ABSTRACT: Benzene is a ubiquitous environmental contaminant and is widely used in industry. Exposure to benzene causes a number of serious health problems, including blood disorders and leukemia. Benzene undergoes complex metabolism in humans, making mechanistic determination of benzene toxicity difficult. We used a functional genomics approach to identify the genes that modulate the cellular toxicity of three of the phenolic metabolites of benzene, hydroquinone (HQ), catechol (CAT) and 1,2,4-benzenetriol (BT), in the model eukaryote Saccharomyces cerevisiae. Benzene metabolites generate oxidative and cytoskeletal stress, and tolerance requires correct regulation of iron homeostasis and the vacuolar ATPase. We have identified a conserved bZIP transcription factor, Yap3p, as important for a HQ-specific response pathway, as well as two genes that encode putative NAD(P)H:quinone oxidoreductases, PST2 and YCP4. Many of the yeast genes identified have human orthologs that may modulate human benzene toxicity in a similar manner and could play a role in benzene exposure-related disease. Genome-Wide Functional Profiling Reveals Genes Required for Tolerance to Benzene Metabolites in Yeast. PLoS ONE 2011, 6(8):e24205 - PMID: 21912624. Pools of homozygous diploid deletion mutants (n = 4,607 strains) were grown in rich media (YPD) at 3 concentrations of hydroquinone (HQ), catechol (CAT) and 1,2,4-benzenetriol (BT) for 5 and 15 generations (5g, 15g). Each strain has a deletion in a different gene. In each strain, the gene was replaced by a deletion cassette containing a antibiotic resistance gene and two BARCODES, up and down tags (Please see Giaever et al, 2002, Nature). These tags in the DNA are specific to each strain. We pool all deletion strains and grow them under a selective condition; extract DNA; amplify barcodes using universal primers and hybridize to arrays containing complemetary sequences to the up and down tags. In this way, we can look at growth of each of the strains. Our strategy is to analyze separately the up and down tags. Therefore, for each array (CEL file), we generate two data files, one for all ups and another one for all downs. In these files, the list of genes are the same but the data come from different set of probes, up or down. The values are log2 averages of replicate probes for the same tag. These pre processed files were used to identify strains with differential growth in benzene metabolites by comparing to the control arrays.
