Application of NMR-based metabolomics to the investigation of salt stress in maize (Zea mays).

INTRODUCTION: High salinity, caused by either natural (e.g. climatic changes) or anthropic factors (e.g. agriculture), is a widespread environmental stressor that can affect development and growth of salt-sensitive plants, leading to water deficit, the inhibition of intake of essential ions and metabolic disorders. OBJECTIVE: The application of an NMR-based metabolic profiling approach to the investigation of saline-induced stress in Maize plants is presented. METHODOLOGY: Zea Maize seedlings were grown in either 0, 50 or 150 mM saline solution. Plants were harvested after 2, 4 and 6 days (n = 5 per class and time point) and (1) H NMR spectroscopy was performed separately on shoot and root extracts. Spectral data were analysed and interpreted using multivariate statistical analyses. RESULTS: A distinct effect of time/growth was observed for the control group with relatively higher concentrations of acetoacetate at day 2 and increased levels of alanine at days 4 and 6 in root extracts, whereas concentration of alanine was positively correlated with the shoot extracts harvested at day 2 and trans-aconitic acid increased at days 4 and 6. A clear dose-dependent effect, superimposed on the growth effect, was observed for saline treated shoot and root extracts. This was correlated with increased levels of alanine, glutamate, asparagine, glycine-betaine and sucrose and decreased levels of malic acid, trans-aconitic acid and glucose in shoots. Correlation with salt-load shown in roots included elevated levels of alanine, γ-amino-N-butyric acid, malic acid, succinate and sucrose and depleted levels of acetoacetate and glucose. CONCLUSIONS: The metabolic effect of high salinity was predominantly consistent with osmotic stress as reported for other plant species and was found to be stronger in the shoots than the roots. Using multivariate data analysis it is possible to investigate the effects of more than one environmental stressor simultaneously.

Generation of in-silico cytochrome P450 1A2, 2C9, 2C19, 2D6, and 3A4 inhibition QSAR models.

In-silico models were generated to predict the extent of inhibition of cytochrome P450 isoenzymes using a set of relatively interpretable descriptors in conjunction with partial least squares (PLS) and regression trees (RT). The former was chosen due to the conservative nature of the resultant models built and the latter to more effectively account for any non-linearity between dependent and independent variables. All models are statistically significant and agree with the known SAR and they could be used as a guide to P450 liability through a classification based on the continuous pIC50 prediction given by the model. A compound is classified as having either a high or low P450 liability if the predicted pIC(50) is at least one root mean square error (RMSE) from the high/low pIC(50) cut-off of 5. If predicted within an RMSE of the cut-off we cannot be confident a compound will be experimentally low or high so an indeterminate classification is given. Hybrid models using bulk descriptors and fragmental descriptors do significantly better in modeling CYP450 inhibition, than bulk property QSAR descriptors alone.

Development, interpretation and temporal evaluation of a global QSAR of hERG electrophysiology screening data.

A 'global' model of hERG K(+) channel was built to satisfy three basic criteria for QSAR models in drug discovery: (1) assessment of the applicability domain, (2) assuring that model decisions can be interpreted by medicinal chemists and (3) assessment of model performance after the model was built. A combination of D-optimal onion design and hierarchical partial least squares modelling was applied to construct a global model of hERG blockade in order to maximize the applicability domain of the model and to enhance its interpretability. Additionally, easily interpretable hERG specific fragment-based descriptors were developed. Model performance was monitored, throughout a time period of 15 months, after model implementation. It was found that after this time duration a greater proportion of molecules were outside the model's applicability domain and that these compounds had a markedly higher average prediction error than those from molecules within the model's applicability domain. The model's predictive performance deteriorated within 4 months after building, illustrating the necessity of regular updating of global models within a drug discovery environment.

Application of directly coupled high performance liquid chromatography-NMR-mass spectometry and 1H NMR spectroscopic studies to the investigation of 2,3-benzofuran metabolism in Sprague-Dawley rats.

The urinary excretion of metabolites of 2,3-benzofuran was studied in Sprague-Dawley rats (n = 5) given a single dose of 150 mg/kg i.p. Urine samples were collected at defined intervals up to 7 days postdose and analyzed using (1). H NMR and directly coupled high performance liquid chromatography (HPLC)-NMR, HPLC-(mass spectrometry) MS and HPLC-MS-NMR methods. The principal metabolites were determined to be 2-hydroxyphenylacetic acid and 2-(2-hydroxyethyl)phenyl hydrogen sulfate, representing 24.3 +/- 6.0% and 19.6 +/- 6.4% of the dose, respectively. This indicates that metabolism of benzofuran to the polar species excreted in urine involves cleavage of the furan ring.

Metabonomic assessment of toxicity of 4-fluoroaniline, 3,5-difluoroaniline and 2-fluoro-4-methylaniline to the earthworm Eisenia veneta (Rosa): identification of new endogenous biomarkers.

High-resolution 1H nuclear magnetic resonance (NMR) spectroscopy can be used to produce a biochemical fingerprint of low-molecular-weight metabolites from complex biological mixtures such as tissue extracts and biofluids. Changes in such fingerprint profiles can be used to characterize the effects of toxic insult in in vivo systems. The technique is nonselective and requires little sample preparation or derivatization. In the present study, earthworms (Eisenia veneta) were exposed to three different model xenobiotics by a standard filter paper contact test, and toxicant-induced biochemical changes were then investigated by characterizing the changes in endogenous metabolites visible in 600-MHz 1H NMR spectra of tissue extracts. The NMR spectral intensities were converted to discrete numerical values and tabulated in order to provide data matrices suitable for multivariate analysis. Principal component analysis showed that changes had occurred in the biochemical profiles relative to the undosed controls. The 2-fluoro-4-methylaniline-treated worms showed a decrease in a resonance from a compound identified as 2-hexyl-5-ethyl-3-furansulfonate using a combination of high-performance liquid chromatography (HPLC)-Fourier transform mass spectrometry (IonSpec, Lake Forest, CA, USA) and 1H and 13C NMR spectroscopy. An increase in inosine monophosphate was also observed. The 4-fluoroaniline-treated worms showed a decrease in maltose concentrations, and 3,5-difluoroaniline exerted the same effect as 2-fluoro-4-methylaniline but to a lesser extent. These changes could potentially be used as novel biomarkers of xenobiotic toxicity and could be used to determine the mechanism of action of other toxic chemicals.