ToxSci Advance Access originally published online on October 16, 2006
Toxicological Sciences 2007 95(1):182-187; doi:10.1093/toxsci/kfl131
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Chemical Genomic Profiling for Identifying Intracellular Targets of Toxicants Producing Parkinson's Disease

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* The Parkinson's Institute, Sunnyvale, California 94087
Stanford Genome Technology Center, California 940304
University of Toronto Donnelley Centre for Cellular and Biomolecular Research, Toronto, Ontario M5S 3E1, Canada
1 To whom correspondence should be addressed at The Parkinson's Institute, 1170 Morse Ave., Sunnyvale, CA 94087. Fax: (408) 734-8522. E-mail: jdoostzadeh{at}thepi.org.
Received June 19, 2006; accepted October 5, 2006
| ABSTRACT |
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The yeast deletion collection includes
4700 strains deleted for both copies of every nonessential gene. This collection is a powerful resource for identifying the cellular pathways that functionally interact with drugs. In the present study, the complete pool of
4700 barcoded homozygous deletion strains of Saccharomyces cerevisiae were surveyed to identify genes/pathways interacting with 1-methyl-4-phenylpyridinium (MPP+) and N,N-dimethyl-4-4-bipiridinium (paraquat), neurotoxicants that can produce Parkinson's disease. Each yeast mutant is molecularly "barcoded" the collections can be grown competitively and ranked for sensitivity by microarray hybridization. Analysis data from these screens allowed us to determine that the multivesicular body pathway is an important element of toxicity induced by both MPP+ and paraquat. When yeast genes that when deleted showed sensitivity to MPP+ and paraquat toxicity were analyzed for their homology to human genes, 80% were found to have highly conserved human homologs (with e < 108). Future work will address if these human genes may also functionally interact with MPP+ and paraquat toxicity. Key Words: chemogenomics; neurotoxicants; Parkinson's disease.
| INTRODUCTION |
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The search for a link between environmental factors and the risk of developing Parkinson's disease (PD) has been investigated for over 20 years. One interesting observation is that the structurally simple molecule, MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine) could induce many of the motor features of PD in humans (Langston, 1985
It has been suggested that paraquat exerts its toxicity by several potential mechanisms: (1) generation of the superoxide anion and the formation of more toxic reactive oxygen species; (2) oxidation of cellular nicotinamide adenine dinucleotide phosphate (reduced), a major source of reducing equivalents for the intracellular reduction of paraquat; and (3) lipid peroxidation, which results in the oxidative degeneration of cellular polyunsaturated fatty acids (Suntres, 2002
). Of particular relevance to PD are several recent studies indicating that paraquat damages nigrostratial dopaminergic neurons when administrated to mice either alone or in combination with other toxicants (Brooks et al., 1999
; McCormack et al., 2002
; Thiruchelvam et al., 2002
).
The molecular mechanism by which MPTP exerts its toxicity is well characterized. Because of its lipophilicity, MPTP readily crosses the blood-brain barrier, and once in the brain, it is biotransformed to 1-methyl-4-phenylpyridinium (MPP+) a reaction catalyzed by monoamine oxidase type B (MAO-B) (Chiba et al., 1985
). This reaction takes place in glia and MPP+ accumulates in mitochondria, where it disrupts cellular respiration (Gluck et al., 1994
) and causes neuronal cell death. Because both of toxins are selective for the system known to be vulnerable in PD (the dopaminergic nigrostriatal system) elucidating the molecular basis of cell death triggered by MPP+ and paraquat could provide valuable insight into the mechanisms underlying their toxicity.
The goal of this study was to identify cellular targets or target pathways that underlie the mechanisms of paraquat and MPP+ toxicity, by taking advantage of a genome-wide assay known as "chemogenomic profiling." This technology allows for profiling of the relative sensitivity to drugs or toxins of the gene products of the entire genome in yeast (Brown et al., 2006
; Giaever et al., 2002
, 2004
; Lum et al., 2004
; Steinmetz et al., 2002
; Winzeler et al., 1999
) and targets or target pathways can be identified. Our results provide a comprehensive in vivo snapshot of the genome-wide cellular response to MPP+ and paraquat perturbation.
| METHODS |
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Strains and media.
Yeast strains were grown in yeast peptone dextrose (YPD) media at 30°C. The homozygous deletion pool was constructed as described (Giaever et al., 2004
Experimental design.
Our experimental design is detailed in Giaever et al. (2002)
. Briefly, 0.062 OD600 units of homozygous deletion pool is diluted into 700 µl of YPD containing compound. This volume of media permits 300 individual cells of each deletion strain to be sufficiently represented at the beginning of the experiment. Cells are grown for five generations until the OD600 reaches 2.0 and cells are harvested.
Harvested cells are subjected to genomic DNA extraction according to the Zymo Research (YeaStar DNA) kit. Fifteen microliters of genomic DNA is added to a 100 µl of PCR reaction, one with "UPTAG" primers and another with "DOWNTAG" primers.
Thirty microliters of each TAG PCR products are mized and hybridized to TAG3 microarrays (Giaever et al., 2002
), stained, and scanned as described in (Fig. 1).
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Data analysis.
Fitness-defect scores were calculated for each strain in the pool for each experiment. These scores are based on a tag-specific algorithm that takes into account the intensities of each tag on the experimental array and the corresponding intensities on a set of control arrays performed on the pool without compound (the control set) (Giaever et al., 2004
Tag intensities are log transformed, mean normalized, and the intensities are averaged into a single value. A mean and standard deviation are calculated for the uptag and downtag intensities for each strain across the set of control arrays. A z-score for upstream and downstream tags for each strain is then calculated by taking the difference of the average intensities between the control and treatment and dividing by the standard deviation of the control-set intensities. The result is two z-score values for the upstream and downstream tags; these are then averaged into a single fitness-defect score for the strain.
| RESULTS AND DISCUSSION |
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Paraquat and MPP+
MPTP is rapidly converted into MPP+, a potent neurotoxin, by MAO-B. Another prototypic neurotoxicant that shares structural homology with MPP+ is paraquat, a common herbicide (Fig. 2). Because the molecular mechanisms involved in MPP+ and paraquat are not completely understood, and because there is sufficient homology between yeast and human biochemical pathways we tested the hypothesis that the chemogenomic assay could reveal new insights into mechanism of action of MPP+ and paraquat.
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Genes Displaying Sensitivity to MPP+ and Paraquat Toxicity
The sensitivity of 4700 homozygous deletion yeast strains was tested in the presence of sublethal doses of MPP+ (250µM) and paraquat (2000µM). These doses were derived from a serial dose response curve to define conditions in which wild-type yeast cells manifest 10% reduction in growth rate. Sensitivity for each treated and untreated strain was obtained by calculating the fitness values for homozygous collections in rich media (YPD) by monitoring the abundance of the molecular barcodes over time as described (Giaever et al., 2002
Analysis of the data allowed us to identify 96 genes for MPP+ and 141 genes for paraquat with a growth defect in the homozygous deletion collection. The list of genes showing a growth defect in the presence of MPP+ and paraquat are provided in Figure 3 and supplementary data. None of these genes, when deleted in homozygous diploids, showed a growth defect in the presence of dimethyl sulfoxide (vehicle control). Those genes showing sensitivity to MPP+ and paraquat (96 genes for MPP+ and 141 genes for paraquat) are dispensable for viability and, in the absence of compound, and are not required for normal growth rate as a homozygote, suggesting that the slow-growth phenotype of these strains is a direct result of MPP+ and/or paraquat toxicity. The description, biological process, molecular function, cellular component, and analyzed array information for all screened genes are provided as supplementary material. The genes enriched for sensitivity to both compounds were specific, that is to say, they were not generally enriched in the majority of other conditions that this collection has been subjected to (see Brown et al., 2006
; Giaever et al., 2002
, 2004
; Steinmetz et al., 2002
).
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A broad functional distribution of enriched genes, genes that are showing sensitivity in the presence of MPP+ and paraquat, when deleted in the homozygote, is illustrated in supplementary figures (saccharomyces genome database [SGD] Gene Ontology [GO] Term Mapper; Dwight et al., 2002
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Ubiquitin-dependent Protein Catabolism via the Multivesicular Body Pathway is Engaged in Toxicity of Both MPP+ and Paraquat
A more precise functional map was determined for enriched genes for both MPP+ and paraquat using (SGD GO Term Mapper; Dwight et al., 2002
The functional map for enriched genes showing slow growth in the presence of paraquat revealed that other processes are engaged in upstream of ubiquitin-dependent protein catabolism via the multivesicular body pathway (with a p value < 108)these processes include (1) late endosome to vacuole transport (NHX1, VPS27, VPS8, VPS60, DID4, VPS24, DID2, SNF7, VPS38, VPS20, VPS4); (2) protein targeting to vacuole (VPS27, CCZ1, STP22, ATG18, MON1, VPS25, DID2, ARP6, SRN2, VPS36, VPS9, SNF8, VPS28, VPS30); (3) intracellular protein transport (LST7, LST4), vacuolar transport (CCZ1 and BRO1); and (4) endosome transport (NHX1, VPS27). For all yeast genes (n = 26) involved in processes with p values < 108 the human homologs were analyzed using the SGD data base (www.yeastgenome.org)the description, e-values, and percentage of human homologs for these yeast genes were also determined (SGD GO and PSIBLAST [www.ncbi.nlm.nih.gov/BLAST/Blast]). The alignment of enriched yeast genes compared to their respective human homologs revealed a high sequence homology (> 80%) for 76% of those genes. These data suggest that chemogenomic assays such has those presented here may be a valuable for filtering and prioritizing both toxins and genes involved in Parkinson's syndromes.
Defining the functional interactions between specific gene products and toxicants is fundamental for determining the molecular mechanisms that underlie their toxicity. Chemogenomic profiling of various compounds demonstrated that this genome-wide assay specifically allows the identification of gene products or in this studygenetic pathways that functionally interact with compounds or toxins (Giaever et al., 2004
; Lum et al., 2004
). In the our study, we used yeast as a model organism to interrogate the complete genome set of homozygous yeast deletion strains to enrich genes that show sensitivity to MPP+ and paraquat. We identified 95 genes and 140 genes that exhibit slow growth, as homozygotes, when exposed to MPP+ and paraquat, respectively. Several of these genes include TSA1, ALD1, TCH1, FET3, FTR1, and CCC2 encode yeast homologs of human proteins involved in pathways important for paraquat toxicity.
It will be of great interest to investigate the role of these homologs in PD model. Because MPP+ cytotoxicity is known to involve respiratory chain coplex I alterations, which Saccharomyces cerevisiae lacks, it is important to assay the contribution of this complex to the toxicity we observe. However, our results do implicate the ubiquitin-dependent catabolism in both MPP+ and paraquat toxicity. A functional map of enriched genes revealed that ubiquitin-dependent protein catabolism via the multivesicular body pathway is most likely engaged in the processes underlying MPP+ and paraquat toxicity (p value < 108). Functional maps of enriched genes that show slow growth in the presence of paraquat revealed that processes such as late endosome to vacuole transport, protein targeting to vacuole, intracellular protein transport, vacuolar transport, and endosome transport are affected by paraquat. Current models suggest that the ESCRT complexes recognize cargo via the ubiquitin tag, and mediate sorting into the lumenal multivesicular bodies (MVB) membranes. The Vps27p-Hse1p complex binds phosphatidylinositol-3 phosphate. Vps27 recruits ESCRT-I (VPS23, VPS28, VPS 37) from the cytoplasm to the endosome, where ESCRT-I interacts with monoubiquitinated cargo. Consequently, ESCRT-I activates ESCRT-II (VPS22, VPS25, VPS 36), which in turn initiates the oligomerization of a group of at least four small coiled-coil proteins (Vps2, Vps24, Vps20, Snf7), resulting in the formation of ESCRT-III. The ESCRT-III (VPS20, VPS32) complex concentrates the MVB cargo and recruits additional factors such as Bro1 and the AAA-type adenosine triphosphatase Vps4. Bro1 functions in the recruitment of the deubiquitinating enzyme Doa4 that removes the ubiquitin tag from cargo proteins.
Mounting pathological, genetic, and experimental evidence suggests that dysfunction of the ubiquitin-dependent protein catabolism, either at the level of the proteasome itself or at the level of ubiquitination, plays a role in the pathogenesis of PD (McNaught and Jenner, 2001
). Our results clearly indicate that toxicity mediated by MPP+ and paraquat is regulated by nonoverlapping sets of conserved genes and engages ubiquitin-dependent protein catabolism. Respectively, 80 and 76% of enriched yeast genes showing sensitivity to MPP+ and paraquat toxicity (with e < 108) have highly conserved homologs in the human genome. The utilization of mammalian animal models in which single genes along the ubiquitin-proteasome pathway are manipulated can be used as powerful tools to expand the results of this study to mammals.
| SUPPLEMENTARY DATA |
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Supplementary data are available online at http://toxsci.oxfordjournals.org/.
| ACKNOWLEDGMENTS |
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This study was supported by a grant from Centers for Parkinson's Disease Environmental ResearchNational Institute of Environmental Health Sciences/National Institutes of Health. We thank Anjani Attili, Carla Camos Alves, Sheri Mohandessi, and Rama Yerramilli for bioinformatic assistance.
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