Novel insights into iron metabolism by integrating deletome and transcriptome analysis in an iron deficiency model of the yeast Saccharomyces cerevisiae

<p>Abstract</p> <p>Background</p> <p>Iron-deficiency anemia is the most prevalent form of anemia world-wide. The yeast <it>Saccharomyces cerevisiae </it>has been used as a model of cellular iron deficiency, in part because many of its cellular pathways are conserved. To better understand how cells respond to changes in iron availability, we profiled the yeast genome with a parallel analysis of homozygous deletion mutants to identify essential components and cellular processes required for optimal growth under iron-limited conditions. To complement this analysis, we compared those genes identified as important for fitness to those that were differentially-expressed in the same conditions. The resulting analysis provides a global perspective on the cellular processes involved in iron metabolism.</p> <p>Results</p> <p>Using functional profiling, we identified several genes known to be involved in high affinity iron uptake, in addition to novel genes that may play a role in iron metabolism. Our results provide support for the primary involvement in iron homeostasis of vacuolar and endosomal compartments, as well as vesicular transport to and from these compartments. We also observed an unexpected importance of the peroxisome for growth in iron-limited media. Although these components were essential for growth in low-iron conditions, most of them were not differentially-expressed. Genes with altered expression in iron deficiency were mainly associated with iron uptake and transport mechanisms, with little overlap with those that were functionally required. To better understand this relationship, we used expression-profiling of selected mutants that exhibited slow growth in iron-deficient conditions, and as a result, obtained additional insight into the roles of <it>CTI6</it>, <it>DAP1</it>, <it>MRS4 </it>and <it>YHR045W </it>in iron metabolism.</p> <p>Conclusion</p> <p>Comparison between functional and gene expression data in iron deficiency highlighted the complementary utility of these two approaches to identify important functional components. This should be taken into consideration when designing and analyzing data from these type of studies. We used this and other published data to develop a molecular interaction network of iron metabolism in yeast.</p

Genome-Wide Functional Profiling Identifies Genes and Processes Important for Zinc-Limited Growth of Saccharomyces cerevisiae

Zinc is an essential nutrient because it is a required cofactor for many enzymes and transcription factors. To discover genes and processes in yeast that are required for growth when zinc is limiting, we used genome-wide functional profiling. Mixed pools of ∼4,600 deletion mutants were inoculated into zinc-replete and zinc-limiting media. These cells were grown for several generations, and the prevalence of each mutant in the pool was then determined by microarray analysis. As a result, we identified more than 400 different genes required for optimal growth under zinc-limiting conditions. Among these were several targets of the Zap1 zinc-responsive transcription factor. Their importance is consistent with their up-regulation by Zap1 in low zinc. We also identified genes that implicate Zap1-independent processes as important. These include endoplasmic reticulum function, oxidative stress resistance, vesicular trafficking, peroxisome biogenesis, and chromatin modification. Our studies also indicated the critical role of macroautophagy in low zinc growth. Finally, as a result of our analysis, we discovered a previously unknown role for the ICE2 gene in maintaining ER zinc homeostasis. Thus, functional profiling has provided many new insights into genes and processes that are needed for cells to thrive under the stress of zinc deficiency

Genome-Wide Functional Profiling Reveals Genes Required for Tolerance to Benzene Metabolites in Yeast

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

Confirmation of functional genomics analysis results by flow cytometry.

a<p>Significance was defined as having a p-value less than 0.05 comparing the mutant prevalence after 15 generations growth in low versus high zinc; NS = not significant.</p

Representative mutants showing growth defects in low zinc.

<p>Yeast pools were grown in zinc-replete (LZM+100 µM ZnCl₂) and zinc-limiting (LZM+1 µM ZnCl₂) media for either five or fifteen generations. The yeast ORFs/genes correspond to representative deletion strains that exhibited a significant change in growth in low zinc when compared to zinc-replete conditions (q<0.05). Numeric values are fitness scores (log₂ ratios); a negative value indicates a growth defect of the mutant in low zinc. Empty cells in the table indicate that any differences observed were not significant at that generation point.</p>a<p>Average fitness scores for representative sensitive strains in four replicate experiments after 5 generations of growth.</p>b<p>Average fitness scores for representative sensitive strains in four replicate experiments after 15 generations of growth.</p

Analysis of low zinc growth by flow cytometry.

<p>Untagged wild-type BY4743 (panels A, B), <i>gal2Δ</i> (panels C, D), and <i>tsa1Δ</i> (panels E, F) cells were mixed with approximately equal numbers of GFP-expressing BY4743 cells and inoculated into zinc-replete (LZM+100 µM ZnCl₂, panels A, C, E) or zinc-limiting (LZM+1 µM ZnCl₂, panels B, D, F) media and grown for fifteen generations prior to analysis by flow cytometry. Approximately 20,000 total cells per culture were assessed for GFP fluorescence (x-axis) and autofluorescence (y-axis). The <i>red line</i> in each panel marks the boundary between the sub-populations of tagged and untagged cells. Quantitation of these data is provided in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002699#pgen-1002699-t001" target="_blank">Table 1</a>. The elongated distribution of fluorescence in zinc-limited cells is likely due to alterations in cell size and cell wall composition relative to zinc-replete cells and was observed for both GFP fluorescence and autofluorescence.</p

A possible role for <i>ICE2</i> in ER zinc homeostasis.

<p>A) Confirmation of the <i>ice2Δ</i> low zinc growth defect. Wild type (BY4743, <i>filled columns</i>) and <i>ice2Δ</i> (BY4743 <i>ice2Δ</i>, <i>open columns</i>) cells were inoculated into LZM supplemented with either 1 or 3 µM ZnCl₂ and grown overnight prior to measuring the culture optical densities at 600 nm (OD₆₀₀). B) Loss of Ice2 causes a zinc-suppressible hyper-induction of the unfolded protein response (UPR). Wild type (BY4743) and homozygous <i>ice2Δ</i> mutant (BY4743 <i>ice2Δ</i>) cells were transformed with the UPRE-lacZ reporter pMCZ-Y and inoculated into low zinc medium (LZM) supplemented with 0.3, 1, 3 or 10 µM ZnCl₂. These cells were then grown overnight prior to measuring β–galactosidase activity. C) Loss of Ice2 exacerbates the zinc-suppressible hyper-induction of the UPR in <i>msc2Δ zrg17Δ zrc1Δ cot1Δ</i> quadruple mutants. JSY5 (<i>msc2Δ zrg17Δ zrc1Δ cot1Δ</i>) and JSY5 <i>ice2Δ</i> (<i>msc2Δ zrg17Δ zrc1Δ cot1Δ ice2Δ</i>) cells were transformed with the UPRE-lacZ reporter pMCZ-Y and inoculated into low zinc medium (LZM) supplemented with 1, 3, 10 or 100 µM ZnCl₂. These cells were then grown overnight prior to measuring β–galactosidase activity. The wild-type strain used was the isogenic CM100 strain. D) Zinc treatment does not inhibit the UPR induction in response to tunicamycin. Wild-type BY4743 cells bearing the UPRE-lacZ reporter were grown to exponential phase in LZM supplemented with the indicated concentration of zinc, then treated for 2 hours with 2 µg/ml tunicamycin prior to β–galactosidase activity assay. Data presented are the averages of triplicate cultures for each condition and the error bars indicate ±1 S.D.</p

Fitness data for all significantly affected sensitive strains identified from this study were mapped onto the <i>S. cerevisiae</i> BioGRID interaction dataset using Cytoscape.

<p>The fitness scores (the difference in the mean of the log₂ hybridization signal between LZM+1 µM ZnCl₂ and LZM+100 µM ZnCl₂) of these sensitive strains were then used to identify and create a smaller sub-network (283 genes) containing the sensitive genes and the non-sensitive and essential genes that link them through known genetic and physical interactions. The sub-network was then assessed for significant overrepresentation of Gene Ontology (GO) Cellular Component categories. These categories were visualized as a linked network. Node color of categories indicates the significance of representation (white = not identified as significant) and node size indicates the number of genes identified present in each category. Edge arrows indicate hierarchy of GO terms. For clarity, only GO Cellular Component categories with a p-value<0.0005 are shown. A separate GO enrichment assessment identified overrepresentation of all GO categories in the sub-network. This analysis was used to generate visual representations of the GO processes and cellular components identified showing the genes involved in these processes. In these cases, node color indicates the sensitivity of each deletion strain in our study (fitness score). The edge color defines the interaction type between nodes (from the BioGRID database).</p

Enrichment among low zinc-sensitive mutants by biological process, cellular component, functional classification, and subcellular localization.

a<p>k = number of genes of specific category identified by screen that when deleted increase sensitivity to low zinc.</p>b<p>f = number of genes in specific GO/MIPS category.</p