Systematic interpretation of genetic interactions using protein networks

Abstract

Genetic interaction analysis, in which two mutations have a combined effect not exhibited by either mutation alone, is a powerful and widespread tool for establishing functional linkages between genes. In the yeast Saccharomyces cerevisiae, ongoing screens have generated >4,800 such genetic interaction data. We demonstrate that by combining these data with information on protein-protein, prote in-DNA or metabolic networks, it is possible to uncover physical mechanisms behind many of the observed genetic effects. Using a probabilistic model, we found that 1,922 genetic interactions are significantly associated with either between-or within-pathway explanations encoded in the physical networks, covering ~40% of known genetic interactions. These models predict new functions for 343 proteins and suggest that between-pathway explanations are better than withinpathway explanations at interpreting genetic interactions identified in systematic screens. This study provides a road map for how genetic and physical interactions can be integrated to reveal pathway organization and function.A major biological challenge is to interpret observed genetic interactions in a physical cellular context 1-3 . There are several major types of genetic interactions: synthetic-lethal interactions, in which mutations in two nonessential genes are lethal when combined; suppressor interactions, in which one mutation is lethal but when combined with a second, cell viability is restored; and an array of other effects such as enhancement and epistasis. Genetic interactions have been used extensively to shed light on pathway organization in model organisms [1][2][3][4] . In humans, genetic interactions are critical in linkage analysis of complex diseases 5 and in discovery of new pharmaceuticals 6 . Although genetic interactions are classically identified by mutant screens 7 , recent studies have applied systematic 'reverse' methods such as synthetic genetic arrays (SGA) 8 or synthetic lethal analysis by microarrays (SLAM) 9 to catalog ~4,000 synthetic-lethal and synthetic-sick interactions in Saccharomyces cerevisiae.Because of the high-throughput nature of SGA, discovery of new genetic interactions is largely automated. However, interpreting the functional significance of each result remains a relatively slow process. The problem is compounded by the large number of genetic interactions measured when screening one gene versus all others (~34 on average 10 ) as well as possible false positives if the interactions are not confirmed by tetrad or random spore analysis. Thus,

Predicting genetic modifier loci using functional gene networks

“…Previous work has shown that it is possible to predict genetic interactions using physical protein-protein interaction networks Wong et al 2004;Kelley and Ideker 2005;Le Meur and Gentleman 2008). We therefore asked whether using functional interactionswhich extend beyond physical interactions-improves the predictive power of a network.…”

“…These screens have highlighted the enormous extent to which mutations in one gene alter the phenotypic outcome of mutations in a second locus. Furthermore, the data from these screens show that most genetic interactions do not represent simple cases of ''redundancy'' between genes encoding similar biochemical functions Wong et al 2004;Kelley and Ideker 2005;Ulitsky and Shamir 2007).…”

“…This would reduce the number of tests being performed, therefore increasing power to detect interactions. Analysis of genetic interaction data from model organisms has shown that genes that participate in common biological processes often show similar patterns of genetic interactions Kelley and Ideker 2005;Ye et al 2005;Ulitsky and Shamir 2007). This means that genetic interactions can, in part, be predicted using physical interaction data to identify proteins that act in common functional pathways (Wong et al 2004;Le Meur and Gentleman 2008).…”

Evaluating Between-Pathway Models with Expression Data

“…We find 228 of the 404 BPMs reported by Kelley and Ideker (2005) satisfy these criteria, as do 39 BPMs from Ulitsky and Shamir (2007b), 78 from Ma et al (2008), and 1062 from Brady et al (2009).…”

Quantitative genetic analysis in Saccharomyces cerevisiae using epistatic miniarray profiles (E-MAPs) and its application to chromatin functions

“…In order to systematically collect information on these functional relationships, high-throughput technologies in Saccharomyces cerevisiae have been developed to qualitatively identify synthetic sick/lethal (SSL) (aggravating) genetic interactions between gene pairs on a genomewide scale (3)(4)(5). While these methods have proven very powerful, it has become clear that individual SSL relationships between non-essential genes are often hard to interpret since they usually identify genes that function in different, potentially parallel pathways (6)(7)(8). Quantitative information on the entire spectrum of genetic interactions would provide a more comprehensive view of the cellular effects of gene mutation.…”