Global Genetic Networks and the Genotype-to-Phenotype Relationship.

Genetic interactions identify combinations of genetic variants that impinge on phenotype. With whole-genome sequence information available for thousands of individuals within a species, a major outstanding issue concerns the interpretation of allelic combinations of genes underlying inherited traits. In this Review, we discuss how large-scale analyses in model systems have illuminated the general principles and phenotypic impact of genetic interactions. We focus on studies in budding yeast, including the mapping of a global genetic network. We emphasize how information gained from work in yeast translates to other systems, and how a global genetic network not only annotates gene function but also provides new insights into the genotype-to-phenotype relationship.

Translocon Declogger Ste24 Protects against IAPP Oligomer-Induced Proteotoxicity.

Aggregates of human islet amyloid polypeptide (IAPP) in the pancreas of patients with type 2 diabetes (T2D) are thought to contribute to ß cell dysfunction and death. To understand how IAPP harms cells and how this might be overcome, we created a yeast model of IAPP toxicity. Ste24, an evolutionarily conserved protease that was recently reported to degrade peptides stuck within the translocon between the cytoplasm and the endoplasmic reticulum, was the strongest suppressor of IAPP toxicity. By testing variants of the human homolog, ZMPSTE24, with varying activity levels, the rescue of IAPP toxicity proved to be directly proportional to the declogging efficiency. Clinically relevant ZMPSTE24 variants identified in the largest database of exomes sequences derived from T2D patients were characterized using the yeast model, revealing 14 partial loss-of-function variants, which were enriched among diabetes patients over 2-fold. Thus, clogging of the translocon by IAPP oligomers may contribute to ß cell failure.

K29-linked free polyubiquitin chains affect ribosome biogenesis and direct ribosomal proteins to the intranuclear quality control compartment.

Ribosome assembly requires precise coordination between the production and assembly of ribosomal components. Mutations in ribosomal proteins that inhibit the assembly process or ribosome function are often associated with ribosomopathies, some of which are linked to defects in proteostasis. In this study, we examine the interplay between several yeast proteostasis enzymes, including deubiquitylases (DUBs) Ubp2 and Ubp14, and E3 ligases Ufd4 and Hul5, and we explore their roles in the regulation of the cellular levels of K29-linked unanchored polyubiquitin (polyUb) chains. Accumulating K29-linked unanchored polyUb chains associate with maturing ribosomes to disrupt their assembly, activate the ribosome assembly stress response (RASTR), and lead to the sequestration of ribosomal proteins at the intranuclear quality control compartment (INQ). These findings reveal the physiological relevance of INQ and provide insights into mechanisms of cellular toxicity associated with ribosomopathies.

Widespread Expansion of Protein Interaction Capabilities by Alternative Splicing.

While alternative splicing is known to diversify the functional characteristics of some genes, the extent to which protein isoforms globally contribute to functional complexity on a proteomic scale remains unknown. To address this systematically, we cloned full-length open reading frames of alternatively spliced transcripts for a large number of human genes and used protein-protein interaction profiling to functionally compare hundreds of protein isoform pairs. The majority of isoform pairs share less than 50% of their interactions. In the global context of interactome network maps, alternative isoforms tend to behave like distinct proteins rather than minor variants of each other. Interaction partners specific to alternative isoforms tend to be expressed in a highly tissue-specific manner and belong to distinct functional modules. Our strategy, applicable to other functional characteristics, reveals a widespread expansion of protein interaction capabilities through alternative splicing and suggests that many alternative "isoforms" are functionally divergent (i.e., "functional alloforms").

Timer-based proteomic profiling of the ubiquitin-proteasome system reveals a substrate receptor of the GID ubiquitin ligase.

Selective protein degradation by the ubiquitin-proteasome system (UPS) is involved in all cellular processes. However, the substrates and specificity of most UPS components are not well understood. Here we systematically characterized the UPS in Saccharomyces cerevisiae. Using fluorescent timers, we determined how loss of individual UPS components affects yeast proteome turnover, detecting phenotypes for 76% of E2, E3, and deubiquitinating enzymes. We exploit this dataset to gain insights into N-degron pathways, which target proteins carrying N-terminal degradation signals. We implicate Ubr1, an E3 of the Arg/N-degron pathway, in targeting mitochondrial proteins processed by the mitochondrial inner membrane protease. Moreover, we identify Ylr149c/Gid11 as a substrate receptor of the glucose-induced degradation-deficient (GID) complex, an E3 of the Pro/N-degron pathway. Our results suggest that Gid11 recognizes proteins with N-terminal threonines, expanding the specificity of the GID complex. This resource of potential substrates and relationships between UPS components enables exploring functions of selective protein degradation.

Repression of essential cell cycle genes increases cellular fitness.

A network of transcription factors (TFs) coordinates transcription with cell cycle events in eukaryotes. Most TFs in the network are phosphorylated by cyclin-dependent kinase (CDK), which limits their activities during the cell cycle. Here, we investigate the physiological consequences of disrupting CDK regulation of the paralogous repressors Yhp1 and Yox1 in yeast. Blocking Yhp1/Yox1 phosphorylation increases their levels and decreases expression of essential cell cycle regulatory genes which, unexpectedly, increases cellular fitness in optimal growth conditions. Using synthetic genetic interaction screens, we find that Yhp1/Yox1 mutations improve the fitness of mutants with mitotic defects, including condensin mutants. Blocking Yhp1/Yox1 phosphorylation simultaneously accelerates the G1/S transition and delays mitotic exit, without decreasing proliferation rate. This mitotic delay partially reverses the chromosome segregation defect of condensin mutants, potentially explaining their increased fitness when combined with Yhp1/Yox1 phosphomutants. These findings reveal how altering expression of cell cycle genes leads to a redistribution of cell cycle timing and confers a fitness advantage to cells.

Skp, Cullin, F-box (SCF)-Met30 and SCF-Cdc4-Mediated Proteolysis of CENP-A Prevents Mislocalization of CENP-A for Chromosomal Stability in Budding Yeast.

Restricting the localization of the histone H3 variant CENP-A (Cse4 in yeast, CID in flies) to centromeres is essential for faithful chromosome segregation. Mislocalization of CENP-A leads to chromosomal instability (CIN) in yeast, fly and human cells. Overexpression and mislocalization of CENP-A has been observed in many cancers and this correlates with increased invasiveness and poor prognosis. Yet genes that regulate CENP-A levels and localization under physiological conditions have not been defined. In this study we used a genome-wide genetic screen to identify essential genes required for Cse4 homeostasis to prevent its mislocalization for chromosomal stability. We show that two Skp, Cullin, F-box (SCF) ubiquitin ligases with the evolutionarily conserved F-box proteins Met30 and Cdc4 interact and cooperatively regulate proteolysis of endogenous Cse4 and prevent its mislocalization for faithful chromosome segregation under physiological conditions. The interaction of Met30 with Cdc4 is independent of the D domain, which is essential for their homodimerization and ubiquitination of other substrates. The requirement for both Cdc4 and Met30 for ubiquitination is specifc for Cse4; and a common substrate for Cdc4 and Met30 has not previously been described. Met30 is necessary for the interaction between Cdc4 and Cse4, and defects in this interaction lead to stabilization and mislocalization of Cse4, which in turn contributes to CIN. We provide the first direct link between Cse4 mislocalization to defects in kinetochore structure and show that SCF-mediated proteolysis of Cse4 is a major mechanism that prevents stable maintenance of Cse4 at non-centromeric regions, thus ensuring faithful chromosome segregation. In summary, we have identified essential pathways that regulate cellular levels of endogenous Cse4 and shown that proteolysis of Cse4 by SCF-Met30/Cdc4 prevents mislocalization and CIN in unperturbed cells.

A method for benchmarking genetic screens reveals a predominant mitochondrial bias.

We present FLEX (Functional evaluation of experimental perturbations), a pipeline that leverages several functional annotation resources to establish reference standards for benchmarking human genome-wide CRISPR screen data and methods for analyzing them. FLEX provides a quantitative measurement of the functional information captured by a given gene-pair dataset and a means to explore the diversity of functions captured by the input dataset. We apply FLEX to analyze data from the diverse cell line screens generated by the DepMap project. We identify a predominant mitochondria-associated signal within co-essentiality networks derived from these data and explore the basis of this signal. Our analysis and time-resolved CRISPR screens in a single cell line suggest that the variable phenotypes associated with mitochondria genes across cells may reflect screen dynamics and protein stability effects rather than genetic dependencies. We characterize this functional bias and demonstrate its relevance for interpreting differential hits in any CRISPR screening context. More generally, we demonstrate the utility of the FLEX pipeline for performing robust comparative evaluations of CRISPR screens or methods for processing them.

Global analysis of genetic circuitry and adaptive mechanisms enabling resistance to the azole antifungal drugs.

Invasive fungal infections caused by the pathogen Candida albicans have transitioned from a rare curiosity to a major cause of human mortality. This is in part due to the emergence of resistance to the limited number of antifungals available to treat fungal infections. Azoles function by targeting the biosynthesis of ergosterol, a key component of the fungal cell membrane. Loss-of-function mutations in the ergosterol biosynthetic gene ERG3 mitigate azole toxicity and enable resistance that depends upon fungal stress responses. Here, we performed a genome-wide synthetic genetic array screen in Saccharomyces cerevisiae to map ERG3 genetic interactors and uncover novel circuitry important for azole resistance. We identified nine genes that enabled erg3-mediated azole resistance in the model yeast and found that only two of these genes had a conserved impact on resistance in C. albicans. Further, we screened a C. albicans homozygous deletion mutant library and identified 13 genes for which deletion enhances azole susceptibility. Two of the genes, RGD1 and PEP8, were also important for azole resistance acquired by diverse mechanisms. We discovered that loss of function of retrograde transport protein Pep8 overwhelms the functional capacity of the stress response regulator calcineurin, thereby abrogating azole resistance. To identify the mechanism through which the GTPase activator protein Rgd1 enables azole resistance, we selected for mutations that restore resistance in strains lacking Rgd1. Whole genome sequencing uncovered parallel adaptive mechanisms involving amplification of both chromosome 7 and a large segment of chromosome 3. Overexpression of a transporter gene on the right portion of chromosome 3, NPR2, was sufficient to enable azole resistance in the absence of Rgd1. Thus, we establish a novel mechanism of adaptation to drug-induced stress, define genetic circuitry underpinning azole resistance, and illustrate divergence in resistance circuitry over evolutionary time.

Pathway-based discovery of genetic interactions in breast cancer.

Breast cancer is the second largest cause of cancer death among U.S. women and the leading cause of cancer death among women worldwide. Genome-wide association studies (GWAS) have identified several genetic variants associated with susceptibility to breast cancer, but these still explain less than half of the estimated genetic contribution to the disease. Combinations of variants (i.e. genetic interactions) may play an important role in breast cancer susceptibility. However, due to a lack of statistical power, the current tests for genetic interactions from GWAS data mainly leverage prior knowledge to focus on small sets of genes or SNPs that are known to have an association with breast cancer. Thus, many genetic interactions, particularly among novel variants, remain understudied. Reverse-genetic interaction screens in model organisms have shown that genetic interactions frequently cluster into highly structured motifs, where members of the same pathway share similar patterns of genetic interactions. Based on this key observation, we recently developed a method called BridGE to search for such structured motifs in genetic networks derived from GWAS studies and identify pathway-level genetic interactions in human populations. We applied BridGE to six independent breast cancer cohorts and identified significant pathway-level interactions in five cohorts. Joint analysis across all five cohorts revealed a high confidence consensus set of genetic interactions with support in multiple cohorts. The discovered interactions implicated the glutathione conjugation, vitamin D receptor, purine metabolism, mitotic prometaphase, and steroid hormone biosynthesis pathways as major modifiers of breast cancer risk. Notably, while many of the pathways identified by BridGE show clear relevance to breast cancer, variants in these pathways had not been previously discovered by traditional single variant association tests, or single pathway enrichment analysis that does not consider SNP-SNP interactions.

Identifying pathogenicity of human variants via paralog-based yeast complementation.

To better understand the health implications of personal genomes, we now face a largely unmet challenge to identify functional variants within disease-associated genes. Functional variants can be identified by trans-species complementation, e.g., by failure to rescue a yeast strain bearing a mutation in an orthologous human gene. Although orthologous complementation assays are powerful predictors of pathogenic variation, they are available for only a few percent of human disease genes. Here we systematically examine the question of whether complementation assays based on paralogy relationships can expand the number of human disease genes with functional variant detection assays. We tested over 1,000 paralogous human-yeast gene pairs for complementation, yielding 34 complementation relationships, of which 33 (97%) were novel. We found that paralog-based assays identified disease variants with success on par with that of orthology-based assays. Combining all homology-based assay results, we found that complementation can often identify pathogenic variants outside the homologous sequence region, presumably because of global effects on protein folding or stability. Within our search space, paralogy-based complementation more than doubled the number of human disease genes with a yeast-based complementation assay for disease variation.

An extended set of yeast-based functional assays accurately identifies human disease mutations.

We can now routinely identify coding variants within individual human genomes. A pressing challenge is to determine which variants disrupt the function of disease-associated genes. Both experimental and computational methods exist to predict pathogenicity of human genetic variation. However, a systematic performance comparison between them has been lacking. Therefore, we developed and exploited a panel of 26 yeast-based functional complementation assays to measure the impact of 179 variants (101 disease- and 78 non-disease-associated variants) from 22 human disease genes. Using the resulting reference standard, we show that experimental functional assays in a 1-billion-year diverged model organism can identify pathogenic alleles with significantly higher precision and specificity than current computational methods.

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 in the presence of a compound, revealing chemical-genetic interactions that can elucidate a compound's mode of action. We developed a highly parallel, 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 the 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 seven 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.

Errata: Functional annotation of chemical libraries across diverse biological processes.

This corrects the article DOI: 10.1038/nchembio.2436.

Errata: Functional annotation of chemical libraries across diverse biological processes.

This corrects the article DOI: 10.1038/nchembio.2436.

Predicting bioprocess targets of chemical compounds through integration of chemical-genetic and genetic interactions.

Chemical-genetic interactions-observed when the treatment of mutant cells with chemical compounds reveals unexpected phenotypes-contain rich functional information linking compounds to their cellular modes of action. To systematically identify these interactions, an array of mutants is challenged with a compound and monitored for fitness defects, generating a chemical-genetic interaction profile that provides a quantitative, unbiased description of the cellular function(s) perturbed by the compound. Genetic interactions, obtained from genome-wide double-mutant screens, provide a key for interpreting the functional information contained in chemical-genetic interaction profiles. Despite the utility of this approach, integrative analyses of genetic and chemical-genetic interaction networks have not been systematically evaluated. We developed a method, called CG-TARGET (Chemical Genetic Translation via A Reference Genetic nETwork), that integrates large-scale chemical-genetic interaction screening data with a genetic interaction network to predict the biological processes perturbed by compounds. In a recent publication, we applied CG-TARGET to a screen of nearly 14,000 chemical compounds in Saccharomyces cerevisiae, integrating this dataset with the global S. cerevisiae genetic interaction network to prioritize over 1500 compounds with high-confidence biological process predictions for further study. We present here a formal description and rigorous benchmarking of the CG-TARGET method, showing that, compared to alternative enrichment-based approaches, it achieves similar or better accuracy while substantially improving the ability to control the false discovery rate of biological process predictions. Additional investigation of the compatibility of chemical-genetic and genetic interaction profiles revealed that one-third of observed chemical-genetic interactions contributed to the highest-confidence biological process predictions and that negative chemical-genetic interactions overwhelmingly formed the basis of these predictions. We also present experimental validations of CG-TARGET-predicted tubulin polymerization and cell cycle progression inhibitors. Our approach successfully demonstrates the use of genetic interaction networks in the high-throughput functional annotation of compounds to biological processes.

Budding Yeast Rif1 Controls Genome Integrity by Inhibiting rDNA Replication.

The Rif1 protein is a negative regulator of DNA replication initiation in eukaryotes. Here we show that budding yeast Rif1 inhibits DNA replication initiation at the rDNA locus. Absence of Rif1, or disruption of its interaction with PP1/Glc7 phosphatase, leads to more intensive rDNA replication. The effect of Rif1-Glc7 on rDNA replication is similar to that of the Sir2 deacetylase, and the two would appear to act in the same pathway, since the rif1Δ sir2Δ double mutant shows no further increase in rDNA replication. Loss of Rif1-Glc7 activity is also accompanied by an increase in rDNA repeat instability that again is not additive with the effect of sir2Δ. We find, in addition, that the viability of rif1Δ cells is severely compromised in combination with disruption of the MRX or Ctf4-Mms22 complexes, both of which are implicated in stabilization of stalled replication forks. Significantly, we show that removal of the rDNA replication fork barrier (RFB) protein Fob1, alleviation of replisome pausing by deletion of the Tof1/Csm3 complex, or a large deletion of the rDNA repeat array all rescue this synthetic growth defect of rif1Δ cells lacking in addition either MRX or Ctf4-Mms22 activity. These data suggest that the repression of origin activation by Rif1-Glc7 is important to avoid the deleterious accumulation of stalled replication forks at the rDNA RFB, which become lethal when fork stability is compromised. Finally, we show that Rif1-Glc7, unlike Sir2, has an important effect on origin firing outside of the rDNA locus that serves to prevent activation of the DNA replication checkpoint. Our results thus provide insights into a mechanism of replication control within a large repetitive chromosomal domain and its importance for the maintenance of genome stability. These findings may have important implications for metazoans, where large blocks of repetitive sequences are much more common.

Uncoupling of GTP hydrolysis from eIF6 release on the ribosome causes Shwachman-Diamond syndrome.

Removal of the assembly factor eukaryotic initiation factor 6 (eIF6) is critical for late cytoplasmic maturation of 60S ribosomal subunits. In mammalian cells, the current model posits that eIF6 release is triggered following phosphorylation of Ser 235 by activated protein kinase C. In contrast, genetic studies in yeast indicate a requirement for the ortholog of the SBDS (Shwachman-Bodian-Diamond syndrome) gene that is mutated in the inherited leukemia predisposition disorder Shwachman-Diamond syndrome (SDS). Here, by isolating late cytoplasmic 60S ribosomal subunits from Sbds-deleted mice, we show that SBDS and the GTPase elongation factor-like 1 (EFL1) directly catalyze eIF6 removal in mammalian cells by a mechanism that requires GTP binding and hydrolysis by EFL1 but not phosphorylation of eIF6 Ser 235. Functional analysis of disease-associated missense variants reveals that the essential role of SBDS is to tightly couple GTP hydrolysis by EFL1 on the ribosome to eIF6 release. Furthermore, complementary NMR spectroscopic studies suggest unanticipated mechanistic parallels between this late step in 60S maturation and aspects of bacterial ribosome disassembly. Our findings establish a direct role for SBDS and EFL1 in catalyzing the translational activation of ribosomes in all eukaryotes, and define SDS as a ribosomopathy caused by uncoupling GTP hydrolysis from eIF6 release.

Genetic Interactions Implicating Postreplicative Repair in Okazaki Fragment Processing.

Ubiquitination of the replication clamp proliferating cell nuclear antigen (PCNA) at the conserved residue lysine (K)164 triggers postreplicative repair (PRR) to fill single-stranded gaps that result from stalled DNA polymerases. However, it has remained elusive as to whether cells engage PRR in response to replication defects that do not directly impair DNA synthesis. To experimentally address this question, we performed synthetic genetic array (SGA) analysis with a ubiquitination-deficient K164 to arginine (K164R) mutant of PCNA against a library of S. cerevisiae temperature-sensitive alleles. The SGA signature of the K164R allele showed a striking correlation with profiles of mutants deficient in various aspects of lagging strand replication, including rad27Δ and elg1Δ. Rad27 is the primary flap endonuclease that processes 5' flaps generated during lagging strand replication, whereas Elg1 has been implicated in unloading PCNA from chromatin. We observed chronic ubiquitination of PCNA at K164 in both rad27Δ and elg1Δ mutants. Notably, only rad27Δ cells exhibited a decline in cell viability upon elimination of PRR pathways, whereas elg1Δ mutants were not affected. We further provide evidence that K164 ubiquitination suppresses replication stress resulting from defective flap processing during Okazaki fragment maturation. Accordingly, ablation of PCNA ubiquitination increased S phase checkpoint activation, indicated by hyperphosphorylation of the Rad53 kinase. Furthermore, we demonstrate that alternative flap processing by overexpression of catalytically active exonuclease 1 eliminates PCNA ubiquitination. This suggests a model in which unprocessed flaps may directly participate in PRR signaling. Our findings demonstrate that PCNA ubiquitination at K164 in response to replication stress is not limited to DNA synthesis defects but extends to DNA processing during lagging strand replication.

Systematic mapping of genetic interaction networks.

Genetic interactions influencing a phenotype of interest can be identified systematically using libraries of genetic tools that perturb biological systems in a defined manner. Systematic screens conducted in the yeast Saccharomyces cerevisiae have identified thousands of genetic interactions and provided insight into the global structure of biological networks. Techniques enabling systematic genetic interaction mapping have been extended to other single-celled organisms, the bacteria Escherichia coli and the yeast Schizosaccharomyces pombe, opening the way to comparative investigations of interaction networks. Genetic interaction screens in Caenorhabditis elegans, Drosophila melanogaster, and mammalian models are helping to improve our understanding of metazoan-specific signaling pathways. Together, our emerging knowledge of the genetic wiring diagrams of eukaryotic and prokaryotic cells is providing a new understanding of the relationship between genotype and phenotype.