作用，以及其它外界因素的影响密切相关。因此有人认为，基因组学和蛋白质组学能够说明可能发生的事件，而代谢组学则反映确实已经发生了的事情。 代谢组学主要研究的是作为各种代谢路径的底物和产物的小分子代谢物（MW<1000）。其样品主要是尿液，血浆或血清，唾液，以及细胞和组织的提取液。主要技术手段是核磁共振（NMR ），液-质联用（LC-MS），气-质联用（GC-MS），色谱（HPLC，GC）等。通过检测一系列样品的谱图，再结合化学模式识别方法，可以判断出生物体的病理生理状态，基因的功能，药物的毒性和药效等，并有可能找出与之相关的生物标志物（biomarker）。 代谢组学在新药的安全性评价，毒理学，生理学，重大疾病的早期诊断，个性化治疗，功能基因组学，中医药现代化，环境评价，营养学等科学领域中都有着极其广泛和重要的应用前景，是一门充满朝气的学科。 在“863”相关课题的部分支持和院创新研究基金的支持下，国家生物医学分析中心于2002年底，在现有仪器条件下，积极开始了基于核磁共振的代谢组学技术方法的建立，并与院内相关实验室展开了合作研究。在2003年初，就将其应用到抗癌新药Z24的毒性研究中，获得了较好的结果，已经发表2篇论文。在此基础之上进行的项目“代谢组学方法的建立及其在新药安全性评价中的应用”获得了国家自然科学基金的资助（30371705）。目前已经利用现有条件，在核磁共振和液-质联用谱仪上初步建立了代谢组学研究的技术，测试了数千个尿液、血浆及组织提取液样品，对我院自主研发的新药和模型化合物的毒性进行了代谢组学实验研究。我们还将代谢组学技术应用到中药现代化研究中，开展了中药毒性的研究，对关木通的肾毒性进行考察，部分结果已经在24届化学年会和波谱学年会上做了报告。我们还准备开展中药复方减毒增效机理的研究，以及代谢组学方法对中药材的质量进行研究。申请的课题“代谢组学方法在中药毒性研究中的应用”获得了国家自然科学基金重大研究计划“中医药学几个关键科学问题的现代研究”项目的资助（90409019）。目前，正准备开展中药复方作用机理和症候模型的代谢组学研究工作。 正常大鼠尿样的600兆核磁共振氢谱 其中包含了成百上千个化合物的谱峰，一张谱图可包含数百种有关疾病和中毒等过程的信息,谱图中的每一段都可以被看作是对应于不同病理生理条件的生物标志物的“窗口”。 引用网址 http://www.ncba.cn/serve/daixiezuxue.htm [ Last edited by zw3210612 on 2005-12-15 at 11:28 ] |
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12楼2006-02-02 13:55:14
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终极“组学”——代谢组学 Metabolomics--A New Exciting Field within the "omics" Sciences <-- end ArticleInfo --> <-- start ArticleView --> Metabolomics can be regarded as the end point of the “omics” cascade. Metabolomics is an emerging field in analytical biochemistry and can be regarded as the end point of the "omics" cascade. Whereas genomics deals with the analysis of the complete genome in order to understand the function of single genes, the majority of functional genomics studies are currently based on the analysis of gene expression (transcriptomics) and comprehensive protein analysis (proteomics). As we are amassing knowledge of the genome, the transcriptome, and the proteome, we have largely forgotten the metabolome. However, changes in the metabolome are the ultimate answer of an organism to genetic alterations, disease, or environmental influences. The metabolome is therefore most predictive of phenotype (Fiehn 2002; Weckwerth 2003). Consequently, the comprehensive and quantitative study of metabolites, or metabolomics, is a desirable tool for either diagnosing disease or studying the effects of toxicants on phenotype. One of course wonders why metabolomics has lagged behind other "omics" technologies. Possibly this is because the number of metabolites varies dramatically based on how they are counted. Investigators also debate about what compounds are considered metabolites; for example, should vitamins or smaller peptides be included? According to a simple and widely used definition, a metabolite is any substance involved in metabolism either as a product of metabolism or necessary for metabolism. In any case 3,000 major metabolites seem a reasonable number. If we attempt a global and quantitative evaluation, the technology involved is daunting because the physical properties of the compounds are so divergent and they vary dramatically in concentration. Moreover, the metabolome is a dynamic system subjected to significant environmental influences, for example, temporal or dietary. It is difficult to envision a single platform being developed in the near future that is able to analyze quantitatively all metabolites simultaneously. Thus with all metabolites as our goal, the technological hurdle seems to be the limiting step. At the other extreme, metabolomics can be seen as metabolite profiling or "just" analytical chemistry. So it is nothing new, simply multi-analyte chemistry that biochemists have been doing for decades. Of course metabolomics is simultaneously both and neither of these. Although an "omics" or global view of metabolism is a goal, by no means is universal coverage of all metabolites required for tremendous biological insight. Also whether we work on complete coverage of a single metabolic pathway or on a more global approach to examine multiple metabolites, such multi-analyte analysis is by no means trivial. Nevertheless, successful implementation of metabolomics requires analytical instrumentation that offers high throughput, resolution, reproducibility, and sensitivity, and only an assembly of different analytical platforms will currently provide maximum coverage of the metabolome. To date, metabolomics-type studies rely primarily on nuclear magnetic resonance (NMR) or mass spectrometry coupled to chromatography. Currently, two complementary approaches are used for metabolomic investigations. In one approach--metabolic profiling--quantitative analytical methods are developed for metabolites in a pathway or for a class of compounds. This approach produces independent information that can be interpreted in terms of known biochemical pathways and physiological interactions. These data represent an independent legacy database since they are quantitative. The disadvantage is that the system is not a universal or "omics" approach. However, the tremendous advances in technology over the past years allow the constant expansion of the number of analytes quantified simultaneously. Technologically, we are at a point where it is often as simple to measure many compounds as to measure one. If we take one step further and assemble a suite of quantitative methods analyzing key metabolites from different biochemical pathways, we can transform metabolic profiling into metabolomics. The second approach is metabolic fingerprinting. In such metabolomic investigations, the intention is not to identify each observed compound but to compare patterns or fingerprints of metabolites that change in response to disease or toxin exposure. Comparison of fingerprints, often NMR or mass spectra or chromatograms, is performed using statistical tools such as hierarchical cluster analysis or principal component analysis. If these types of analyse results in sample segregation into unique metabolic clusters, further efforts can be made to elucidate the discriminating compounds and subsequently to evaluate these monocytes as potential biomarkers. Being semiquantitative and simultaneously applicable to a wide range of metabolites--this is a true "omics" approach. Such methods are attractive, as they allow investigators to cast a wide net both generating and testing hypotheses. However, the nature of the data makes the observations instrument/platform dependent. The implementation of NMR-based metabolic fingerprinting has marked the beginning of a metabolomics approach as a tool in biochemistry and has proven to be a powerful technique (Nicholson et al. 2002). However, it will only detect high abundance metabolites. Complementary to NMR, mass spectrometry-based tools will provide coverage for metabolic fingerprinting in a lower concentration range, and their use is increasing steadily (Plumb et al. 2003). The combination of metabolic profiling and fingerprinting will lead to the realization of metabolomics. In one approach, changes in fingerprints correlating to metabolite profiles will be linked to a physiological state, without exact knowledge of fingerprint components. In another approach, discriminating compounds identified in fingerprints will become the focus for quantitative metabolite analyses. Therefore, metabolomics will contribute to our biological understanding both in a mechanistic as well as a predictive manner. However, it could also assist us in improving human health and may be among the first of the "omics" technologies to reach the clinic. Through multiple metabolomics projects, a powerful list of likely markers of variations in health can evolve (Watkins and German 2002). Analyzing this set of biomarkers in a single high throughput assay will provide the clinician with a powerful diagnostic tool. In genomics and transcriptomics we saw economies of scale as institutional support developed generating infrastructure behind the technologies. Similar support will be necessary to advance metabolomics. For example, a centralized effort to provide isotopic-labeled standards for a wide range of metabolites would tremendously accelerate work in metabolomics as would the development of an integrated pathway map to aid in data interpretation. Such a map would introduce us also to the next level of measuring flux through pathways. Although metabolomics is still in an early evolutionary stage, we can expect to see exciting new developments in the near future. As more quantitative metabolomic databases evolve, we can integrate them with data sets from the other "omics" technologies to enhance the data value and provide greater biological insight than any one "omics" technique alone can offer. Katja Dettmer Bruce D. Hammock Cancer Research Center University of California, Davis Davis, California E-mail: bdhammock@ucdavis.edu Katja Dettmer is a postdoctoral researcher in professor Bruce Hammock's laboratory at the University of California, Davis. She is conducting research in the field of metabolomics, focusing on the development of mass spectrometry-based tools for metabolic fingerprinting in biofluids as well as metabolic profiling methods. Bruce Hammock is a Distinguished Professor of Entomology and a scientist in the Cancer Research Center at the University of California, Davis. He directs an analytical-metabolomics laboratory that pioneered the use of immunochemical diagnostics in the environmental field. His research interests include development of recombinant viral pesticides, mammalian xenobiotic metabolism, environmental chemistry, and biosensor development. References Fiehn O. 2002. Metabolomics--the link between genotypes and phenotypes. Plant Mol Biol 48:155-171. Plumb RS, Stumpf CL, Granger JH, Castro-Perez J, Haselden JN, Dear GJ. 2003. Use of liquid chromatography/time-of-flight mass spectrometry and multivariate statistical analysis shows promise for the detection of drug metabolites in biological fluids. Rapid Commun Mass Spectrom 17:2632-2638. Nicholson JK, Connelly J, Lindon JC, Holmes E. 2002. Metabonomics: a platform for studying drug toxicity and gene function. Nat Rev Drug Discov 1:153-161. Watkins SM, German J B. 2002. Toward the implementation of metabolomic assessments of human health and nutrition. Curr Opin Biotechnol 13:512-516. Weckwerth W. 2003. Metabolomics in systems biology. Annu Rev Plant Biol 54:669-689. |
2楼2005-12-15 11:20:31
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近年来有关代谢组学研究进展 The Populus Genome Science Plan Panel on Metabolic Characterization and Metabolomics Panel Members: Tim Tschaplinski, Thomas Moritz, Andrea Polle, Scott Harding, Janice Cooke, and Reinhard Jetter BACKGROUND AND SCOPE The emerging science of metabolomics couples metabolite profiling with the analysis of mutant and transgenic lines to elucidate protein function, the structure of metabolic pathways, and offers tremendous potential to discover and assign function to novel genes. Stated explicitly, metabolic profiling is the unbiased, relative quantification of the broad array of cellular metabolites, and their fluxes. As such, metabolic profiling can provide information on how gene function affects the complex biochemical network, and the levels of regulation of biochemical networks that are not revealed by DNA microarray technology. A comprehensive functional genomics research platform, that links metabolite profiling to gene expression arrays and protein profiles, will facilitate the cataloguing of genes. About 500-1000 metabolites may be expected to accumulate to detectable levels in a typical eukaryotic genome, which codes for >10,000 proteins. Therefore, it is unlikely that a single gene knockout or up-regulation event will often lead to direct relationships of a single gene completely regulating the production and accumulation of a single metabolite. Some examples indicate that genetic mutations can lead to changes that are highly pleiotropic, depending on where the mutation is operating in the metabolic networks. However, the ability to detect a wide array of metabolites (and their fluxes) will permit determination of how biochemical networks, with their distributed control (regulation), have been perturbed. Successful deployment of metabolite profiling requires the development of rapid, reliable, and efficient assays for detecting phenotypes that are metabolic variants within natural or mutated populations. Assays need to be developed which will allow the detection of as many metabolites as possible and preferably at high-throughput rates. Although the desire is to have a single analysis that captures all metabolites in a short time, there is, as yet, no single “silver bullet” analysis that will be appropriate for all metabolites with a high degree of sensitivity and resolution. The varying chemical characteristics of the different classes of compounds will necessitate several analyses, but it is possible to standardize the use of a limited number of protocols that rapidly captures the bulk of small molecules. A number of analytical approaches are currently available that can image a large number of metabolites, but they need to address the problems of co-eluting interference, and be able to accurately identify as many of the peaks as possible. The current status of promising analytical approaches and what is needed to forward these approaches will be the focus of this plan. Included are sample preparation needs, description of the advantages and disadvantages of a given analytical approach and how a combination of multiple approaches can circumvent limitations. Overall, the current-best protocols need to be modified for high throughput, while simultaneously developing the next generation of high-throughput protocols that are scalable and address difficult-to-measure metabolites. Classes of challenging metabolites include intermediate-sized molecules (1000-2500 Da), and charged molecules, such as phosphorylated compounds. In addition to determination of the steady-state concentrations of large numbers of metabolites, in many cases, it is the flux of these metabolites that will provide key insight in to which gene was perturbed. The concentrations and fluxes of metabolites will need to be assessed in biochemical pathway inference models to probe pathway linkages. Recognizing that the greatest gains in functional genomics analysis will be derived from the integration of the different data streams, tools and approaches that combine the different classes of genomic data that are available, including DNA sequence data, mRNA expression profiles, protein profiles, and metabolite profiles, need to be developed. 3- Itemized list of goalsSCIENTIFIC OBJECTIVES Sampling and Sample Preparation Given that many compounds are unstable, have very high turnover rates, or exhibit diurnal variation in concentration, etc., sampling is important. Sample extraction prior the chemical analysis must be adapted for each type of sample (e.g., extraction protocols for leaf tissue from Populus might be different from extraction protocols for developing xylem tissue). The metabolites represent many different classes of compounds, and therefore the chemical properties of the metabolites are highly variable. Depending on the extraction protocol, different classes of compounds show different extraction efficiency under specific conditions. It is unlikely that a single extraction procedure for plant tissues allows accurate quantification for all compounds, but the goal is to capture as many metabolites as possible. Short-term goal: To establish standardized extraction protocols that are tailored to each tissue-type of Populus with high recovery and reproducibility. The extraction protocols will include addition of internal standards representing all major classes of compounds (e.g. carbohydrates, organic acids, steroids, amines, etc.) prior to extraction to increase accuracy and precision of the analysis. Long-term goal: To establish methods for reproducible fractionation of extracts using solid phase extraction (SPE) columns or other methods. The goal of fractionation is to concentrate metabolites, permitting more of the extract to be analyzed by the gas chromatography (GC)- or liquid chromatography-mass spectrometry (LC-MS), and lessening the probability of saturating columns or MS-detectors. Derivatization for GC-MS analysis GC-MS analysis of extracts containing such varied metabolites as organic acids, sugars, sugar alcohols, amino acids and steroids is complicated. Many of the metabolites are not volatile and must be derivatized prior analysis by GC. Methoxymation in combination with trimethylsilylation (methoxy-TMS) is widely used as the main derivatization protocol. By first protecting the carbonyl group(s), the coupled derivatizations are more efficient (than silylation alone) for low molecular weight organic acids, but the relatively low temperature of the protocol may limit the derivatization of the more difficult to derivatize metabolites, including secondary carbon compounds that are typical of Populus. The organic acids that are most vulnerable to TMS derivatization alone can also be captured in other separations and analyses. Short-term goal: Confirm the method(s) that minimizes sample preparatory time and maximizes spectral output (i.e., maximum metabolites observed) of the Populus species under investigation. The protocols must ensure the stability of the derivatized compounds and reproducibility of the data generated. Long-term goal: Develop standardized extraction and derivatization protocols that are suitable for complete automation. Analytical Techniques for Steady-State Metabolite Analyses High-throughput GC-MS Comparison of GC-MS techniques: The “oldest” and best-established coupling of methods to MS for metabolite analysis is GC-MS. The thermo-stable samples are vaporized and then ionized by either electron-impact (EI) or chemical-ionization (CI). For metabolomics analyses there are in practice two types of instruments used: 1) quadrupole (single stage MS and two stage MS/MS (ion trap)) and 2) time-of-flight (TOF) MS instruments. Both instruments have advantages and disadvantages, e.g. the quadrupole instruments have large dynamic range, are robust and easy to use. GC-TOF instruments have the capability to perform rapid spectral acquisition (up to 4-500 spectra/s over the full mass range), which results in possibility to speed up the analyses (high throughput), as narrow, short GC columns are used. The deconvolution of overlapping peaks is also greatly improved because of spectral continuity across a peak (no skewing of different masses). A disadvantage with GC-TOF instruments has been a reduced dynamic range (in practice) compared to quadrupole instruments, which can be a problem when analyzing complex samples with high variation in concentrations of compounds. GC-TOF MS is the approach of choice for high-throughput GC analyses, but as instrument manufacturers produce new quadrupole instruments with much higher scan rates, their ease-of-use may make them more versatile. 1D vs 2D GCxGC-MS Rapid advancement in two-dimensional (2D) gas chromatography (GCxGC) makes it a powerful tool when coupled with high-speed TOF-MS for the deconvolution of metabolites that co-elute in traditional one-dimensional (1D) GC-MS. The GCxGC-TOF-MS approach couples columns of different polarity and operated at different temperatures to shift retention times of co-eluting metabolites. The peak capacity is approx. equal to the product of the separation capacities of the individual columns. The eluent from the first column is pulsed into a second column, generating an array of high-speed secondary chromatograms that can be detected by the high speed, high capacity time-array detector of TOF-MS. All of the eluent from the first dimension is subjected to separation in the second dimension, not just a single congested area of the chromatogram. The approach can be used for deconvolution to ensure the proper assignment of fragments to the metabolites being deconvolved, which can then be incorporated into the data extraction algorithms of the 1D analyses. As a deconvolution tool, it can be applied with the introduction of each novel heterogeneous matrix (e.g., a new poplar species/tissue). Given that the approach requires the second column to function much faster than the first column, it is unlikely that GCxGC-TOF-MS will be deployed (in the near-term) as the standard data acquisition approach for high throughput profiling, until detectors with even more rapid acquisition rates become available. |
3楼2005-12-15 11:22:10
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代谢组学在毒理学中的角色【转帖】 以下是引用longer在2003-6-2 4:10:43的发言： Contemporary issues in toxicology the role of metabonomics in toxicology and its evaluation by the COMET project John C. Lindon, , a, Jeremy K. Nicholsona, Elaine Holmesa, Henrik Anttia, 1, Mary E. Bollarda, Hector Keuna, Olaf Beckonerta, Timothy M. Ebbelsa, Michael D. Reilyb, Donald Robertsonb, Gregory J. Stevensc, Peter Luked, Alan P. Breaue, Glenn H. Cantorf, Roy H. Biblee, Urs Niederhauserg, Hans Senng, Goetz Schlotterbeckg, Ulla G. Sidelmannh, Steen M. Laursenh, Adrienne Tymiaki, Bruce D. Cari, Lois Lehman-McKeemani, Jean-Marie Coletj, Ali Loukacij and Craig Thomasj, 2 a Biological Chemistry, Biomedical Sciences Division, Faculty of Medicine, Imperial College of Science, Technology and Medicine, Sir Alexander Fleming Building, South Kensington, London SW7 2AZ, UK b Pfizer Global R&D, 2800 Plymouth Road, Ann Arbor, MI 48105, USA c Pfizer Global R&D, La Jolla, CA, 92121, USA d Pfizer Global R&D, Sandwich, Kent CT13 9NJ, UK e The Pharmacia Corporation, 4901 Searle Parkway, Skokie, IL 60077, USA f The Pharmacia Corporation, 301 Henrietta St., Kalamazoo, MI 49007, USA g Hoffmann-La Roche AG, Grenzacherstrasse, CH-4070, Basel, Switzerland h Novo Nordisk, Novo Nordisk Park, 2760, Maaloev, Denmark i Bristol-Myers-Squibb Company, PO Box 4000, Princeton, NJ 08543, USA j Lilly Research Laboratories, Eli Lilly and Co., Lilly Development Centre S.A., 1348, Mont-Saint-Guibert, Belgium Received 19 September 2002; accepted 9 December 2002. ; Available online 6 March 2003. Abstract The role that metabonomics has in the evaluation of xenobiotic toxicity studies is presented here together with a brief summary of published studies. To provide a comprehensive assessment of this approach, the Consortium for Metabonomic Toxicology (COMET) has been formed between six pharmaceutical companies and Imperial College of Science, Technology and Medicine (IC), London, UK. The objective of this group is to define methodologies and to apply metabonomic data generated using 1H NMR spectroscopy of urine and blood serum for preclinical toxicological screening of candidate drugs. This is being achieved by generating databases of results for a wide range of model toxins which serve as the raw material for computer-based expert systems for toxicity prediction. The project progress on the generation of comprehensive metabonomic databases and multivariate statistical models for prediction of toxicity, initially for liver and kidney toxicity in the rat and mouse, is reported. Additionally, both the analytical and biological variation which might arise through the use of metabonomics has been evaluated. An evaluation of intersite NMR analytical reproducibility has revealed a high degree of robustness. Second, a detailed comparison has been made of the ability of the six companies to provide consistent urine and serum samples using a study of the toxicity of hydrazine at two doses in the male rat, this study showing a high degree of consistency between samples from the various companies in terms of spectral patterns and biochemical composition. Differences between samples from the various companies were small compared to the biochemical effects of the toxin. A metabonomic model has been constructed for urine from control rats, enabling identification of outlier samples and the metabolic reasons for the deviation. Building on this success, and with the completion of studies on approximately 80 model toxins, first expert systems for prediction of liver and kidney toxicity have been generated. Author Keywords: Metabonomics; Toxin; Pharmaceutical; NMR; Chemometrics; Expert system Article Outline Introduction Metabonomics background NMR spectroscopy of biofluids MAS NMR of tissues Chemometric analysis of metabolic NMR data Identification of novel biomarkers of toxicity Expert system development The COMET project Experimental methods used in COMET Animal experiments and sample collection Sample preparation and NMR spectroscopy Overview of results to date Critical issues requiring resolution A statistical model for control rat and mouse urine Intersite consistency of biofluid sample provision Consistency of NMR measurement study Overview of toxicity results The future: a perspective on metabonomics References Introduction The importance of postgenomic technologies for improving the understanding of drug adverse effects has been highlighted recently (Aardema and MacGregor 2002 and Cockerell et al 2002) and these approaches have been recognized to include metabonomics. While there is a comprehensive literature on the use of metabonomics to investigate xenobiotic toxicity and this has been reviewed recently (Nicholson et al., 2002), a rigorous and comprehensive evaluation would be of considerable value. To this end, a consortium has been formed to investigate the utility of NMR-based metabonomic approaches to the toxicological assessment of drug candidates. The main aim of the consortium is to use 1H NMR spectroscopy of biofluids (and, in selected cases, tissues), with the application of computer-based pattern recognition and expert system methods to classify the biofluids in terms of known pathological effects caused by administration of substances causing toxic effects. The project is hosted at Imperial College of Science, Technology and Medicine (IC), University of London, UK, and involves funding by six pharmaceutical companies, namely, Bristol-Myers-Squibb, Eli Lilly and Co., Hoffman–La Roche, NovoNordisk, Pfizer Incorporated, and The Pharmacia Corporation. The main objectives of the project are (1) provision of a detailed multivariate description of normal physiological and biochemical variation of metabolites in urine, blood serum, and selected tissues, for primarily selected male rat and mouse strains, based on 1H NMR spectra; (2) development of a database of 1H NMR spectra from animals dosed with model toxins, initially concentrating on liver and kidney effects; (3) development of expert systems for the detection of the toxic effects of xenobiotics based on a chemometric analysis of their NMR-detected changes in biofluid metabolite profiles; (4) identification of combination biomarkers of the various defined classes; (5) testing of the methods to assess the ability of metabonomics to distinguish between toxic and nontoxic analogues and to assess the specificity of the predictive expert systems. The classes of chemicals used and the types of toxicity investigated are as diverse as possible to assist the validation of NMR methods for use in early "broad" screening of candidates for toxicity. In this concise review, the background to metabonomics is presented together with a survey of literature results where metabonomics has been used to probe xenobiotic toxicity based on target organ, regions within target organs, and biochemical mechanisms of action. The methods used in COMET are briefly summarized and results are used to exemplify the approach. Finally, a perspective on the future uses of metabonomics in drug safety assessment is given. Metabonomics background NMR spectroscopy of biofluids When toxins interact with cells and tissues they disturb the ratios, concentrations, and fluxes of endogenous biochemicals in key intermediary cellular metabolic pathways. Under mild toxic stress, cells attempt to maintain homeostasis and metabolic control by varying the composition of the body fluids that either perfuse them or are secreted by them. In more severe toxicity states, cell death leads to loss of organ function and more marked biochemical changes occur in biofluids due to loss of whole body homeostasis and metabolite leakage from damaged cells. Consequently, following either scenario there are characteristic organ-specific and mechanism-specific alterations in biofluid composition. Clearly, the detection of toxic lesions via biochemical effects is most difficult close to the toxic threshold, yet these are often the most important effects to define. Previously, the detection of novel biomarkers of toxic effect has mainly been serendipitous. However, it is now possible to use a combined NMR–expert system approach to systematically explore the relationships between biofluid composition and toxicity and to generate novel combination biomarkers of toxicity. The approach of characterizing the metabolic profile of a specific tissue or biofluid has been termed "metabonomics" by analogy with genomics and proteomics. Metabonomics has been defined as the study of the time-related quantitative multivariate metabolic response to pathophysiological processes or genetic modification in cells, tissues, and whole organisms (Nicholson et al., 1999). 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 (Nicholson and Wilson, 1989). 1H NMR spectroscopy is well suited to the study of toxic events, as multicomponent analyses on biological materials can be made simultaneously, without bias imposed by expectations of the type of toxin-induced metabolic changes. This is particularly true for NMR spectra of urine in situations where damage has occurred to the kidney or liver. Quantitative changes in NMR spectroscopic metabolite patterns have also been shown to give information on the location and severity of toxic lesions, as well as insights into the underlying molecular mechanisms of toxicity. (Nicholson et al 1985 and Nicholson and Wilson 1989). The first studies of using PR to classify biofluid samples used a simple scoring system used to describe the levels of 18 endogenous metabolites in urine from rats which either were in a control group or had received a specific organ toxin which affected the liver, the testes, the renal cortex, or the renal medulla (Gartland et al 1990 and Gartland et al 1991). This study showed that samples corresponding to different organ toxins mapped into distinctly different regions. Various refinements in the data analysis were investigated, including taking scored data at three time points after the toxin exposure for the nephrotoxins only (this used only 16 metabolites, as taurine and creatine were not altered in this data subset) as well as using a simple dual scoring system (the time and magnitude of the greatest change from control). The maps derived from the full time course information provided the best discrimination between toxin classes. This study was further extended (Anthony et al., 1994b) to incorporate actual metabolite NMR resonance intensities rather than simple scores. This was carried out for the nephrotoxins in the earlier group plus additional nephrotoxic compounds. A good separation of renal medullary from renal cortical toxins was achieved. In addition, it was possible to differentiate cortical toxins according to the region of the proximal tubule which was affected and also by the biochemical mechanism of the toxic effect. The time course of metabolic urinary changes induced by two renal toxins was first investigated in detail by metabonomics using Fisher 344 rats administered a single acute dose of the renal cortical toxin mercuric chloride and the medullary toxin 2-bromoethanamine (Holmes et al., 1992). The rat urine was collected for up to 9 days after dosing and was analyzed using 1H NMR spectroscopy. The onset, progression, and recovery of the lesions were also followed using histopathology to provide a definitive classification of the toxic state relating to each urine sample. The concentrations of 20 endogenous urinary metabolites were measured at eight time points after dosing and mapping methods were used to reduce the data dimensionality. These showed that the points on the plot can be related to the development of, and recovery from, the lesions. A wide range of toxins has now been investigated using this metabonomics approach, including the kidney cortical toxins mercury chloride (Nicholson et al., 1985), p-aminophenol (Gartland et al 1989 and Sanins et al 1990b), uranyl nitrate (Anthony et al., 1994a), ifosfamide (Foxall et al., 1996), cephaloridine (Anthony et al., 1992), the kidney medullary toxins propylene imine and 2-bromoethanamine hydrochloride (Holmes et al., 1992; Robertson et al., 2000), and the liver toxins hydrazine, allyl alcohol, thioacetamide, -naphthylisothiocyanate, and carbon tetrachloride Sanins et al 1990a and Sanins et al 1990b; Nicholls et al., 2001; Robertson et al., 2000). The testicular toxin cadmium chloride has also been investigated in detail (Nicholson et al, 1989), including the effects of chronic exposure at environmentally realistic levels (Griffin et al., 2001). The incidence of phospholipidosis caused by amiodarone, chloroquine, DMP-777 (a neutrophil elastase inhibitor), and a number of Glaxo Wellcome compounds has been evaluated using metabonomics (Espina et al 2001 and Nicholls et al 2000), as has the effect of dexamethasone on vascular lesions (Slim et al., 2002). Other studies include the toxicity of the aldose reductase inhibitor HOE-843 (Hoyle et al., 1992) and lanthanum nitrate (Feng et al., 2002). Toxic stress in earthworms has also been investigated using metabonomics (Bundy et al., 2001). Extensions to the earlier chemometric approaches include a toxicological assessment approach based on neural network software to ascertain whether the methods provide a robust approach which could lead to automatic toxin classification (Anthony et al., 1995). The neural network approach to sample classification, based on 1H NMR spectra of urine, was in general predictive of the sample class. It appears to be reasonably robust and once the network is trained, the prediction of new samples is rapid and automatic. However, the principal disadvantage is common to all neural network studies in that it is difficult to ascertain from the network which of the original sample descriptors are responsible for the classification. Nevertheless as shown recently, probabilistic neural networks appear to be a useful and effective method for sample classification (Holmes et al., 2001). The use of statistical batch processing has been explored as a means of characterizing the biochemical changes associated with hydrazine-induced toxicity (Antti et al., 2002). Recently, comprehensive studies have been published using pattern recognition to predict and classify drug toxicity effects, including lesions in the liver (Beckwith-Hall et al., 1998) and kidney (Holmes et al., 1998a), and using supervised methods as an approach to an expert system (Holmes et al 1998a; Holmes et al 1998b and Holmes et al 2000). |
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