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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.

We present a novel application of the heteronuclear statistical total correlation spectroscopy (HET-STOCSY) approach utilizing statistical correlation between one-dimensional [sup.19]F/[sup.1]H NMR spectroscopic data sets collected in parallel to study drug metabolism. Parallel one-dimensional (1D) 800 MHz [sup.1]H and 753 MHz [sup.19]F{[sup.1]H} spectra (n = 21) were obtained on urine samples collected from volunteers (n = 6) at various intervals up to 24 h after oral dosing with 500 mg of flucloxacillin. A variety of statistical relationships between and within the spectroscopic datasets were explored without significant loss of the typically high 1 D spectral resolution, generating [sup.1]H-[sup.1]H STOCSY plots, and novel [sup.19]F-[sup.1]H HET-STOCSY, [sup.19]F-[sup.19]F STOCSY, and [sup.19]F-edited [sup.1]H-[sup.1]H STOCSY (X-STOCSY) spectroscopic maps, with a resolution of ~0.8 Hz/pt for both nuclei. The efficient statistical editing provided by these methods readily allowed the collection of drug metabolic data and assisted structure elucidation. This approach is of general applicability for studying the metabolism of other fluorine-containing drugs, including important anticancer agents such as 5-fluorouracil and flutamide, and is extendable to any drug metabolism study where there is a spin-active X-nucleus (e.g., [sup.13]C, [sup.15]N, [sup.31]P) label present.

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.

Quantitative characterization of 13 C-labeled metabolites is an important part of the stable isotope tracing method widely used in metabolic flux analysis. Given the long relaxation time and low sensitivity of 13 C nuclei, direct measurement of 13 C-labeled metabolites using one-dimensional 13 C NMR often fails to meet the demand of metabolomics studies, especially with large numbers of samples and metabolites having low abundance. Although HSQC-based 2D NMR methods have improved sensitivity with inversion detection, they are time-consuming and thus unsuitable for high-throughput absolute quantification of 13 C-labeled metabolites. In this study, we developed a method for absolute quantification of 13 C-labeled metabolites using naturally abundant TSP as a reference with the first increment of the HMQC pulse sequence, taking polarization transfer efficiencies into consideration. We validated this method using a mixture of 13 C-labeled alanine, methionine, glucose, and formic acid together with a mixture of alanine, lactate, glycine, uridine, cytosine, and hypoxanthine, which have natural 13 C abundance with known concentrations. We subsequently applied this method to analyze the flux of glucose in HepG2 cells infected with hepatitis B virus (HBV). The results showed that HBV infection increased the cellular uptake of glucose, stimulated glycolysis, and enhanced the pentose phosphate and hexosamine pathways for biosynthesis of RNA and DNA and nucleotide sugars to facilitate HBV replication. This method saves experimental time and provides a possibility for absolute quantitative tracking of the 13 C-labeled metabolites for high-throughput studies.

We present a novel application of the heteronuclear statistical total correlation spectroscopy (HET-STOCSY) approach utilizing statistical correlation between one-dimensional 19F/1H NMR spectroscopic data sets collected in parallel to study drug metabolism. Parallel one-dimensional (1D) 800 MHz 1H and 753 MHz 19F{1H} spectra (n = 21) were obtained on urine samples collected from volunteers (n = 6) at various intervals up to 24 h after oral dosing with 500 mg of flucloxacillin. A variety of statistical relationships between and within the spectroscopic datasets were explored without significant loss of the typically high 1D spectral resolution, generating 1H-1H STOCSY plots, and novel 19F-1H HET-STOCSY, 19F-19F STOCSY, and 19F-edited 1H-1H STOCSY (X-STOCSY) spectroscopic maps, with a resolution of approximately 0.8 Hz/pt for both nuclei. The efficient statistical editing provided by these methods readily allowed the collection of drug metabolic data and assisted structure elucidation. This approach is of general applicability for studying the metabolism of other fluorine-containing drugs, including important anticancer agents such as 5-fluorouracil and flutamide, and is extendable to any drug metabolism study where there is a spin-active X-nucleus (e.g., 13C, 15N, 31P) label present.

Previously we have demonstrated the use of 1H 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 1H NMR spectra (spin-echo, one-dimensional, and diffusion-edited) and 31P-{1H} 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 1H and 31P-{1H} 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 phospholipid/triglyceride vesicle pool in intact tissue. The application of 31P-1H HET-STOCSY allowed the cross-assignment of major 31P signals to their equivalent 1H 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 31P-1H 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.