Gene function prediction from congruent synthetic lethal interactions in yeast

Abstract

We predicted gene function using synthetic lethal genetic interactions between null alleles in Saccharomyces cerevisiae. Phenotypic and protein interaction data indicate that synthetic lethal gene pairs function in parallel or compensating pathways. Congruent gene pairs, defined as sharing synthetic lethal partners, are in single pathway branches. We predicted benomyl sensitivity and nuclear migration defects using congruence; these phenotypes were uncorrelated with direct synthetic lethality. We also predicted YLL049W as a new member of the dynein-dynactin pathway and provided new supporting experimental evidence. We performed synthetic lethal screens of the parallel mitotic exit network (MEN) and Cdc14 early anaphase release pathways required for late cell cycle. Synthetic lethal interactions bridged genes in these pathways, and high congruence linked genes within each pathway. Synthetic lethal interactions between MEN and all components of the Sin3/Rpd3 histone deacetylase revealed a novel function for Sin3/Rpd3 in promoting mitotic exit in parallel to MEN. These in silico methods can predict phenotypes and gene functions and are applicable to genomic synthetic lethality screens in yeast and analogous RNA interference screens in metazoans.

Essential global role of CDC14 in DNA synthesis revealed by chromosome underreplication unrecognized by checkpoints in cdc14 mutants

“…However, the prolonged cell-cycle span of the import defect, including the G1-S transition (when Cdc14p is inactive), suggests a possibility that the import machinery itself is also a substrate of the Cdc14 phosphatase. Indeed, a number of nuclear pore components interacting with Kap60p are CDK substrates (52), that is, potential Cdc14p targets; mutations in the Nup84 subcomplex are synthetically lethal with FEAR pathway mutations (53).…”

Predicting genetic modifier loci using functional gene networks

“…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).…”

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.…”

“…This includes interactions that are alleviating (buffering or suppressing), where the double mutants grow more rapidly than would be expected given the growth rate of each of the single mutants. Such interactions often occur between pairs of genes working in the same cellular pathway (7)(8)(9). To enable highthroughput analysis of the entire spectrum of interactions for large groups of genes we developed the E-MAPs (Epistatic MiniArray Profiles) (7) technology that allows for the collection of quantitative genetic interaction data on logically selected subsets of genes.…”