Past Projects

Characterization of natural metabolic variation in wild yeast strains

Like humans, yeasts from around the world have different genetic backgrounds. We are exploring the metabolic variation among strains.  In collaboration with several labs in the Yeast Resource Center we have collected samples from yeast strains from around the world. We are interested in identifying the metabolic differences that exist among these strains.  Our collaborators have also measured RNA and protein levels in these strains allowing us the unique opportunity to characterize the natural variation at a variety of different levels. (Skelley et al. 2013)


Yeast as model for statin efficacy

Statins are a commonly prescribed drug to lower cholesterol. We were interested in using a combination of genomics and metabolomics to screen for compounds that affect sterol synthesis in yeast in the presence of the drug, lovastatin. Using this approach, we identified zinc and copper to significantly alter the effect of statins on sterol production. Combining expression data and metabolomics data, we showed that zinc and copper increase the production of ergosterol by increasing the flux through the entire sterol biosynthetic pathway. Because zinc and copper are consumed in the human diet, there may be an effect of these metals on statin effectiveness in a clinical setting. (Fowler et al. 2011)


Amino acid profiling in the yeast deletion collection

My initial work in the field of metabolomics and metabolite profiling focused on screening a relatively small number of metabolites in a large number of strains.  We developed a high-throughput method to quantify primary amine-containing metabolites in the yeast Saccharomyces cerevisiae by the use of capillary electrophoresis in combination with fluorescent derivatization of cell extracts. We measured amino acid levels in the yeast deletion collection, a set of approximately 5000 strains each lacking a single gene, and developed a computational pipeline for data analysis.  Global analysis of the deletion collection was carried out using clustering methods. We grouped strains based on their metabolite profiles, revealing groups of mutants enriched for genes encoding mitochondrial proteins, urea cycle enzymes, and vacuolar ATPase functions. (Cooper et al. 2010)


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