Metabonomics: a platform for studying drug toxicity and gene function

Application of high-throughput Fourier-transform infrared spectroscopy in toxicology studies: contribution to a study on the development of an animal model for idiosyncratic toxicity

An evaluation of high-throughput Fourier-transform infrared spectroscopy (FT-IR) as a technology that could support a "metabonomics" component in toxicological studies of drug candidates is presented. The hypothesis tested in this study was that FT-IR had sufficient resolving power to discriminate between urine collected from control rat populations and rats subjected to treatment with a potent inflammatory agent, bacterial lipopolysaccharide (LPS). It was also hypothesized that co-administration of LPS with ranitidine, a drug associated with reports of idiosyncratic susceptibility, would induce hepatotoxicity in rats and that this could be detected non-invasively by an FT-IR-based metabonomics approach. The co-administration of LPS with "idiosyncratic" drugs represents an attempt to develop a predictive model of idiosyncratic toxicity and FT-IR is used herein to support characterization of this model. FT-IR spectra are high dimensional and the use of genetic programming to identify spectral sub-regions that most contribute to discrimination is demonstrated. FT-IR is rapid, reagentless, highly reproducible and inexpensive. Results from this pilot study indicate it could be extended to routine applications in toxicology and to supporting characterization of a new animal model for idiosyncratic susceptibility.

Non-invasive measurement of brain damage in a primate model of multiple sclerosis

Early recognition of whether a product has potential as a new therapy for treating multiple sclerosis (MS) relies upon the quality of the animal models used in the preclinical trials. The promising effects of new treatments in rodent models of experimental autoimmune encephalomyelitis (EAE) have rarely been reproduced in patients suffering from MS. EAE in outbred marmoset monkeys, Callithrix jacchus, is a valid new model, and might provide an experimental link between EAE in rodent models and human MS. Using magnetic resonance imaging techniques similar to those used in patients suffering from MS pathological abnormalities in the brain, white matter of the animal can be visualized and quantified. Moreover, NMR spectroscopy, in combination with pattern recognition, offers an advanced uroscopic technique for the identification of biomarkers of inflammatory demyelination.

Early prediction of drug metabolism and toxicity: systems biology approach and modeling

Many of the drug candidates that fail in clinical trials are withdrawn because of unforeseen effects of human metabolism, such as toxicity and unfavorable pharmacokinetic profiles. Early pre-clinical elimination of such compounds is important but not yet possible. An ideal system would enable researchers to make a confident elimination decision based purely on the structure of a new compound, and incorporate and use multiple pre-clinical experimental data to support such a decision. Currently available resources can be split into three categories: (i). structure-activity relationships (SAR) computational models based on compound structure; (ii). 'pattern' databases of tissue or organ response to drugs, compiled from high-throughput experiments; and (iii). 'systems biology' databases of metabolic pathways, genes and regulatory networks. In this review, we outline the advantages and drawbacks of each of these systems and suggest directions for their integration.

Nanoprobe NMR spectroscopy and in vivo microdialysis: new analytical methods to study brain neurochemistry

Nuclear magnetic resonance (NMR) spectroscopy was used to study the chemical composition of cerebrospinal fluid (CSF) microdialysate from the rat brain. In vivo microdialysis techniques were used in several brain regions including the frontal cortex, amygdala, striatum, nucleus accumbens and third ventricle and dialysate samples (20microl) were subsequently analyzed by one and two-dimensional 1H NMR experiments using a Varian nanoprobe. Neurochemical resonances were assigned on the basis of published chemical shifts [Lindon et al., Ann. Rep. NMR Spectrosc. 38 (1999) 1-88], correlation experiments and addition of standard compounds. Glucose, lactate, formate, pyruvate, creatinine, gamma-hydroxybutyrate, acetate, glutamate, glycine, tyrosine, isoleucine, leucine, alanine and choline were some of the neurochemicals unambiguously assigned. Additional studies in the frontal cortex showed that amino acids such as glutamate, alanine and isoleucine were sensitive to local tetrodotoxin (TTX) infusion. The NMR spectra were also subjected to multivariate statistical methods to compare the different brain regions examined. To our knowledge, the present experiments are the first to describe the combination of nanoprobe NMR technology with in vivo microdialysis for the analysis of brain neurochemistry in freely-moving rats.

Use of Metabonomics to Identify Impaired Fatty Acid Metabolism as the Mechanism of a Drug-Induced Toxicity

An increased diversity of therapeutic targets in the pharmaceutical industry in recent years has led to a greater diversity of toxicological effects. This, and the increased pace of drug discovery, leads to a need for new technologies for the rapid elucidation of toxicological mechanisms. As part of an evaluation of the utility of metabonomics in drug safety assessment, 1H NMR spectra were acquired on urine and liver tissue samples obtained from rats administered vehicle or a development compound (MrkA) previously shown to induce hepatotoxicity in several animal species. Multivariate statistical analysis of the urinary NMR data clearly discriminated drug-treated from control animals, due to a depletion in tricarboxylic acid cycle intermediates, and the appearance of medium chain dicarboxylic acids. High-resolution magic angle spinning NMR data acquired on liver samples exhibited elevated triglyceride levels that were correlated with changes in the urinary NMR data. Urinary dicarboxylic aciduria is associated with defective metabolism of fatty acids; subsequent in vitro experiments confirmed that MrkA impairs fatty acid metabolism. The successful application of metabonomics to characterize an otherwise ill-defined mechanism of drug-induced toxicity supports the practicality of this approach for resolving toxicity issues for drugs in discovery and development.

Improved methods for the acquisition and interpretation of NMR metabolomic data

The high spectral congestion typically observed in one-dimensional (1D) 1H nuclear magnetic resonance (NMR) spectra of tissue extracts and biofluids limits the metabolic information that can be extracted. This study evaluates the application of two-dimensional J-resolved (JRES) spectroscopy for metabolomics, which can provide proton-decoupled projected 1D spectra (p-JRES). This approach is illustrated by an investigation of embryogenesis in Japanese medaka (Oryzias latipes), an established fish model for developmental toxicology. When combined with optimized spectral pre-processing,(2) including a 0.005-ppm bin width for data segmentation and a logarithmic transformation, the reduced congestion in the p-JRES spectra increases the likelihood that a specific metabolite can be accurately integrated and thus increases the extractable information content of the spectra. Principal components analysis of the p-JRES spectra reveals the concept of a developmental trajectory that summarizes the changes in the NMR-visible metabolome throughout medaka embryogenesis. Advantages and potential disadvantages of the p-JRES approach are discussed.

NMR-based metabolomics: a powerful approach for characterizing the effects of environmental stressors on organism health

It is important to assess the chronic effects of chemical, physical, and biological stressors on organisms in the environment. Appropriate methods must enable rapid, inexpensive, and multibiomarker analyses of organism health. Here we investigate withering syndrome in red abalone (Haliotis rufescens), an important wild and farmed shellfish species along the Pacific coast, using a metabolomic approach that combines the metabolic profiling capabilities of nuclear magnetic resonance spectroscopy (NMR) with pattern recognition methods. Foot muscle, digestive gland, and hemolymph samples were collected from healthy, stunted, and diseased abalone, and the extracts were analyzed by NMR. Following spectral preprocessing, principal components analyses of the metabolite profiles were conducted. Our results confirm that NMR-based metabolomics can successfully distinguish the biochemical profiles of the three groups of animals, in every type of tissue or biofluid studied. Furthermore, this discovery-based approach successfully identified novel metabolic biomarker profiles associated with withering syndrome. The application of these methods for investigating other environmental stressors is discussed, as are the advantages of NMR-based metabolomics for biomonitoring, particularly in conjunction with gene and protein expression profiling.

Toxicogenomic approach for assessing toxicant-related disease

The problems of identifying environmental factors involved in the etiology of human disease and performing safety and risk assessments of drugs and chemicals have long been formidable issues. Three principal components for predicting potential human health risks are: (1) the diverse structure and properties of thousands of chemicals and other stressors in the environment; (2) the time and dose parameters that define the relationship between exposure and disease; and (3) the genetic diversity of organisms used as surrogates to determine adverse chemical effects. The global techniques evolving from successful genomics efforts are providing new exciting tools with which to address these intractable problems of environmental health and toxicology. In order to exploit the scientific opportunities, the National Institute of Environmental Health Sciences has created the National Center for Toxicogenomics (NCT). The primary mission of the NCT is to use gene expression technology, proteomics and metabolite profiling to create a reference knowledge base that will allow scientists to understand mechanisms of toxicity and to be able to predict the potential toxicity of new chemical entities and drugs. A principal scientific objective underpinning the use of microarray analysis of chemical exposures is to demonstrate the utility of signature profiling of the action of drugs or chemicals and to utilize microarray methodologies to determine biomarkers of exposure and potential adverse effects. The initial approach of the NCT is to utilize proof-of-principle experiments in an effort to "phenotypically anchor" the altered patterns of gene expression to conventional parameters of toxicity and to define dose and time relationships in which the expression of such signature genes may precede the development of overt toxicity. The microarray approach is used in conjunction with proteomic techniques to identify specific proteins that may serve as signature biomarkers. The longer-range goal of these efforts is to develop a reference relational database of chemical effects in biological systems (CEBS) that can be used to define common mechanisms of toxicity, chemical and drug actions, to define cellular pathways of response, injury and, ultimately, disease. In order to implement this strategy, the NCT has created a consortium of research organizations and private sector companies to actively collaborative in populating the database with high quality primary data. The evolution of discrete databases to a knowledge base of toxicogenomics will be accomplished through establishing relational interfaces with other sources of information on the structure and activity of chemicals such as that of the National Toxicology Program (NTP) and with databases annotating gene identity, sequence, and function.

Magnetic resonance microscopy approaches to molecular imaging: sensitivity vs. specificity

Magnetic resonance imaging (MRI) has become a staple of diagnostic radiology. Despite its diagnostic utility the resolving power of typical clinical MRI instruments is only on the order of 1 mm. This has led to the development of magnetic resonance microscopy (MRM), which employs the same physical imaging principals used in MRI, but with instrumentation designed to resolve structural details down to the level of 10-100 microns in samples ranging from less than 1mm to several centimeters in size. Until recently, major advancements in MRM have focused on hardware and software developments allowing the detection of radio-frequency signals originating from very small volume elements within the sample. Such high-resolution images have facilitated the early detection of diseased tissue by focusing on sub-millimeter structural changes induced in the tissue. To sensitize the MRM technique to pathologic tissue changes, investigators have developed techniques, such as chemical shift imaging to detect pre-cancerous changes in tissue metabolism and MR relaxometry to detect changes in tissue composition during the earliest stages of degeneration for diseases such as osteoarthritis or multiple sclerosis. However, such non-specific measurements can only serve as surrogate measures of disease progression and potential measures of treatment efficacy. As disease diagnosis moves from the anatomic to the molecular stage, scientists will require imaging techniques that can detect molecular events deep inside the human body. To meet this goal, MR scientists are working to improve imaging resolutions in vivo and they are developing molecular probes that can dramatically amplify the MR signal in response to specific and highly localized molecular events. This article will identify current trends in the MRM field aimed at meeting the challenges imposed by molecular imaging and areas for future development in this highly promising imaging field.

Complementary on-line preconcentration strategies for steroids by capillary electrophoresis

Complementary on-line preconcentration strategies are needed when analyzing different classes of solutes in real samples by capillary electrophoresis (CE) with UV detection. The performance of three different on-line preconcentration (focusing) techniques under alkaline conditions was examined in terms of their selectivity and sensitivity enhancement for a group of steroids, including classes of androgens, corticosteroids and estrogens. Electrokinetic focusing of large sample injection plugs (up to 28% of effective capillary length or 22.1 cm) directly on-capillary can be tuned for specific classes of steroids based on changes in their mobility (velocity) using a multi-section electrolyte system in CE. A dynamic pH junction was applied for the selective resolution and focusing of weakly acidic estrogens using borate, pH 11.0 and pH 8.0 in the background electrolyte and the sample, respectively. Sweeping, using an anionic bile acid surfactant and neutral gamma-cyclodextrin (gamma-CD) under alkaline conditions (pH 8), resulted in focusing and separation of the moderately hydrophobic (non-ionic) classes of steroids, such as androgen and corticosteroids. Optimal focusing and resolution of all test steroids under a single buffer condition was realized by a dynamic pH junction-sweeping format using borate, pH 11.0 and bile acid surfactant with gamma-CD in the BGE, whereas the sample is devoid of surfactant at pH 8.0. The design of selective on-line focusing strategies in CE is highlighted by the analysis of microgram amounts of ethynyl estradiol derived from a female contraceptive pill extract using the dynamic pH junction method, which resulted in over a 100-fold enhancement in concentration sensitivity.

What is the relevance of bioinformatics to pharmacology?

Although bioinformatics achieved prominence because of its central role in genome data storage, management and analysis, its focus has shifted as the life sciences exploit these data. In pharmacology, genomic, transcriptomic and proteomic data are being used in the quest for drugs that fulfill unmet medical needs, are disease modifying or curative and are more effective and safer than current drugs. Bioinformatics is used in drug target identification and validation and in the development of biomarkers and toxicogenomic and pharmacogenomic tools to maximize the therapeutic benefit of drugs. Now that the 'parts list' of cellular signalling pathways is available, integrated computational and experimental programmes are being developed, with the goal of enabling in silico pharmacology by linking the genome, transcriptome and proteome to cellular pathophysiology.

Clinical applications of proteomics

Proteomics, the systematic evaluation of changes in the protein constituency of a cell, is more than just the generation of lists of proteins that increase or decrease in expression as a cause or consequence of disease. The ultimate goal is to characterize the information flow through protein pathways that interconnect the extracellular microenvironment with the control of gene transcription. The nature of this information can be a cause or a consequence of disease processes. Clinical applications of proteomics involve the use of proteomic technologies at the bedside. The analysis of human cancer as a model for how proteomics can have an impact at the bedside is now employing several new proteomic technologies that are being developed for early detection, therapeutic targeting and finally, patient-tailored therapy.

On-line preconcentration strategies for trace analysis of metabolites by capillary electrophoresis

Analysis of low concentrations of metabolites is required for new fields of biological research, such as metabolomics. In this review, recent work in our laboratory aimed at developing improved strategies for on-line sample preconcentration of metabolites by capillary electrophoresis (CE) is presented. Dynamic pH junction, sweeping and dynamic pH junction-sweeping represent three complementary methods for electrokinetic focusing of large volumes of sample directly on-capillary. Focusing selectivity and focusing efficiency are two factors that can be used to assess the suitability of each method for different classes of metabolites. Buffer properties can be selected to enhance the focusing of specific types of metabolites based on knowledge of the analyte physicochemical properties. The application of on-line preconcentration CE for trace analysis of metabolites in real samples of interest, such as biological fluids and cellular extracts, is also demonstrated. Under optimum conditions, up to three orders of magnitude increase in concentration sensitivity can be realized for several classes of metabolites, including catecholamines, purines, nucleosides, nucleotides, amino acids, steroids and coenzymes. Recent work on hyphenating on-line preconcentration with multiplexed CE is highlighted as a promising platform for sensitive and high-throughput analyses of metabolites.

Building with a scaffold: emerging strategies for high- to low-level cellular modeling

Computational cellular models are becoming crucial for the analysis of complex biological systems. An important new paradigm for cellular modeling involves building a comprehensive scaffold of molecular interactions and then mining this scaffold to reveal a hierarchy of signaling, regulatory and metabolic pathways. We review the important trends that make this approach feasible and describe how they are spurring the development of models at multiple levels of abstraction. Pathway maps can be extracted from the scaffold using "high-level" computational models, which identify the key components, interactions and influences required for more detailed "low-level" models. Large-scale experimental measurements validate high-level models, whereas targeted experimental manipulations and measurements test low-level models.

Toxicogenomics: challenges and opportunities

Toxicogenomics describes the measurement of global gene expression changes in biological samples exposed to toxicants. This new technology promises to greatly facilitate research into toxicant mechanisms, with the possibility of assisting in the detection of compounds with the potential to cause adverse health effects earlier in the development of pharmaceutical and chemical products. In this short review, I discuss the opportunities presented by toxicogenomics, the challenges we face in the application of these tools, and the progress we have made in realising the potential of these new genomic approaches.

Proteomic applications for the early detection of cancer

The ability of physicians to effectively treat and cure cancer is directly dependent on their ability to detect cancers at their earliest stages. Proteomic analyses of early-stage cancers have provided new insights into the changes that occur in the early phases of tumorigenesis and represent a new resource of candidate biomarkers for early-stage disease. Studies that profile proteomic patterns in body fluids also present new opportunities for the development of novel, highly sensitive diagnostic tools for the early detection of cancer.

Contemporary issues in toxicology the role of metabonomics in toxicology and its evaluation by the COMET project

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 (1)H 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.

An integrated metabonomic investigation of acetaminophen toxicity in the mouse using NMR spectroscopy

An integrated metabonomics study using high-resolution 1H NMR spectroscopy has been applied to investigate the biochemical composition of intact liver tissue (using magic angle spinning), liver tissue extracts, and blood plasma samples obtained from control and acetaminophen-treated mice. Principal components analysis was used to visualize similarities and differences in biochemical profiles. The time- and dose-dependent biochemical effects of acetaminophen were related to the drug toxicity, as determined using histopathology. Metabolic effects in intact liver tissue and lipid soluble liver tissue extracts from animals treated with the high dose level of acetaminophen included an increase in lipid triglycerides and monounsaturated fatty acids together with a decrease in polyunsaturated fatty acids, indicating mitochondrial malfunction with concomitant compensatory increase of peroxisomal activity. In addition, a depletion of phospholipids was observed in treated liver tissue, which suggested an inhibition of enzymes involved in phospholipid synthesis. There was also a depletion in the levels of liver glucose and glycogen. In addition, the aqueous soluble liver tissue extracts from high dose animals also revealed an increase in lactate, alanine, and other amino acids, together with a decrease in glucose. Plasma spectra showed increases in glucose, acetate, pyruvate, and lactate. These observations all provide evidence for an increased rate of glycolysis. These findings could indicate a mitochondrial inability to use pyruvate in the citric acid cycle and also reveal the impairment of fatty acid beta-oxidation in liver mitochondria of such treated mice.

The role of investigative molecular toxicology in early stage drug development

Molecular toxicology, the application of molecular biology principles and technologies to preclinical safety assessment, represents a key tool for understanding mechanisms of toxicity and assessing the risks associated with specific toxicities. The application of gene expression markers to early stage preclinical safety assessment has the potential to impact pipelines in two main areas: lead optimisation and issue management. Lead optimisation focuses on deprioritising leads with significant, development-limiting toxicological liabilities while advancing those compounds with the greatest chance of successfully navigating the gauntlet of preclinical and clinical safety studies. Issue management utilises mechanistic toxicology studies to position non-development-limiting findings prior to the onset of Good Laboratory Practice studies in full development, and can help to identify and validate gene expression markers predictive of adverse events to avoid issues in second-generation projects. In this review, the authors describe the application of molecular toxicology to a standard pharmaceutical testing funnel, provide examples of the successful application of gene expression markers, and discuss the potential for future impact in several broad categories of clinically relevant toxicity.

Plant metabolomics: large-scale phytochemistry in the functional genomics era

Metabolomics or the large-scale phytochemical analysis of plants is reviewed in relation to functional genomics and systems biology. A historical account of the introduction and evolution of metabolite profiling into today's modern comprehensive metabolomics approach is provided. Many of the technologies used in metabolomics, including optical spectroscopy, nuclear magnetic resonance, and mass spectrometry are surveyed. The critical role of bioinformatics and various methods of data visualization are summarized and the future role of metabolomics in plant science assessed.

Metabolomic analysis of the consequences of cadmium exposure in Silene cucubalus cell cultures via 1H NMR spectroscopy and chemometrics

Several essential and non-essential metals (typically those from periods 4, 5 and 6 in groups 11-15 in the periodic table) are commonly detoxified in higher plants by complexation with phytochelatin. The genetic and gross metabolic basis of metal tolerance in plants is, however, poorly understood. Here, we have analyzed plant cell extracts using 1H NMR spectroscopy combined with multivariate statistical analysis of the data to investigate the biochemical consequences of Cd(2+) exposure in Silene cucubalus cell cultures. Principal components analysis of 1H NMR spectra showed clear discrimination between control and Cd(2+) dosed groups, demonstrating the metabolic effects of Cd(2+) and thus allowing the identification of increases in malic acid and acetate, and decreases in glutamine and branched chain amino acids as consequences of Cd(2+) exposure. This work shows the value of NMR-based metabolomic approaches to the determination of biochemical effects of pollutants in naturally selected populations.

Metabolite profiling in rat urine by liquid chromatography/electrospray ion trap mass spectrometry. Application to the study of heavy metal toxicity

This work reports the use of reverse-phase liquid chromatography coupled to electrospray ion trap (QIT) mass spectrometry for the analysis of the metabolome in rat urine. An injection of 20 microL of urine into the chromatographic system is followed by a slow gradient elution and mass spectrometric detection in the scanning mode from m/z 100-1000 in both positive and negative modes. Over a time scale of 90 min, 30 and 20 resolved peaks were observed in the positive and the negative modes, respectively, corresponding to the presence of a few hundred m/z ratios. By using a QIT analyzer, data-dependent tandem mass spectrometry of selected m/z ratios identified several compounds in rat urine and characterized various chemical families, including carboxylic acids, amines, sulfated compounds, glucuronides and glycosides, by the observation of characteristic fragment ions or neutral losses. The method has been applied to the investigation of the chronic toxicity of heavy metals in rat urine. A few tens of m/z ratios, differing in intensity more than threefold from control values, were observed in both positive and negative modes. The time variations for some selected ions suggest that LC/ESI-MS could allow selective characterization of biomarkers in response to specific toxic compounds.

Vitamin E deficiency and metabolic deficits in neuronal ceroid lipofuscinosis described by bioinformatics

The mnd mouse, a model of neuronal ceroid lipofusinosis (NCL), has a profound vitamin E deficiency in sera and brain, associated with cerebral deterioration characteristic of NCL. In this study, the vitamin E deficiency is corrected using dietary supplementation. However, the histopathological features associated with NCL remained. With use of a bioinformatics approach based on high-resolution solid and solution state 1H-NMR spectroscopy and principal component analysis (PCA), the deficits associated with NCL are defined in terms of a metabolic phenotype. Although vitamin E supplementation reversed some of the metabolic abnormalities, in particular the concentration of phenylalanine in extracts of cerebral tissue, PCA demonstrated that metabolic deficits associated with NCL were greater than any effects produced from vitamin E supplementation. These deficits included increased glutamate and N-acetyl-L-aspartate and decreased creatine and glutamine concentrations in aqueous extracts of the cortex, as well as profound accumulation of lipid in intact cerebral tissue. This is discussed in terms of faulty production of mitochondrial-associated membranes, thought to be central to the deficits in mnd mice.

Computer algebra in the life sciences

This note (1) provides references to recent work that applies computer algebra (CA) to the life sciences, (2) cites literature that explains the biological background of each application, (3) states the mathematical methods that are used, (4) mentions the benefits of CA, and (5) suggests some topics for future work.

Metabolic profiles of dystrophin and utrophin expression in mouse models of Duchenne muscular dystrophy

Metabolic profiles from (1)H nuclear magnetic resonance spectroscopy have been used to describe both one and two protein systems in four mouse models related to Duchenne muscular dystrophy using the pattern recognition technique partial least squares. Robust statistical models were built for extracts and intact cardiac tissue, distinguishing mice according to expression of dystrophin. Using metabolic profiles of diaphragm, models were built describing dystrophin and utrophin, a dystrophin related protein, expression. Increased utrophin expression counteracted some of the deficits associated with dystrophic tissue. This suggests the method may be ideal for following treatment regimes such as gene therapy.

NMR-based metabonomic studies on the biochemical effects of commonly used drug carrier vehicles in the rat

The biochemical effects of a series of commonly used drug carrier vehicles were investigated using (1)H NMR spectroscopic and pattern recognition based metabonomic analysis. Animals were treated by oral gavage with six dosage vehicles: 0.5% (w/v) sodium carboxymethylcellulose/0.2% (v/v)tween; microemulsion (consisting of propylene glycol, ethanol, cremophor, and corn oil glycerides); labrafil [consisting of poly(ethylene glycol) 300 esterified with oleic acid] (30%)/corn oil (70%); 0.1 M sodium phosphate buffered water; poly(ethylene glycol) 300 and 0.5% methocel. Urine samples (n = 7) collected over a 96 h period post administration were analyzed using 600 MHz (1)H NMR spectroscopy, and principal components analysis of the spectral data was used to analyze these data. Of the six vehicles studied, three (labrafil/corn oil, PEG 300 and microemulsion) gave rise to strong vehicle-related signals in the (1)H NMR spectra of urine and were, therefore, deemed to be less suitable for NMR-based toxicity studies. To investigate any biochemical consequences of vehicle dosing, PCA was used to analyze spectral regions that did not contain vehicle-related signals, i.e., the NMR-detectable endogenous metabolite profile. PEG 300 and labrafil/corn oil induced changes in the biochemical composition of urine including increased concentrations of dicarboxylic acids, creatinine, taurine, and sugars, indicating that these vehicles were bioactive in their own right and that this might confound interpretation of biochemical effects of weakly toxic drugs dosed in these carriers. This study shows the importance of selecting appropriate vehicles for NMR-based metabonomic studies with a view to minimizing the possibility of vehicle resonances obscuring endogenous compound peaks. Furthermore, we have shown that at least two of the commonly used drug carrier vehicles caused metabolic perturbations in the urine profile. These alterations in the biochemical profile reflect vehicle-induced changes in the physiological status of the organism that may obscure the pharmacologic or toxicologic effects of drugs.

Clinical proteomics: translating benchside promise into bedside reality

The ultimate goal of proteomics is to characterize the information flow through protein networks. This information can be a cause, or a consequence, of disease processes. Clinical proteomics is an exciting new subdiscipline of proteomics that involves the application of proteomic technologies at the bedside, and cancer, in particular, is a model disease for studying such applications. Here, we describe proteomic technologies that are being developed to detect cancer earlier, to discover the next generation of targets and imaging biomarkers, and finally to tailor the therapy to the patient.

Clinical proteomics for cancer biomarker discovery and therapeutic targeting

As we emerge into the post-genome era, proteomics finds itself as the driving force field as we translate the nucleic acid information archive into understanding how the cell actually works and how disease processes operate. Even so, the traditionally held view of proteomics as simply cataloging and developing lists of the cellular protein repertoire of a cell are now changing, especially in the sub-discipline of clinical proteomics. The most relevant information archive to clinical applications and drug development involves the elucidation of the information flow of the cell; the "software" of protein pathway networks and circuitry. The deranged circuitry of the cell as the drug target itself as well as the effect of the drug on not just the target, but also the entire network, is what we now are striving towards. Clinical proteomics, as a new and most exciting sub-discipline of proteomics, involves the bench-to-bedside clinical application of proteomic tools. Unlike the genome, there are potentially thousands of proteomes: each cell type has its own unique proteome. Moreover, each cell type can alter its proteome depending on the unique tissue microenvironment in which it resides, giving rise to multiple permutations of a single proteome. Since there is no polymerase chain reaction equivalent to proteomics- identifying and discovering the "wiring diagram" of a human diseased cell in a biopsy specimen remains a daunting challenge. New micro-proteomic technologies are being and still need to be developed to drill down into the proteomes of clinically relevant material. Cancer, as a model disease, provides a fertile environment to study the application of proteomics at the bedside. The promise of clinical proteomics and the new technologies that are developed is that we will detect cancer earlier through discovery of biomarkers, we will discover the next generation of targets and imaging biomarkers, and we can then apply this knowledge to patient-tailored therapy.

Earthworm species of the genus Eisenia can be phenotypically differentiated by metabolic profiling

The universality of low molecular weight metabolites allows rapid and straightforward investigation of the biochemistry of genetically uncharacterised species. Thus ex vivo metabolic profiling in combination with multivariate data analysis (metabonomics) offers great potential in comparative biology. Here we present the first use of high resolution nuclear magnetic resonance (NMR) spectroscopy to distinguish closely related animal species via their metabolic phenotype (metabotype). We have profiled the three Eisenia (Oligochaeta, Lumbricidae) species Eisenia fetida, Eisenia andrei and Eisenia veneta using tissue extracts and coelomic fluid analysis. The low molecular weight biochemical profiles of tissue extracts were highly conserved for all three species, with E. fetida and E. andrei being more similar to each other than to E. veneta. However the metabolic profiles of the coelomic fluid of the different species were highly distinctive - the NMR spectra allowed unequivocal identification of species. Multivariate statistics were also used to quantify these spectral differences and to enable simplified graphical visualisation of species similarity. These results show that two morphologically undistinguishable species (E. fetida and E. andrei) differ markedly in their biochemical profiles despite apparently occupying the same ecological niche, and indicate that metabolic phenotype profiling can be used as a powerful functional genomics tool.

Metabonomic assessment of physiological disruptions using 1H-13C HMBC-NMR spectroscopy combined with pattern recognition procedures performed on filtered variables

Metabonomic characterization of long-lasting although weak physiological events such as anabolic disruptions remains poorly investigated. We have validated 1H-13C HMBC-NMR as a suitable generator of instrumental variables that are strongly linked to the concentration of endogenous metabolites in biological fluids. This method is interfaced to multivariate pattern recognition procedures. Fingerprints established from urine sample collected on cattle treated with anabolic steroids were used to validate this method. Four main results arise from this study. (i) 2D NMR is as informative as 1D NMR. (ii) 2D NMR variable clustering highlights successfully a contingent redundancy of variables, although a relevant hierarchical model of statistical correlations covering from structural relationships to physiologic links can also be evidenced. (iii) To enhance pattern recognition performances, we have validated a variable selection algorithm for accurate prediction of unknown individuals belonging to predetermined groups achieved by linear discriminant analysis (LDA). This algorithm synthesizes the whole information contained in the data set by selecting preferentially nonredundant variables. Parameters generating variable subsets are validated by predicted variance efficiency obtained when minimizing error rates calculated by cross-validation methods. (iv) Provided variables are correctly filtered, LDA fairly competes with partial least-squares methods for both classification of individuals and statistical interpretation of metabolic responses obtained in such a physiological disruption context.

Metabonomics: the use of electrospray mass spectrometry coupled to reversed-phase liquid chromatography shows potential for the screening of rat urine in drug development

The application of liquid chromatography/mass spectrometry (LC/MS) followed by principal components analysis (PCA) has been successfully applied to the screening of rat urine following the administration of three candidate pharmaceuticals. With this methodology it was possible to differentiate the control samples from the dosed samples and to identify the components of the mass spectrum responsible for the separation. These data clearly show that LC/MS is a viable alternative, or complementary, technique to proton NMR for metabonomics applications in drug discovery and development.