Nuclear Magnetic Resonance Metabonomics

Prediction of Low-Dose Aspirin-Induced Gastric Toxicity Using Nuclear Magnetic Resonance Spectroscopy-Based Pharmacometabolomics in Rats

BACKGROUND

Low-dose aspirin (LDA) is the backbone for secondary prevention of coronary artery disease, although limited by gastric toxicity. This study aimed to identify novel metabolites that could predict LDA-induced gastric toxicity using pharmacometabolomics.

METHODS

Pre-dosed urine samples were collected from male Sprague-Dawley rats. The rats were treated with either LDA (10 mg/kg) or 1% methylcellulose (10 mL/kg) per oral for 28 days. The rats' stomachs were examined for gastric toxicity using a stereomicroscope. The urine samples were analyzed using a proton nuclear magnetic resonance spectroscopy. Metabolites were systematically identified by exploring established databases and multivariate analyses to determine the spectral pattern of metabolites related to LDA-induced gastric toxicity.

RESULTS

Treatment with LDA resulted in gastric toxicity in 20/32 rats (62.5%). The orthogonal projections to latent structures discriminant analysis (OPLS-DA) model displayed a goodness-of-fit (R2Y) value of 0.947, suggesting near-perfect reproducibility and a goodness-of-prediction (Q2Y) of -0.185 with perfect sensitivity, specificity and accuracy (100%). Furthermore, the area under the receiver operating characteristic (AUROC) displayed was 1. The final OPLS-DA model had an R2Y value of 0.726 and Q2Y of 0.142 with sensitivity (100%), specificity (95.0%) and accuracy (96.9%). Citrate, hippurate, methylamine, trimethylamine N-oxide and alpha-keto-glutarate were identified as the possible metabolites implicated in the LDA-induced gastric toxicity.

CONCLUSION

The study identified metabolic signatures that correlated with the development of a low-dose Aspirin-induced gastric toxicity in rats. This pharmacometabolomic approach could further be validated to predict LDA-induced gastric toxicity in patients with coronary artery disease.

Metabolomic changes in patients with chronic obstructive pulmonary disease with abnormal Savda syndrome

The aim of this study was to determine the metabolic biomarkers for abnormal Savda syndrome in patients with chronic obstructive pulmonary disease (COPD). Based on Traditional Uyghur Medicine (TUM) theory, a total of 103 patients with COPD were classified into abnormal Savda and non-abnormal Savda syndrome groups and 52 healthy volunteers acted as the control group. Blood samples from the three groups were analyzed using nuclear magnetic resonance (NMR) spectroscopy combined with orthogonal projection to latent structure-discriminant analysis. NMR tests showed that the regional distributions of the patients with COPD with abnormal Savda syndrome, those with non-abnormal Savda syndrome and the control group were completely separate (P>0.05). The patients with COPD with abnormal Savda syndrome exhibited relatively low levels of amino acids, glycoproteins and unsaturated lipids (P<0.05) but significantly higher levels of lactic acid, carnitine, acetone and acetoacetate (P<0.05) compared with the healthy controls. Abnormal Savda syndrome was one of the main types of syndrome among the patients with COPD; increased age, a longer duration of illness and a higher disease severity were characteristic of this type of syndrome. In addition, the present study provided biochemical evidence for the TUM theory-based classification of patients with COPD; these biomarkers can be used in the clinic for the diagnosis of COPD with abnormal Savda syndrome. The study also demonstrated that the plasma metabolic disorder in patients with COPD with abnormal Savda syndrome was more serious than that in the control and COPD with non-abnormal Savda syndrome groups. The plasma metabolic disorder was also associated with a low immune function of the body and endocrine and energy metabolism disorders.

NMR methods for metabolomics of mammalian cell culture bioreactors

Metabolomics has become an important tool for measuring pools of small molecules in mammalian cell cultures expressing therapeutic proteins. NMR spectroscopy has played an important role, largely because it requires minimal sample preparation, does not require chromatographic separation, and is quantitative. The concentrations of large numbers of small molecules in the extracellular media or within the cells themselves can be measured directly on the culture supernatant and on the supernatant of the lysed cells, respectively, and correlated with endpoints such as titer, cell viability, or glycosylation patterns. The observed changes can be used to generate hypotheses by which these parameters can be optimized. This chapter focuses on the sample preparation, data acquisition, and analysis to get the most out of NMR metabolomics data from CHO cell cultures but could easily be extended to other in vitro culture systems.

Application of NMR and molecular docking in structure-based drug discovery

Drug discovery is a complex and costly endeavor, where few drugs that reach the clinical testing phase make it to market. High-throughput screening (HTS) is the primary method used by the pharmaceutical industry to identify initial lead compounds. Unfortunately, HTS has a high failure rate and is not particularly efficient at identifying viable drug leads. These shortcomings have encouraged the development of alternative methods to drive the drug discovery process. Specifically, nuclear magnetic resonance (NMR) spectroscopy and molecular docking are routinely being employed as important components of drug discovery research. Molecular docking provides an extremely rapid way to evaluate likely binders from a large chemical library with minimal cost. NMR ligand-affinity screens can directly detect a protein-ligand interaction, can measure a corresponding dissociation constant, and can reliably identify the ligand binding site and generate a co-structure. Furthermore, NMR ligand affinity screens and molecular docking are perfectly complementary techniques, where the combination of the two has the potential to improve the efficiency and success rate of drug discovery. This review will highlight the use of NMR ligand affinity screens and molecular docking in drug discovery and describe recent examples where the two techniques were combined to identify new and effective therapeutic drugs.

Automatic phase correction of 2D NMR spectra by a whitening method

A new method for the automatic phase correction of multidimensional NMR spectra is described. It is based on the whitening concept formulated as the 'maximization of the number of white pixels into a bitmap that corresponds to the spectrum'. This process of maximization can be factorized along the individual axes of the spectrum and this property makes the method robust and fast. It employs a statistic measure based on a large number of spectral data points and, for this reason, is very tolerant to low signal-to-noise ratio (SNR) and local artifacts. The algorithm can efficiently phase either homonuclear or heteronuclear experiments and, unlike other previous methods, it can also process automatically spectra containing positive or negative peaks so that it is not necessary to deal with individual or special cases.

GC-MS with ethyl chloroformate derivatization for comprehensive analysis of metabolites in serum and its application to human uremia

An optimized method based on GC-MS with ethyl chloroformate derivatization has been developed for the comprehensive analysis of endogenous metabolites in serum. Twenty-two reference standards and serum samples were used to validate the proposed method. The correlation coefficient was higher than 0.9900 for each of the standards, and the LOD varied from 125 to 300 pg on-column. The analytical equipment exhibited good repeatability (RSD<10%) for all of the standards. Both the repeatability and the within-48-h stability of the analytical method were satisfactory (RSD<10%) for the 18 metabolites identified in the serum samples. Mean recovery was acceptable for the 18 metabolites, ranging from 70% to 120% with RSDs of less than 10%. Using the optimized protocol and a subsequent multivariate statistical technique, complete differentiation was achieved between the metabolic profile of uremic patients and that of age- and sex-matched normal subjects. Significantly decreased levels of valine, leucine, and isoleucine and increased levels of myristic acid and linoleic acid were observed in the patient group. This work demonstrated that this method is suitable for serum-based metabolic profiling studies.

Metabonomic study on ageing: NMR-based investigation into rat urinary metabolites and the effect of the total flavone of Epimedium

The aim of this work was to investigate the effects of ageing on rat urinary metabolites and to evaluate the anti-ageing effects of the total flavone of Epimedium (TFE). Proton nuclear magnetic resonance based metabonomic analyses was performed on urine from rats aged 4, 10, 18 and 24 months, and rats administered with TFE. By multivariate analysis, 26 characteristic resonances were found to be highly related with the age of rats, and ten of them were structurally postulated as creatinine, lactate, alanine, acetate, acetone, succinate, allantoin, methylamine, dimethylamine and trimethylamine-N-oxide; these metabolites involve creatinine metabolites, aliphatic amines metabolites and some important intermediates or end products of energy metabolism. Principal components analysis revealed that the metabolic profiles of 24-month-old rats treated with TFE closely resembled those of rats aged 18 months. In addition, most of these characteristic resonances were reset to younger levels by TFE intervention. The result suggests that TFE administration can markedly influence the ageing process and shows anti-ageing effects, which might due to the melioration of pyruyate metabolism and oxidative phosphorylation.

Modeling of NMR processing, toward efficient unattended processing of NMR experiments

Many alternative processing techniques have recently been proposed in the literature. Most of these techniques rely on specific acquisition protocols as well as on specific data processing techniques, the need for an efficient versatile and expandable NMR processing tool would be a particularly timely addition to the modern NMR spectroscopy laboratory. The work presented here consists in a modeling of the various possible NMR data processing approaches. This modeling presents a common working frame for most of the modern acquisition/processing protocols. Two different data modeling approaches are presented, strong modeling and weak modeling, depending whether the system under study or the measurement is modeled. The emphasis is placed on the weak modeling approach. This modeling is implemented in a computer program developed in python and called NPK standing (standing for NMR Processing Kernel), organized in four logical layers (i) mathematical kernel; (ii) elementary actions; (iii) processing phases; (iv) processing strategies. This organisation, along with default values for most processing parameters allows the use of the program in an unattended manner, producing close to optimal spectra. Examples are shown for 1D and 2D processing, and liquid and solid NMR spectroscopy. NPK is available from the site: http://abcis.cbs.cnrs.fr/NPK.