Search

Over the past decade, the analysis of metabolic data with advanced chemometric techniques has offered the potential to explore functional relationships among biological compartments in relation to the structure and function of the intestine. However, the employed methodologies, generally based on regression modeling techniques, have given emphasis to region-specific metabolic patterns, while providing only limited insights into the spatiotemporal metabolic features of the complex gastrointestinal system. Hence, novel approaches are needed to analyze metabolic data to reconstruct the metabolic biological space associated with the evolving structures and func6ons of an organ such as the gastrointestinal tract. Here, we report the application of multivariate curve resolution (MCR) methodology to model metabolic relationships along the gastrointestinal compartments in relation to its structure and function using data from our previous metabonomic analysis. The method simultaneously summarizes metabolite occurrence and contribution to continuous metabolic signatures of the different biological compartments of the gut tract. This methodology sheds new light onto the complex web of metabolic interac6ons with gut symbionts that modulate host cell metabolism in surrounding gut tissues. In the future, such an approach will be key to provide new insights into the dynamic onset of metabolic deregulations involved in region-specific gastrointestinal disorders, such as Crohn's disease or ulcerative colitis. 10.1021/ac102015n

Isotope labeled tracers are commonly used to quantify the turnover rates of various metabolic intermediates and yield information regarding physiological regulation. Studies often only consider either one nutritional state (fasted or fed) and/or one question (e.g., measure of lipid or protein turnover). In this article, we consider a novel application combining the global approach of metabonomics with widespread stable isotope labeling as a way of being able to map metabolism in open mammalian systems, an approach we call 'isotopomics'. A total of 45 15-week-old male Zucker rats were administrated different amounts (from 0.5 to 8 mmol/kg) of sodium [1,2-[sup.13][C.sub.2]] acetate. Plasma samples taken at 1, 4, and 24 h were analyzed with [sup.13]C nuclear magnetic resonance (NMR) and gas chromatography/mass spectrometry (GC/MS) to measure [sup.13]C isotopic enrichment of 39 plasma metubolites across a wide range of compound classes (amino acids, short-chain fatty acids, lactate, glucose, and free fatty acids). Isotopic enrichment from 0.1-7.1 mole percent excess (MPE) for the highest dose could be reliably measured in 16 metabolites, and the kinetics of their [sup.13]C isotopic enrichment are reported. Clustering metabolites based on [sup.13]C kinetic curves enabled highlighting of time dependent patterns of 13C distribution through the key metabolic pathways. These kinetic and quantitative data were reported into a biochemical map. This type of isotopomic approach for mapping dynamic metabolism in an open system has great potential for advancing our mechanistic knowledge of how different interventions and diseases can impact the metabolic response of animals and humans. 10.1021/ac902086g

The following study investigates the preparation of human blood plasma for metabolomic profiling analysis by ultrahigh performance liquid chromatography coupled to time-of-flight mass spectrometry (UPLC/TOFMS) in a novel two-step design study. Four different organic solvents (acetonitrile, acetone, methanol, and ethanol) were used to assess human blood plasma preparation via protein precipitation. The optimal conditions for sample preparation were investigated, with consideration to the number of extracted markers, data quality/reproducibility, and column lifetime prolongation. Isotopically labeled internal standards were used to monitor data quality/reproducibility. Gel electrophoresis was also used to measure the protein content in the supernatant of the 'first design step' allowing assessment of the amount of protein that would be injected/accumulate onto the column after many injections that would be apparent in a global metabolic profiling study. The second design step followed on from the results obtained in step one, with four of the best conditions selected and further investigated, looking at the effects of vortex time and temperature on precipitation/extraction. Two choices of solvent compositions were found to be 'optimal' for preparation of plasma for global metabolic profiling analysis; these were 'methanol/ethanol' (1:1, v/v) and 'methanol/acetonitrile/acetone' (1: 1:1, v/v/v) added to plasma (4:1 ratio, 400 [micro]L total volume).

In this article we highlight a novel variation on dynamic time warping (DTW) for aligning chromatogram signals. We are interested in sets of signals that can be aligned well locally, but not globally, by shifting individual signals in time. This kind of alignment is often sufficient for aligning gas chromatography data. Regular DTW often 'over-warps' signals and introduces artificial features into the aligned data. To overcome this we introduce a variable penalty into the DTW process. The penalty is added to the distance metric whenever a nondiagonal step is taken. We select our penalty based on a morphological dilation of the two signals. We showcase our method by aligning GC/MS datafiles from 712 blood plasma samples processed in 23 batches over the course of 6 months. The use of variable penalty DTW significantly reduces the number of nondiagonal moves. In the examples presented here, this reduction is by a factor of 30, with no cost to visual quality of the alignment.

A novel model-free statistical approach (self modeling curve resolution, SMCR) has been applied to recover biochemical information from complex overlapping signals in [sup.1]H NMR spectra of blood serum in a long-term study of caloric restriction (CR) in the dog (n = 24 control fed (CF) and n = 24 CR animals). A new statistical spectroscopic construct, the spectrotype, is proposed which is a spectroscopic subset description or component of a metabolic phenotype. Characterization of the [sup.1]H NMR profiles according to their evolutionary contribution of each spectrotype gives clues to the kinetics of the macro-biochemical response profiles and the identity of the underlying biochemical constituents, governing the evolutionary global response to an intervention. This information can be used to monitor and predict the end point of the biological process and to identify the mechanisms responsible for those changes. Here a SMCR strategy together with a pattern recognition method, principal component analysis (PCA) was used to resolve sets of spectrotypes, without a priori information. From the [sup.1]H NMR evolutionary response profiles, two spectrotypes were identified and resolved; spectrotype 1 dominated by lipids featuring contributions from phosphatidylcholine, lipoprotein lipid fatty acyl groups from triglycerides, phospholipids, and cholesteryl esters plus total cholesterol (i.e., both esterified and unesterified); spectrotype 2 comprising glucose signals and a poorly resolved envelope of albumin and N-acetylated glycoprotein resonances. The relative contributions of these spectrotypes in each sample were calculated. For both caloric restricted (CR) and control fed (CF) dogs between ages 1 and 9 years, the contribution of spectrotype 2 > spectrotype 1, whereas for dogs aged between 9 and 12 years spectrotype 1 > spectrotype 2. Therefore, SMCR analysis pinpointed ages where nutrition and aging metabolic changes became significant within serum samples as well as providing the individual longitudinal contribution profiles associated with each spectrotype, which could potentially be used as part of a strategy to monitor and predict longevity and morbidity in populations. Hence SMCR is a useful addition to the chemometric 'toolbox' for metabolic analysis and should have diverse applications within other biomedical conditions characterized by subtle time-dependent changes.

Previously we have demonstrated the use of [sup.1]H magic angle spinning (MAS) NMR spectroscopy for the topographical variations in functional metabolic signatures of intact human intestinal biopsy samples. Here we have analyzed a series of MAS [sup.1]H NMR spectra (spin--echo, one-dimensional, and diffusion-edited) and [sup.31]P--{[sup.1]H} spectra and focused on analyzing the enhancement of information recovery by use of the statistical total correlation spectroscopy (STOCSY) method. We have applied a heterospectroscopic cross-examination performed on the same samples and between [sup.1]H and [sup.31]P--{[sup.1]H} spectra (heteronuclear STOCSY) to recover latent metabolic information. We show that heterospectroscopic correlation can give new information on the molecular compartmentation of metabolites in intact tissues, including the statistical 'isolation' of a phosphlipid/triglyceride vesicle pool in intact tissue. The application of [sup.31]P--[sup.1]H HETSTOCSY allowed the cross-assignment of major [sup.31]P signals to their equivalent [sup.1]H NMR spectra, e.g., for phosphorylcholine and phosphorylethanolamine. We also show pathway correlations, e.g., the ascorbate--glutathione pathway, in the STOCSY analysis of intact tissue spectra. These [sup.31]P--[sup.1] HET-STOCSY spectra also showed different topographical regions, particular for minor signals in different tissue microenvironments. This approach could be extended to allow the detection of altered distributions within metabolic subcompartments as well as conventional metabonomics concentration-based diagnostics.

A large metabolomics study was performed on 600 plasma samples taken at four time points before and after a single intake of a high fat test meal by obese and lean subjects. All samples were analyzed by a liquid chromatography-mass spectrometry (LC-MS) lipidomic method for metabolic profiling. A pragmatic approach combining several well-established statistical methods was developed for processing this large data set in order to detect small differences in metabolic profiles in combination with a large biological variation. Such metabolomics studies require a careful analytical and statistical protocol. The strategy included data preprocessing, data analysis, and validation of statistical models. After several data preprocessing steps, partial least-squares discriminant analysis (PLS-DA) was used for finding biomarkers. To validate the found biomarkers statistically, the PLS-DA models were validated by means of a permutation test, biomarker models, and noninformative models. Univariate plots of potential biomarkers were used to obtain insight in up-or downregulation. The strategy proposed proved to be applicable for dealing with large-scale human metabolomics studies.