Chiral Metabonomics: ¹H NMR-Based Enantiospecific Differentiation of Metabolites in Human Urine via Direct Cosolvation with β-Cyclodextrin

Differences in molecular chirality remain an important issue in drug metabolism and pharmacokinetics for the pharmaceutical industry and regulatory authorities, and chirality is an important feature of many endogenous metabolites. We present a method for the rapid, direct differentiation and identification of chiral drug enantiomers in human urine without pretreatment of any kind. Using the well-known anti-inflammatory chemical ibuprofen as one example, we demonstrate that the enantiomers of ibuprofen and the diastereoisomers of one of its main metabolites, the glucuronidated carboxylate derivative, can be resolved by ¹H NMR spectroscopy as a consequence of direct addition of the chiral cosolvating agent (CSA) β-cyclodextrin (βCD). This approach is simple, rapid, and robust, involves minimal sample manipulation, and does not require derivatization or purification of the sample. In addition, the method should allow the enantiodifferentiation of endogenous chiral metabolites, and this has potential value for differentiating metabolites from mammalian and microbial sources in biofluids. From these initial findings, we propose that more extensive and detailed enantiospecific metabolic profiling could be possible using CSA-NMR spectroscopy than has been previously reported

Robust Data Processing and Normalization Strategy for MALDI Mass Spectrometric Imaging

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) provides localized information about the molecular content of a tissue sample. To derive reliable conclusions from MSI data, it is necessary to implement appropriate processing steps in order to compare peak intensities across the different pixels comprising the image. Here, we review commonly used normalization methods, and propose a rational data processing strategy, for robust evaluation and modeling of MSI data. The approach includes newly developed heuristic methods for selecting biologically relevant peaks and pixels to reduce the size of a data set and remove the influence of the applied MALDI matrix. The methods are demonstrated on a MALDI MSI data set of a sagittal section of rat brain (4750 bins, <i>m</i>/<i>z</i> = 50–1000, 111 × 185 pixels) and the proposed preferred normalization method uses the median intensity of selected peaks, which were determined to be independent of the MALDI matrix. This was found to effectively compensate for a range of known limitations associated with the MALDI process and irregularities in MS image sampling routines. This new approach is relevant for processing of all MALDI MSI data sets, and thus likely to have impact in biomarker profiling, preclinical drug distribution studies, and studies addressing underlying molecular mechanisms of tissue pathology

Statistical Total Correlation Spectroscopy Scaling for Enhancement of Metabolic Information Recovery in Biological NMR Spectra

The high level of complexity in nuclear magnetic resonance (NMR) metabolic spectroscopic data sets has fueled the development of experimental and mathematical techniques that enhance latent biomarker recovery and improve model interpretability. We previously showed that statistical total correlation spectroscopy (STOCSY) can be used to <i>edit</i> NMR spectra to remove drug metabolite signatures that obscure metabolic variation of diagnostic interest. Here, we extend this “STOCSY editing” concept to a generalized scaling procedure for NMR data that enhances recovery of latent biochemical information and improves biological classification and interpretation. We call this new procedure STOCSY-scaling (STOCSY^S). STOCSY^S exploits the fixed proportionality in a set of NMR spectra between resonances from the same molecule to suppress or enhance features correlated with a resonance of interest. We demonstrate this new approach using two exemplar data sets: (a) a streptozotocin rat model (<i>n</i> = 30) of type 1 diabetes and (b) a human epidemiological study utilizing plasma NMR spectra of patients with metabolic syndrome (<i>n</i> = 67). In both cases significant biomarker discovery improvement was observed by using STOCSY^S: the approach successfully suppressed interfering NMR signals from glucose and lactate that otherwise dominate the variation in the streptozotocin study, which then allowed recovery of biomarkers such as glycine, which were otherwise obscured. In the metabolic syndrome study, we used STOCSY^S to enhance variation from the high-density lipoprotein cholesterol peak, improving the prediction of individuals with metabolic syndrome from controls in orthogonal projections to latent structures discriminant analysis models and facilitating the biological interpretation of the results. Thus, STOCSY^S is a versatile technique that is applicable in any situation in which variation, either biological or otherwise, dominates a data set at the expense of more interesting or important features. This approach is generally appropriate for many types of NMR-based complex mixture analyses and hence for wider applications in bioanalytical science

Stability and Robustness of Human Metabolic Phenotypes in Response to Sequential Food Challenges

High-resolution spectroscopic profiles of biofluids can define metabolic phenotypes, providing a window onto the impact of diet on health to reflect gene–environment interactions. ¹H NMR spectroscopic profiling was used to characterize the effect of nutritional intervention on the stability of the metabolic phenotype of 7 individuals following a controlled 7 day dietary protocol. Inter-individual metabolic differences influenced proportionally more of the spectrum than dietary modulation, with certain individuals displaying a greater stability of metabolic phenotypes than others. Correlation structures between urinary metabolites were identified and used to map inter-individual pathway differences. Choline degradation was the pathway most affected by the individual, suggesting that the gut microbiota influence host metabolic phenotypes. This influence was further emphasized by the highly correlated excretion of the microbial–mammalian co-metabolites phenylacetylglutamine, 4-cresylsulfate (<i>r</i> = 0.87), and indoxylsulfate (<i>r</i> = 0.67) across all individuals. Above the background of inter-individual differences, clear biochemical effects of single type dietary interventions, animal protein, fruit and wine intake, were observed; for example, the spectral variance introduced by fruit ingestion was attributed to the metabolites tartrate, proline betaine, hippurate, and 4-hydroxyhippurate. This differential metabolic baseline and response to selected dietary challenges highlights the importance of understanding individual differences in metabolism and provides a rationale for evaluating dietary interventions and stratification of individuals with respect to guiding nutrition and health programmes

Pharmacometabonomic Characterization of Xenobiotic and Endogenous Metabolic Phenotypes That Account for Inter-individual Variation in Isoniazid-Induced Toxicological Response

An NMR-based pharmacometabonomic approach was applied to investigate inter-animal variation in response to isoniazid (INH; 200 and 400 mg/kg) in male Sprague–Dawley rats, alongside complementary clinical chemistry and histopathological analysis. Marked inter-animal variability in central nervous system (CNS) toxicity was identified following administration of a high dose of INH, which enabled characterization of CNS responders and CNS non-responders. High-resolution post-dose urinary ¹H NMR spectra were modeled both by their xenobiotic and endogenous metabolic information sets, enabling simultaneous identification of the differential metabolic fate of INH and its associated endogenous metabolic consequences in CNS responders and CNS non-responders. A characteristic xenobiotic metabolic profile was observed for CNS responders, which revealed higher urinary levels of pyruvate isonicotinylhydrazone and β-glucosyl isonicotinylhydrazide and lower levels of acetylisoniazid compared to CNS non-responders. This suggested that the capacity for acetylation of INH was lower in CNS responders, leading to increased metabolism <i>via</i> conjugation with pyruvate and glucose. In addition, the endogenous metabolic profile of CNS responders revealed higher urinary levels of lactate and glucose, in comparison to CNS non-responders. Pharmacometabonomic analysis of the pre-dose ¹H NMR urinary spectra identified a metabolic signature that correlated with the development of INH-induced adverse CNS effects and may represent a means of predicting adverse events and acetylation capacity when challenged with high dose INH. Given the widespread use of INH for the treatment of tuberculosis, this pharmacometabonomic screening approach may have translational potential for patient stratification to minimize adverse events

Pharmacometabonomic Investigation of Dynamic Metabolic Phenotypes Associated with Variability in Response to Galactosamine Hepatotoxicity

Galactosamine (galN) is widely used as an <i>in vivo</i> model of acute liver injury. We have applied an integrative approach, combining histopathology, clinical chemistry, cytokine analysis, and nuclear magnetic resonance (NMR) spectroscopic metabolic profiling of biofluids and tissues, to study variability in response to galactosamine following successive dosing. On re-challenge with galN, primary non-responders displayed galN-induced hepatotoxicity (induced response), whereas primary responders exhibited a less marked response (adaptive response). A systems-level metabonomic approach enabled simultaneous characterization of the xenobiotic and endogenous metabolic perturbations associated with the different response phenotypes. Elevated serum cytokines were identified and correlated with hepatic metabolic profiles to further investigate the inflammatory response to galN. The presence of urinary <i>N</i>-acetylglucosamine (glcNAc) correlated with toxicological outcome and reflected the dynamic shift from a resistant to a sensitive phenotype (induced response). In addition, the urinary level of glcNAc and hepatic level of UDP-<i>N</i>-acetylhexosamines reflected an adaptive response to galN. The unique observation of galN-pyrazines and altered gut microbial metabolites in fecal profiles of non-responders suggested that gut microfloral metabolism was associated with toxic outcome. Pharmacometabonomic modeling of predose urinary and fecal NMR spectroscopic profiles revealed a diverse panel of metabolites that classified the dynamic shift between a resistant and sensitive phenotype. This integrative pharmacometabonomic approach has been demonstrated for a model toxin; however, it is equally applicable to xenobiotic interventions that are associated with wide variation in efficacy or toxicity and, in particular, for prediction of susceptibility to toxicity

Subset Optimization by Reference Matching (STORM): An Optimized Statistical Approach for Recovery of Metabolic Biomarker Structural Information from ¹H NMR Spectra of Biofluids

We describe a new multivariate statistical approach to recover metabolite structure information from multiple ¹H NMR spectra in population sample sets. Subset optimization by reference matching (STORM) was developed to select subsets of ¹H NMR spectra that contain specific spectroscopic signatures of biomarkers differentiating between different human populations. STORM aims to improve the visualization of structural correlations in spectroscopic data by using these reduced spectral subsets containing smaller numbers of samples than the number of variables (<i>n</i> ≪ <i>p</i>). We have used statistical shrinkage to limit the number of false positive associations and to simplify the overall interpretation of the autocorrelation matrix. The STORM approach has been applied to findings from an ongoing human metabolome-wide association study on body mass index to identify a biomarker metabolite present in a subset of the population. Moreover, we have shown how STORM improves the visualization of more abundant NMR peaks compared to a previously published method (statistical total correlation spectroscopy, STOCSY). STORM is a useful new tool for biomarker discovery in the “omic” sciences that has widespread applicability. It can be applied to any type of data, provided that there is interpretable correlation among variables, and can also be applied to data with more than one dimension (e.g., 2D NMR spectra)

Optimized Phenotypic Biomarker Discovery and Confounder Elimination via Covariate-Adjusted Projection to Latent Structures from Metabolic Spectroscopy Data

Metabolism is altered by genetics, diet, disease status, environment, and many other factors. Modeling either one of these is often done without considering the effects of the other covariates. Attributing differences in metabolic profile to one of these factors needs to be done while controlling for the metabolic influence of the rest. We describe here a data analysis framework and novel confounder-adjustment algorithm for multivariate analysis of metabolic profiling data. Using simulated data, we show that similar numbers of true associations and significantly less false positives are found compared to other commonly used methods. Covariate-adjusted projections to latent structures (CA-PLS) are exemplified here using a large-scale metabolic phenotyping study of two Chinese populations at different risks for cardiovascular disease. Using CA-PLS, we find that some previously reported differences are actually associated with external factors and discover a number of previously unreported biomarkers linked to different metabolic pathways. CA-PLS can be applied to any multivariate data where confounding may be an issue and the confounder-adjustment procedure is translatable to other multivariate regression techniques

Precision High-Throughput Proton NMR Spectroscopy of Human Urine, Serum, and Plasma for Large-Scale Metabolic Phenotyping

Proton nuclear magnetic resonance (NMR)-based metabolic phenotyping of urine and blood plasma/serum samples provides important prognostic and diagnostic information and permits monitoring of disease progression in an objective manner. Much effort has been made in recent years to develop NMR instrumentation and technology to allow the acquisition of data in an effective, reproducible, and high-throughput approach that allows the study of general population samples from epidemiological collections for biomarkers of disease risk. The challenge remains to develop highly reproducible methods and standardized protocols that minimize technical or experimental bias, allowing realistic interlaboratory comparisons of subtle biomarker information. Here we present a detailed set of updated protocols that carefully consider major experimental conditions, including sample preparation, spectrometer parameters, NMR pulse sequences, throughput, reproducibility, quality control, and resolution. These results provide an experimental platform that facilitates NMR spectroscopy usage across different large cohorts of biofluid samples, enabling integration of global metabolic profiling that is a prerequisite for personalized healthcare

Hyperspectral Visualization of Mass Spectrometry Imaging Data

The acquisition of localized molecular spectra with mass spectrometry imaging (MSI) has a great, but as yet not fully realized, potential for biomedical diagnostics and research. The methodology generates a series of mass spectra from discrete sample locations, which is often analyzed by visually interpreting specifically selected images of individual masses. We developed an intuitive color-coding scheme based on hyperspectral imaging methods to generate a single overview image of this complex data set. The image color-coding is based on spectral characteristics, such that pixels with similar molecular profiles are displayed with similar colors. This visualization strategy was applied to results of principal component analysis, self-organizing maps and t-distributed stochastic neighbor embedding. Our approach for MSI data analysis, combining automated data processing, modeling and display, is user-friendly and allows both the spatial and molecular information to be visualized intuitively and effectively