Abstract
BackgroundUnderstanding gene function and genetic relationships is fundamental to our efforts to better understand biological systems. Previous studies systematically describing genetic interactions on a global scale have either focused on core biological processes in protozoans or surveyed catastrophic interactions in metazoans. Here, we describe a reliable high-throughput approach capable of revealing both weak and strong genetic interactions in the nematode Caenorhabditis elegans.ResultsWe investigated interactions between 11 'query' mutants in conserved signal transduction pathways and hundreds of 'target' genes compromised by RNA interference (RNAi). Mutant-RNAi combinations that grew more slowly than controls were identified, and genetic interactions inferred through an unbiased global analysis of the interaction matrix. A network of 1,246 interactions was uncovered, establishing the largest metazoan genetic-interaction network to date. We refer to this approach as systematic genetic interaction analysis (SGI). To investigate how genetic interactions connect genes on a global scale, we superimposed the SGI network on existing networks of physical, genetic, phenotypic and coexpression interactions. We identified 56 putative functional modules within the superimposed network, one of which regulates fat accumulation and is coordinated by interactions with bar-1(ga80), which encodes a homolog of β-catenin. We also discovered that SGI interactions link distinct subnetworks on a global scale. Finally, we showed that the properties of genetic networks are conserved between C. elegans and Saccharomyces cerevisiae, but that the connectivity of interactions within the current networks is not.ConclusionsSynthetic genetic interactions may reveal redundancy among functional modules on a global scale, which is a previously unappreciated level of organization within metazoan systems. Although the buffering between functional modules may differ between species, studying these differences may provide insight into the evolution of divergent form and function.
Highlights
Understanding gene function and genetic relationships is fundamental to our efforts to better understand biological systems
We examined how the recall and precision of the systematic genetic interaction analysis (SGI) network compared with other large eukaryotic interaction networks, including a previously described C. elegans geneticinteraction network (Lehner et al [24]), a C. elegans proteininteraction network (Li et al [37]), a eukaryotic protein-interaction network that augments the C. elegans protein-interaction network with orthologous interactions from S. cerevisiae, Drosophila melanogaster, and human protein interactions contained in BioGRID [41], an mRNA coexpression network constructed from C. elegans, S. cerevisiae, D. melanogaster, and human expression data [38,40], an S. cerevisiae synthetic genetic-interaction network (Tong et al [12]), and a network we created based on the similarity of C. elegans RNA interference (RNAi)-induced phenotypes [3,4,22,42] (Figure 4c, and Materials and methods)
We extended the comparison between the SGI and Lehner networks by using previously computed prediction scores for C. elegans genetic interactions based on characterized physical interactions, gene expression, phenotypes, and functional annotation from C. elegans, D. melanogaster, and S. cerevisiae (Zhong and Sternberg [44])
Summary
Understanding gene function and genetic relationships is fundamental to our efforts to better understand biological systems. Genetic disruption yields no detectable phenotype in a laboratory setting. One way to uncover biological roles for phenotypically silent genes is through genetic modifier screens. Genetic modifiers are traditionally identified through a random mutagenesis of individuals harboring one mutant gene followed by a screen for second-site mutations that either enhance or suppress the primary phenotype (reviewed in [5]). Modifying genes identified in this way clearly participate in the regulation of the process of interest, yet often have no detectable phenotype on their own [6,7,8,9,10]. Forward genetic modifier screens are a useful but indirect approach to ascribe function to genes that otherwise have no phenotype
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