Review: toxicometabolomics

Abstract

Metabolomics use in toxicology is rapidly increasing, particularly owing to advances in mass spectroscopy, which is widely used in the life sciences for phenotyping disease states. Toxicology has the advantage of having the disease agent, the toxicant, available for experimental induction of metabolomics changes monitored over time and dose. This review summarizes the different technologies employed and gives examples of their use in various areas of toxicology. A prominent use of metabolomics is the identification of signatures of toxicity - patterns of metabolite changes predictive of a hazard manifestation. Increasingly, such signatures indicative of a certain hazard manifestation are identified, suggesting that certain modes of action result in specific derangements of the metabolism. This might enable the deduction of underlying pathways of toxicity, which, in their entirety, form the Human Toxome, a key concept for implementing the vision of Toxicity Testing for the 21st century. This review summarizes the current state of metabolomics technologies and principles, their uses in toxicology and gives a thorough overview on metabolomics bioinformatics, pathway identification and quality assurance. In addition, this review lays out the prospects for further metabolomics application also in a regulatory context.

Keywords: analytical chemistry; bioinformatics; metabolomics; pathways of toxicity; quality assurance; toxicity testing for the 21st century.

Figure 1

Relative sensitivity and selectivity of different analytical tools for metabolomics.

Figure 2

Illustration of the large data reduction involved in a typical toxicometabolomics study. Metabolomics data are usually represented in their raw form as (A) chromatograms, i.e. 3D matrix of masses, retention times and intensities. Depending on the experiment study design and the strategy adopted (untargeted approach in this case), (B) biological replicates representing different conditions are acquired and using specialized software, (C) mass signals or “features” are extracted and aligned across all samples. Statistical analysis tools highlight variability and/or similarity between and within the different groups, e.g. (D) Principle components analysis (PCA) and (E) Hierarchical clustering The result may be displayed as a list of differentially abundant metabolites with corresponding p-value, fold change, etc. and (F) putative identity of the relevant metabolites established before confirmation using standard compounds and inclusion in a pathway enrichment analysis.

Figure 3

General workflow of a research project adapted and modified from Mathur-De (2000).

Figure 4

Typical metabolomics workflow.