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In (1)H NMR metabolomic datasets, there are often over a thousand peaks per spectrum, many of which change position drastically between samples. Automatic alignment, annotation, and quantification of all the metabolites of interest in such datasets have not been feasible. In this work we propose a fully automated annotation and quantification procedure which requires annotation of metabolites only in a single spectrum. The reference database built from that single spectrum can be used for any number of (1)H NMR datasets with a similar matrix. The procedure is based on the generalized fuzzy Hough transform (GFHT) for alignment and on Principal-components analysis (PCA) for peak selection and quantification. We show that we can establish quantities of 21 metabolites in several (1)H NMR datasets and that the procedure is extendable to include any number of metabolites that can be identified in a single spectrum. The procedure speeds up the quantification of previously known metabolites and also returns a table containing the intensities and locations of all the peaks that were found and aligned but not assigned to a known metabolite. This enables both biopattern analysis of known metabolites and data mining for new potential biomarkers among the unknowns.

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This work addresses the subject of time-series analysis of comprehensive (1)H-NMR data of biological origin. One of the problems with toxicological and efficacy studies is the confounding of correlation between the administered drug, its metabolites and the systemic changes in molecular dynamics, i.e., the flux of drug-related molecules correlates with the molecules of system regulation. This correlation poses a problem for biomarker mining since this confounding must be untangled in order to separate true biomarker molecules from dose-related molecules. One way of achieving this goal is to perform pharmacokinetic analysis. The difference in pharmacokinetic time profiles of different molecules can aid in the elucidation of the origin of the dynamics, this can even be achieved regardless of whether the identity of the molecule is known or not. This mode of analysis is the basis for metabonomic studies of toxicology and efficacy. One major problem concerning the analysis of (1)H-NMR data generated from metabonomic studies is that of the peak positional variation and of peak overlap. These phenomena induce variance in the data, obscuring the true information content and are hence unwanted but hard to avoid. Here, we show that by using the generalized fuzzy Hough transform spectral alignment, variable selection, and parallel factor analysis, we can solve both the alignment and the confounding problem stated above. Using the outlined method, several different temporal concentration profiles can be resolved and the majority of the studied molecules and their respective fluxes can be attributed to these resolved kinetic profiles. The resolved time profiles hereby simplifies finding true biomarkers and bio-patterns for early detection of biological conditions as well as providing more detailed information about the studied biological system. The presented method represents a significant step forward in time-series analysis of biological (1)H-NMR data as it provides almost full automation of the whole data analysis process and is able to analyze over 800 unique features per sample. The method is demonstrated using a (1)H-NMR rat urine dataset from a toxicology study and is compared with a classical approach: COW alignment followed by bucketing.

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S-GPT elevated due to ethionine (Eth) administration was suppressed by thiol compounds such as tiopronin (2-mercaptopropionylglycine), glutathione, cysteine, in which tiopronin proved to be more effective than glutathione or cysteine. In thin-layer chromatography of urinary metabolites, Eth and ethionine sulfoxide were detected with administration of Eth, and S-ethyltiopronin plus Eth and ethionine sulfoxide by the administrations of Eth and tiopronin. These S-ethyl derivatives were not detected in the urine with administration of Eth and glutathione or cysteine. In the analysis of Eth and its metabolites by gas chromatography, cumulative urinary excretion of Eth within 72 hr after Eth administration was 40.7% in the Eth administered group, 23.6% in the Eth-tiopronin administered group and 38.2% in the Eth-glutathione administered group, respectively. In the urine of the Eth-tiopronin administered group, S-ethyltiopronin was excreted by 13.6%. Detoxicating effect of tiopronin on Eth induced liver damage was considered to involve the following mechanism. Tiopronin is considered to excrete part of Eth as S-ethyltiopronin by being an acceptor of transfer reaction of the ethyl group of Eth. Neither glutathione nor cysteine was an acceptor of the ethyl group and a detoxicating effect on Eth was not observed.

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The concentration of S-adenosylethionine in the liver of ethionine-fed rats was increased gradually during the process of carcinogenesis. This increase may have been due to the decreased capacity of the treated rats to acetylate ethionine sulfoxide. Ethionine sulfoxide is considered as the main reserve pool of ethionine for the synthesis of S-adenosylethionine. When the ethionine diet was supplemented by DL-methionine (0.3 to 0.9%), the increase in the concentration of S-adenosylethionine during the period of observation (28 to 150 days) was lower and the acetylation of ethionine sulfoxide was significantly higher. The concentration of the total S-adenosyl compounds in the liver of rats on a diet supplemented with DL-methionine was increased over the concentration of S-adenosylethionine in rats fed ethionine alone, and the S-adenosylethionine portion of this fraction was only about 30% lower. The supplementation of the diet with methionine restored the diurnal oscillation of adenosine 5'-triphosphate in the liver, which had been absent in rats ingesting only ethionine.

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The L-[ethyl-1-14C]ethionine metabolites soluble in trichloroacetic acid were studied in rats by the use of column chromatography. After p.o. application of ethionine, its absorption from intestinal lumen was rapid and was complete in less than 2 hr. Any unabsorbed ethionine was later excreted in the feces. During the passage through the gastrointestinal tract, a portion of ethionine was metabolized. The chemical nature and biological significance of these metabolites is not yet known. The fate of absorbed ethionine was investigated in the small intestine, liver, blood, kidney, and urine as a function of time after application. A great part of ethionine was quickly oxidized to ethionine sulfoxide. In liver and kidney, the concentration of ethionine sulfoxide was higher than that of free ethionine. In all organs, the presence of N-acetylethionine sulfoxide was also demonstrated. Ethionine sulfoxide can be reduced and N-acetylethionine can be deacetylated in vivo as demonstrated by the formation of S-adenosylethionine from ethionine sulfoxide and N-acetylethionine. In urine, 4 main components were observed: N-acetylethionine sulfoxide, S-adenosylethionine, ethionine sulfoxide, and free ethionine. Some minor components, as yet unidentified, were also present in the urine and in different organs. The probable site of origin of urinary S-adenosylethionine is the kidney.

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The pancreas is the tissue which traps the most intensively the trace-dosis injected ethionine -14C; 30 min after the injection, the pancreas fixes the labelled product twice more than the liver and five times more than the stomach. This trapping might explain the pancreatic modifications occuring during the intoxication. In the same experimental conditions, the pancreas fixes the ethionine -14C twice less than methionine. Urinary excretion of ethionine is faster and more important than that of methionine.

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