An Integrated Metabolomic Approach to Understanding Drug Function
Metabolites are a unique and highly diverse class of elements and compounds that constitute the “business end” of biochemistry. For example, the budding yeast S. cerevisiae is estimated to contain thousands of unique metabolites at a wide range of concentrations. In addition to being both substrates for and products of protein action, metabolites have profound regulatory effects ranging from simple enzymatic product inhibition to allostery to initiation of complex signaling cascades that regulate gene expression programs. Furthermore, exogenous metabolites, acquired for nutritive purposes or used as chemical defenses, greatly expand the diversity of metabolites a cell might encounter. Thus, we hypothesized that examination of the effect of excess metabolites on a drug phenotype could provide rich, systems-level information about both cellular and drug function.
To test this principle, we screened a small pilot library of about 50 metabolites in a yeast-based model against lovastatin. Statin drugs inhibit HMG-CoA reductase, which is the rate-limiting enzyme in the synthesis of cholesterol. Consequently, they are among the most widely prescribed drugs in the world, used to treat high cholesterol and atherosclerosis. We chose to investigate statins because, despite being one of the first drugs designed with a specific molecular target in mind, statins have poorly understood pleiotropic effects. For example, in addition to lowering cholesterol by inhibiting HMG-CoA reductase, statins can reduce the risk of death from stroke. Statins can also have significant side effects including musculoskeletal deterioration and rhabdomyolysis, but how these deleterious effects occur is not known.
Statins are effective in inhibiting the yeast orthologs of HMG-CoA reductase and lowering levels of the yeast cholesterol analog, ergosterol. Statin treatment produces dose-dependent growth inhibition in yeast, presumably owing to the requirement for ergosterol for generation of new membrane. We screened our pilot metabolite library against a S. cerevisiae model of statin action. Our metabolite-statin screen revealed that the divalent metal ions zinc, copper and manganese were all effective in alleviating statin mediated growth inhibition. We characterized metal mediated statin rescue using an integrated approach that included biochemical, metabolomic and genomic approaches.
•Doug Fowler & Jason Stephany
Published Results:
Fowler DM, Cooper SJ, Stephany JJ, Hendon N, Nelson S and Fields S. Suppression of statin effectiveness by copper and zinc in yeast and human cells. Mol. BioSyst. 2011, Feb;7(2):533-544. download pdf
Metabolite profiling in yeast
Metabolism encompasses all the processes by which a cell generates energy and other essential molecules from nutrients. These pathways rely on hundreds of genes and involve thousands of small molecule intermediates, vitamins and cofactors. Interest in these molecules has led to development of technologies that allow high-throughput profiling of metabolic intermediates. We have optimized capillary electrophoresis methods for profiling amines, thiols and organic acids in the yeast Saccharomyces cerevisiae. Using these protocols, we have screened the yeast deletion collection and shown that clustering based on metabolite profiles allows us to identify related genes and pathways.
Figure 1: Amino acid profiling of a wild-type yeast extract using fluorescent derivatization of amine groups in combination with capillary electrophoresis separation.
Figure 2: Panel A shows amino acid profiling of the yeast deletion collection clustered by common metabolite profile. Panel B shows that arginine mutants show low levels of arginine and accumulation of arginine precursors such as ornithine and lysine. This cluster is also enriched for mitochondrial genes. Since arginine biosynthesis occurs in the mitochondria we propose that genes affecting mitochondrial function also affect arginine biosynthesis.
We have also begun metabolite profiling using gas chromatography and mass spectrometry (GCxGC-TOF). Preliminary experiments demonstrate that we can identify hundreds of unique compounds, including amino acids, sugars, organic acids and sterols. We are currently applying this method to better understand sterol biosynthesis in yeast. These complementary approaches provide a systematic view of metabolism in yeast. Other applications of this technology that we are working on include metabolite profiling in human urine samples from individuals with kidney disease and characterizing natural variation in yeast by assaying metabolic profiles along with transcription and protein levels in wild yeast strains.
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