Yeast has proven an invaluable tool to assign functions to genes, which is a central challenge of the post-genomic era. It was the first domesticated microorganism and was used for baking bread and brewing beer. Besides, it was the first eukaryote whose genome was completely sequenced (Goffeau et al., 1996). The genome contains 12 megabases of information on 16 linear chromosomes and stores about 6000 genes. More than 40% of the yeast proteins have human homologs, thus providing a potential model for human diseases (Lander et al., 2001).
A great step towards an understanding of the yeast genome was the construction of the first deletion collection (also known as yeast knock-out [YKO] collection) containing mutants in which each open reading frame (ORF) is replaced by a cassette conferring resistance to an antibiotic (Winzeler et al., 1999; Giaever et al., 2002). This collection exists in different variations, which contain haploid mutants with different mating type or hetero- and homozygous diploids (Giaever and Nislow, 2014).
Later, additional libraries were produced covering essential genes with a titratable promoter or genes whose mRNA stability is disturbed (Mnaimneh et al., 2004; Breslow et al., 2008). More than 1000 genome-wide screens were performed and led to an expanding annotation of the genome (Giaever and Nislow, 2014). Several of these screens addressed genes required for mitochondrial activity and morphology and expanded our knowledge about mitochondrial biogenesis (Dimmer et al., 2002; Altmann and Westermann, 2005; Luban et al., 2005; Merz and Westermann, 2009).
Comprehensive, functional information was not only derived from deletion mutant analysis but also from a strain collection containing all ORFs fused to a GFP coding sequence allowing the microscopic localization of proteins under different conditions (Huh et al., 2003; Breker et al., 2013). Protein-protein interactions were assessed by the yeast tandem affinity purification collection (Krogan et al., 2006), genome-scale two-hybrid studies (Ito et al., 2001) and protein-fragment complementation assay (PCA) collections (Tarassov et al., 2008).
Nonetheless, many genes remain functionally unclassified. Only about 20% of the yeast genes are essential, suggesting a great amount of redundancy among the genes (Winzeler et al., 1999; Giaever et al., 2002). The identification of genetic interactions is one way to take advantage of this redundancy in order to uncover gene functions. Genetic interactions occur when two mutations of different genes are combined and produce an unexpected phenotype; e. g., when deletions of two genes, which individually are not harmful to the cell, result in synthetic lethality of the double mutant (summarized in Dixon et al., 2009). This concept is based on the assumption that a combination of mutations, which individually result in a growth defect, has a multiplicative effect. If mutant a has a fitness of 0.7 compared to wild type and mutant b has a fitness of 0.4, one expects a fitness of 0.7 x 0.4 = 0.28 for the double mutant ab (Figure 6A). There are two classes of genetic interactions:
17 negative and positive ones. If ab has fitness below 0.28, it is a negative interaction, if the fitness exceeds 0.28, it is a positive one.
The concept intuitively becomes clear in the case of symmetric positive interactions. If the products of two genes C and D are components of a complex and if disintegration of the complex by deletion of either gene results in a growth defect of 0.6, the combination of the two deletions will not result in a double mutant cd with a fitness of 0.6 x 0.6 = 0.36 but 0.6, since the complex is dysfunctional to the same extent in the single mutants and the double mutant and hence they have the same fitness (Figure 6B). This fitness is better than expected and demonstrates that gene products physically interacting with each other have the tendency to show positive genetic interactions (Collins et al., 2007). Another possible cause for positive interactions is that the genes function in antagonistic pathways and the double mutants have a more wild type-like situation like in the case of num1
mmr1 double mutants. Loss of NUM1 results in a mitochondrial distribution shifted towards the bud, while Mmr1 depletion leads to a shift towards the mother. Double mutants, however, show a rather wild type-like distribution (Klecker et al., 2013). Alternatively, the genes work in the same pathway and blocking the pathway flux results in a comparable outcome in both single and double mutants. Negative interactions can occur when genes work in parallel or redundant pathways contributing to the same biological process. Cells can cope with deletion of either gene but have a severe fitness defect when the deletions are combined with the extreme case of being inviable (synthetic lethality) as in the case of the two formin coding genes BNI1 and BNR1.
Figure 6. Genetic interactions. (A) Two hypothetical single mutants a and b have a reduced fitness compared to wild type AB. The actual fitness of the double mutant ab can equal the expected fitness of the combined single mutants’ fitness (no interaction), be lower (negative interaction) or higher (positive interaction) than the expected fitness. (B) A special case of a hypothetical symmetric positive interaction, where the single mutants c and d have the same fitness as the double mutant cd.
Genetic interaction networks have turned out to be powerful tools and contributed to the identification of genes working in chromosome biology, lipid quality control and many other processes (Collins et al., 2007; Dixon et al., 2009; Surma et al., 2013). In order to identify genetic interactions on a genome-wide scale, the synthetic genetic array (SGA) technology was developed. In SGA technology, a query strain carrying a mutation is crossed to an array of mutants. Selectable markers allow the subsequent isolation of double mutants, whose fitness can be quantified and
18 genetic interactions can be uncovered (Tong et al., 2001). Automated replica-plating enabled the high-throughput screening of more than a thousand query mutations, resulting in the first “genetic landscape of a cell” (Tong et al., 2004; Costanzo et al., 2010). Applying this method with a focus on mitochondria gave rise to the MITO-MAP and led to the discovery of six genes involved in the biogenesis of cristae (Hoppins et al., 2011). Exactly the same genes were found at the same time by two independent groups using different methods (Harner et al., 2011; von der Malsburg et al., 2011), which demonstrates that genetic interactions are a powerful tool in order to assign functions to genes.
The technology is now broadly applied by geneticists and the automation of the process will lead to a highly anticipated map, where all digenic interaction data from deletion mutants are integrated.
However, the genetic interactomes of essential genes remain largely unknown since deletion mutants are not available. Hypomorphic alleles with reduced expression of the query gene have been screened but may not yield a comprehensive picture of the interactomes (Breslow et al., 2008;
Costanzo et al., 2010). Yet, determination of the interactomes of specific point mutants of essential genes with a defined spectrum of phenotypes are an attractive alternative to hypomorphic alleles and will expand our knowledge about biological processes, which are essential in yeast.
19