Functional Profiling Discovers the Dieldrin Organochlorinated Pesticide Affects Leucine Availability in Yeast

Department of Nutritional Science and Toxicology, University of California, Berkeley, California 94720.
Toxicological Sciences (Impact Factor: 3.85). 01/2013; 132(2). DOI: 10.1093/toxsci/kft018
Source: PubMed


Exposure to organochlorinated pesticides such as dieldrin has been linked to Parkinson’s and Alzheimer’s diseases, endocrine
disruption, and cancer, but the cellular and molecular mechanisms of toxicity behind these effects remain largely unknown.
Here we demonstrate, using a functional genomics approach in the model eukaryote Saccharomyces cerevisiae, that dieldrin alters leucine availability. This model is supported by multiple lines of congruent evidence: (1) mutants
defective in amino acid signaling or transport are sensitive to dieldrin, which is reversed by the addition of exogenous leucine;
(2) dieldrin sensitivity of wild-type or mutant strains is dependent upon leucine concentration in the media; (3) overexpression
of proteins that increase intracellular leucine confer resistance to dieldrin; (4) leucine uptake is inhibited in the presence
of dieldrin; and (5) dieldrin induces the amino acid starvation response. Additionally, we demonstrate that appropriate negative
regulation of the Ras/protein kinase A pathway, along with an intact pyruvate dehydrogenase complex, is required for dieldrin
tolerance. Many yeast genes described in this study have human orthologs that may modulate dieldrin toxicity in humans.

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    • "Inherent in high-throughput functional toxicology methods is the use of computational analyses to decipher toxicant mechanisms of action. Various studies have utilized overenrichment or Cytoscape software (Shannon et al., 2003) to uncover yeast genetic networks affected by a toxicant (Zhou et al., 2009; North et al., 2011; Gaytán et al., 2013a,b; Kaiser et al., 2013). Others, by integrating data from many distinct yeast chemical-genetic datasets, have assembled chemical-phenotype networks that helped identify potential effects elicited by various compounds (Venancio et al., 2010). "
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