We describe ASCA, a new method that can deal with complex multivariate datasets containing an underlying experimental design, such as metabolomics datasets. It is a direct generalization of analysis of variance (ANOVA) for univariate data to the multivariate case. The method allows for easy interpretation of the variation induced by the different factors of the design. The method is illustrated with a dataset from a metabolomics experiment with time and dose factors.

The introduction of the concept of systems biology, enabling the study of living systems from a holistic perspective based on the profiling of a multitude of biochemical components, opens up a unique and novel opportunity to reinvestigate natural products. In the study of their bioactivity, the necessary reductionistic approach on single active components has been successful in the discovery of new medicines, but at the same time the synergetic effects of components were lost. Systems biology, and especially metabolomics, is the ultimate phenotyping. It opens up the possibility of studying the effect of complex mixtures, such as those used in Traditional Chinese Medicine, in complex biological systems; abridging it with molecular pharmacology. This approach is considered to have the potential to revolutionize natural product research and to advance the development of scientific based herbal medicine.

Dietary fatty acids have a profound impact on atherosclerosis, but mechanisms are not fully understood. We studied the effects of a saturated fat diet supplemented with fish oil, trans10,cis12 conjugated linoleic acid (CLA), or elaidic acid on lipid and glucose metabolism and liver protein levels of APOE*3 Leiden transgenic mice, a model for lipid metabolism and atherosclerosis. Fish oil lowered plasma and liver cholesterol and triglycerides, plasma free fatty acids, and glucose but increased plasma insulin. CLA lowered plasma cholesterol but increased plasma and liver triglycerides, plasma beta-hydroxybutyrate, and insulin. Elaidic acid lowered plasma and liver cholesterol. Proteomics identified significant regulation of 65 cytosolic and 8-membrane proteins. Many of these proteins were related to lipid and glucose metabolism, and to oxidative stress. Principal component analysis revealed that fish oil had a major impact on cytosolic proteins, and elaidic acid on membrane proteins. Correlation analysis between physiological and protein data revealed novel clusters of correlated variables, among which a metabolic syndrome cluster. The combination of proteomics and physiology gave new insights in mechanisms by which these dietary fatty acids regulate lipid metabolism and related pathways, for example, by altering protein levels of long-chain acyl-CoA thioester hydrolase and adipophilin in the liver.

This study showed that a urine metabolite profile may serve as a novel discriminating biomarker of OA.

Osteoarthritis (OA), one of the most common diseases among the elderly, is characterized by the progressive destruction of joint tissues. Its etiology is largely unclear and no effective disease-modifying treatment is currently available. Metabolic fingerprinting provides a novel tool for the identification of biomarkers. A metabolic fingerprint consists of a typical combination of metabolites in a biological fluid and is identified by a combination of (1)H NMR spectroscopy and multivariate data analysis (MVDA). The current feasibility study was aimed at identifying a metabolic fingerprint for OA and applying this in a nutritional intervention study. Urine samples were collected from osteoarthritic male Hartley guinea pigs (n = 44) at 10 and 12 mo of age, treated from 4 mo onward with variable vitamin C doses (2.5-3, 30 and 150 mg/d) and from healthy male Strain 13 guinea pigs (n = 8) at 12 mo of age, treated with 30 mg vitamin C/d. NMR measurements were performed on all urine samples. Subsequently, MVDA was carried out on the data obtained using NMR. An NMR fingerprint was identified that reflected the osteoarthritic changes in guinea pigs. The metabolites that comprised the fingerprint indicate that energy and purine metabolism are of major importance in OA. Metabolic fingerprinting also allowed detection of differences in OA-specific metabolites induced by different dietary vitamin C intakes. This study demonstrates the feasibility of metabolic fingerprinting to identify disease-specific profiles of urinary metabolites. NMR fingerprinting is a promising means of identifying new disease markers and of gaining fresh insights into the pathophysiology of disease.

We introduce a matrix-assisted laser desorption ionization-Fourier transform ion cyclotron resonance (MALDI-FT-ICR) method for quantitative peptide profiling, using peak height as a measure for abundance. Relative standard deviations in peak height of peptides spiked over 3 orders of magnitude in concentration were below 10% and allowed for accurate comparisons between multiple sclerosis and controls. Application on a set of 163 cerebrospinal fluid (CSF) samples showed significantly differential abundant peptides, which were subsequently identified into proteins (e.g., chromogranin A, clusterin, and complement C3).

This study investigated whether integrated analysis of transcriptomics and metabolomics data increased the sensitivity of detection and provided new insight in the mechanisms of hepatotoxicity. Metabolite levels in plasma or urine were analyzed in relation to changes in hepatic gene expression in rats that received bromobenzene to induce acute hepatic centrilobular necrosis. Bromobenzene-induced lesions were only observed after treatment with the highest of 3 dose levels. Multivariate statistical analysis showed that metabolite profiles of blood plasma were largely different from controls when the rats were treated with bromobenzene, also at doses that did not elicit histopathological changes. Changes in levels of genes and metabolites were related to the degree of necrosis, providing putative novel markers of hepatotoxicity. Levels of endogenous metabolites like alanine, lactate, tyrosine and dimethylglycine differed in plasma from treated and control rats. The metabolite profiles of urine were found to be reflective of the exposure levels. This integrated analysis of hepatic transcriptomics and plasma metabolomics was able to more sensitively detect changes related to hepatotoxicity and discover novel markers. The relation between gene expression and metabolite levels was explored and additional insight in the role of various biological pathways in bromobenzene-induced hepatic necrosis was obtained, including the involvement of apoptosis and changes in glycolysis and amino acid metabolism.The complete Table 2 is available as a supplemental file online at http://taylorandfrancis.metapress.com/openurlasp?genre=journal&issn=0192-6233. To access the file, click on the issue link for 33(4), then select this article. A download option appears at the bottom of this abstract. In order to access the full article online, you must either have an individual subscription or a member subscription accessed through www.toxpath.org.

In most MALDI peptide profiling cases, sequencing is required to identify peptides of interest, preferentially by using different mass spectrometry techniques. Using identical samples, we determined the number of false positive matches in sequence of peptide identification using different mass spectrometers. This paper demonstrates that the reliability of the identification phase greatly benefits from concerted MS-technologies and determines the influence of mass accuracy, signal-to-noise and statistical score on peptide identification.