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: lindquist_admin@wi.mit.edu (S. L.); fraenkel-admin@mit.edu (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-prot