Key Points
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Large-scale gene-deletion analyses reveal that mutations in most eukaryotic genes have little discernable effect.
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Rarely, pairwise combinations of mutant alleles can be inviable even though the single mutants themselves are viable, a phenomenon that is termed synthetic lethality. Synthetic lethality identifies a functional relationship between genes, indicating that they work together and impinge on the same essential function.
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Two different methods, synthetic genetic array (SGA) analysis and diploid synthetic-lethal analysis by microarray (dSLAM), enable large-scale mapping of synthetic-lethal genetic interactions in yeast.
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Collections of yeast genes that have been cloned into vectors to enable their overexpression allow systematic analysis of synthetic dosage suppression, whereby the overexpression of a gene compensates for the defect in another gene, or of synthetic dosage lethality, whereby the overexpression of a gene exaggerates the defect that is associated with another gene.
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Quantification of fitness phenotypes enables genetic interactions to be scored as alleviating or aggravating when the double-mutant fitness is compared with the expected value, derived from a multiplicative model for combining the single-mutant fitnesses.
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There are ∼1,000 essential genes in yeast and, ∼200,000 synthetic-lethal/sick double-mutant combinations, indicating that there are ∼200-fold more ways of creating the same mutant phenotype through a digenic interaction.
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Large-scale mapping of synthetic-genetic interactions has showed that, for both non-essential and essential genes, genetic interactions tend to occur among functionally related genes.
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The yeast synthetic-lethal genetic network shows 'small world' properties with a short characteristic path length and dense local neighbourhoods, in which genes tend to interact with their immediate neighbours. The dense neighbourhood characteristic of small-world networks is of particular interest because it can be exploited to predict interactions from a sparsely mapped network.
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Although synthetic-lethal genetic interactions overlap with protein–protein interactions more often than expected by chance, such overlap is relatively rare, occurring at a frequency of less than 1%, and largely confined to genes within pathways that contain an essential gene. Most synthetic-lethal genetic interactions occur between different pathways and are, therefore. orthogonal to protein–protein interactions.
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Genes whose products function within the same pathway or complex often show a similar pattern of genetic interactions; therefore, clustering can be used to identify genes encoding pathway or complex components.
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RNAi libraries that target all predicted genes in metazoan models and in human cell lines offers the potential for genome-wide analysis in complex systems and have already been applied successfully to Caenorhabditis elegans.
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Deletion of a gene encoding the target of an inhibitory compound should cause cellular effects similar to inhibition of the target by drug treatment. So, the complete matrix of synthetic-lethal interactions for yeast should serve as a key for deciphering the chemical–genetic profile of a specific compound, for example the set of yeast mutants that are hypersensitive to the compound, thereby linking the compound to a target pathway.
Abstract
The development and application of genetic tools and resources has enabled a partial genetic-interaction network for the yeast Saccharomyces cerevisiae to be compiled. Analysis of the network, which is ongoing, has already provided a clear picture of the nature and scale of the genetic interactions that robustly sustain biological systems, and how cellular buffering is achieved at the molecular level. Recent studies in yeast have begun to define general principles of genetic networks, and also pave the way for similar studies in metazoan model systems. A comparative understanding of genetic-interaction networks promises insights into some long-standing genetic problems, such as the nature of quantitative traits and the basis of complex inherited disease.
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Acknowledgements
Genetic interaction network projects in the Andrews and Boone laboratories are supported by grants from the Canadian Institutes of Health Research (CIHR) and Genome Canada through the Ontario Genomics Institute. C.B. is an International Scholar of the Howard Hughes Medical Institute and holds a Canada Research Chair in Functional Genomics. We would like to thank Amy Hin Yan Tong and Michael Costanzo for help with the figures and comments on the manuscript.
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Glossary
- Synthetic enhancement
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The situation in which a mutation in one gene exacerbates the phenotypic severity of a mutation in a second gene.
- Synthetic lethality
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The situation in which two genes that are non-essential when individually mutated cause lethality when they are combined as a double mutant.
- Haploinsufficiency
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The situation in diploid cells in which heterozygous mutants that produce a reduced amount of functional gene product can be less robust than the wild type to perturbations that affect essential functions.
- Tetrad analysis
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The four haploid cells that are produced by an individual meiosis in budding yeast are referred to as a tetrad. The tetrad is enclosed in a sac called an ascus. Tetrad analysis involves the isolation and analysis of the haploid meiotic spores of individual asci for the segregation of genetic markers.
- N-end rule
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Relates the in vivo half-life of a protein to the identity of its N-terminal residue. In eukaryotes, the N-end rule pathway is part of the ubiquitin system.
- Hypomorphic
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Describes an allele that carries a mutation that causes a partial loss of gene function.
- Synthetic genetic array analysis
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A robotic procedure that is used to create, select and systematically examine the growth phenotypes of yeast double-mutant haploid strains.
- Pinning
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The use of hand-held or robotic tools, which are composed of small floating pinheads, to replicate yeast colonies to different media for genetic tests (typical formats include 96, 384, 768 and 1,536 pinheads per replica tool).
- Suppression
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The situation in which a mutation in one gene counteracts the effects of a mutation in another, so that the phenotype of the double mutant is more like that of the wild type.
- Nodes
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In typical network diagrams, genes or proteins are represented as nodes, whereas the connections between the nodes are termed edges.
- Clustering algorithms
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Algorithms that group together objects that are 'similar'; objects belonging to other clusters are 'dissimilar'. Clustering algorithms have been used extensively to view large collections of biological data, such as microarray expression profiles and genetic-interaction data.
- Congruency score
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A numerical ranking of the degree of partner sharing in a network.
- Isogenic
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Strains or organisms that share identical genotypes.
- Gene association studies
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Studies that assess whether genotype frequencies are different between two groups that differ in phenotype.
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Boone, C., Bussey, H. & Andrews, B. Exploring genetic interactions and networks with yeast. Nat Rev Genet 8, 437–449 (2007). https://doi.org/10.1038/nrg2085
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DOI: https://doi.org/10.1038/nrg2085
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