Quantitative genetic interaction mapping using the E-MAP approach

A multilineage screen identifies actionable synthetic lethal interactions in human cancers

Cancers are driven by alterations in diverse genes, creating dependencies that can be therapeutically targeted. However, many genetic dependencies have proven inconsistent across tumors. Here we describe SCHEMATIC, a strategy to identify a core network of highly penetrant, actionable genetic interactions. First, fundamental cellular processes are perturbed by systematic combinatorial knockouts across tumor lineages, identifying 1,805 synthetic lethal interactions (95% unreported). Interactions are then analyzed by hierarchical pooling, revealing that half segregate reliably by tissue type or biomarker status (51%) and a substantial minority are penetrant across lineages (34%). Interactions converge on 49 multigene systems, including MAPK signaling and BAF transcriptional regulatory complexes, which become essential on disruption of polymerases. Some 266 interactions translate to robust biomarkers of drug sensitivity, including frequent genetic alterations in the KDM5C/6A histone demethylases, which sensitize to inhibition of TIPARP (PARP7). SCHEMATIC offers a context-aware, data-driven approach to match genetic alterations to targeted therapies.

Developing a Novel and Optimized Yeast Model for Human VDAC Research

The voltage-dependent anion-selective channel (VDAC) plays a crucial role in mitochondrial function, and VDAC paralogs are considered to ensure the differential integration of mitochondrial functions with cellular activities. Heterologous expression of VDAC paralogs in the yeast Saccharomyces cerevisiae por1Δ mutant cells is often employed in studies of functional differentiation of human VDAC paralogs (hVDAC1-hVDAC3) regardless of the presence of the yeast second VDAC paralog (yVDAC2) encoded by the POR2 gene. Here, we applied por1Δpor2Δ double mutants and relevant por1Δ and por2Δ single mutants, derived from two S. cerevisiae strains (M3 and BY4741) differing distinctly in auxotrophic markers but commonly used for heterologous expression of hVDAC paralogs, to study the effect of the presence of yVDAC2 and cell genotypes including MET15, the latter resulting in a low level of hydrogen sulfide (H2S), on the complementation potential of heterologous expression of hVDAC paralogs. The results indicated that yVDAC2 might contribute to the complementation potential. Moreover, the possibility to reverse the growth phenotype through heterologous expression of hVDAC paralogs in the presence of the applied yeast cell genotype backgrounds was particularly diverse for hVDAC3 and depended on the presence of the protein cysteine residues and expression of MET15. Thus, the difference in the set of auxotrophic markers in yeast cells, including MET15 contributing to the H2S level, may create a different background for the modification of cysteine residues in hVDAC3 and thus explain the different effects of the presence and deletion of cysteine residues in hVDAC3 in M3-Δpor1Δpor2 and BY4741-Δpor1Δpor2 cells. The different phenotypes displayed by BY4741-Δpor1Δpor2 and M3-Δpor1Δpor2 cells following heterologous expression of a particular hVDAC paralog make them valuable models for the study of human VDAC proteins, especially hVDAC3, as a representative of VDAC protein sensitive to the reduction-oxidation state.

The Cross-Regulation Between Set1, Clr4, and Lsd1/2 in Schizosaccharomyces pombe

Eukaryotic chromatin is organized into either silenced heterochromatin or relaxed euchromatin regions, which controls the accessibility of transcriptional machinery and thus regulates gene expression. In fission yeast, Schizosaccharomyces pombe, Set1 is the sole H3K4 methyltransferase and is mainly enriched at the promoters of actively transcribed genes. In contrast, Clr4 methyltransferase initiates H3K9 methylation, which has long been regarded as a hallmark of heterochromatic silencing. Lsd1 and Lsd2 are two highly conserved H3K4 and H3K9 demethylases. As these histone-modifying enzymes perform critical roles in maintaining histone methylation patterns and, consequently, gene expression profiles, cross-regulations among these enzymes are part of the complex regulatory networks. Thus, elucidating the mechanisms that govern their signaling and mutual regulations remains crucial. Here, we demonstrated that C-terminal truncation mutants, lsd1-ΔHMG and lsd2-ΔC, do not compromise the integrity of the Lsd1/2 complex but impair their chromatin-binding capacity at the promoter region of target genomic loci. We identified protein-protein interactions between Lsd1/2 and Raf2 or Swd2, which are the subunits of the Clr4 complex (CLRC) and Set1-associated complex (COMPASS), respectively. We showed that Clr4 and Set1 modulate the protein levels of Lsd1 and Lsd2 in opposite ways through the ubiquitin-proteasome-dependent pathway. During heat stress, the protein levels of Lsd1 and Lsd2 are upregulated in a Set1-dependent manner. The increase in protein levels is crucial for differential gene expression under stress conditions. Together, our results support a cross-regulatory model by which Set1 and Clr4 methyltransferases control the protein levels of Lsd1/2 demethylases to shape the dynamic chromatin landscape.

Predicting and explaining the impact of genetic disruptions and interactions on organismal viability

MOTIVATION

Existing computational models can predict single- and double-mutant fitness but they do have limitations. First, they are often tested via evaluation metrics that are inappropriate for imbalanced datasets. Second, all of them only predict a binary outcome (viable or not, and negatively interacting or not). Third, most are uninterpretable black box machine learning models.

RESULTS

Budding yeast datasets were used to develop high-performance Multinomial Regression (MN) models capable of predicting the impact of single, double and triple genetic disruptions on viability. These models are interpretable and give realistic non-binary predictions and can predict negative genetic interactions (GIs) in triple-gene knockouts. They are based on a limited set of gene features and their predictions are influenced by the probability of target gene participating in molecular complexes or pathways. Furthermore, the MN models have utility in other organisms such as fission yeast, fruit flies and humans, with the single gene fitness MN model being able to distinguish essential genes necessary for cell-autonomous viability from those required for multicellular survival. Finally, our models exceed the performance of previous models, without sacrificing interpretability.

AVAILABILITY AND IMPLEMENTATION

All code and processed datasets used to generate results and figures in this manuscript are available at our Github repository at https://github.com/KISRDevelopment/cell_viability_paper. The repository also contains a link to the GI prediction website that lets users search for GIs using the MN models.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Gradients in gene essentiality reshape antibacterial research

Essential genes encode the processes that are necessary for life. Until recently, commonly applied binary classifications left no space between essential and non-essential genes. In this review, we frame bacterial gene essentiality in the context of genetic networks. We explore how the quantitative properties of gene essentiality are influenced by the nature of the encoded process, environmental conditions and genetic background, including a strain's distinct evolutionary history. The covered topics have important consequences for antibacterials, which inhibit essential processes. We argue that the quantitative properties of essentiality can thus be used to prioritize antibacterial cellular targets and desired spectrum of activity in specific infection settings. We summarize our points with a case study on the core essential genome of the cystic fibrosis pathobiome and highlight avenues for targeted antibacterial development.

Systems-level effects of allosteric perturbations to a model molecular switch

Molecular switch proteins whose cycling between states is controlled by opposing regulators^1,2 are central to biological signal transduction. As switch proteins function within highly connected interaction networks³, the fundamental question arises of how functional specificity is achieved when different processes share common regulators. Here we show that functional specificity of the small GTPase switch protein Gsp1 in Saccharomyces cerevisiae (the homologue of the human protein RAN)⁴ is linked to differential sensitivity of biological processes to different kinetics of the Gsp1 (RAN) switch cycle. We make 55 targeted point mutations to individual protein interaction interfaces of Gsp1 (RAN) and show through quantitative genetic⁵ and physical interaction mapping that Gsp1 (RAN) interface perturbations have widespread cellular consequences. Contrary to expectation, the cellular effects of the interface mutations group by their biophysical effects on kinetic parameters of the GTPase switch cycle and not by the targeted interfaces. Instead, we show that interface mutations allosterically tune the GTPase cycle kinetics. These results suggest a model in which protein partner binding, or post-translational modifications at distal sites, could act as allosteric regulators of GTPase switching. Similar mechanisms may underlie regulation by other GTPases, and other biological switches. Furthermore, our integrative platform to determine the quantitative consequences of molecular perturbations may help to explain the effects of disease mutations that target central molecular switches.

Chemical-genetic interrogation of RNA polymerase mutants reveals structure-function relationships and physiological tradeoffs

The multi-subunit bacterial RNA polymerase (RNAP) and its associated regulators carry out transcription and integrate myriad regulatory signals. Numerous studies have interrogated RNAP mechanism, and RNAP mutations drive Escherichia coli adaptation to many health- and industry-relevant environments, yet a paucity of systematic analyses hampers our understanding of the fitness trade-offs from altering RNAP function. Here, we conduct a chemical-genetic analysis of a library of RNAP mutants. We discover phenotypes for non-essential insertions, show that clustering mutant phenotypes increases their predictive power for drawing functional inferences, and demonstrate that some RNA polymerase mutants both decrease average cell length and prevent killing by cell-wall targeting antibiotics. Our findings demonstrate that RNAP chemical-genetic interactions provide a general platform for interrogating structure-function relationships in vivo and for identifying physiological trade-offs of mutations, including those relevant for disease and biotechnology. This strategy should have broad utility for illuminating the role of other important protein complexes.

Identification of Synthetic Lethal Interactions Using High-Throughput, Arrayed CRISPR/Cas9-Based Platforms

Over the past two decades, the concept of synthetic lethality (SL) that queries genetic relationships between gene pairs has gradually emerged as one of the best strategies to selectively eliminate cancer cells. Some of the most successful approaches to identify synthetic lethal interactions (SLIs) were largely dependent on pooled screening formats that require heavy validation in order to mitigate false positives. Here, we describe a high-throughput method to identify SLIs using CRISPR-based strategy that covers, high-throughput production of plasmid DNA preparations, lentiviral production, and subsequent cellular transduction using single guide RNAs (sgRNAs). This method could be adopted to query hundreds of SLIs. As an example, we describe the methods associated with building an interaction map for DNA damage and repair (DDR) genes. The use of multiwell plates and image-based quantification allows a comparative measurement of SLIs at a high-resolution on a one-by-one basis. Furthermore, this scalable, arrayed CRISPR screening method can be applied to multiple cancer cell types, and genes of interest, resulting in new functional discoveries that can be exploited therapeutically.

Genetic interaction mapping informs integrative structure determination of protein complexes

Determining structures of protein complexes is crucial for understanding cellular functions. Here, we describe an integrative structure determination approach that relies on in vivo measurements of genetic interactions. We construct phenotypic profiles for point mutations crossed against gene deletions or exposed to environmental perturbations, followed by converting similarities between two profiles into an upper bound on the distance between the mutated residues. We determine the structure of the yeast histone H3-H4 complex based on ~500,000 genetic interactions of 350 mutants. We then apply the method to subunits Rpb1-Rpb2 of yeast RNA polymerase II and subunits RpoB-RpoC of bacterial RNA polymerase. The accuracy is comparable to that based on chemical cross-links; using restraints from both genetic interactions and cross-links further improves model accuracy and precision. The approach provides an efficient means to augment integrative structure determination with in vivo observations.

A Selective Autophagy Pathway for Phase-Separated Endocytic Protein Deposits

Autophagy eliminates cytoplasmic content selected by autophagy receptors, which link cargo to the membrane-bound autophagosomal ubiquitin-like protein Atg8/LC3. Here, we report a selective autophagy pathway for protein condensates formed by endocytic proteins in yeast. In this pathway, the endocytic protein Ede1 functions as a selective autophagy receptor. Distinct domains within Ede1 bind Atg8 and mediate phase separation into condensates. Both properties are necessary for an Ede1-dependent autophagy pathway for endocytic proteins, which differs from regular endocytosis and does not involve other known selective autophagy receptors but requires the core autophagy machinery. Cryo-electron tomography of Ede1-containing condensates, at the plasma membrane and in autophagic bodies, shows a phase-separated compartment at the beginning and end of the Ede1-mediated selective autophagy route. Our data suggest a model for autophagic degradation of macromolecular protein complexes by the action of intrinsic autophagy receptors.

Pyphe, a python toolbox for assessing microbial growth and cell viability in high-throughput colony screens

Microbial fitness screens are a key technique in functional genomics. We present an all-in-one solution, pyphe, for automating and improving data analysis pipelines associated with large-scale fitness screens, including image acquisition and quantification, data normalisation, and statistical analysis. Pyphe is versatile and processes fitness data from colony sizes, viability scores from phloxine B staining or colony growth curves, all obtained with inexpensive transilluminating flatbed scanners. We apply pyphe to show that the fitness information contained in late endpoint measurements of colony sizes is similar to maximum growth slopes from time series. We phenotype gene-deletion strains of fission yeast in 59,350 individual fitness assays in 70 conditions, revealing that colony size and viability provide complementary, independent information. Viability scores obtained from quantifying the redness of phloxine-stained colonies accurately reflect the fraction of live cells within colonies. Pyphe is user-friendly, open-source and fully documented, illustrated by applications to diverse fitness analysis scenarios.

A genetic interaction map centered on cohesin reveals auxiliary factors involved in sister chromatid cohesion in S. cerevisiae

Eukaryotic chromosomes are replicated in interphase and the two newly duplicated sister chromatids are held together by the cohesin complex and several cohesin auxiliary factors. Sister chromatid cohesion is essential for accurate chromosome segregation during mitosis, yet has also been implicated in other processes, including DNA damage repair, transcription and DNA replication. To assess how cohesin and associated factors functionally interconnect and coordinate with other cellular processes, we systematically mapped the genetic interactions of 17 cohesin genes centered on quantitative growth measurements of >52,000 gene pairs in the budding yeast Saccharomyces cerevisiae Integration of synthetic genetic interactions unveiled a cohesin functional map that constitutes 373 genetic interactions, revealing novel functional connections with post-replication repair, microtubule organization and protein folding. Accordingly, we show that the microtubule-associated protein Irc15 and the prefoldin complex members Gim3, Gim4 and Yke2 are new factors involved in sister chromatid cohesion. Our genetic interaction map thus provides a unique resource for further identification and functional interrogation of cohesin proteins. Since mutations in cohesin proteins have been associated with cohesinopathies and cancer, it may also help in identifying cohesin interactions relevant in disease etiology.

A Quantitative Genetic Interaction Map of HIV Infection

We have developed a platform for quantitative genetic interaction mapping using viral infectivity as a functional readout and constructed a viral host-dependency epistasis map (vE-MAP) of 356 human genes linked to HIV function, comprising >63,000 pairwise genetic perturbations. The vE-MAP provides an expansive view of the genetic dependencies underlying HIV infection and can be used to identify drug targets and study viral mutations. We found that the RNA deadenylase complex, CNOT, is a central player in the vE-MAP and show that knockout of CNOT1, 10, and 11 suppressed HIV infection in primary T cells by upregulating innate immunity pathways. This phenotype was rescued by deletion of IRF7, a transcription factor regulating interferon-stimulated genes, revealing a previously unrecognized host signaling pathway involved in HIV infection. The vE-MAP represents a generic platform that can be used to study the global effects of how different pathogens hijack and rewire the host during infection.

Splicing Defects of the Profilin Gene Alter Actin Dynamics in an S. pombe SMN Mutant

Spinal muscular atrophy (SMA) is a devastating motor neuron disorder caused by mutations in the survival motor neuron (SMN) gene. It remains unclear how SMN deficiency leads to the loss of motor neurons. By screening Schizosaccharomyces pombe, we found that the growth defect of an SMN mutant can be alleviated by deletion of the actin-capping protein subunit gene acp1⁺. We show that SMN mutated cells have splicing defects in the profilin gene, which thus directly hinder actin cytoskeleton homeostasis including endocytosis and cytokinesis. We conclude that deletion of acp1⁺ in an SMN mutant background compensates for actin cytoskeleton alterations by restoring redistribution of actin monomers between different types of cellular actin networks. Our data reveal a direct correlation between an impaired function of SMN in snRNP assembly and defects in actin dynamics. They also point to important common features in the pathogenic mechanism of SMA and ALS.

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Splicing defects in the profilin gene in an S. pombe SMN mutant

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SMN mutant contains excessively polymerized actin

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Altered actin dynamics in the SMN mutant hinders endocytosis and cytokinesis

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Deletion of the acp1 subunit restores actin dynamics in the SMN mutant

Mapping genetic interactions in cancer: a road to rational combination therapies

The discovery of synthetic lethal interactions between poly (ADP-ribose) polymerase (PARP) inhibitors and BRCA genes, which are involved in homologous recombination, led to the approval of PARP inhibition as a monotherapy for patients with BRCA1/2-mutated breast or ovarian cancer. Studies following the initial observation of synthetic lethality demonstrated that the reach of PARP inhibitors is well beyond just BRCA1/2 mutants. Insights into the mechanisms of action of anticancer drugs are fundamental for the development of targeted monotherapies or rational combination treatments that will synergize to promote cancer cell death and overcome mechanisms of resistance. The development of targeted therapeutic agents is premised on mapping the physical and functional dependencies of mutated genes in cancer. An important part of this effort is the systematic screening of genetic interactions in a variety of cancer types. Until recently, genetic-interaction screens have relied either on the pairwise perturbations of two genes or on the perturbation of genes of interest combined with inhibition by commonly used anticancer drugs. Here, we summarize recent advances in mapping genetic interactions using targeted, genome-wide, and high-throughput genetic screens, and we discuss the therapeutic insights obtained through such screens. We further focus on factors that should be considered in order to develop a robust analysis pipeline. Finally, we discuss the integration of functional interaction data with orthogonal methods and suggest that such approaches will increase the reach of genetic-interaction screens for the development of rational combination therapies.

Regulation of Skn7-dependent, oxidative stress-induced genes by the RNA polymerase II-CTD phosphatase, Fcp1, and Mediator kinase subunit, Cdk8, in yeast

Fcp1 is a protein phosphatase that facilitates transcription elongation and termination by dephosphorylating the C-terminal domain of RNA polymerase II. High-throughput genetic screening and gene expression profiling of fcp1 mutants revealed a novel connection to Cdk8, the Mediator complex kinase subunit, and Skn7, a key transcription factor in the oxidative stress response pathway. Briefly, Skn7 was enriched as a regulator of genes whose mRNA levels were altered in fcp1 and cdk8Δ mutants and was required for the suppression of fcp1 mutant growth defects by loss of CDK8 under oxidative stress conditions. Targeted analysis revealed that mutating FCP1 decreased Skn7 mRNA and protein levels as well as its association with target gene promoters but paradoxically increased the mRNA levels of Skn7-dependent oxidative stress-induced genes (TRX2 and TSA1) under basal and induced conditions. The latter was in part recapitulated via chemical inhibition of transcription in WT cells, suggesting that a combination of transcriptional and posttranscriptional effects underscored the increased mRNA levels of TRX2 and TSA1 observed in the fcp1 mutant. Interestingly, loss of CDK8 robustly normalized the mRNA levels of Skn7-dependent genes in the fcp1 mutant background and also increased Skn7 protein levels by preventing its turnover. As such, our work suggested that loss of CDK8 could overcome transcriptional and/or posttranscriptional alterations in the fcp1 mutant through its regulatory effect on Skn7. Furthermore, our work also implicated FCP1 and CDK8 in the broader response to environmental stressors in yeast.

Network dynamics of the yeast methyltransferome

Sulfur assimilation and the biosynthesis of methionine, cysteine and S-adenosylmethionine (SAM) are critical to life. As a cofactor, SAM is required for the activity of most methyltransferases (MTases) and as such has broad impact on diverse cellular processes. Assigning function to MTases remains a challenge however, as many MTases are partially redundant, they often have multiple cellular roles and these activities can be condition-dependent. To address these challenges, we performed a systematic synthetic genetic analysis of all pairwise MTase double mutations in normal and stress conditions (16°C, 37°C, and LiCl) resulting in an unbiased comprehensive overview of the complexity and plasticity of the methyltransferome. Based on this network, we performed biochemical analysis of members of the histone H3K4 COMPASS complex and the phospholipid methyltransferase OPI3 to reveal a new role for a phospholipid methyltransferase in mediating histone methylation (H3K4) which underscores a potential link between lipid homeostasis and histone methylation. Our findings provide a valuable resource to study methyltransferase function, the dynamics of the methyltransferome, genetic crosstalk between biological processes and the dynamics of the methyltransferome in response to cellular stress.

Yeast genetic interaction screens in the age of CRISPR/Cas

The ease of performing both forward and reverse genetics in Saccharomyces cerevisiae, along with its stable haploid state and short generation times, has made this budding yeast the consummate model eukaryote for genetics. The major advantage of using budding yeast for reverse genetics is this organism's highly efficient homology-directed repair, allowing for precise genome editing simply by introducing DNA with homology to the chromosomal target. Although plasmid- and PCR-based genome editing tools are quite efficient, they depend on rare spontaneous DNA breaks near the target sequence. Consequently, they can generate only one genomic edit at a time, and the edit must be associated with a selectable marker. However, CRISPR/Cas technology is efficient enough to permit markerless and multiplexed edits in a single step. These features have made CRISPR/Cas popular for yeast strain engineering in synthetic biology and metabolic engineering applications, but it has not been widely employed for genetic screens. In this review, we critically examine different methods to generate multi-mutant strains in systematic genetic interaction screens and discuss the potential of CRISPR/Cas to supplement or improve on these methods.

Ultrahigh-Density Screens for Genome-Wide Yeast EMAPs in a Single Plate

Systematic measurements of genetic interactions have been used to classify gene functions and to categorize genes into protein complexes, functional pathways and biological processes. This protocol describes how to perform a high-throughput genetic interaction screen in S. cerevisiae using a variant of epistatic miniarray profiles (E-MAP) in which the fitnesses of 6144 colonies are measured simultaneously. We also describe the computational methods to analyze the resulting data.

Genetic analysis reveals functions of atypical polyubiquitin chains

Although polyubiquitin chains linked through all lysines of ubiquitin exist, specific functions are well-established only for lysine-48 and lysine-63 linkages in Saccharomyces cerevisiae. To uncover pathways regulated by distinct linkages, genetic interactions between a gene deletion library and a panel of lysine-to-arginine ubiquitin mutants were systematically identified. The K11R mutant had strong genetic interactions with threonine biosynthetic genes. Consistently, we found that K11R mutants import threonine poorly. The K11R mutant also exhibited a strong genetic interaction with a subunit of the anaphase-promoting complex (APC), suggesting a role in cell cycle regulation. K11-linkages are important for vertebrate APC function, but this was not previously described in yeast. We show that the yeast APC also modifies substrates with K11-linkages in vitro, and that those chains contribute to normal APC-substrate turnover in vivo. This study reveals comprehensive genetic interactomes of polyubiquitin chains and characterizes the role of K11-chains in two biological pathways.

Synthetic Lethal Networks for Precision Oncology: Promises and Pitfalls

Synthetic lethal interactions, in which the simultaneous loss of function of two genes produces a lethal phenotype, are being explored as a means to therapeutically exploit cancer-specific vulnerabilities and expand the scope of precision oncology. Currently, three Food and Drug Administration-approved drugs work by targeting the synthetic lethal interaction between BRCA1/2 and PARP. This review examines additional efforts to discover networks of synthetic lethal interactions and discusses both challenges and opportunities regarding the translation of new synthetic lethal interactions into the clinic.

Genome maintenance functions of the INO80 chromatin remodeller

Chromatin modification is conserved in all eukaryotes and is required to facilitate and regulate DNA-templated processes. For example, chromatin manipulation, such as histone post-translational modification and nucleosome positioning, play critical roles in genome stability pathways. The INO80 chromatin-remodelling complex, which regulates the abundance and positioning of nucleosomes, is particularly important for proper execution of inducible responses to DNA damage. This review discusses the participation and activity of the INO80 complex in DNA repair and cell cycle checkpoint pathways, with emphasis on the Saccharomyces cerevisiae model system. Furthermore, the role of ATM/ATR kinases, central regulators of DNA damage signalling, in the regulation of INO80 function will be reviewed. In addition, emerging themes of chromatin remodelling in mitotic stability pathways and chromosome segregation will be introduced. These studies are critical to understanding the dynamic chromatin landscape that is rapidly and reversibly modified to maintain the integrity of the genome.This article is part of the themed issue 'Chromatin modifiers and remodellers in DNA repair and signalling'.

Genetic Interaction Score (S-Score) Calculation, Clustering, and Visualization of Genetic Interaction Profiles for Yeast

This protocol describes computational analysis of genetic interaction screens, ranging from data capture (plate imaging) to downstream analyses. Plate imaging approaches using both digital camera and office flatbed scanners are included, along with a protocol for the extraction of colony size measurements from the resulting images. A commonly used genetic interaction scoring method, calculation of the S-score, is discussed. These methods require minimal computer skills, but some familiarity with MATLAB and Linux/Unix is a plus. Finally, an outline for using clustering and visualization software for analysis of resulting data sets is provided.

High-Throughput Quantitative Genetic Interaction Mapping in the Fission Yeast Schizosaccharomyces pombe

Epistasis mapping, in which the phenotype that emerges from combining pairs of mutations is measured quantitatively, is a powerful tool for unbiased study of gene function. When performed at a large scale, this approach has been used to assign function to previously uncharacterized genes, define functional modules and pathways, and study their cross talk. These experiments rely heavily on methods for rapid sampling of binary combinations of mutant alleles by systematic generation of a series of double mutants. Epistasis mapping technologies now exist in various model systems. Here we provide an overview of different epistasis mapping technologies, including the pombe epistasis mapper (PEM) system designed for the collection of quantitative genetic interaction data in fission yeast Schizosaccharomyces pombe Comprising a series of high-throughput selection steps for generation and characterization of double mutants, the PEM system has provided insight into a wide range of biological processes as well as facilitated evolutionary analysis of genetic interactomes across different species.

Systematic Gene-to-Phenotype Arrays: A High-Throughput Technique for Molecular Phenotyping

We have developed a highly parallel strategy, systematic gene-to-phenotype arrays (SGPAs), to comprehensively map the genetic landscape driving molecular phenotypes of interest. By this approach, a complete yeast genetic mutant array is crossed with fluorescent reporters and imaged on membranes at high density and contrast. Importantly, SGPA enables quantification of phenotypes that are not readily detectable in ordinary genetic analysis of cell fitness. We benchmark SGPA by examining two fundamental biological phenotypes: first, we explore glucose repression, in which SGPA identifies a requirement for the Mediator complex and a role for the CDK8/kinase module in regulating transcription. Second, we examine selective protein quality control, in which SGPA identifies most known quality control factors along with U34 tRNA modification, which acts independently of proteasomal degradation to limit misfolded protein production. Integration of SGPA with other fluorescent readouts will enable genetic dissection of a wide range of biological pathways and conditions.

The Dfm1 Derlin Is Required for ERAD Retrotranslocation of Integral Membrane Proteins

Endoplasmic reticulum (ER)-associated degradation (ERAD) removes misfolded proteins from the ER membrane and lumen by the ubiquitin-proteasome pathway. Retrotranslocation of ubiquitinated substrates to the cytosol is a universal feature of ERAD that requires the Cdc48 AAA-ATPase. Despite intense efforts, the mechanism of ER exit, particularly for integral membrane (ERAD-M) substrates, has remained unclear. Using a self-ubiquitinating substrate (SUS), which undergoes normal retrotranslocation independently of known ERAD factors, and the new SPOCK (single plate orf compendium kit) micro-library to query all yeast genes, we found the rhomboid derlin Dfm1 was required for retrotranslocation of both HRD and DOA ERAD pathway integral membrane substrates. Dfm1 recruited Cdc48 to the ER membrane with its unique SHP motifs, and it catalyzed substrate extraction through its conserved rhomboid motifs. Surprisingly, dfm1Δ can undergo rapid suppression, restoring wild-type ERAD-M. This unexpected suppression explained earlier studies ruling out Dfm1, and it revealed an ancillary ERAD-M retrotranslocation pathway requiring Hrd1.

Protein Moonlighting Revealed by Noncatalytic Phenotypes of Yeast Enzymes

A single gene can partake in several biological processes, and therefore gene deletions can lead to different-sometimes unexpected-phenotypes. However, it is not always clear whether such pleiotropy reflects the loss of a unique molecular activity involved in different processes or the loss of a multifunctional protein. Here, using Saccharomyces cerevisiae metabolism as a model, we systematically test the null hypothesis that enzyme phenotypes depend on a single annotated molecular function, namely their catalysis. We screened a set of carefully selected genes by quantifying the contribution of catalysis to gene deletion phenotypes under different environmental conditions. While most phenotypes were explained by loss of catalysis, slow growth was readily rescued by a catalytically inactive protein in about one-third of the enzymes tested. Such noncatalytic phenotypes were frequent in the Alt1 and Bat2 transaminases and in the isoleucine/valine biosynthetic enzymes Ilv1 and Ilv2, suggesting novel "moonlighting" activities in these proteins. Furthermore, differential genetic interaction profiles of gene deletion and catalytic mutants indicated that ILV1 is functionally associated with regulatory processes, specifically to chromatin modification. Our systematic study shows that gene loss phenotypes and their genetic interactions are frequently not driven by the loss of an annotated catalytic function, underscoring the moonlighting nature of cellular metabolism.

Computational Approaches to Identify Genetic Interactions for Cancer Therapeutics

The development of improved cancer therapies is frequently cited as an urgent unmet medical need. Here we describe how genetic interactions are being therapeutically exploited to identify novel targeted treatments for cancer. We discuss the current methodologies that use 'omics data to identify genetic interactions, in particular focusing on synthetic sickness lethality (SSL) and synthetic dosage lethality (SDL). We describe the experimental and computational approaches undertaken both in humans and model organisms to identify these interactions. Finally we discuss some of the identified targets with licensed drugs, inhibitors in clinical trials or with compounds under development.

Learning directed acyclic graphs from large-scale genomics data

In this paper, we consider the problem of learning the genetic interaction map, i.e., the topology of a directed acyclic graph (DAG) of genetic interactions from noisy double-knockout (DK) data. Based on a set of well-established biological interaction models, we detect and classify the interactions between genes. We propose a novel linear integer optimization program called the Genetic-Interactions-Detector (GENIE) to identify the complex biological dependencies among genes and to compute the DAG topology that matches the DK measurements best. Furthermore, we extend the GENIE program by incorporating genetic interaction profile (GI-profile) data to further enhance the detection performance. In addition, we propose a sequential scalability technique for large sets of genes under study, in order to provide statistically significant results for real measurement data. Finally, we show via numeric simulations that the GENIE program and the GI-profile data extended GENIE (GI-GENIE) program clearly outperform the conventional techniques and present real data results for our proposed sequential scalability technique.

Interrogation of γ-tubulin alleles using high-resolution fitness measurements reveals a distinct cytoplasmic function in spindle alignment

γ-Tubulin has a well-established role in nucleating the assembly of microtubules, yet how phosphorylation regulates its activity remains unclear. Here, we use a time-resolved, fitness-based SGA approach to compare two γ-tubulin alleles, and find that the genetic interaction profile of γtub-Y362E is enriched in spindle positioning and cell polarity genes relative to that of γtub-Y445D, which is enriched in genes involved in spindle assembly and stability. In γtub-Y362E cells, we find a defect in spindle alignment and an increase in the number of astral microtubules at both spindle poles. Our results suggest that the γtub-Y362E allele is a separation-of-function mutation that reveals a role for γ-tubulin phospho-regulation in spindle alignment. We propose that phosphorylation of the evolutionarily conserved Y362 residue of budding yeast γ-tubulin contributes to regulating the number of astral microtubules associated with spindle poles, and promoting efficient pre-anaphase spindle alignment.

Vps13-Mcp1 interact at vacuole-mitochondria interfaces and bypass ER-mitochondria contact sites

Interorganelle membrane contacts work in a networked fashion to allow exchange of metabolites throughout the cell. In yeast, mitochondria–vacuole contacts act redundantly with ER–mitochondria contacts. We show that the yeast mitochondrial protein Mcp1 binds the endosomal/vacuolar protein Vps13 to mediate the physiological function of vacuole–mitochondria contacts.

Membrane contact sites between endoplasmic reticulum (ER) and mitochondria, mediated by the ER–mitochondria encounter structure (ERMES) complex, are critical for mitochondrial homeostasis and cell growth. Defects in ERMES can, however, be bypassed by point mutations in the endosomal protein Vps13 or by overexpression of the mitochondrial protein Mcp1. How this bypass operates remains unclear. Here we show that the mitochondrial outer membrane protein Mcp1 functions in the same pathway as Vps13 by recruiting it to mitochondria and promoting its association to vacuole–mitochondria contacts. Our findings support a model in which Mcp1 and Vps13 work as functional effectors of vacuole–mitochondria contact sites, while tethering is mediated by other factors, including Vps39. Tethered and functionally active vacuole–mitochondria interfaces then compensate for the loss of ERMES-mediated ER–mitochondria contact sites.

Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation

Metabolic networks, which are mathematical representations of organismal metabolism, are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses, such as interrogation of phylogenetic relationships within the network, can provide further guidance on the modification of metabolic circuitries. Chlamydomonas reinhardtii, a biofuel relevant green alga that has retained key genes with plant, animal, and protist affinities, serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational levels. Here, using detailed topological and functional analyses, coupled with transcriptomics studies on a metabolic network that we have reconstructed for C. reinhardtii, we show that network connectivity has a significant concordance with the co-conservation of genes; however, a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast, genes with predicted synthetic interactions, or genes involved in coupled reactions, show significant enrichment for both shorter and longer phylogenetic distances. Based on our results, we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically, while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii's network in dealing with varied environmental challenges that the species may face. The defined evolutionary constraints within the network, which identify important pairings of genes in metabolism, may offer guidance on synthetic biology approaches to optimize the production of desirable metabolites.

PP2A Controls Genome Integrity by Integrating Nutrient-Sensing and Metabolic Pathways with the DNA Damage Response

Mec1^ATR mediates the DNA damage response (DDR), integrating chromosomal signals and mechanical stimuli. We show that the PP2A phosphatases, ceramide-activated enzymes, couple cell metabolism with the DDR. Using genomic screens, metabolic analysis, and genetic and pharmacological studies, we found that PP2A attenuates the DDR and that three metabolic circuits influence the DDR by modulating PP2A activity. Irc21, a putative cytochrome b5 reductase that promotes the condensation reaction generating dihydroceramides (DHCs), and Ppm1, a PP2A methyltransferase, counteract the DDR by activating PP2A; conversely, the nutrient-sensing TORC1-Tap42 axis sustains DDR activation by inhibiting PP2A. Loss-of-function mutations in IRC21, PPM1, and PP2A and hyperactive tap42 alleles rescue mec1 mutants. Ceramides synergize with rapamycin, a TORC1 inhibitor, in counteracting the DDR. Hence, PP2A integrates nutrient-sensing and metabolic pathways to attenuate the Mec1^ATR response. Our observations imply that metabolic changes affect genome integrity and may help with exploiting therapeutic options and repositioning known drugs.

Kinase Assay-Linked Phosphoproteomics: Discovery of Direct Kinase Substrates

Dissection of direct kinase-substrate relationships provides invaluable information about phosphorylation pathways and can highlight both pathogenic mechanisms and possible drug targets for diseases in which abnormal kinase activity is linked to onset and progression. Here, we describe a mass spectrometry-based strategy to define the direct substrates of a kinase of interest. The kinase assay-linked phosphoproteomics approach examines putative kinase substrates both in vitro and in vivo to produce a list of highly confident substrates.

Combined Action of Histone Reader Modules Regulates NuA4 Local Acetyltransferase Function but Not Its Recruitment on the Genome

Recognition of histone marks by reader modules is thought to be at the heart of epigenetic mechanisms. These protein domains are considered to function by targeting regulators to chromosomal loci carrying specific histone modifications. This is important for proper gene regulation as well as propagation of epigenetic information. The NuA4 acetyltransferase complex contains two of these reader modules, an H3K4me3-specific plant homeodomain (PHD) within the Yng2 subunit and an H3K36me2/3-specific chromodomain in the Eaf3 subunit. While each domain showed a close functional interaction with the respective histone mark that it recognizes, at the biochemical level, genetic level (as assessed with epistatic miniarray profile screens), and phenotypic level, cells with the combined loss of both readers showed greatly enhanced phenotypes. Chromatin immunoprecipitation coupled with next-generation sequencing experiments demonstrated that the Yng2 PHD specifically directs H4 acetylation near the transcription start site of highly expressed genes, while Eaf3 is important downstream on the body of the genes. Strikingly, the recruitment of the NuA4 complex to these loci was not significantly affected. Furthermore, RNA polymerase II occupancy was decreased only under conditions where both PHD and chromodomains were lost, generally in the second half of the gene coding regions. Altogether, these results argue that methylated histone reader modules in NuA4 are not responsible for its recruitment to the promoter or coding regions but, rather, are required to orient its acetyltransferase catalytic site to the methylated histone 3-bearing nucleosomes in the surrounding chromatin, cooperating to allow proper transition from transcription initiation to elongation.

Chemogenetic profiling identifies RAD17 as synthetically lethal with checkpoint kinase inhibition

Chemical inhibitors of the checkpoint kinases have shown promise in the treatment of cancer, yet their clinical utility may be limited by a lack of molecular biomarkers to identify specific patients most likely to respond to therapy. To this end, we screened 112 known tumor suppressor genes for synthetic lethal interactions with inhibitors of the CHEK1 and CHEK2 checkpoint kinases. We identified eight interactions, including the Replication Factor C (RFC)-related protein RAD17. Clonogenic assays in RAD17 knockdown cell lines identified a substantial shift in sensitivity to checkpoint kinase inhibition (3.5-fold) as compared to RAD17 wild-type. Additional evidence for this interaction was found in a large-scale functional shRNA screen of over 100 genotyped cancer cell lines, in which CHEK1/2 mutant cell lines were unexpectedly sensitive to RAD17 knockdown. This interaction was widely conserved, as we found that RAD17 interacts strongly with checkpoint kinases in the budding yeast Saccharomyces cerevisiae. In the setting of RAD17 knockdown, CHEK1/2 inhibition was found to be synergistic with inhibition of WEE1, another pharmacologically relevant checkpoint kinase. Accumulation of the DNA damage marker γH2AX following chemical inhibition or transient knockdown of CHEK1, CHEK2 or WEE1 was magnified by knockdown of RAD17. Taken together, our data suggest that CHEK1 or WEE1 inhibitors are likely to have greater clinical efficacy in tumors with RAD17 loss-of-function.

Nonsense-mediated decay regulates key components of homologous recombination

Cells frequently experience DNA damage that requires repair by homologous recombination (HR). Proteins involved in HR are carefully coordinated to ensure proper and efficient repair without interfering with normal cellular processes. In Saccharomyces cerevisiae, Rad55 functions in the early steps of HR and is regulated in response to DNA damage through phosphorylation by the Mec1 and Rad53 kinases of the DNA damage response. To further identify regulatory processes that target HR, we performed a high-throughput genetic interaction screen with RAD55 phosphorylation site mutants. Genes involved in the mRNA quality control process, nonsense-mediated decay (NMD), were found to genetically interact with rad55 phospho-site mutants. Further characterization revealed that RAD55 transcript and protein levels are regulated by NMD. Regulation of HR by NMD extends to multiple targets beyond RAD55, including RAD51, RAD54 and RAD57 Finally, we demonstrate that loss of NMD results in an increase in recombination rates and resistance to the DNA damaging agent methyl methanesulfonate, suggesting this pathway negatively regulates HR under normal growth conditions.

Synthetic Genetic Arrays: Automation of Yeast Genetics

Genome-sequencing efforts have led to great strides in the annotation of protein-coding genes and other genomic elements. The current challenge is to understand the functional role of each gene and how genes work together to modulate cellular processes. Genetic interactions define phenotypic relationships between genes and reveal the functional organization of a cell. Synthetic genetic array (SGA) methodology automates yeast genetics and enables large-scale and systematic mapping of genetic interaction networks in the budding yeast,Saccharomyces cerevisiae SGA facilitates construction of an output array of double mutants from an input array of single mutants through a series of replica pinning steps. Subsequent analysis of genetic interactions from SGA-derived mutants relies on accurate quantification of colony size, which serves as a proxy for fitness. Since its development, SGA has given rise to a variety of other experimental approaches for functional profiling of the yeast genome and has been applied in a multitude of other contexts, such as genome-wide screens for synthetic dosage lethality and integration with high-content screening for systematic assessment of morphology defects. SGA-like strategies can also be implemented similarly in a number of other cell types and organisms, includingSchizosaccharomyces pombe,Escherichia coli, Caenorhabditis elegans, and human cancer cell lines. The genetic networks emerging from these studies not only generate functional wiring diagrams but may also play a key role in our understanding of the complex relationship between genotype and phenotype.

Translation of Genotype to Phenotype by a Hierarchy of Cell Subsystems

Accurately translating genotype to phenotype requires accounting for the functional impact of genetic variation at many biological scales. Here we present a strategy for genotype-phenotype reasoning based on existing knowledge of cellular subsystems. These subsystems and their hierarchical organization are defined by the Gene Ontology or a complementary ontology inferred directly from previously published datasets. Guided by the ontology's hierarchical structure, we organize genotype data into an "ontotype," that is, a hierarchy of perturbations representing the effects of genetic variation at multiple cellular scales. The ontotype is then interpreted using logical rules generated by machine learning to predict phenotype. This approach substantially outperforms previous, non-hierarchical methods for translating yeast genotype to cell growth phenotype, and it accurately predicts the growth outcomes of two new screens of 2,503 double gene knockouts impacting DNA repair or nuclear lumen. Ontotypes also generalize to larger knockout combinations, setting the stage for interpreting the complex genetics of disease.

Strategies for target identification of antimicrobial natural products

Despite a pervasive decline in natural product research at many pharmaceutical companies over the last two decades, natural products have undeniably been a prolific and unsurpassed source for new lead antibacterial compounds.

Cell-based screens and phenomics with fission yeast

Next-generation sequencing approaches have considerably advanced our understanding of genome function and regulation. However, the knowledge of gene function and complex cellular processes remains a challenge and bottleneck in biological research. Phenomics is a rapidly emerging area, which seeks to rigorously characterize all phenotypes associated with genes or gene variants. Such high-throughput phenotyping under different conditions can be a potent approach toward gene function. The fission yeast Schizosaccharomyces pombe (S. pombe) is a proven eukaryotic model organism that is increasingly used for genomewide screens and phenomic assays. In this review, we highlight current large-scale, cell-based approaches used with S. pombe, including computational colony-growth measurements, genetic interaction screens, parallel profiling using barcodes, microscopy-based cell profiling, metabolomic methods and transposon mutagenesis. These diverse methods are starting to offer rich insights into the relationship between genotypes and phenotypes.

Prediction of Genetic Interactions Using Machine Learning and Network Properties

A genetic interaction (GI) is a type of interaction where the effect of one gene is modified by the effect of one or several other genes. These interactions are important for delineating functional relationships among genes and their corresponding proteins, as well as elucidating complex biological processes and diseases. An important type of GI - synthetic sickness or synthetic lethality - involves two or more genes, where the loss of either gene alone has little impact on cell viability, but the combined loss of all genes leads to a severe decrease in fitness (sickness) or cell death (lethality). The identification of GIs is an important problem for it can help delineate pathways, protein complexes, and regulatory dependencies. Synthetic lethal interactions have important clinical and biological significance, such as providing therapeutically exploitable weaknesses in tumors. While near systematic high-content screening for GIs is possible in single cell organisms such as yeast, the systematic discovery of GIs is extremely difficult in mammalian cells. Therefore, there is a great need for computational approaches to reliably predict GIs, including synthetic lethal interactions, in these organisms. Here, we review the state-of-the-art approaches, strategies, and rigorous evaluation methods for learning and predicting GIs, both under general (healthy/standard laboratory) conditions and under specific contexts, such as diseases.

Differential genetic interactions of yeast stress response MAPK pathways

Genetic interaction screens have been applied with great success in several organisms to study gene function and the genetic architecture of the cell. However, most studies have been performed under optimal growth conditions even though many functional interactions are known to occur under specific cellular conditions. In this study, we have performed a large-scale genetic interaction analysis in Saccharomyces cerevisiae involving approximately 49 × 1,200 double mutants in the presence of five different stress conditions, including osmotic, oxidative and cell wall-altering stresses. This resulted in the generation of a differential E-MAP (or dE-MAP) comprising over 250,000 measurements of conditional interactions. We found an extensive number of conditional genetic interactions that recapitulate known stress-specific functional associations. Furthermore, we have also uncovered previously unrecognized roles involving the phosphatase regulator Bud14, the histone methylation complex COMPASS and membrane trafficking complexes in modulating the cell wall integrity pathway. Finally, the osmotic stress differential genetic interactions showed enrichment for genes coding for proteins with conditional changes in phosphorylation but not for genes with conditional changes in gene expression. This suggests that conditional genetic interactions are a powerful tool to dissect the functional importance of the different response mechanisms of the cell.

Genetic interaction mapping reveals a role for the SWI/SNF nucleosome remodeler in spliceosome activation in fission yeast

Although numerous regulatory connections between pre-mRNA splicing and chromatin have been demonstrated, the precise mechanisms by which chromatin factors influence spliceosome assembly and/or catalysis remain unclear. To probe the genetic network of pre-mRNA splicing in the fission yeast Schizosaccharomyces pombe, we constructed an epistatic mini-array profile (E-MAP) and discovered many new connections between chromatin and splicing. Notably, the nucleosome remodeler SWI/SNF had strong genetic interactions with components of the U2 snRNP SF3 complex. Overexpression of SF3 components in ΔSWI/SNF cells led to inefficient splicing of many fission yeast introns, predominantly those with non-consensus splice sites. Deletion of SWI/SNF decreased recruitment of the splicing ATPase Prp2, suggesting that SWI/SNF promotes co-transcriptional spliceosome assembly prior to first step catalysis. Importantly, defects in SWI/SNF as well as SF3 overexpression each altered nucleosome occupancy along intron-containing genes, illustrating that the chromatin landscape both affects—and is affected by—co-transcriptional splicing.

It has recently become apparent that most introns are removed from pre-mRNA while the transcript is still engaged with RNA polymerase II (RNAPII). To gain insight into possible roles for chromatin in co-transcriptional splicing, we generated a genome-wide genetic interaction map in fission yeast and uncovered numerous connections between splicing and chromatin. The SWI/SNF remodeling complex is typically thought to activate gene expression by relieving barriers to polymerase elongation imposed by nucleosomes. Here we show that this remodeler is important for an early step in splicing in which Prp2, an RNA-dependent ATPase, is recruited to the assembling spliceosome to promote catalytic activation. Interestingly, introns with sub-optimal splice sites are particularly dependent on SWI/SNF, suggesting the impact of nucleosome dynamics on the kinetics of spliceosome assembly and catalysis. By monitoring nucleosome occupancy, we show significant alterations in nucleosome density in particular splicing and chromatin mutants, which generally paralleled the levels of RNAPII. Taken together, our findings challenge the notion that nucleosomes simply act as barriers to elongation; rather, we suggest that polymerase pausing at nucleosomes can activate gene expression by allowing more time for co-transcriptional splicing.

Linking tumor mutations to drug responses via a quantitative chemical-genetic interaction map

There is an urgent need in oncology to link molecular aberrations in tumors with therapeutics that can be administered in a personalized fashion. One approach identifies synthetic-lethal genetic interactions or dependencies that cancer cells acquire in the presence of specific mutations. Using engineered isogenic cells, we generated a systematic and quantitative chemical-genetic interaction map that charts the influence of 51 aberrant cancer genes on 90 drug responses. The dataset strongly predicts drug responses found in cancer cell line collections, indicating that isogenic cells can model complex cellular contexts. Applying this dataset to triple-negative breast cancer, we report clinically actionable interactions with the MYC oncogene, including resistance to AKT-PI3K pathway inhibitors and an unexpected sensitivity to dasatinib through LYN inhibition in a synthetic lethal manner, providing new drug and biomarker pairs for clinical investigation. This scalable approach enables the prediction of drug responses from patient data and can accelerate the development of new genotype-directed therapies.

SIGNIFICANCE

Determining how the plethora of genomic abnormalities that exist within a given tumor cell affects drug responses remains a major challenge in oncology. Here, we develop a new mapping approach to connect cancer genotypes to drug responses using engineered isogenic cell lines and demonstrate how the resulting dataset can guide clinical interrogation.

Single-cell and multivariate approaches in genetic perturbation screens

Large-scale genetic perturbation screens are a classical approach in biology and have been crucial for many discoveries. New technologies can now provide unbiased quantification of multiple molecular and phenotypic changes across tens of thousands of individual cells from large numbers of perturbed cell populations simultaneously. In this Review, we describe how these developments have enabled the discovery of new principles of intracellular and intercellular organization, novel interpretations of genetic perturbation effects and the inference of novel functional genetic interactions. These advances now allow more accurate and comprehensive analyses of gene function in cells using genetic perturbation screens.

Quantitative analysis of triple-mutant genetic interactions

The quantitative analysis of genetic interactions between pairs of gene mutations has proven to be effective for characterizing cellular functions, but it can miss important interactions for functionally redundant genes. To address this limitation, we have developed an approach termed triple-mutant analysis (TMA). The procedure relies on a query strain that contains two deletions in a pair of redundant or otherwise related genes, which is crossed against a panel of candidate deletion strains to isolate triple mutants and measure their growth. A central feature of TMA is to interrogate mutants that are synthetically sick when two other genes are deleted but interact minimally with either single deletion. This approach has been valuable for discovering genes that restore critical functions when the principal actors are deleted. TMA has also uncovered double-mutant combinations that produce severe defects because a third protein becomes deregulated and acts in a deleterious fashion, and it has revealed functional differences between proteins presumed to act together. The protocol is optimized for Singer ROTOR pinning robots, takes 3 weeks to complete and measures interactions for up to 30 double mutants against a library of 1,536 single mutants.

Characterization of yeast mutants lacking alkaline ceramidases YPC1 and YDC1

Humans and yeast possess alkaline ceramidases located in the early secretory pathway. Single deletions of the highly homologous yeast alkaline ceramidases YPC1 and YDC1 have very little genetic interactions or phenotypes. Here, we performed chemical-genetic screens to find deletions/conditions that would alter the growth of ypc1∆ydc1∆ double mutants. These screens were essentially negative, demonstrating that ceramidase activity is not required for cell growth even under genetic stresses. A previously reported protein targeting defect of ypc1∆ could not be reproduced and reported abnormalities in sphingolipid biosynthesis detected by metabolic labeling do not alter the mass spectrometric lipid profile of ypc1∆ydc1∆ cells. Ceramides of ypc1∆ydc1∆ remained normal even in presence of aureobasidin A, an inhibitor of inositolphosphorylceramide synthase. Moreover, in caloric restriction conditions Ypc1p reduces chronological life span. A novel finding is that, when working backwards as a ceramide synthase in vivo, Ypc1p prefers C24 and C26 fatty acids as substrates, whereas it prefers C16:0, when solubilized in detergent and working in vitro. Therefore, its physiological activity may not only concern the minor ceramides containing C14 and C16. Intriguingly, so far the sole discernable benefit of conserving YPC1 for yeast resides with its ability to convey relative resistance toward H2O2.

Conditional genetic interactions of RTT107, SLX4, and HRQ1 reveal dynamic networks upon DNA damage in S. cerevisiae

The DNA damage response (DDR) is a dynamic process that is crucial for protecting the cell from challenges to genome integrity. Although many genome-wide studies in Saccharomyces cerevisiae have identified genes that contribute to resistance to DNA-damaging agents, more work is needed to elucidate the changes in genetic interaction networks in response to DNA lesions. Here we used conditional epistatic miniarray profiling to analyze the genetic interaction networks of the DDR genes RTT107, SLX4, and HRQ1 under three DNA-damaging conditions: camptothecin, hydroxyurea, and methyl methanesulfonate. Rtt107 and its interaction partner Slx4 are targets of the checkpoint kinase Mec1, which is central to the DDR-signaling cascades. Hrq1 recently was identified as a novel member of the RecQ helicase family in S. cerevisiae but is still poorly characterized. The conditional genetic networks that we generated revealed functional insights into all three genes and showed that there were varied responses to different DNA damaging agents. We observed that RTT107 had more genetic interactions under camptothecin conditions than SLX4 or HRQ1, suggesting that Rtt107 has an important role in response to this type of DNA lesion. Although RTT107 and SLX4 function together, they also had many distinct genetic interactions. In particular, RTT107 and SLX4 showed contrasting genetic interactions for a few genes, which we validated with independently constructed strains. Interestingly, HRQ1 had a genetic interaction profile that correlated with that of SLX4 and both were enriched for very similar gene ontology terms, suggesting that they function together in the DDR.