A metabolomics investigation of non-genotoxic carcinogenicity in the rat.

Non-genotoxic carcinogens (NGCs) promote tumor growth by altering gene expression, which ultimately leads to cancer without directly causing a change in DNA sequence. As a result NGCs are not detected in mutagenesis assays. While there are proposed biomarkers of carcinogenic potential, the definitive identification of non-genotoxic carcinogens still rests with the rat and mouse long-term bioassay. Such assays are expensive and time-consuming and require a large number of animals, and their relevance to human health risk assessments is debatable. Metabolomics and lipidomics in combination with pathology and clinical chemistry were used to profile perturbations produced by 10 compounds that represented a range of rat non-genotoxic hepatocarcinogens (NGC), non-genotoxic non-hepatocarcinogens (non-NGC), and a genotoxic hepatocarcinogen. Each compound was administered at its maximum tolerated dose level for 7, 28, and 91 days to male Fisher 344 rats. Changes in liver metabolite concentration differentiated the treated groups across different time points. The most significant differences were driven by pharmacological mode of action, specifically by the peroxisome proliferator activated receptor alpha (PPAR-α) agonists. Despite these dominant effects, good predictions could be made when differentiating NGCs from non-NGCs. Predictive ability measured by leave one out cross validation was 87% and 77% after 28 days of dosing for NGCs and non-NGCs, respectively. Among the discriminatory metabolites we identified free fatty acids, phospholipids, and triacylglycerols, as well as precursors of eicosanoid and the products of reactive oxygen species linked to processes of inflammation, proliferation, and oxidative stress. Thus, metabolic profiling is able to identify changes due to the pharmacological mode of action of xenobiotics and contribute to early screening for non-genotoxic potential.

Metabolomic strategies to study lipotoxicity in cardiovascular disease.

Cardiovascular disease arises from a combination of dyslipidaemia and systemic inflammation in both humans and mouse models of the disease. Given the strong metabolic component and also the strong interaction between diet and disease, one would expect strategies based on the global profiling of metabolism should hold substantial promise in defining the mechanism involved in this collection of pathologies. This review examines how metabolomics is being used both as a research tool to understand mechanisms of pathology and as an approach for biomarker discovery in cardiovascular disease. While the lipid fraction of blood plasma has a profound influence on the development of cardiovascular disease, there is also a growing body of evidence that the aqueous fraction of metabolites also have a role in following the effects of myocardial infarction and monitoring the development of atherosclerosis. Metabolomics has also been used in conjunction with proteomics and transcriptomics as part of a systems biology description of cardiovascular disease and in high-throughput approaches to profile large numbers of patients as part of epidemiology studies to understand how the genome interacts with the development of atherosclerosis.

An integrated functional genomic study of acute phenobarbital exposure in the rat.

BACKGROUND: Non-genotoxic carcinogens are notoriously difficult to identify as they do not damage DNA directly and have diverse modes of action, necessitating long term in vivo studies. The early effects of the classic rodent non-genotoxic hepatocarcinogen phenobarbital have been investigated in the Fisher rat using a combination of metabolomics and transcriptomics, to investige early stage mechanistic changes that are predictive of longer term pathology. RESULTS: Liver and blood plasma were profiled across 14 days, and multivariate statistics used to identify perturbed pathways. Both metabolomics and transcriptomics detected changes in the liver which were dose dependent, even after one day of exposure. Integration of the two datasets associated perturbations with specific pathways. Hepatic glycogen was decreased due to a decrease in synthesis, and plasma triglycerides were decreased due to an increase in fatty acid uptake by the liver. Hepatic succinate was increased and this was associated with increased heme biosynthesis. Glutathione synthesis was also increased, presumably in response to oxidative stress. Liquid Chromatography Mass Spectrometry demonstrated a remodeling of lipid species, possibly resulting from proliferation of the smooth endoplasmic reticulum. CONCLUSIONS: The data fusion of metabolomic and transcriptomic changes proved to be a highly sensitive approach for monitoring early stage changes in altered hepatic metabolism, oxidative stress and cytochrome P450 induction simultaneously. This approach is particularly useful in interpreting changes in metabolites such as succinate which are hubs of metabolism.