Olaf Beckonert

Metabolic profiling, metabolomic and metabonomic procedures for NMR spectroscopy of urine, plasma, serum and tissue extracts

Metabolic profiling, metabolomic and metabonomic studies mainly involve the multicomponent analysis of biological fluids, tissue and cell extracts using NMR spectroscopy and/or mass spectrometry (MS). We summarize the main NMR spectroscopic applications in modern metabolic research, and provide detailed protocols for biofluid (urine, serum/plasma) and tissue sample collection and preparation, including the extraction of polar and lipophilic metabolites from tissues. 1H NMR spectroscopic techniques such as standard 1D spectroscopy, relaxation-edited, diffusion-edited and 2D J-resolved pulse sequences are widely used at the analysis stage to monitor different groups of metabolites and are described here. They are often followed by more detailed statistical analysis or additional 2D NMR analysis for biomarker discovery. The standard acquisition time per sample is 4–5 min for a simple 1D spectrum, and both preparation and analysis can be automated to allow application to high-throughput screening for clinical diagnostic and toxicological studies, as well as molecular phenotyping and functional genomics.

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.

High-resolution magic-angle-spinning NMR spectroscopy for metabolic profiling of intact tissues

Metabolic profiling, metabolomic and metabonomic studies require robust study protocols for any large-scale comparisons and evaluations. Detailed methods for solution-state NMR spectroscopy have been summarized in an earlier protocol. This protocol details the analysis of intact tissue samples by means of high-resolution magic-angle-spinning (HR-MAS) NMR spectroscopy and we provide a detailed description of sample collection, preparation and analysis. Described here are 1H NMR spectroscopic techniques such as the standard one-dimensional, relaxation-edited, diffusion-edited and two-dimensional J-resolved pulse experiments, as well as one-dimensional 31P NMR spectroscopy. These are used to monitor different groups of metabolites, e.g., sugars, amino acids and osmolytes as well as larger molecules such as lipids, non-invasively. Through the use of NMR-based diffusion coefficient and relaxation times measurements, information on molecular compartmentation and mobility can be gleaned. The NMR methods are often combined with statistical analysis for further metabonomics analysis and biomarker identification. The standard acquisition time per sample is 8–10 min for a simple one-dimensional 1H NMR spectrum, giving access to metabolite information while retaining tissue integrity and hence allowing direct comparison with histopathology and MRI/MRS findings or the evaluation together with biofluid metabolic-profiling data.

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.

A metabolic system-wide characterisation of the pig: a model for human physiology

The pig is a single-stomached omnivorous mammal and is an important model of human disease and nutrition. As such, it is necessary to establish a metabolic framework from which pathology-based variation can be compared. Here, a combination of one and two-dimensional (1)H and (13)C nuclear magnetic resonance spectroscopy (NMR) and high-resolution magic angle spinning (HR-MAS) NMR was used to provide a systems overview of porcine metabolism via characterisation of the urine, serum, liver and kidney metabolomes. The metabolites observed in each of these biological compartments were found to be qualitatively comparable to the metabolic signature of the same biological matrices in humans and rodents. The data were modelled using a combination of principal components analysis and Venn diagram mapping. Urine represented the most metabolically distinct biological compartment studied, with a relatively greater number of NMR detectable metabolites present, many of which are implicated in gut-microbial co-metabolic processes. The major inter-species differences observed were in the phase II conjugation of extra-genomic metabolites; the pig was observed to conjugate p-cresol, a gut microbial metabolite of tyrosine, with glucuronide rather than sulfate as seen in man. These observations are important to note when considering the translatability of experimental data derived from porcine models.

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.

Identification of human urinary biomarkers of cruciferous vegetable consumption by metabonomic profiling.

Consumption of cruciferous vegetables (CVs) is inversely correlated to many human diseases including cancer (breast, lung, and bladder), diabetes, and cardiovascular and neurological disease. Presently, there are no readily measurable biomarkers of CV consumption and intake of CVs has relied on dietary recall. Here, biomarkers of CV intake were identified in the urine of 20 healthy Caucasian adult males using (1)H NMR spectroscopy with multivariate statistical modeling. The study was separated into three phases of 14 days: a run-in period with restricted CV consumption (phase I); a high CV phase where participants consumed 250 g/day of both broccoli and Brussels sprouts (phase II); a wash-out phase with a return to restricted CV consumption (phase III). Each study participant provided a complete cumulative urine collection over 48 h at the end of each phase; a spot urine (U0), 0-10 h (U0-10), 10-24 h (U10-24), and 24-48 h (U24-48) urine samples. Urine samples obtained after consumption of CVs were differentiated from low CV diet samples by four singlet (1)H NMR spectroscopic peaks, one of which was identified as S-methyl-l-cysteine sulfoxide (SMCSO) and the three other peaks were tentatively identified as other metabolites structurally related to SMCSO. These stable urinary biomarkers of CV consumption will facilitate future assessment of CVs in nutritional population screening and dietary intervention studies and may correlate to population health outcomes.

Temporal metabonomic modeling of l-arginine-induced exocrine pancreatitis.

The time-related metabolic responses to l-arginine (ARG)-induced exocrine pancreatic toxicity were investigated using single ip doses of 1,000 and 4,000 mg/kg body weight over a 7 day experimental period in male Sprague-Dawley rats. Sequential timed urine and plasma samples were analyzed using high resolution (1)H NMR spectroscopy together with complementary clinical chemistry and histopathology analyses. Principal components analysis (PCA) and orthogonal projection on latent structures discriminant analysis (O-PLS-DA) were utilized to analyze the (1)H NMR data and to extract and identify candidate biomarkers and to construct metabolic trajectories post ARG administration. Low doses of ARG resulted in virtually no histopathological damage and distinct reversible metabolic response trajectories. High doses of ARG caused pancreatic acinar degeneration and necrosis and characteristic metabolic trajectory profiles with several distinct phases. The initial trajectory phase (0-8 h) involved changes in the urea cycle and transamination indicating a homeostatic response to detoxify excess ammonia generated from ARG catabolism. By 48 h, there was a notable enhancement of the excretion of the gut microbial metabolites, phenylacetylglycine (PAG), 4-cresol-glucuronide and 4-cresol-sulfate, suggesting that compromised pancreatic function impacts on the activity of the gut microbiota giving potential rise to a novel class of surrogate extragenomic biomarkers of pancreatic injury. The implied compromise of microbiotal function may also contribute to secondary hepatic and pancreatic toxic responses. We show here for the first time the value of metabonomic studies in investigating metabolic disruption due to experimental pancreatitis. The variety of observed systemic responses suggests that this approach may be of general value in the assessment of other animal models or human pancreatitis.

Heteronuclear 19F−1H Statistical Total Correlation Spectroscopy as a Tool in Drug Metabolism: Study of Flucloxacillin Biotransformation

We present a novel application of the heteronuclear statistical total correlation spectroscopy (HET-STOCSY) approach utilizing statistical correlation between one-dimensional 19F/1H NMR spectroscopic data sets collected in parallel to study drug metabolism. Parallel one-dimensional (1D) 800 MHz 1H and 753 MHz 19F{1H} spectra (n = 21) were obtained on urine samples collected from volunteers (n = 6) at various intervals up to 24 h after oral dosing with 500 mg of flucloxacillin. A variety of statistical relationships between and within the spectroscopic datasets were explored without significant loss of the typically high 1D spectral resolution, generating 1H-1H STOCSY plots, and novel 19F-1H HET-STOCSY, 19F-19F STOCSY, and 19F-edited 1H-1H STOCSY (X-STOCSY) spectroscopic maps, with a resolution of approximately 0.8 Hz/pt for both nuclei. The efficient statistical editing provided by these methods readily allowed the collection of drug metabolic data and assisted structure elucidation. This approach is of general applicability for studying the metabolism of other fluorine-containing drugs, including important anticancer agents such as 5-fluorouracil and flutamide, and is extendable to any drug metabolism study where there is a spin-active X-nucleus (e.g., 13C, 15N, 31P) label present.

Cluster analysis statistical spectroscopy using nuclear magnetic resonance generated metabolic data sets from perturbed biological systems.

We present a new approach for analysis, information recovery, and display of biological (1)H nuclear magnetic resonance (NMR) spectral data, cluster analysis statistical spectroscopy (CLASSY), which profiles qualitative and quantitative changes in biofluid metabolic composition by utilizing a novel local-global correlation clustering scheme to identify structurally related spectral peaks and arrange metabolites by similarity of temporal dynamic variation. Underlying spectral data sets are presented in a novel graphical format to represent high-dimensionality biochemical information conveying both statistical metabolite relationships and their responses to experimental perturbation simultaneously in a high-throughput and intuitive manner. The method is exemplified using multiple 600 MHz (1)H NMR spectra of rat (n = 40) urine samples collected over 160 h following the development of experimental pancreatitis induced by L-arginine (ARG) and a wider range of model toxins including acetaminophen, galactosamine, and 2-bromoethanamine. The CLASSY approach deconvolutes complex biofluid mixture spectra into quantitative fold-change metabolic trajectories and clusters metabolites by commonalities of coexpression patterns. We demonstrate that the developing pathological processes cause coordinated changes in the levels of many compounds which share similar pathway connectivities. Variability in individual responses to toxin exposure is also readily detected and visualized allowing the assessment of interanimal variability. As an untargeted, unsupervised approach, CLASSY provides significant advantages in biological information recovery in terms of increased throughput, interpretability, and robustness and has wide potential metabonomic/metabolomic applications in clinical, toxicological, and nutritional studies of biofluids as well as in studies of cellular biochemistry, microbial fermentation monitoring, and functional genomics.