Magnetic Resonance in Chemistry

NMR is an integral component of the drug discovery process and is currently making significant contributions in the burgeoning area of metabolomics. NMR is successfully being used to identify biomarkers for various diseases, to analyze drug toxicity and to determine a drug's in vivo efficacy and selectivity. This review describes advances in NMR-based metabolomics and discusses some recent applications.

Compounds known to induce the biosynthesis of cytochrome 2B enzymes increased the urinary excretion of gulonic and ascorbic acids. The compounds also modulated the hepatic expression of genes involved in ascorbic acid biosynthesis and reutilization.

NMR-based metabolomics of mouse urine was used in conjunction with the traditional staining and imaging of aortas for the characterization of plaque formation in apolipoprotein-E knockout mice. Metabolomics was able to differentiate the captopril-treated from the untreated mice in general agreement with the staining results. Allantoin, a marker attributed to captopril treatment, was elevated in the drug-treated mice and xanthine and ascorbate were elevated in the untreated mice as possible markers of plaque formation.

A metabonomic approach was used to reveal the time-related metabolic effects of 1-cyano-2-hydroxy-3-butene (CHB, crambene) exposure in rats using high-resolution ¹H NMR spectroscopy of urine and serum in combination with pattern recognition methods. The spectral correlation technique STOCSY was applied to identify CHB metabolites in urine and to detect both pharmacological and toxicological connectivities between xenobiotic and endogenous metabolites for the first time. The approach may be of wider application in drug metabolism studies for identification of unknown toxic species.

850 MHz ¹H NMR spectroscopy was used to characterize changes in metabolic profiles of mice urine and fecal extracts following gut sterilization using the broad-spectrum fluoroquinoline antibiotic enrofloxacin (Baytril). Principal components analysis (PCA) indicated 17 metabolites changed that could be attributed to loss of bacterial species in the gut. The figure shows a section of the loadings plot derived from PCA of NMR of fecal extracts was used to identify NMR resonances that changed in intensity after antibiotic treatment.

Referencing ¹H magic angle spinning (MAS) NMR spectra of intact biological tissue samples to the chemical shift of glucose rather than that of trimethylsilylpropionic acid (TSP) improves the reliability of multivariate statistical models of the data. This study demonstrates that a number of alternative internal reference chemical shifts are also possible, in the absence of glucose in the tissue.

Fecal extracts contain useful biological information that can reflect the health status of the colon. Appropriate analytical techniques and data analysis methods are essential to extract all that information. Studies reviewed in this article demonstrate that MR metabolomics has a potential to play a role in that regard, particularly in colorectal cancer and inflammatory bowel disease. Methodological considerations on sample collection, preparation, data acquisition and data analysis have been discussed with some recommendations provided that can make the technique robust.

High-field ¹H nuclear magnetic resonance (NMR) spectroscopy has been applied to obtain metabolic profiles of chick embryos at early stages of neural development. Neural tube defects (NTDs) are severe congenital malformations, and evidence exists for prevention of NTDs by periconceptional supplementation of the diet with folate. The metabolic profiles of chick embryos following inhibition of enzymes of the methylation cycle, a factor believed to be inked with the observed folate-protection, and the effect of their action on neural tube closure were investigated.

An NMR based metabolomics approach is used to discriminate serum samples of colorectal cancer patients from controls. By using Hadamard encoded TOCSY spectra a significant advantage in acquisition time and resolution of the spectra is achieved. Using this method in combination with principal component analysis good separation between cancer samples and controls is achieved. Moreover, staging correlates well with the metabolic analysis.

In this study, fingerprinting of a class of lipid metabolites was achieved by ³¹P labeling using the derivatizing agent 2-chloro-4,4,5,5-tetramethyldioxaphospholane. Lipids containing hydroxyl, aldehyde and carboxyl groups were selectively tagged with ³¹P and then detected with good resolution using ³¹P NMR by exploiting the 100% natural abundance and wide chemical shift range of ³¹P. The ³¹P derivatization approach is simple, reproducible and highly quantitative, and has the potential to profile a number of important lipids in complex biological samples.

A standard operating procedure (SOP) for high-throughput metabonomics studies is presented. The procedure is designed to minimize random and systematic variations of NMR data collection. In addition, a protocol is described to assess the quality of the data acquired by a given NMR spectroscopist.

The suitability of 2-dimensional ¹H J-resolved (JRES) NMR spectroscopy for quantitative metabolite analysis, based upon derived mathematical functions to describe the line-shapes, has been evaluated. The NMR line-shapes and signal intensities have been shown to be affected by the application of apodisation functions (SINE and SEM) and JRES-specific processing (tilting and symmetrising). Overall, the 2D JRES approach is comparable to the effectiveness of a traditional 1D NMR measurement for metabolite quantification.

The application of fuzzy K-means (FKM) clustering method for the analysis of 1D ¹H NMR metabolomics measurements of different types of cell lines as well as urine samples is presented. In FKM clustering, each sample is assigned to all clusters with varying memberships. The FKM clustering allowed more accurate sample classification relative to principal component analysis, hierarchical clustering and K-means clustering. Fuzzy clustering method provided clear separation of sample types, and the analysis of membership values allowed assignment of subtypes.

Multivariate curve resolution via weighted alternation squares (MCR-wALS) has been shown to be an effective tool for modeling time course metabolomics data. Two data sets were used to demonstrate the utility of this algorithm. Concentration profiles that qualitatively describe the evolution of metabolites during the course of the experiment were obtained along with corresponding spectral profiles that can aid in identification of individual metabolites in a complex mixture of small molecules in biofluids and tissue extracts.

NMR spectroscopy has a unique potential for the elucidation of the chemical composition of biological samples, which is a main focus of systems biology and metabolomics. To streamline this task, a web server suite, termed COLMAR, has been created, which includes a covariance processing server for 2D NMR TOCSY, a server that deconvolutes the spectrum into the subspectra of individual components, and finally a server that screens the subspectra against a NMR database for automated compound identification.

A new open source software package (rNMR) is introduced. rNMR is a flexible data analysis tool that simplifies the repetitive resonance assignment and quantification tasks inherent to NMR-based metabolomics. The program provides a simple graphics-based method for visualizing NMR signals across multiple spectra and makes the quantitative analyses of complex NMR data more transparent.

This work determines metabolite profiles of tropical wines produced in northeast Brazil, São Francisco River Valley, by using ¹H NMR spectroscopy in combination with chemometric methods to analyze and discriminate wine samples from 2007 vintages produced by different wineries.

This article introduces a screening method based on 1H-NMR for quality control of fruit juices. NMR allows the analysis in two ways within one experiment: namely, target and non-targeted. Targeted stands for the safe identification and quantification of individual compounds, whereas untargeted means the detection of all deviations visible by NMR using statistical analysis based on normality models. Statistical analysis is also used for classification, e.g. the determination of the origin of the juice.

Cryoprobe-assisted ¹H and ¹³C NMR methods for identifying and quantifying concentrations of metabolites in H₂O above 10 µM from fermenting cyanobacteria with respect to time are presented. Relaxation times (T1) of several metabolites in H₂O were measured. Methods should be broadly applicable to other microbial systems.

This work compares several commonly used metabolite extraction solvents for their effects on the ¹H-NMR metabolic profile of the model plant Arabidopsis thaliana. Perchloric acid and D₂O extracts showed evidence of sucrose degradation. Mixed organic-aqueous extraction solvents had better stability but gave rise to problems due to contamination from lipid resonances. The best results were obtained performing the extraction with CD₃CN/D₂O followed by removal of solvent and reconstitution in D₂O buffer.

Metabolite identification is a key step for metabonomics study. A fully automated hyphenated technique is an efficient method to determine the structure of an unknown metabolite. In this review, applications of HPLC-DAD-MS-SPE-NMR in identifying novel and trace metabolites in plant extracts and drug metabolism are discussed.

Two dimensional Diffusion Ordered SpectroscopY (DOSY) ¹H NMR was applied to the analysis of 17 formulations of sildenafil, one being genuine Viagra and the others illegally manufactured formulations of this drug. It enabled distinguishing imitations or counterfeit from the authentic formulation, detecting the presence of sildenafil or adulterants, gaining information on formulation process by detection of various excipients, thus giving a precise and global “signature” of the manufacturer.