A comparative integrated transcript analysis and functional characterization of differential mechanisms for induction of liver hypertrophy in the rat.

The main goal of the present work was to better understand the molecular mechanisms underlying liver hypertrophy (LH), a recurrent finding observed following acute or repeated drug administration to animals, using transcriptomic technologies together with the results from conventional toxicology methods. Administration of 5 terminated proprietary drug candidates from participating companies involved in the EU Innomed PredTox Project or the reference hepatotoxicant troglitazone to rats for up to a 14-day duration induced LH as the main liver phenotypic toxicity outcome. The integrated analysis of transcriptomic liver expression data across studies turned out to be the most informative approach for the generation of mechanistic models of LH. In response to a xenobiotic stimulus, a marked increase in the expression of xenobiotic metabolizing enzymes (XME) was observed in a subset of 4 studies. Accumulation of these newly-synthesized proteins within the smooth endoplasmic reticulum (SER) would suggest proliferation of this organelle, which most likely is the main molecular process underlying the LH observed in XME studies. In another subset of 2 studies (including troglitazone), a marked up-regulation of genes involved in peroxisomal fatty acid ß-oxidation was noted, associated with induction of genes involved in peroxisome proliferation. Therefore, an increase in peroxisome abundance would be the main mechanism underlying LH noted in this second study subset. Together, the use of transcript profiling provides a means to generate putative mechanistic models underlying the pathogenesis of liver hypertrophy, to distinguish between subtle variations in subcellular organelle proliferation and creates opportunities for improved mechanism-based risk assessment.

Metabonomic study of ochratoxin a toxicity in rats after repeated administration: phenotypic anchoring enhances the ability for biomarker discovery.

For early detection of toxicity and improved mechanistic understanding, GC/MS-, 1H NMR-, and LC/MS-based metabonomics were applied to urine samples from a rodent toxicity study on the mycotoxin and renal carcinogen ochratoxin A (OTA). OTA was administered at doses of 0, 21, 70, and 210 microg/kg body wt for up to 90 days. Urine samples were collected at 24 h intervals 14, 28, and 90 days after the start of treatment and analyzed with GC/MS, 1H NMR, and LC/MS. Principal component analysis and orthogonal projection to latent structures discriminate analysis (OPLS-DA) based on GC/MS and 1H NMR data discriminated controls from animals dosed with 210 microg/kg body wt OTA as early as 14 days and animals dosed with 70 microg/kg body wt 28 days after the start of treatment, correlating with mild histopathological changes in the kidney. Integration of histopathology scores as discriminators in OPLS-DA models resulted in better multivariate model predictivity and facilitated marker identification. Decreased 2-oxoglutarate and citrate excretion and increased glucose, creatinine, pseudouridine, 5-oxoproline, and myo-inositol excretion were detected with GC/MS. Decreased 2-oxoglutarate and citrate excretion and increased amino acid excretion were found with 1H NMR. Increased urinary glucose is a well-established indicator of kidney damage, and altered excretion of TCA cycle intermediates (citrate and 2-oxoglutarate) is found as a general response to toxic insult in many metabonomics studies. Other markers are associated with cell proliferation (pseudouridine), changes in renal osmolyte handling (myo-inositol), and oxidative stress (5-oxoproline), established mechanisms of OTA toxicity. LC/MS was also able to discriminate controls and treated animals but contained more noise, and marker annotation was only speculative due to lack of reference databases. Use of multiple analytical platforms for metabonomics analysis may result in a more comprehensive metabolite coverage and may be applied to obtain mechanistic information from conventional rodent toxicity studies.

Rapid and sensitive HILIC-ESI-MS/MS quantitation of polar metabolites of acrylamide in human urine using column switching with an online trap column.

The carcinogen acrylamide (AA) is formed during the processing of food. AA is metabolized to mercapturic acids, which are excreted with urine. A hydrophilic interaction liquid chromatography tandem mass spectrometry method (HILIC-MS/MS) using a zwitterionic stationary phase (Zic-HILIC) was developed and validated to quantitate the mercapturic acids of AA (AAMA) and glycidamide (GAMA), and AAMA-sulfoxide in human urine. In contrast to reversed phases, the application of Zic-HILIC resulted in efficient retention and separation of these highly polar compounds. Off-line sample workup was avoided by application of column switching with a Stability BS-C17 trap column prior to the analytical column, thus minimizing interferences with the urinary matrix. Limit of quantification values (LOQs) were 0.5 microg/L (AAMA), 2.0 microg/L (AAMA-sulfoxide), and 1.0 microg/L (GAMA) in human urine. Median concentrations in urine samples ( n = 54) of six nonsmoking human subjects were 24.0 microg/L (AAMA, 7.8-79.8 microg/L), 16.7 microg/L (AAMA-sulfoxide, 6.8-70.1 microg/L), and 3.82 microg/L (GAMA, 1.0-23.6 microg/L).

Tools in metabonomics: an integrated validation approach for LC-MS metabolic profiling of mercapturic acids in human urine.

While for 1H NMR techniques there already exist common analytical and reporting standards, this does not apply to LC-MS metabolic profiling approaches. These standards are the more recommended when applying metabonomics to human biofluids, particularly urine samples, due to the high degree of biological variation compared to animals. A control study was performed, and urine samples of 30 healthy male and female human subjects were collected at intervals of 8 h twice a day for three consecutive days. Using selective multiple reaction monitoring in combination with a column-switching tool for the analysis of the mercapturate pattern, samples were screened for time and gender differences, the most common confounders. Data preprocessing parameters, alignment, scaling to internal standards, and normalization techniques were optimized by PCA, PLS-DA, and OPLS models. Great care was taken in the validation process of both analytical and chemometric protocols. Additionally, a problem of LC-MS, the combination of "different-batch" data to "one-batch" data could be solved by a batchwise scaling procedure. Based on these results, the use of metabolic profiling via mercapturates will be feasible for the detection of disease or toxicity markers in the future since mercapturates are important biomarkers of reactive metabolites known to be involved in many toxic processes.