Elena Kuzmin

A global genetic interaction network maps a wiring diagram of cellular function

INTRODUCTION Genetic interactions occur when mutations in two or more genes combine to generate an unexpected phenotype. An extreme negative or synthetic lethal genetic interaction occurs when two mutations, neither lethal individually, combine to cause cell death. Conversely, positive genetic interactions occur when two mutations produce a phenotype that is less severe than expected. Genetic interactions identify functional relationships between genes and can be harnessed for biological discovery and therapeutic target identification. They may also explain a considerable component of the undiscovered genetics associated with human diseases. Here, we describe construction and analysis of a comprehensive genetic interaction network for a eukaryotic cell. RATIONALE Genome sequencing projects are providing an unprecedented view of genetic variation. However, our ability to interpret genetic information to predict inherited phenotypes remains limited, in large part due to the extensive buffering of genomes, making most individual eukaryotic genes dispensable for life. To explore the extent to which genetic interactions reveal cellular function and contribute to complex phenotypes, and to discover the general principles of genetic networks, we used automated yeast genetics to construct a global genetic interaction network. RESULTS We tested most of the ~6000 genes in the yeast Saccharomyces cerevisiae for all possible pairwise genetic interactions, identifying nearly 1 million interactions, including ~550,000 negative and ~350,000 positive interactions, spanning ~90% of all yeast genes. Essential genes were network hubs, displaying five times as many interactions as nonessential genes. The set of genetic interactions or the genetic interaction profile for a gene provides a quantitative measure of function, and a global network based on genetic interaction profile similarity revealed a hierarchy of modules reflecting the functional architecture of a cell. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections associated with defects in cell cycle progression or cellular proteostasis. Importantly, the global network illustrates how coherent sets of negative or positive genetic interactions connect protein complex and pathways to map a functional wiring diagram of the cell. CONCLUSION A global genetic interaction network highlights the functional organization of a cell and provides a resource for predicting gene and pathway function. This network emphasizes the prevalence of genetic interactions and their potential to compound phenotypes associated with single mutations. Negative genetic interactions tend to connect functionally related genes and thus may be predicted using alternative functional information. Although less functionally informative, positive interactions may provide insights into general mechanisms of genetic suppression or resiliency. We anticipate that the ordered topology of the global genetic network, in which genetic interactions connect coherently within and between protein complexes and pathways, may be exploited to decipher genotype-to-phenotype relationships. A global network of genetic interaction profile similarities. (Left) Genes with similar genetic interaction profiles are connected in a global network, such that genes exhibiting more similar profiles are located closer to each other, whereas genes with less similar profiles are positioned farther apart. (Right) Spatial analysis of functional enrichment was used to identify and color network regions enriched for similar Gene Ontology bioprocess terms. We generated a global genetic interaction network for Saccharomyces cerevisiae, constructing more than 23 million double mutants, identifying about 550,000 negative and about 350,000 positive genetic interactions. This comprehensive network maps genetic interactions for essential gene pairs, highlighting essential genes as densely connected hubs. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections among gene pairs, rather than shared functionality. The global network illustrates how coherent sets of genetic interactions connect protein complex and pathway modules to map a functional wiring diagram of the cell.

Dosage suppression genetic interaction networks enhance functional wiring diagrams of the cell

Dosage suppression is a genetic interaction in which overproduction of one gene rescues a mutant phenotype of another gene. Although dosage suppression is known to map functional connections among genes, the extent to which it might illuminate global cellular functions is unclear. Here we analyze a network of interactions linking dosage suppressors to 437 essential genes in yeast. For 424 genes, we curated interactions from the literature. Analyses revealed that many dosage suppression interactions occur between functionally related genes and that the majority do not overlap with other types of genetic or physical interactions. To confirm the generality of these network properties, we experimentally identified dosage suppressors for 29 genes from pooled populations of temperature-sensitive mutant cells transformed with a high-copy molecular-barcoded open reading frame library, MoBY-ORF 2.0. We classified 87% of the 1,640 total interactions into four general types of suppression mechanisms, which provided insight into their relative frequencies. This work suggests that integrating the results of dosage suppression studies with other interaction networks could generate insights into the functional wiring diagram of a cell.

Exploring genetic suppression interactions on a global scale

A global genetic suppression network The genetic background of an organism can influence the overall effects of new genetic variants. Some mutations can amplify a deleterious phenotype, whereas others can suppress it. Starting with a literature survey and expanding into a genomewide assay, van Leeuwen et al. generated a large-scale suppression network in yeast. The data set reveals a set of general properties that can be used to predict suppression interactions. Furthermore, the study provides a template for extending suppression studies to other genes or to more complex organisms. Science, this issue p. 599 A large-scale study in yeast reveals how defects associated with a mutation in one gene can be compensated for by a second mutation in a suppressor gene. INTRODUCTION Genetic suppression occurs when the phenotypic defects caused by a mutated gene are rescued by a mutation in another gene. These genetic interactions can connect genes that work within the same pathway or biological process, providing new mechanistic insights into cellular function, or they can correct defects in gene expression or protein production. More generally, suppression interactions may play an important role in the genetics underlying human diseases, such as the diverse penetrance of Mendelian disease variants. Our ability to interpret personal genome sequences remains limited, in part, because we lack an understanding of how sequence variants interact in nonadditive ways to generate profound phenotypes, including genetic suppression. RATIONALE Genetic interactions, in which mutations in two different genes combine to generate an unexpected phenotype, may underlie a significant component of trait heritability. Although genetic interactions that compromise fitness, such as synthetic lethality, have been mapped extensively, suppression interactions have not been explored systematically. To understand the general principles of genetic suppression and to examine the extent to which these interactions reflect cellular function, we harnessed the powerful genetics of the budding yeast Saccharomyces cerevisiae to assemble aglobal network of genetic suppression interactions. RESULTS By analyzing hundreds of published papers, we assembled a network of genetic suppression interactions involving ~1300 different yeast genes and ~1800 unique interactions. Through automated genetic mapping and whole-genome sequencing, we also isolated an unbiased, experimental set of ~200 spontaneous suppressor mutations that correct the fitness defects of deletion or hypomorphic mutant alleles. Integrating these results yielded a global suppression network. The majority of suppression interactions identified novel gene-gene connections, thus providing new information about the functional wiring diagram of a cell. Most suppression pairs connected functionally related genes, including genes encoding members of the same pathway or complex. The functional enrichments observed for suppression gene pairs were several times as high as those found for other types of genetic interactions; this highlighted their discovery potential for assigning gene function. Our systematic suppression analysis also identified a prevalent allele-specific mechanism of suppression, whereby growth defects of hypomorphic alleles can be overcome by mutations that compromise either protein or mRNA degradation machineries. From whole-genome sequencing of suppressor strains, we also identified additional secondary mutations, the vast majority of which appeared to be random passenger mutations. However, a small subset of genes was enriched for secondary mutations, several of which did not affect growth rate but rather appeared to delay the onset of the stationary phase. This delay suggests that they are selected for under laboratory growth conditions because they increase cell abundance within a propagating population. CONCLUSION A global network of genetic suppression interactions highlights the major potential for systematic studies of suppression to map cellular function. Our findings allowed us to formulate and quantify the general mechanisms of genetic suppression, which has the potential to guide the identification of modifier genes affecting the penetrance of genetic traits, including human disease. Global analysis of genetic suppression. Genetic suppression interactions occur when the detrimental effects of a primary mutation can be overcome by a secondary mutation. Both literature-curated and experimentally derived suppression interactions were collected and yielded a genetic suppression network. This global network was enriched for functional relationships and defined distinct mechanistic classes of genetic suppression. Genetic suppression occurs when the phenotypic defects caused by a mutation in a particular gene are rescued by a mutation in a second gene. To explore the principles of genetic suppression, we examined both literature-curated and unbiased experimental data, involving systematic genetic mapping and whole-genome sequencing, to generate a large-scale suppression network among yeast genes. Most suppression pairs identified novel relationships among functionally related genes, providing new insights into the functional wiring diagram of the cell. In addition to suppressor mutations, we identified frequent secondary mutations,in a subset of genes, that likely cause a delay in the onset of stationary phase, which appears to promote their enrichment within a propagating population. These findings allow us to formulate and quantify general mechanisms of genetic suppression.

SGAtools: one-stop analysis and visualization of array-based genetic interaction screens

Screening genome-wide sets of mutants for fitness defects provides a simple but powerful approach for exploring gene function, mapping genetic networks and probing mechanisms of drug action. For yeast and other microorganisms with global mutant collections, genetic or chemical-genetic interactions can be effectively quantified by growing an ordered array of strains on agar plates as individual colonies, and then scoring the colony size changes in response to a genetic or environmental perturbation. To do so, requires efficient tools for the extraction and analysis of quantitative data. Here, we describe SGAtools (http://sgatools.ccbr.utoronto.ca), a web-based analysis system for designer genetic screens. SGAtools outlines a series of guided steps that allow the user to quantify colony sizes from images of agar plates, correct for systematic biases in the observations and calculate a fitness score relative to a control experiment. The data can also be visualized online to explore the colony sizes on individual plates, view the distribution of resulting scores, highlight genes with the strongest signal and perform Gene Ontology enrichment analysis.

A Comparative Genomic Approach for Identifying Synthetic Lethal Interactions in Human Cancer

Synthetic lethal interactions enable a novel approach for discovering specific genetic vulnerabilities in cancer cells that can be exploited for the development of therapeutics. Despite successes in model organisms such as yeast, discovering synthetic lethal interactions on a large scale in human cells remains a significant challenge. We describe a comparative genomic strategy for identifying cancer-relevant synthetic lethal interactions whereby candidate interactions are prioritized on the basis of genetic interaction data available in yeast, followed by targeted testing of candidate interactions in human cell lines. As a proof of principle, we describe two novel synthetic lethal interactions in human cells discovered by this approach, one between the tumor suppressor gene SMARCB1 and PSMA4, and another between alveolar soft-part sarcoma-associated ASPSCR1 and PSMC2. These results suggest therapeutic targets for cancers harboring mutations in SMARCB1 or ASPSCR1 and highlight the potential of a targeted, cross-species strategy for identifying synthetic lethal interactions relevant to human cancer.

Chemical Genomics-Based Antifungal Drug Discovery: Targeting Glycosylphosphatidylinositol (GPI) Precursor Biosynthesis

Steadily increasing antifungal drug resistance and persistent high rates of fungal-associated mortality highlight the dire need for the development of novel antifungals. Characterization of inhibitors of one enzyme in the GPI anchor pathway, Gwt1, has generated interest in the exploration of targets in this pathway for further study. Utilizing a chemical genomics-based screening platform referred to as the Candida albicans fitness test (CaFT), we have identified novel inhibitors of Gwt1 and a second enzyme in the glycosylphosphatidylinositol (GPI) cell wall anchor pathway, Mcd4. We further validate these targets using the model fungal organism Saccharomyces cerevisiae and demonstrate the utility of using the facile toolbox that has been compiled in this species to further explore target specific biology. Using these compounds as probes, we demonstrate that inhibition of Mcd4 as well as Gwt1 blocks the growth of a broad spectrum of fungal pathogens and exposes key elicitors of pathogen recognition. Interestingly, a strong chemical synergy is also observed by combining Gwt1 and Mcd4 inhibitors, mirroring the demonstrated synthetic lethality of combining conditional mutants of GWT1 and MCD4. We further demonstrate that the Mcd4 inhibitor M720 is efficacious in a murine infection model of systemic candidiasis. Our results establish Mcd4 as a promising antifungal target and confirm the GPI cell wall anchor synthesis pathway as a promising antifungal target area by demonstrating that effects of inhibiting it are more general than previously recognized.

Synthetic Genetic Array Analysis for Global Mapping of Genetic Networks in Yeast

Genetic interactions occur when mutant alleles of two or more genes collaborate to generate an unusual composite phenotype, one that would not be predicted based on the expected combined effects of the individual mutant alleles. Synthetic Genetic Array (SGA) methodology was developed to automate yeast genetic analysis and enable systematic genetic interaction studies. In its simplest form, SGA consists of a series of replica pinning steps, which enable the construction of haploid double mutants through mating and meiotic recombination. For example, a strain carrying a query mutation, such as a deletion allele of a nonessential gene or a conditional temperature sensitive allele of an essential gene, could be crossed to an input array of yeast mutants, such as the complete set of ~5,000 viable deletion mutants, to generate an output array of double mutants, that can be scored for genetic interactions based on estimates of cellular fitness derived from colony-size measurements. A simple quantitative measure of genetic interactions can be derived from colony size, which serves as a proxy for fitness. Furthermore, SGA can be applied in a variety of other contexts, such as Synthetic Dosage Lethality (SDL), in which a query mutation is crossed into an array of yeast strains, each of which overexpresses a different gene, thus making use of SGA to probe for gain-of-function phenotypes in specific genetic backgrounds. High-Content Screening (HCS) also integrates SGA to perform genome-wide screens for quantitative analysis of morphological phenotypes or pathway activity based upon fluorescent markers, extending genetic interaction analysis beyond fitness-based measurements. Genetic interaction studies offer insight into gene function, pathway structure, and buffering, and thus a complete genetic interaction network of yeast will generate a global functional wiring diagram for a eukaryotic cell.

Systematic analysis of complex genetic interactions

Trigenic interactions in yeast link bioprocesses To dissect the genotype-phenotype landscape of a cell, it is necessary to understand interactions between genes. Building on the digenic protein-protein interaction network, Kuzmin et al. created a trigenic landscape of yeast by using a synthetic genetic array (see the Perspective by Walhout). Triple-mutant analyses indicated that the majority of genes with trigenic associations functioned within the same biological processes. These converged on networks identified in the digenic interaction landscape. Although the overall effects were weaker for trigenic than for digenic interactions, trigenic interactions were more likely to bridge biological processes in the cell. Science, this issue p. eaao1729; see also p. 269 Trigenic interactions in yeast link bioprocesses are explored. INTRODUCTION Genetic interactions occur when mutations in different genes combine to result in a phenotype that is different from expectation based on those of the individual mutations. Negative genetic interactions occur when a combination of mutations leads to a fitness defect that is more exacerbated than expected. For example, synthetic lethality occurs when two mutations, neither of which is lethal on its own, generate an inviable double mutant. Alternatively, positive genetic interactions occur when genetic perturbations combine to generate a double mutant with a greater fitness than expected. Global digenic interaction studies have been useful for understanding the functional wiring diagram of the cell and may also provide insight into the genotype-to-phenotype relationship, which is important for tracking the missing heritability of human health and disease. Here we describe a network of higher-order trigenic interactions and explore its implications. RATIONALE Variation in phenotypic outcomes in different individuals is caused by genetic determinants that act as modifiers. Modifier loci are prevalent in human populations, but knowledge regarding how variants interact to modulate phenotype in different individuals is lacking. Similarly, in yeast, traits including conditional essentiality—in which certain genes are essential in one genetic background but nonessential in another—often result from an interplay of multiple modifier loci. Because complex modifiers may underlie the genetic basis of physiological states found in natural populations, it is critical to understand the landscape of higher-order genetic interactions. RESULTS To survey trigenic interactions, we designed query strains that sampled key features of the global digenic interaction network: (i) digenic interaction strength, (ii) average number of digenic interactions, and (iii) digenic interaction profile similarity. In total, we tested ~400,000 double and ~200,000 triple mutants for fitness defects and identified ~9500 digenic and ~3200 trigenic negative interactions. Although trigenic interactions tend to be weaker than digenic interactions, they were both enriched for functional relationships. About one-third of trigenic interactions identified “novel” connections that were not observed in our digenic control network, whereas the remaining approximately two-thirds of trigenic interactions “modified” a digenic interaction, suggesting that the global digenic interaction network is important for understanding the trigenic interaction network. Despite their functional enrichment, trigenic interactions also bridged distant bioprocesses. We estimate that the global trigenic interaction network is ~100 times as large as the global digenic network, highlighting the potential for complex genetic interactions to affect the biology of inheritance. CONCLUSION The extensive network of trigenic interactions and their ability to generate functionally diverse phenotypes suggest that higher-order genetic interactions may play a key role in the genotype-to-phenotype relationship, genome size, and speciation. Systematic analysis of trigenic interactions. We surveyed for trigenic interactions and found that they are ~100 times as prevalent as digenic interactions, often modify a digenic interaction, and connect functionally related genes as well as genes in more diverse bioprocesses (multicolored nodes). PPI, protein-protein interaction. To systematically explore complex genetic interactions, we constructed ~200,000 yeast triple mutants and scored negative trigenic interactions. We selected double-mutant query genes across a broad spectrum of biological processes, spanning a range of quantitative features of the global digenic interaction network and tested for a genetic interaction with a third mutation. Trigenic interactions often occurred among functionally related genes, and essential genes were hubs on the trigenic network. Despite their functional enrichment, trigenic interactions tended to link genes in distant bioprocesses and displayed a weaker magnitude than digenic interactions. We estimate that the global trigenic interaction network is ~100 times as large as the global digenic network, highlighting the potential for complex genetic interactions to affect the biology of inheritance, including the genotype-to-phenotype relationship.

Synthetic Genetic Array Analysis

Genetic interaction studies have been used to characterize unknown genes, assign membership in pathway and complex, and build a comprehensive functional map of a eukaryotic cell. Synthetic genetic array (SGA) methodology automates yeast genetic analysis and enables systematic mapping of genetic interactions. In its simplest form, SGA consists of a series of replica pinning steps that enable construction of haploid double mutants through automated mating and meiotic recombination. Using this method, a strain carrying a query mutation, such as a deletion allele of a nonessential gene or a conditional temperature-sensitive allele of an essential gene, can be crossed to an input array of yeast mutants, such as the complete set of approximately 5000 viable deletion mutants. The resulting output array of double mutants can be scored for genetic interactions based on estimates of cellular fitness derived from colony-size measurements. The SGA score method can be used to analyze large-scale data sets, whereas small-scale data sets can be analyzed using SGAtools, a simple web-based interface that includes all the necessary analysis steps for quantifying genetic interactions.

KIBRA (WWC1) Is a Metastasis Suppressor Gene Affected by Chromosome 5q Loss in Triple-Negative Breast Cancer

Summary Triple-negative breast cancers (TNBCs) display a complex spectrum of mutations and chromosomal aberrations. Chromosome 5q (5q) loss is detected in up to 70% of TNBCs, but little is known regarding the genetic drivers associated with this event. Here, we show somatic deletion of a region syntenic with human 5q33.2–35.3 in a mouse model of TNBC. Mechanistically, we identify KIBRA as a major factor contributing to the effects of 5q loss on tumor growth and metastatic progression. Re-expression of KIBRA impairs metastasis in vivo and inhibits tumorsphere formation by TNBC cells in vitro. KIBRA functions co-operatively with the protein tyrosine phosphatase PTPN14 to trigger mechanotransduction-regulated signals that inhibit the nuclear localization of oncogenic transcriptional co-activators YAP/TAZ. Our results argue that the selective advantage produced by 5q loss involves reduced dosage of KIBRA, promoting oncogenic functioning of YAP/TAZ in TNBC.