Abstract
BackgroundEpistatic Miniarray Profiling(E-MAP) quantifies the net effect on growth rate of disrupting pairs of genes, often producing phenotypes that may be more (negative epistasis) or less (positive epistasis) severe than the phenotype predicted based on single gene disruptions. Epistatic interactions are important for understanding cell biology because they define relationships between individual genes, and between sets of genes involved in biochemical pathways and protein complexes. Each E-MAP screen quantifies the interactions between a logically selected subset of genes (e.g. genes whose products share a common function). Interactions that occur between genes involved in different cellular processes are not as frequently measured, yet these interactions are important for providing an overview of cellular organization.ResultsWe introduce a method for combining overlapping E-MAP screens and inferring new interactions between them. We use this method to infer with high confidence 2,240 new strongly epistatic interactions and 34,469 weakly epistatic or neutral interactions. We show that accuracy of the predicted interactions approaches that of replicate experiments and that, like measured interactions, they are enriched for features such as shared biochemical pathways and knockout phenotypes. We constructed an expanded epistasis map for yeast cell protein complexes and show that our new interactions increase the evidence for previously proposed inter-complex connections, and predict many new links. We validated a number of these in the laboratory, including new interactions linking the SWR-C chromatin modifying complex and the nuclear transport apparatus.ConclusionOverall, our data support a modular model of yeast cell protein network organization and show how prediction methods can considerably extend the information that can be extracted from overlapping E-MAP screens.
Highlights
IntroductionEpistatic Miniarray Profiling(E-MAP) quantifies the net effect on growth rate of disrupting pairs of genes, often producing phenotypes that may be more (negative epistasis) or less (positive epistasis) severe than the phenotype predicted based on single gene disruptions
Epistatic Miniarray Profiling(E-MAP) quantifies the net effect on growth rate of disrupting pairs of genes, often producing phenotypes that may be more or less severe than the phenotype predicted based on single gene disruptions
The representation of genetic interactions as networks emerges from continuing studies aimed at characterizing the functions of individual genes, and anticipates systems biology analyses that focus on dynamic network behavior
Summary
Epistatic Miniarray Profiling(E-MAP) quantifies the net effect on growth rate of disrupting pairs of genes, often producing phenotypes that may be more (negative epistasis) or less (positive epistasis) severe than the phenotype predicted based on single gene disruptions. Epistatic interactions are important for understanding cell biology because they define relationships between individual genes, and between sets of genes involved in biochemical pathways and protein complexes. A number of genetic [9,10] and biochemical methods [11,12] allowed the mapping of > 30,000 protein-protein interactions [13], that could be represented as a large (~4000 node) undirected graph. Within such networks, proteins often form local densely connected network structures that correspond to stable physically associating heteropolymeric complexes that form in vivo (e.g. the ribosome, the proteasome). Because protein functions in the cell operate over different time scales, in different locations, and in different biochemical contexts, understanding how the cell organizes biological events in terms of protein-protein interaction networks has been a major challenge
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