Bridging the gap between high-throughput genetic and transcriptional data reveals cellular pathways responding to alpha-synuclein toxicity

Cells respond to stimuli by changes in various processes, including signaling pathways and gene expression. Efforts to identify components of these responses increasingly depend on mRNA profiling and genetic library screens, yet the functional roles of the genes identified by these assays often remain enigmatic. By comparing the results of these two assays across various cellular responses, we found that they are consistently distinct. Moreover, genetic screens tend to identify response regulators, while mRNA profiling frequently detects metabolic responses. We developed an integrative approach that bridges the gap between these data using known molecular interactions, thus highlighting major response pathways. We harnessed this approach to reveal cellular pathways related to alpha-synuclein, a small lipid-binding protein implicated in several neurodegenerative disorders including Parkinson disease. For this we screened an established yeast model for alphasynuclein toxicity to identify genes that when overexpressed alter cellular survival. Application of our algorithm to these data and data from mRNA profiling provided functional explanations for many of these genes and revealed novel relations between alpha-synuclein toxicity and basic cellular pathways. Cells live in a dynamic environment in which they confront various perturbations such as sudden environmental changes, toxins, and mutations. The response to such perturbations is #To whom correspondence should be addressed. E-mail: [email protected] (S. L.); [email protected] (E.F.). 7Present Address: Department of Cell and Developmental Biology, The University of Pennsylvania, Philadelphia, PA, USA 8Present Address: Medical College of Georgia, Augusta, GA, USA 9Present Address: Boston Biomedical Research Institute, Watertown, MA, USA. *These authors contributed equally to this work +These authors contributed equally to this work Summary: A novel approach that integrates genetic hits, differentially expressed genes and known molecular interactions reveals a dramatically enhanced view of cellular responses and was used to create the first cellular map of alpha-synuclein toxicity. NIH Public Access Author Manuscript Nat Genet. Author manuscript; available in PMC 2009 September 1. Published in final edited form as: Nat Genet. 2009 March ; 41(3): 316–323. doi:10.1038/ng.337. N IH PA Athor M anscript N IH PA Athor M anscript N IH PA Athor M anscript typically complex and comprises signaling and metabolic changes, as well as changes in gene expression. Revealing the cellular mechanisms responding to a specific perturbation may unravel its nature, thus illuminating disease mechanisms1 or a drug’s mode of action2 ,3, and identify points of intervention with potential therapeutic value4. High-throughput experimental techniques including mRNA profiling and genetic screening are commonly used for revealing components of these response pathways because they provide a genomeand proteome-wide view of molecular changes. mRNA profiling experiments rapidly identify genes that are differentially expressed following stimuli. Genetic screening, including deletion, overexpression and RNAi library screens, identify genetic “hits”, genes whose individual manipulation alters the phenotype of stimulated cells. However, each technique has obvious limitations for revealing the full nature of cellular responses. mRNA profiling experiments do not target the series of events that led to the differential expression. Genetic screens provide strong evidence that a gene is functionally related to the response process. Yet, this relationship is often indirect and hard to decipher, especially in highthroughput experiments that typically result in scores of relevant genes with various functions. It has been noted previously in a few specific instances 2,5–9 that genetic screens do not identify the same genes as mRNA assays conducted in the same conditions. By analyzing the relationship between genetic hits and differentially expressed genes across 179 diverse conditions, we found that this discrepancy is, in fact, a general rule. Furthermore, we found a striking bias in each technique that led us to a new, more coherent view of cellular responses. To bridge the gap between the two forms of high throughput analysis we developed an algorithm that exploits these experimental biases and that takes advantage of molecular interactions data. This approach simultaneously reveals (i) the functional context of genetic hits, and (ii) additional proteins that participate in the response yet were not detected by either the genetic or the mRNA profiling assays themselves. Having validated our approach in a wide array of perturbations, we applied it to unravel cellular responses to increased expression of alpha-synuclein. Alpha-synuclein is a small human protein implicated in Parkinson disease whose native function and role in the etiology of the disease remain unclear 10. We screened an established yeast model for alpha-synuclein toxicity 11,12 using an additional set of 3,500 overexpression yeast strains, exposing the multifaceted toxicity of alpha-synuclein. Application of our approach to the high-throughput genetic and transcriptional data of the yeast model illuminated response pathways whose manipulation altered cellular survival, and provided the first cellular map of the proteins and genes responding to alpha-synuclein expression. The relationship between genetic hits and differentially expressed genes In order to derive a comprehensive view of the relationship between genetic hits and differentially expressed genes identified in a particular condition, we analyzed published mRNA profiles and genetic hits for 179 distinct perturbations in yeast (Methods). These data included responses to a wide array of chemical and genetic insults affecting a multitude of cellular processes. For 30 of these perturbations complete genetic screens were reported, typically identifying >100 genetic hits; only partial genetic data are available for the remaining perturbations. The number of genetic hits, differentially expressed genes and genes common to both for each perturbation are given in Table 1 and Supplementary Table 1. Intriguingly, in almost all cases the overlap was astonishingly small and statistically insignificant (p>0.05, Methods). One possible explanation for the poor overlap between genetic hits and differentially expressed genes is that each assay may be biased toward distinct aspects of cellular responses. Analysis Yeger-Lotem et al. Page 2 Nat Genet. Author manuscript; available in PMC 2009 September 1. N IH PA Athor M anscript N IH PA Athor M anscript N IH PA Athor M anscript of Gene Ontology (GO) enrichment confirmed this hypothesis (Methods). The combined hits from all 179 genetic screens were highly enriched for the annotations biological regulation (23.3%, p<10−82), transcription (14%, p<10−44) and signal transduction (6.3%, p<10−30). In contrast, the regulated genes from all perturbations were enriched mostly for various metabolic processes (e.g., organic acid metabolic process 7.1%, p<10−18) and oxidoreductase activities 7.2%, p< 10−34). To ensure these patterns of enrichment do not stem from a handful of data sources but reflect a general tendency, we also analyzed the 30 perturbations for which complete data were available. We found the same enrichment trends, regardless of whether these perturbations were analyzed individually (Supplementary Table 2) or whether all 30 datasets were combined (Supplementary Table 3). Complete enrichment analyses appear in Supplementary Text. Thus, we find that genetic assays tend to probe the regulation of cellular responses, while mRNA profiling assays tend to probe the metabolic aspects of cellular responses. The striking differences in annotations between genetic hits and differentially expressed genes imply that each gene set alone often provides a limited and biased view of cellular responses. In fact, this hypothesis was often borne out in cases where the pathways are well-studied by other, more classical methods of genetic and molecular biological research. In the yeast DNA damage response pathway, for example, a genetic screen 4 detected proteins that sense DNA damage (Mec3, Ddc1, Rad17 and Rad24), while mRNA profiling detected repair enzymes such as Rnr4 13. Yet core components of this pathway that had been uncovered by other intense investigations over many years, such as the signal transducers Mec1 and Rad53 and the transcription factor Rfx1, remained undetected by either high-throughput assay. If we are to fully reap the benefits of applying high-throughput methods to new problems and under-explored biological processes, it is essential that we find new routes to connect these data and obtain a true picture of the regulation of cellular responses. Here we provide a novel framework that bridges the gap between genetic and transcriptional data. Based on known pathways such as the response to DNA damage discussed above, we expect that some of the genetic hits, which are enriched for response regulators, will be connected via regulatory pathways to the differentially regulated genes, which are the output of such pathways. Discovering these pathways may uncover additional components of the cellular response to perturbation that are missing from the experimental data (Figure 1). ResponseNet algorithm for identification of response networks The ResponseNet algorithm identifies molecular interaction paths connecting genetic hits and differentially expressed genes that may include hidden components of the cellular response (Figure 1). The yeast Saccharomyces cerevisiae provides a powerful model system for such analysis due to the extensive molecular interactions data now available (Methods and Supplementary Table 4). Taking advantage of these resources we assembled an integrated network model of the yeast interactome that contains protein-protein interactions, metabolic relations and protein-DNA interactions detected by various methods with different levels of reliability14. The resulting interactome relates 5,622 interacting proteins and 5,510 regulated genes, which are represented by network nodes, via 57,955 molecular interactions, which are represented by network edges. Our representation of the interactome has two important features that facilitate identification of pathways relating genetic hits to transcriptional changes. First, we chose to highlight the role of transcriptional regulatory proteins in determining expression changes by representing differentially expressed genes and their protein products as separate nodes. The only protein nodes that are connected to gene nodes are transcriptional regulatory proteins, and the edges between protein and gene nodes represent observed protein-DNA interactions. Edges between Yeger-Lotem et al. Page 3 Nat Genet. Author manuscript; available in PMC 2009 September 1. N IH PA Athor M anscript N IH PA Athor M anscript N IH PA Athor M anscript two protein nodes represent protein-protein interaction data. Thus, all pathways connecting genetic hits to the differentially expressed genes must pass through a transcriptional regulatory protein (Supplementary Figure 1). Second, because interactions vary in their reliability, each edge was given a weight that represents the probability that the connected nodes interact in a response pathway. The probabilities were computed using a Bayesian method that considers the types of experimental data supporting the putative interaction and that favors interactions among proteins acting in a common cellular response pathway (Methods). Due to the vast number of edges, a search for all interaction paths connecting the genetic hits to the differentially expressed genes typically results in “hairball” networks that are very hard to interpret (Figure 2A). One approach to this problem is to identify the highest probability paths. However, pioneering approaches that searched an interactome for high-probability paths had to limit the output path lengths to 3 edges for computational complexity issues15,16. We aimed for a solution that would (i) pick the subset of genetic hits most likely to modulate the differentially expressed genes without limiting it a priori to known regulatory genes, (ii) identify and rank intermediary proteins that are likely to be part of response pathways but escaped detection by high-throughput methods, and (iii) connect the proteins via a highprobability interactome sub-network without restricting its topology. We reasoned that these requirements could elegantly be met using a “flow algorithm”, a computational method that has been employed previously to analyze signaling or metabolic networks that have already known topology (e.g.,17). In these algorithms flow goes from a source node to a sink node through the graph edges; edges can be associated with a capacity that limits the flow and are also associated with a cost. (As a loose analogy, this resembles water finding the path of least resistance through a complex landscape.) Because we sought to discover the topology of the unknown response pathways connecting genetic hits and differentially expressed genes we required that flow pass from genetic hits through interactome edges to transcriptional regulators of the regulated genes (Supplementary Figure 1). We then formulated our goal as a minimumcost flow optimization problem 18: Cost was defined as the negative log of the probability of an edge. Hence, by minimizing the cost the algorithm gives preference to high-probability paths (Methods). The solution to the optimization problem is a relatively sparse network that connects many of the genetic hits to many of the regulated genes through known interactions and intermediary proteins (Figure 2B). These intermediary proteins were not identified by either high-throughput genetic analysis or mRNA profiling, but are predicted by the algorithm to play a part in the response. The proteins in the solution are ranked by the amount of flow they carry. The more flow that passes through a protein, the more important it is in connecting the genetic and transcriptional datasets. Validation of the ResponseNet algorithm To determine if ResponseNet provides valid biological insights, we used it to connect genetic and transcriptional data from perturbations in well-studied pathways. We then asked if ResponseNet revealed the proteins and pathways that are missing from the genetic and transcriptional data, but that had previously been gleaned from individual analyses. For example, we used ResponseNet to analyze genetic hits19 and transcriptional20 data collected from a strain deleted for the gene encoding Ste5, a scaffold protein that coordinates the MAP kinase cascade activated by pheromone (Figure 2B). The nodes selected by ResponseNet were highly enriched for proteins functioning in the pheromone response pathway (46%, p<10−18), thus revealing the perturbed biological process. The highly ranked intermediary proteins provided biologically meaningful connections between the data sets, as they included key regulators of the pheromone response as well as Ste5, the source of perturbation. Yeger-Lotem et al. Page 4 Nat Genet. Author manuscript; available in PMC 2009 September 1. N IH PA Athor M anscript N IH PA Athor M anscript N IH PA Athor M anscript The algorithm also performed well in analyzing the much more complex cellular response to DNA damage4,21,22. The nodes discovered by ResponseNet were highly enriched for the GO categories response to DNA damage stimulus (21%, p<10−14) and DNA repair (19%, p<10−14). Indeed, the most highly ranked part of the network contained core members of the pathway that had previously been uncovered by years of intense investigation but were not detected by high-throughput screens, including the signal transducers Mec1 and Rad53, members of the RFC complex (Rfc2-Rfc5) and the transcription factor Rfx1 (Figure 2C). To test ResponseNet more broadly, we evaluated its ability to identify hidden components in the cellular response to over one hundred distinct perturbations corresponding to inactivations of well-annotated genes (Methods). For each such perturbation the genetic hits set consisted of the genetic interactors of the inactivated gene (e.g., synthetic lethals), and the differentially expressed genes were based on mRNA profiling of the inactivated strain 20. The identity of the inactivated gene was hidden from the algorithm, and was used to evaluate the predicted network. ResponseNet output was considered successful in revealing the cellular response to the perturbation if the hidden nodes it identified fulfilled one of two criteria: (i) they included the inactivated gene that was the source of perturbation, or (ii) they were significantly enriched for a specific biological process attributed to the inactivated gene. Significance was determined relative to networks generated using randomization techniques (Methods and supplementary text). ResponseNet success rates are given in Table 2 and Supplementary Table 5. In total, ResponseNet predictions were successful in 63% of the cases. This rate of success is relatively high considering that for the majority of the cases (85%) genetic hits data were rather limited (a median of 14 genetic hits) and no high-throughput genetic screening data are yet available. Notably, ResponseNet typically selected only 1% of the yeast proteins as relevant for the response. Despite the fact that relevant interactions might be missing from our data or have low probability compared with alternative paths, in a third of the cases the inactivated gene was highly ranked among this small fraction. A map of cellular pathways responding to alpha-synuclein toxicity Having established the validity of our method to uncover connections between otherwise disparate high-throughput datasets, we applied ResponseNet to investigate the cellular toxicity associated with alpha-synuclein (α-syn). α-syn is a small lipid-binding protein that is natively unfolded when not bound to lipids and prone to forming toxic oligomers 23. It that has been implicated in several neurodegenerative disorders, most particularly Parkinson disease (PD). α-syn is the main component of Lewy bodies, cytoplasmic proteinaceous inclusions that are a hallmark of PD 24; locus duplication or triplication of α-syn lead to familial forms of PD 25, 26, and increased expression of α-syn leads to neurodegeneration in several animal models 27. α-syn is linked to alterations in vesicle trafficking 12,28 and mitochondrial function 29, yet despite immense efforts, the cellular pathways by which α-syn leads to cell death are just beginning to be uncovered. The yeast S. cerevisiae provides a powerful system for studying the molecular basis of α-syn toxicity that result from its intrinsic physical properties. Expression of human α-syn in yeast yields several dosage-dependent defects that are also found in mammalian systems, such as lipid droplet accumulation in the cytosol, the production of reactive oxygen species and impairment of the ubiquitin-proteasome system 11. An initial overexpression screen in yeast for genes that modify α-syn toxicity tested 2,000 strains and identified a class of genes functioning in ER to Golgi vesicle trafficking, leading to the observation that α-syn causes an ER to Golgi vesicle trafficking block. One of these genes, Ypt1/Rab1, a GTPase protein, was Yeger-Lotem et al. Page 5 Nat Genet. Author manuscript; available in PMC 2009 September 1. N IH PA Athor M anscript N IH PA Athor M anscript N IH PA Athor M anscript tested in neuronal models of PD and was found to rescue dopaminergic neurons from α-syn toxicity 12. We now report other results from that screen and the results of screening an additional set of 3,500 overexpression yeast strains, thereby covering in total 85% of the yeast proteome. We identified a diverse group of genes including 54 suppressors and 23 enhancers of α-syn toxicity, many with clear human orthologs (Table 3). Major classes of genes that emerged include vesicle-trafficking genes, kinases and phosphatases, ubiquitin-related proteins, transcriptional regulators, manganese transporters, and trehalose biosynthesis genes. Significantly enriched GO categories included ER to Golgi vesicle-mediated transport (12%, p=6.2*10−5), phosphatases (9.1%, p=0.0028) and transcription factors (6.5%, p=0.047). While the identification of additional vesicle trafficking and ubiquitin-related genes is consistent with the defects caused by α-syn expression in yeast, the identification of trehalose biosynthesis genes and manganese transporters was new and intriguing. Trehalose was recently shown to promote the clearance of misfolded mutant α-syn 30, and manganese exposure has been linked with Parkinson-like symptoms albeit with a distinct underlying pathology31. Notably, another suppressor we identified is homologous to the human PD gene PARK9. Park9 and the human homologs of seven other genetic modifiers from diverse functional classes (Hrd1, Ubp3, Pde2, Cdc5, Yck3, Sit4 and Pmr1) were found to be efficacious in neuronal models, validating the yeast model as meaningful to α-syn toxicity in neurons (Gitler et al.; manuscript submitted). The genes identified by the screen therefore begin to unravel the surprisingly multifaceted toxicity of α-syn. Importantly, they provide novel causal relations between α-syn expression and toxicities previously associated with PD but not specifically linked to α-syn. A detailed description of the various gene classes and their potential relation to PD appears in the Supplementary Text. The transcriptional profile occurring in response to α-syn toxicity was determined in a separate study (Supplementary Text; Su et al.; manuscript submitted). Up-regulated genes prominently included genes with oxidoreductase activities (13%, p<10−9). Down-regulated genes included ribosomal genes (28%, p<10−30), as commonly observed under stress 32. More specific to αsyn toxicity, the down-regulated genes were strikingly enriched for genes encoding proteins localized to the mitochondria (60%, p<10−44) and for genes involved in generation of precursor metabolites and energy (18%, p<10−15). The genetic and transcriptional data obtained in this model system exemplify both the power and the limitations of the current approaches. These technologies reveal the wide range of cellular functions that are altered by α-syn expression. Yet the precise roles of the genetic hits and differentially expressed genes in the cellular response are unclear. For example, we checked whether the ubiquitin-related proteins that emerged from the genetic screen affect αsyn degradation. However, in strains overexpressing these ubiquitin-related genes we did not detect changes by flow cytometry in steady-state α-syn protein levels (Supplementary Figure 2). As with our previous analyses (above), the overlap between the data obtained from the genome-wide genetic screen and mRNA profiling assay was minor and statistically insignificant (four genes, p=0.96). Applying ResponseNet to these disparate datasets revealed a more coherent view of the cellular response (Supplementary Figure 3). The resulting network provided context to a large portion of the data: 34 (44%) genetic hits and 166 (27%) differentially expressed genes were linked to each other through 106 intermediate connections. These include two thirds of the protein kinase, phosphatase and ubiquitin-related genetic hits, illuminating their intricate role in the response to α-syn. For example, ResponseNet suggests that the genetic suppressor Rck1, a kinase known to respond to oxidative stress, functions through its interactions with the Cad1 Yeger-Lotem et al. Page 6 Nat Genet. Author manuscript; available in PMC 2009 September 1. N IH PA Athor M anscript N IH PA Athor M anscript N IH PA Athor M anscript transcription factor, and that this sub-network explains the differential transcriptional of seven genes (Supplementary Figure 3J). Similarly, ResponseNet identifies a set of transcriptional changes that it traces back to the genetic hits Bre5 and Ubp3, which form a deubiquitination complex (Supplementary Figure 3C). The major cellular pathways responding to α-syn toxicity included ubiquitin-dependent protein degradation, cell cycle regulation and vesicle trafficking pathways, all of which have previously been associated with PD (Supplementary text and Supplementary Figure 3). Impairment of the ubiquitin proteasome system33 and mutations in ubiquitin-related genes (parkin and uch-L1) underlie sporadic and familial forms of PD. Interestingly, parkin is associated with the SCF ubiquitin ligase complex 34, components of which were selected by ResponseNet. Inappropriate cell cycle regulation has also been implicated in neuronal cell death in PD 35,36, and ResponseNet predicted several regulators of mitosis and early meiosis. Below we focus on additional ResponseNet predictions that relate to known aspects of PD including nitrosylation, mitochondrial dysfunction and the heat shock response. Nitrosative stress Fzf1 was the only gene identified in the screen related to nitrosative stress 37. However, ResponseNet connected it to four up-regulated transcripts, including Pdi1, a protein disulfide isomerase (PDI) (Figure 3A). Intriguingly, the up-regulation of human PDI protects neuronal cells from neurotoxicity associated with ER stress and protein misfolding (both of which are linked to α-syn expression), and, further, PDI has been found to be S-nitrosylated in PD 38. We found that increased expression of α-syn causes increased S-nitrosylation of proteins (Figure 3B). This result is surprising as nitrosative responses in yeast cells were long thought to represent a defense mechanism against other microbes. Very recently it was shown that yeast synthesize NO in response to exogenous H2O2 39, suggesting that the nitrosylation of specific proteins is a highly conserved response to oxidative stress. Mitochondrial dysfunction Mitochondrial dysfunction and oxidative stress have been strongly linked with PD 40, and were recently associated specifically with α-syn (e.g., 41). Although mitochondrial dysfunction was a prominent signature in the microarray data (Su et al.; manuscript submitted), the genetic hits contained only a few genes clearly related to mitochondria. ResponseNet identified two connected components related to mitochondrial dysfunction. One component contained the suppressor Hap4, a transcriptional activator of respiratory genes, directly connected to several of the differentially expressed genes (Supplementary Figure 3B). The other component contained regulators of the retrograde signaling pathway, which senses mitochondrial dysfunction (Mks1, Rtg2 and Grr1 42, Supplementary Figure 3E). Heat shock The induction of heat shock response directly or via chemical inhibition of Hsp90 43 suppresses α-syn toxicity in many model systems including yeast, flies, mice and human cells (e.g., 44, 45). However, heat shock related genes were conspicuously absent among the list of genetic suppressors. Nonetheless, ResponseNet predicted the involvement of two highly conserved heat shock regulators, the chaperone Hsp90 (isoform Hsp82, Supplementary Figure 3A) and the heat shock transcription factor Hsf1 (Figure 4A). Interestingly, ResponseNet predicted that the toxicity suppressor Gip2, a putative regulatory subunit of the Glc7 phosphatase, interacts with Gac1. Gac1 is a regulatory subunit of the Glc7 complex, which is known to activate Hsf146. This connection suggested that Gip2 overexpression might induce a heat shock response and prompted us to test it. Indeed, we found that strains overexpressing Gip2 show elevated levels of heat shock proteins (Figure 4B). ResponseNet therefore provided a Yeger-Lotem et al. Page 7 Nat Genet. Author manuscript; available in PMC 2009 September 1. N IH PA Athor M anscript N IH PA Athor M anscript N IH PA Athor M anscript mechanistic explanation for the suppression of α-syn toxicity achieved by Gip2 overexpression and identified a new player in the regulation of the ancient heat shock response. We also identified cellular pathways whose relation to α-syn toxicity was initially obscure, raising the possibility that they may be interesting avenues for future research. Below we focus on two such highly-conserved pathways, the mevalonate/ergosterol pathway that is targeted by the cholesterol lowering statin drugs, and the target of rapamycin (TOR) pathway. The mevalonate/ergosterol biosynthesis pathway not only synthesizes sterols, but also synthesizes other products with connections to α-syn toxicity such as farnesyl groups required for vesicle trafficking proteins and ubiquinone required for mitochondrial respiration. ResponseNet ranked highly Hrd1, which regulates the protein target of statins, and the predicted intermediary Hap1, a proposed transcriptional regulator of the pathway 47 (Supplementary Figure 3A). In addition, the α-syn mRNA profile was modestly correlated with the profile of yeast treated with lovastatin (r=0.32, p< 10−93, Su et al; manuscript submitted), and several genetic hits could be also associated with products of the pathway (dependent enzymes Bet4 and Cax4, farnesylated proteins Ypt1 and Ykt6 and putative sterol carriers Sut2, Osh2, and Osh3). We therefore tested the effect of lovastatin, which selectively inhibits the highly conserved HMG-CoA reductase of yeast as well as that of mammalian cells, on α-syn toxicity. Addition of 5μM lovastatin to the media caused a further reduction in growth to strains overexpressing α-syn (Figure 5A), but did not reduce growth of either wild-type controls or of cells expressing another toxic protein, a glutamine-expansion variant of huntingtin exon I 48 (Supplementary Figure 4). We further tested ubiquinone, a downstream output of this pathway, reasoning that its down-regulation through the action of α-syn might increase cellular vulnerability. Indeed, the addition of ubiquinone-2 to the media provided a modest suppression against α-syn toxicity. Ubiquinone is an antioxidant, but this was not a non-specific antioxidant response as the antioxidant N-acetylcysteine had no effect (Supplementary Figure 5). The TOR pathway has been related to other neurodegenerative diseases 49,50. ResponseNet identified the TOR pathway proteins Tor1, Tor2 and their target transcription factors as intermediary between the genetic hit Lst8 and several up-regulated genes involved in spore wall formation (a vectorially directed secretory process in yeast) and vacuolar protein degradation (Figure 5B). We found that addition of the TOR-inhibitor rapamycin to the media markedly enhanced the toxicity of α-syn. Indeed, a low dose α-syn, which is otherwise innocuous, became toxic (Figure 5C). Establishing the specificity of this effect to α-syn, rapamycin did not reduce growth of cells expressing glutamine expansion variants of huntingtin exon I (Supplementary Figure 6).