Lipidomics Profiling of Human Adipose Tissue Identifies a Pattern of Lipids Associated with Fish Oil Supplementation.

To understand the interaction between diet and health, biomarkers that accurately reflect consumption of foods of perceived health relevance are needed. The aim of this investigation was to use direct infusion-mass spectrometry (DI-MS) lipidomics to determine the effects of fish oil supplementation on lipid profiles of human adipose tissue. Adipose tissue samples from an n-3 polyunsaturated fatty acid (PUFA) supplementation study (n = 66) were analyzed to compare the pattern following supplementation equivalent to zero or four portions of oily fish per week. Eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) were incorporated into highly unsaturated (≥5 double bonds) triglycerides (TGs), phosphocholines, and phosphoethanolamines as well as being detected directly as the nonesterified fatty acid forms. Multivariate statistics demonstrated that phospholipids were the most accurate and sensitive lipids for the assessing EPA and DHA incorporation into adipose tissue. Potential confounding factors (adiposity, age, and sex of the subject) were also considered in the analysis, and adiposity was also associated with an increase in highly unsaturated TGs as a result of incorporation of the n-6 PUFA arachidonic acid. DI-MS provides a high-throughput analysis of fatty acid status that can monitor oily fish consumption, suitable for use in cohort studies.

Metabolic assessment of human liver transplants from biopsy samples at the donor and recipient stages using high-resolution magic angle spinning 1H NMR spectroscopy.

This work presents the first application of high-resolution magic angle spinning (HR-MAS) 1H NMR spectroscopy to human liver biopsy samples, allowing a determination of their metabolic profiles before removal from donors, during cold perfusion, and after implantation into recipients. The assignment of peaks observed in the 1H HR-MAS NMR spectra was aided by the use of two-dimensional J-resolved, TOCSY and 1H-13C HMQC spectra. The spectra were dominated by resonances from triglycerides, phospholipids, and glycogen and from a variety of small molecules including glycerophosphocholine (GPC), glucose, lactate, creatine, acetate, amino acids, and nucleoside-related compounds such as uridine and adenosine. In agreement with histological data obtained on the same biopsies, two of the six livers were found to contain high amounts of triglycerides by NMR spectroscopy, which also indicated that these tissues contained a higher degree of unsaturated lipids and a lower proportion of phospholipids and low molecular weight compounds. Additionally, proton T2 relaxation times indicated two populations of lipids, a higher mobility triglyceride fraction and a lower mobility phospholipid fraction, the proportions of which changed according to the degree of fat content. GPC was found to decrease from the pretransplant to the posttransplant biopsy of all livers except for one with a histologically confirmed high lipid content, and this might represent a biomarker of liver function posttransplantation. NMR signals produced by the liver preservation solution were clearly detected in the cold perfusion stage biopsies of all livers but remained in the posttransplant spectra of only the two livers with a high lipid content and were prominent mainly in the graft that later developed primary graft dysfunction. This study has shown biochemical differences between livers used for transplants that can be related to the degree and type of lipid composition. This technology might therefore provide a novel screening approach for donor organ quality and a means to assess function in the recipient after transplantation.

NMR-based metabonomic approaches for evaluating physiological influences on biofluid composition.

Strategies such as genomics, proteomics and metabonomics are being applied with increasing frequency in the pharmaceutical industry. For each of these approaches, toxicological response can be measured by terms of deviation from control or baseline status. However, in order to accurately define drug-induced response, it is necessary to characterize the normal degree of physiological variation in the absence of stimuli. Here, 1H NMR spectroscopic-based analyses of the metabolic composition of urine in experimental animals under various normal physiological conditions are reviewed. In particular, the effects of inter-animal and diurnal variation, gender, age, diet, species, strain, hormonal status and stress on the biochemical composition of urine are explored. Pattern recognition methods facilitate the comparison of urine NMR spectra over a given time-course, enabling the establishment of changes in profile and highlighting the dynamic metabolic status of an organism. Thus metabonomic approaches based on information-rich spectroscopic data sets can be used to evaluate normal physiological variation and for investigation of drug safety issues.

Metabonomics technologies and their applications in physiological monitoring, drug safety assessment and disease diagnosis.

In this review, metabonomics, a combination of data-rich analytical chemical measurements and chemometrics for profiling metabolism in complex systems, is described and its applications are reviewed. Metabonomics is typically carried out using biofluids or tissue samples. The relevance of the technique is reviewed in relation to other '-omics', and it is shown how the methods can be applied to physiological evaluation, drug safety assessment, characterization of genetically modified animal models of disease, diagnosis of human disease, and drug therapy monitoring. The different types of analytical data, mainly from nuclear magnetic resonance spectroscopy and mass spectrometry, are summarized. The outputs from a metabonomics study allow sample classification, for example according to phenotype, drug safety or disease diagnosis, and interpretation of the reasons for classification yields information on combination biomarkers of effect. Transcriptomic and metabonomic data is currently being further integrated into a holistic understanding of systems biology. An assessment of the possible future role and impact of metabonomics is presented.