Anastasia Baryshnikova

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.

Genetic Interaction Networks: Toward an Understanding of Heritability

Understanding the relationship between the genotypes and phenotypes of individuals is key for identifying genetic variants responsible for disease and developing successful therapeutic strategies. Mapping the phenotypic effects of individual genetic variants and their combinations in human populations presents numerous practical and statistical challenges. However, model organisms, such as the budding yeast Saccharomyces cerevisiae, provide an incredible set of molecular tools and advanced technologies that should be able to efficiently perform this task. In particular, large-scale genetic interaction screens in yeast and other model systems have revealed common properties of genetic interaction networks, many of which appear to be maintained over extensive evolutionary distances. Indeed, despite relatively low conservation of individual genes and their pairwise interactions, the overall topology of genetic interaction networks and the connections between broad biological processes may be similar in most organisms. Taking advantage of these general principles should provide a fundamental basis for mapping and predicting genetic interaction networks in humans.

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.

Chromosome-Specific and Global Effects of Aneuploidy in Saccharomyces cerevisiae

Aneuploidy, an unbalanced karyotype in which one or more chromosomes are present in excess or reduced copy number, causes an array of known phenotypes including proteotoxicity, genomic instability, and slowed proliferation. However, the molecular consequences of aneuploidy are poorly understood and an unbiased investigation into aneuploid cell biology is lacking. We performed high-throughput screens for genes the deletion of which has a synthetic fitness cost in aneuploid Saccharomyces cerevisiae cells containing single extra chromosomes. This analysis identified genes that, when deleted, decrease the fitness of specific disomic strains as well as those that impair the proliferation of a broad range of aneuploidies. In one case, a chromosome-specific synthetic growth defect could be explained fully by the specific duplication of a single gene on the aneuploid chromosome, highlighting the ability of individual dosage imbalances to cause chromosome-specific phenotypes in aneuploid cells. Deletion of other genes, particularly those involved in protein transport, however, confers synthetic sickness on a broad array of aneuploid strains. Indeed, aneuploid cells, regardless of karyotype, exhibit protein secretion and cell-wall integrity defects. Thus, we were able to use this screen to identify novel cellular consequences of aneuploidy, dependent on both specific chromosome imbalances and caused by many different aneuploid karyotypes. Interestingly, the vast majority of cancer cells are highly aneuploid, so this approach could be of further use in identifying both karyotype-specific and nonspecific stresses exhibited by cancer cells as potential targets for the development of novel cancer therapeutics.

trappc11 is required for protein glycosylation in zebrafish and humans

The trappc11 mutation in zebrafish causes a stressed UPR, leading to fatty liver disease. This phenotype is not due to a trafficking defect but instead results from N-glycosylation defects. Hypoglycosylation and lipid accumulation are also found in patient cells with knockdown or mutated TRAPPC11 genes, suggesting a common etiology with zebrafish.

Functional annotation of chemical libraries across diverse biological processes.

Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells to a compound, revealing chemical-genetic interactions that can elucidate a compound’s mode of action. We developed a highly parallel and unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized, diagnostic mutant collection that is predictive for all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode sequencing protocol, enabling assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened 7 different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.

Exploratory Analysis of Biological Networks through Visualization, Clustering, and Functional Annotation in Cytoscape

Biological networks define how genes, proteins, and other cellular components interact with one another to carry out specific functions, providing a scaffold for understanding cellular organization. Although in-depth network analysis requires advanced mathematical and computational knowledge, a preliminary visual exploration of biological networks is accessible to anyone with basic computer skills. Visualization of biological networks is used primarily to examine network topology, identify functional modules, and predict gene functions based on gene connectivity within the network. Networks are excellent at providing a birds-eye view of data sets and have the power of illustrating complex ideas in simple and intuitive terms. In addition, they enable exploratory analysis and generation of new hypotheses, which can then be tested using rigorous statistical and experimental tools. This protocol describes a simple procedure for visualizing a biological network using the genetic interaction similarity network for Saccharomyces cerevisiae as an example. The visualization procedure described here relies on the open-source network visualization software Cytoscape and includes detailed instructions on formatting and loading the data, clustering networks, and overlaying functional annotations.

Unification of Protein Abundance Datasets Yields a Quantitative Saccharomyces cerevisiae Proteome

Protein activity is the ultimate arbiter of function in most cellular pathways, and protein concentration is fundamentally connected to protein action. While the proteome of yeast has been subjected to thexa0most comprehensive analysis of any eukaryote, existing datasets are difficult to compare, and therexa0is no consensus abundance value for each protein.xa0We evaluated 21 quantitative analyses of the S.xa0cerevisiae proteome, normalizing and converting all measurements of protein abundance into the intuitive measurement of absolute molecules per cell. We estimate the cellular abundance of 92% of the proteins in the yeast proteome and assess the variation in each abundance measurement. Using our protein abundance dataset, we find that a global response to diverse environmental stresses is not detected at the level of protein abundance, we find that protein tags have only a modest effect on protein abundance, and we identify proteins that are differentially regulated at the mRNA abundance, mRNA translation, and protein abundance levels.

ER-associated retrograde SNAREs and the Dsl1 complex mediate an alternative, Sey1p-independent homotypic ER fusion pathway.

SNAREs and the Dsl1 complex mediate an alternative, Sey1p-independent homotypic endoplasmic reticulum (ER) fusion pathway in yeast. When both pathways and the reticulons are simultaneously disrupted, cells are inviable. This demonstrates that homotypic ER fusion is an essential process in yeast and that the Dsl1 complex has vesicle trafficking-independent functions.