Mary E. Bollard

Metabonomics technologies and their applications in physiological monitoring, drug safety assessment and disease diagnosis

In this review, metabonomics, a combination of data-rich analytical chemical measurements and chemometrics for profiling metabolism in complex systems, is described and its applications are reviewed. Metabonomics is typically carried out using biofluids or tissue samples. The relevance of the technique is reviewed in relation to other ‘-omics’, and it is shown how the methods can be applied to physiological evaluation, drug safety assessment, characterization of genetically modified animal models of disease, diagnosis of human disease, and drug therapy monitoring. The different types of analytical data, mainly from nuclear magnetic resonance spectroscopy and mass spectrometry, are summarized. The outputs from a metabonomics study allow sample classification, for example according to phenotype, drug safety or disease diagnosis, and interpretation of the reasons for classification yields information on combination biomarkers of effect. Transcriptomic and metabonomic data is currently being further integrated into a holistic understanding of systems biology. An assessment of the possible future role and impact of metabonomics is presented.

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.

Improved analysis of multivariate data by variable stability scaling: application to NMR-based metabolic profiling

Abstract Variable scaling alters the covariance structure of data, affecting the outcome of multivariate analysis and calibration. Here we present a new method, variable stability (VAST) scaling, which weights each variable according to a metric of its stability. The beneficial effect of VAST scaling is demonstrated for a data set of 1 H NMR spectra of urine acquired as part of a metabonomic study into the effects of unilateral nephrectomy in an animal model. The application of VAST scaling improved the class distinction and predictive power of partial least squares discriminant analysis (PLS-DA) models. The effects of other data scaling and pre-processing methods, such as orthogonal signal correction (OSC), were also tested. VAST scaling produced the most robust models in terms of class prediction, outperforming OSC in this aspect. As a result the subtle, but consistent, metabolic perturbation caused by unilateral nephrectomy could be accurately characterised despite the presence of much greater biological differences caused by normal physiological variation. VAST scaling presents itself as an interpretable, robust and easily implemented data treatment for the enhancement of multivariate data analysis.

High-resolution 1H and 1H-13C magic angle spinning NMR spectroscopy of rat liver

High‐resolution magic angle spinning (MAS) 1H NMR spectra of small samples (ca. 8 mg) of intact rat liver are reported for the first time. One dimensional spectra reveal a number of large well‐resolved NMR signals mainly from low to medium molecular weight compounds (generally <1000 Daltons) from a variety of chemical classes. A range of 2D MAS‐NMR experiments were performed, including 1H J‐resolved (JRES), 1H‐1H total correlation spectroscopy (TOCSY) and 1H‐13C heteronuclear multiple quantum coherence (HMQC) to enable detailed signal assignment. Resonances were assigned from α‐ and β‐glucose, glycerol, alanine, glutamate, glycine, dimethylglycine, lysine, and threonine, together with phosphocholine, choline, lactate, trimethylamine‐N‐oxide (TMAO), and certain fatty acids. Well‐resolved 1H NMR signals from glycogen (poly 1‐4 α‐glucose) were observed directly in intact liver using MAS‐NMR spectroscopy. In addition, the resonances from the glycogen C1H proton in α(1→4) linked glucose units with either α(1→4) units adjacent or α(1→6) linked branches could be resolved in a high‐resolution 1H NMR experiment giving direct in situ information on the ratio of α(1→4) to α(1→6) units. This indicates that despite the relatively high MW (>1,000,000 Daltons) there is considerable segmental motion in the glycogen molecules giving long 1H T2 relaxation times. Magn Reson Med 44:201–207, 2000.

Toxicity classification from metabonomic data using a density superposition approach: ‘CLOUDS’

Predicting and avoiding the potential toxicity of candidate drugs is of fundamental importance to the pharmaceutical industry. The consortium for metabonomic toxicology (COMET) project aims to construct databases and metabolic models of drug toxicity using ca. 100,000 600 MHz 1 H NMR spectra of biofluids from laboratory rats and mice treated with model toxic compounds. Chemometric methods are being used to characterise the time-related and dose-specific effects of toxins on the endogenous metabolite profiles. Here we present a probabilistic approach to the classification of a large data set of COMET samples using Classification Of Unknowns by Density Superposition (CLOUDS), a novel non-neural implementation of a classification technique developed from probabilistic neural networks. NMR spectra of urine from rats from 19 different treatment groups, collected over 8 days, were processed to produce a data matrix with 2844 samples and 205 spectral variables. The spectra were normalised to account for gross concentration differences in the urine and regions corresponding to non-endogenous metabolites (0.4% of the data) were treated as missing values. Modeling the data according to organ of effect (control, liver, kidney or other organ), with a 50/50 train/test set split, over 90% of the test samples were classified as belonging to the correct group. In particular, samples from liver and kidney treatments were classified with 77 and 90% success, respectively, with only a 2% misclassification rate between these classes. Further analysis of the data, counting each of the 19 treatment groups as separate classes, resulted in a mean success rate across groups of 74%. Finally, as a severe test, the data were split into 88 classes, each representing a particular toxin at a particular time point. Fifty-four percent of the spectra from non-control samples were classified correctly, particularly successful when compared to the null success rate of ∼1% expected from random class assignment. The CLOUDS technique has advantages when modelling complex multi-dimensional distributions, giving a probabilistic rather than absolute class description of the data and is particularly amenable to inclusion of prior knowledge such as uncertainties in the data descriptors. This work shows that it is possible to construct viable and informative models of metabonomic data using the CLOUDS methodology, delineating the whole time course of toxicity. These models will be useful in building hybrid expert systems for predicting toxicology, which are the ultimate goal of the COMET project.

A study of metabolic compartmentation in the rat heart and cardiac mitochondria using high-resolution magic angle spinning 1H NMR spectroscopy.

High‐resolution magic angle spinning (MAS) 1H nuclear magnetic resonance (NMR) spectroscopy is increasingly being used to monitor metabolic abnormalities within cells and intact tissues. Many toxicological insults and metabolic diseases affect subcellular organelles, particularly mitochondria. In this study high‐resolution 1H NMR spectroscopy was used to examine metabolic compartmentation between the cytosol and mitochondria in the rat heart to investigate whether biomarkers of mitochondrial dysfunction could be identified and further define the mitochondrial environment. High‐resolution MAS spectra of mitochondria revealed NMR signals from lactate, alanine, taurine, choline, phosphocholine, creatine, glycine and lipids. However, spectra from mitochondrial extracts contained additional well‐resolved resonances from valine, methionine, glutamine, acetoacetate, succinate, and aspartate, suggesting that a number of metabolites bound within the mitochondrial membranes occur in ‘NMR invisible’ environments. This effect was further investigated using diffusion‐weighted measurements of water and NMR spectroscopy during state 2 and state 3 respiration. State 3 respiration caused a decrease in the resonance intensity of endogenous succinate compared with state 2 respiration, suggesting that coupled respiration may also modulate the NMR detection of metabolites within mitochondria.

Integrated Histopathological and Urinary Metabonomic Investigation of the Pathogenesis of Microcystin-LR Toxicosis

Patterns of change of endogenous metabolites may closely reflect systemic and organ-specific toxic changes. The authors examined the metabolic effects of the cyanobacterial (blue-green algal) toxin microcystin-LR by 1H-nuclear magnetic resonance (NMR) analysis of urinary endogenous metabolites. Rats were treated with a single sublethal dose, either 20 or 80 µg/kg intraperitoneally, and sacrificed at 2 or 7 days post dosing. Changes in the high-dose, 2-day sacrifice group included centrilobular hepatic necrosis and congestion, accompanied in some animals by regeneration and neovascularization. By 7 days, animals had recovered, the necrotizing process had ended, and the centrilobular areas had been replaced by regenerative, usually hypertrophic hepatocytes. There was considerable interanimal variation in the histologic process and severity, which correlated with the changes in patterns of endogenous metabolites in the urine, thus providing additional validation of the biomarker and biochemical changes. Similarity of the shape of the metabolic trajectories suggests that the mechanisms of toxic effects and recovery are similar among the individual animals, albeit that the magnitude and timing are different for the individual animals. Initial decreases in urinary citrate, 2-oxoglutarate, succinate, and hippurate concentrations were accompanied by a temporary increase in betaine and taurine, then creatine from 24 to 48 hours. Further changes were an increase in guanidinoacetate, dimethylglycine, urocanic acid, and bile acids. As a tool, urine can be repeatedly and noninvasively sampled and metabonomics utilized to study the onset and recovery after toxicity, thus identifying time points of maximal effect. This can help to employ histopathological examination in a guided and effective fashion.