Nuclear magnetic resonance metabonomic profiling using tO2PLS.

Blood plasma collected from adult fish (black bream, Sparidae) exposed to a dose of 5 mg kg(-1) 17ß-estradiol underwent metabonomic profiling using nuclear magnetic resonance (NMR). An extension of the orthogonal 2 projection to latent structure (O2PLS) analysis, tO2PLS, was proposed and utilized to classify changes between the control and experimental metabolic profiles. As a bidirectional modeling tool, O2PLS examines the (variable) commonality between two different data blocks, and extracts the joint correlations as well as the unique variations present within each data block. tO2PLS is a proposed matrix transposition of O2PLS to allow for commonality between experiments (spectral profiles) to be observed, rather than between sample variables. tO2PLS analysis highlighted two potential biomarkers, trimethylamine-N-oxide (TMAO) and choline, that distinguish between control and 17ß-estradiol exposed fish. This study presents an alternative way of examining spectroscopic (metabolite) data, providing a method for the visual assessment of similarities and differences between control and experimental spectral features in large data sets.

Building multivariate systems biology models.

Systems biology methods using large-scale "omics" data sets face unique challenges: integrating and analyzing near limitless data space, while recognizing and removing systematic variation or noise. Herein we propose a complementary multivariate analysis workflow to both integrate "omics" data from disparate sources and analyze the results for specific and unique sample correlations. This workflow combines principal component analysis (PCA), orthogonal projections to latent structures discriminate analysis (OPLS-DA), orthogonal 2 projections to latent structures (O2PLS), and shared and unique structures (SUS) plots. The workflow is demonstrated using data from a study in which ApoE3Leiden mice were fed an atherogenic diet consisting of increasing cholesterol levels followed by therapeutic intervention (fenofibrate, rosuvastatin, and LXR activator T-0901317). The levels of structural lipids (lipidomics) and free fatty acids in liver were quantified via liquid chromatography-mass spectrometry (LC-MS). The complementary workflow identified diglycerides as key hepatic metabolites affected by dietary cholesterol and drug intervention. Modeling of the three therapeutics for mice fed a high-cholesterol diet further highlighted diglycerides as metabolites of interest in atherogenesis, suggesting a role in eliciting chronic liver inflammation. In particular, O2PLS-based SUS2 plots showed that treatment with T-0901317 or rosuvastatin returned the diglyceride profile in high-cholesterol-fed mice to that of control animals.

General and hybrid correlation nuclear magnetic resonance analysis of phosphorus in Phytophthora palmivora.

Generalized two-dimensional (Gen2D) correlation analysis and hybrid correlation analysis have been applied to a series of dynamic (31)P nuclear magnetic resonance (NMR) spectra to monitor the in vivo metabolic changes of the plant pathogen Phytophthora palmivora in the presence and absence of phosphonate over an 18-h period. Results indicate that phosphonate exposure causes cleavage in organism polyphosphate chains as well as an increase in total sugar phosphates. In the presence of phosphonate, the NMR resonances attributed to terminal polyphosphate phosphorus reduced at a lower rate than those of middle polyphosphate phosphorus, indicating a change in average chain length and suggesting cleavage in the middle of the chain as well as at the ends. The correlation analysis techniques serve to identify and confirm spectral regions undergoing major change in the time-series data and facilitate the analysis of these dynamic changes.

Spectroscopic correlation analysis of NMR-based metabonomics in exercise science.

Spectroscopic studies of complex clinical fluids have led to the application of a more holistic approach to their chemical analysis becoming more popular and widely employed. The efficient and effective interpretation of multidimensional spectroscopic data relies on many chemometric techniques and one such group of tools is represented by so-called correlation analysis methods. Typical of these techniques are two-dimensional correlation analysis and statistical total correlation spectroscopy (STOCSY). Whilst the former has largely been applied to optical spectroscopic analysis, STOCSY was developed and has been applied almost exclusively to NMR metabonomic studies. Using a (1)H NMR study of human blood plasma, from subjects recovering from exhaustive exercise trials, the basic concepts and applications of these techniques are examined. Typical information from their application to NMR-based metabonomics is presented and their value in aiding interpretation of NMR data obtained from biological systems is illustrated. Major energy metabolites are identified in the NMR spectra and the dynamics of their appearance and removal from plasma during exercise recovery are illustrated and discussed. The complementary nature of two-dimensional correlation analysis and statistical total correlation spectroscopy are highlighted.

Generalised 2D-correlation NMR analysis of a wine fermentation.

A wine fermentation has been monitored on a daily basis by (1)H NMR spectroscopy. Following data pre-processing that includes synthesis of the spectra to ensure all peaks are of constant half-width, the series of spectra were examined using generalised two-dimensional correlation techniques. Synchronous and asynchronous data maps have been generated and employed to interpret the changes in the fermentation process as a function of time. The results illustrate the potential of high resolution NMR with multivariate data analysis as a tool for process monitoring and the manner in which two-dimensional correlation mapping can aid in data interpretation.