Applications of metabolomics and proteomics to the mdx mouse model of Duchenne muscular dystrophy: lessons from downstream of the transcriptome

The completion of genome projects, such as those associated with the mouse [1] and humans [2], heralded the field of functional genomics, in which high-throughput approaches are used to profile a tier of organization in a cell, tissue or even organism after perturbation of a gene's function in order to deduce what the function of that gene is. By far the commonest approach used in the armory of functional genomic technology is probably the DNA microarray, which is used to profile the transcriptome that results from a gene manipulation. Although this technology has proven to be incredibly powerful in deducing the consequences of certain genetic modifications, there are situations in which such an approach may not be successful. Approaches based on transcriptomics make the assumption that altered concentrations of mRNA are reflected in the proteome, but this may not be true if the concentration of a given protein is determined by the rate of its destruction. Furthermore, although long-term changes in the function of a cell or tissue may be caused by changes in gene expression, in mammals many medium-term changes arise from protein modifications, such as phosphorylation, acetylation and ubiquitylation, although short-term changes are often caused by allosteric modifications, reflecting rapid transient changes in metabolites. This has led to tools to profile the proteome and the metabolome of a cell, tissue or organism to complement approaches using transcriptomics.In addition to understanding gene function, functional genomic technologies have also been used to help in phenotyping organisms. One of the first applications of metabolomics was in the phenotyping of yeast (Saccharomyces cerevisiae) mutants in which genetic modifications had produced 'silent phenotypes' in terms of the growth rate, the main phenotype used to distinguish mutants [3]. Raamsdonk and colleagues [4] described an approach described as 'functional analysis by co-responses in yeast' (FANCY), which