Significant conservation of synthetic lethal genetic interaction networks between distantly related eukaryotes

Discovery of Novel Ortho-Aminophenol Derivatives Targeting Lipid Peroxidation with Potent Antiferroptotic Activities

The concept of ferroptosis inhibition has gained growing recognition as a promising therapeutic strategy for addressing a wide range of diseases. Here, we present the discovery of four series of ortho-aminophenol derivatives as potential ferroptosis inhibitors beginning with the endogenous substance 3-hydroxyanthranilic acid (3-HA) by employing quantum chemistry techniques, in vitro and in vivo assays. Our findings reveal that these ortho-aminophenol derivatives exhibit unique intra-H bond interactions, compelling ortho-amines to achieve enhanced alignment with the aromatic π-system, thereby expanding their activity. Notably, compounds from all four series display remarkable activity against RSL3-induced ferroptosis, showcasing an activity 100 times more than that of 3-HA. Furthermore, these compounds also demonstrate robust in vivo efficacy in protecting mice from kidney ischemia-reperfusion injury and acetaminophen-induced hepatotoxicity. In summary, we provide four distinct series of active scaffolds that significantly expand the chemical space of ferroptosis inhibitors, serving as valuable insights for future structural modifications.

Joint embedding of biological networks for cross-species functional alignment

MOTIVATION

Model organisms are widely used to better understand the molecular causes of human disease. While sequence similarity greatly aids this cross-species transfer, sequence similarity does not imply functional similarity, and thus, several current approaches incorporate protein-protein interactions to help map findings between species. Existing transfer methods either formulate the alignment problem as a matching problem which pits network features against known orthology, or more recently, as a joint embedding problem.

RESULTS

We propose a novel state-of-the-art joint embedding solution: Embeddings to Network Alignment (ETNA). ETNA generates individual network embeddings based on network topological structure and then uses a Natural Language Processing-inspired cross-training approach to align the two embeddings using sequence-based orthologs. The final embedding preserves both within and between species gene functional relationships, and we demonstrate that it captures both pairwise and group functional relevance. In addition, ETNA's embeddings can be used to transfer genetic interactions across species and identify phenotypic alignments, laying the groundwork for potential opportunities for drug repurposing and translational studies.

AVAILABILITY AND IMPLEMENTATION

https://github.com/ylaboratory/ETNA.

The SAGA histone acetyltransferase module targets SMC5/6 to specific genes

BACKGROUND

Structural Maintenance of Chromosomes (SMC) complexes are molecular machines driving chromatin organization at higher levels. In eukaryotes, three SMC complexes (cohesin, condensin and SMC5/6) play key roles in cohesion, condensation, replication, transcription and DNA repair. Their physical binding to DNA requires accessible chromatin.

RESULTS

We performed a genetic screen in fission yeast to identify novel factors required for SMC5/6 binding to DNA. We identified 79 genes of which histone acetyltransferases (HATs) were the most represented. Genetic and phenotypic analyses suggested a particularly strong functional relationship between the SMC5/6 and SAGA complexes. Furthermore, several SMC5/6 subunits physically interacted with SAGA HAT module components Gcn5 and Ada2. As Gcn5-dependent acetylation facilitates the accessibility of chromatin to DNA-repair proteins, we first analysed the formation of DNA-damage-induced SMC5/6 foci in the Δgcn5 mutant. The SMC5/6 foci formed normally in Δgcn5, suggesting SAGA-independent SMC5/6 localization to DNA-damaged sites. Next, we used Nse4-FLAG chromatin-immunoprecipitation (ChIP-seq) analysis in unchallenged cells to assess SMC5/6 distribution. A significant portion of SMC5/6 accumulated within gene regions in wild-type cells, which was reduced in Δgcn5 and Δada2 mutants. The drop in SMC5/6 levels was also observed in gcn5-E191Q acetyltransferase-dead mutant.

CONCLUSION

Our data show genetic and physical interactions between SMC5/6 and SAGA complexes. The ChIP-seq analysis suggests that SAGA HAT module targets SMC5/6 to specific gene regions and facilitates their accessibility for SMC5/6 loading.

Genetic-interaction screens uncover novel biological roles and regulators of transcription factors in fission yeast

In Schizosaccharomyces pombe, systematic analyses of single transcription factor deletion or overexpression strains have made substantial advances in determining the biological roles and target genes of transcription factors, yet these characteristics are still relatively unknown for over a quarter of them. Moreover, the comprehensive list of proteins that regulate transcription factors remains incomplete. To further characterize Schizosaccharomyces pombe transcription factors, we performed synthetic sick/lethality and synthetic dosage lethality screens by synthetic genetic array. Examination of 2,672 transcription factor double deletion strains revealed a sick/lethality interaction frequency of 1.72%. Phenotypic analysis of these sick/lethality strains revealed potential cell cycle roles for several poorly characterized transcription factors, including SPBC56F2.05, SPCC320.03, and SPAC3C7.04. In addition, we examined synthetic dosage lethality interactions between 14 transcription factors and a miniarray of 279 deletion strains, observing a synthetic dosage lethality frequency of 4.99%, which consisted of known and novel transcription factor regulators. The miniarray contained deletions of genes that encode primarily posttranslational-modifying enzymes to identify putative upstream regulators of the transcription factor query strains. We discovered that ubiquitin ligase Ubr1 and its E2/E3-interacting protein, Mub1, degrade the glucose-responsive transcriptional repressor Scr1. Loss of ubr1⁺ or mub1⁺ increased Scr1 protein expression, which resulted in enhanced repression of flocculation through Scr1. The synthetic dosage lethality screen also captured interactions between Scr1 and 2 of its known repressors, Sds23 and Amk2, each affecting flocculation through Scr1 by influencing its nuclear localization. Our study demonstrates that sick/lethality and synthetic dosage lethality screens can be effective in uncovering novel functions and regulators of Schizosaccharomyces pombe transcription factors.

Humanized yeast to model human biology, disease and evolution

For decades, budding yeast, a single-cellular eukaryote, has provided remarkable insights into human biology. Yeast and humans share several thousand genes despite morphological and cellular differences and over a billion years of separate evolution. These genes encode critical cellular processes, the failure of which in humans results in disease. Although recent developments in genome engineering of mammalian cells permit genetic assays in human cell lines, there is still a need to develop biological reagents to study human disease variants in a high-throughput manner. Many protein-coding human genes can successfully substitute for their yeast equivalents and sustain yeast growth, thus opening up doors for developing direct assays of human gene function in a tractable system referred to as 'humanized yeast'. Humanized yeast permits the discovery of new human biology by measuring human protein activity in a simplified organismal context. This Review summarizes recent developments showing how humanized yeast can directly assay human gene function and explore variant effects at scale. Thus, by extending the 'awesome power of yeast genetics' to study human biology, humanizing yeast reinforces the high relevance of evolutionarily distant model organisms to explore human gene evolution, function and disease.

An orthologous gene coevolution network provides insight into eukaryotic cellular and genomic structure and function

The evolutionary rates of functionally related genes often covary. We present a gene coevolution network inferred from examining nearly 3 million orthologous gene pairs from 332 budding yeast species spanning ~400 million years of evolution. Network modules provide insight into cellular and genomic structure and function. Examination of the phenotypic impact of network perturbation using deletion mutant data from the baker's yeast Saccharomyces cerevisiae, which were obtained from previously published studies, suggests that fitness in diverse environments is affected by orthologous gene neighborhood and connectivity. Mapping the network onto the chromosomes of S. cerevisiae and Candida albicans revealed that coevolving orthologous genes are not physically clustered in either species; rather, they are often located on different chromosomes or far apart on the same chromosome. The coevolution network captures the hierarchy of cellular structure and function, provides a roadmap for genotype-to-phenotype discovery, and portrays the genome as a linked ensemble of genes.

Barcoded Bulk QTL mapping reveals highly polygenic and epistatic architecture of complex traits in yeast

Mapping the genetic basis of complex traits is critical to uncovering the biological mechanisms that underlie disease and other phenotypes. Genome-wide association studies (GWAS) in humans and quantitative trait locus (QTL) mapping in model organisms can now explain much of the observed heritability in many traits, allowing us to predict phenotype from genotype. However, constraints on power due to statistical confounders in large GWAS and smaller sample sizes in QTL studies still limit our ability to resolve numerous small-effect variants, map them to causal genes, identify pleiotropic effects across multiple traits, and infer non-additive interactions between loci (epistasis). Here, we introduce barcoded bulk quantitative trait locus (BB-QTL) mapping, which allows us to construct, genotype, and phenotype 100,000 offspring of a budding yeast cross, two orders of magnitude larger than the previous state of the art. We use this panel to map the genetic basis of eighteen complex traits, finding that the genetic architecture of these traits involves hundreds of small-effect loci densely spaced throughout the genome, many with widespread pleiotropic effects across multiple traits. Epistasis plays a central role, with thousands of interactions that provide insight into genetic networks. By dramatically increasing sample size, BB-QTL mapping demonstrates the potential of natural variants in high-powered QTL studies to reveal the highly polygenic, pleiotropic, and epistatic architecture of complex traits.

Functional profiling of long intergenic non-coding RNAs in fission yeast

Eukaryotic genomes express numerous long intergenic non-coding RNAs (lincRNAs) that do not overlap any coding genes. Some lincRNAs function in various aspects of gene regulation, but it is not clear in general to what extent lincRNAs contribute to the information flow from genotype to phenotype. To explore this question, we systematically analysed cellular roles of lincRNAs in Schizosaccharomyces pombe. Using seamless CRISPR/Cas9-based genome editing, we deleted 141 lincRNA genes to broadly phenotype these mutants, together with 238 diverse coding-gene mutants for functional context. We applied high-throughput colony-based assays to determine mutant growth and viability in benign conditions and in response to 145 different nutrient, drug, and stress conditions. These analyses uncovered phenotypes for 47.5% of the lincRNAs and 96% of the protein-coding genes. For 110 lincRNA mutants, we also performed high-throughput microscopy and flow cytometry assays, linking 37% of these lincRNAs with cell-size and/or cell-cycle control. With all assays combined, we detected phenotypes for 84 (59.6%) of all lincRNA deletion mutants tested. For complementary functional inference, we analysed colony growth of strains ectopically overexpressing 113 lincRNA genes under 47 different conditions. Of these overexpression strains, 102 (90.3%) showed altered growth under certain conditions. Clustering analyses provided further functional clues and relationships for some of the lincRNAs. These rich phenomics datasets associate lincRNA mutants with hundreds of phenotypes, indicating that most of the lincRNAs analysed exert cellular functions in specific environmental or physiological contexts. This study provides groundwork to further dissect the roles of these lincRNAs in the relevant conditions.

Biology and applications of co-produced, synergistic antimicrobials from environmental bacteria

Environmental bacteria, such as Streptomyces spp., produce specialized metabolites that are potent antibiotics and therapeutics. Selected specialized antimicrobials are co-produced and function together synergistically. Co-produced antimicrobials comprise multiple chemical classes and are produced by a wide variety of bacteria in different environmental niches, suggesting that their combined functions are ecologically important. Here, we highlight the exquisite mechanisms that underlie the simultaneous production and functional synergy of 16 sets of co-produced antimicrobials. To date, antibiotic and antifungal discovery has focused mainly on single molecules, but we propose that methods to target co-produced antimicrobials could widen the scope and applications of discovery programs.

Crosstalk between H2A variant-specific modifications impacts vital cell functions

Selection of C-terminal motifs participated in evolution of distinct histone H2A variants. Hybrid types of variants combining motifs from distinct H2A classes are extremely rare. This suggests that the proximity between the motif cases interferes with their function. We studied this question in flowering plants that evolved sporadically a hybrid H2A variant combining the SQ motif of H2A.X that participates in the DNA damage response with the KSPK motif of H2A.W that stabilizes heterochromatin. Our inventory of PTMs of H2A.W variants showed that in vivo the cell cycle-dependent kinase CDKA phosphorylates the KSPK motif of H2A.W but only in absence of an SQ motif. Phosphomimicry of KSPK prevented DNA damage response by the SQ motif of the hybrid H2A.W/X variant. In a synthetic yeast expressing the hybrid H2A.W/X variant, phosphorylation of KSPK prevented binding of the BRCT-domain protein Mdb1 to phosphorylated SQ and impaired response to DNA damage. Our findings illustrate that PTMs mediate interference between the function of H2A variant specific C-terminal motifs. Such interference could explain the mutual exclusion of motifs that led to evolution of H2A variants.

Machine Learning to Identify Gene Interactions from High-Throughput Mutant Crosses

Advances in molecular genetics through high-throughput gene mutagenesis and genetic crossing have enabled gene interaction mapping across whole genomes. Detecting gene interactions in even small microbial genomes relies on measuring growth phenotypes in thousands of crossed strains followed by statistical analysis to compare single and double mutants. The preferred computational approach is to use a multiplicative model that factors phenotype scores of single gene mutants to identify gene interactions in double mutants. Here we present how machine learning models that consider the characteristics of the phenotypic data improve on the classical multiplicative model. Importantly, machine learning improves the selection of cutoff values to identify gene interactions from phenotypic scores.

Genes affecting the extension of chronological lifespan in Schizosaccharomyces pombe (fission yeast)

So far, more than 70 genes involved in the chronological lifespan (CLS) of Schizosaccharomyces pombe (fission yeast) have been reported. In this mini‐review, we arrange and summarize these genes based on the reported genetic interactions between them and the physical interactions between their products. We describe the signal transduction pathways that affect CLS in S. pombe: target of rapamycin complex 1, cAMP‐dependent protein kinase, Sty1, and Pmk1 pathways have important functions in the regulation of CLS extension. Furthermore, the Php transcription complex, Ecl1 family proteins, cyclin Clg1, and the cyclin‐dependent kinase Pef1 are important for the regulation of CLS extension in S. pombe. Most of the known genes involved in CLS extension are related to these pathways and genes. In this review, we focus on the individual genes regulating CLS extension in S. pombe and discuss the interactions among them.

Revealing Functional Crosstalk between Distinct Bioprocesses through Reciprocal Functional Tests of Genetically Interacting Genes

To systematically explore the genes mediating functional crosstalk between metazoan biological processes, we apply comparative genetic interaction (GI) mapping in Saccharomyces cerevisiae and Caenorhabditis elegans to generate an inter-bioprocess network consisting of 178 C. elegans GIs. The GI network spans six annotated biological processes including aging, intracellular transport, microtubule-based processes, cytokinesis, lipid metabolic processes, and anatomical structure development. By proposing a strategy called "reciprocal functional test" for interacting gene pairs, we discover a group of genes that mediate crosstalk between distinct biological processes. In particular, we identify the ribosomal S6 Kinase/RSKS-1, previously characterized as an mTOR (mechanistic target of rapamycin) effector, as a regulator of DAF-2 endosomal recycling transport, which traces a functional correlation between endocytic recycling and aging processes. Together, our results provide an alternative and effective strategy for identifying genes and pathways that mediate crosstalk between bioprocesses with little prior knowledge.

Functional responses between PMP3 small membrane proteins and membrane potential

The Plasma Membrane Proteolipid 3 (PMP3, UPF0057 family in Uniprot) family consists of abundant small hydrophobic polypeptides with two predicted transmembrane helices. Plant homologues were upregulated in response to drought/salt-stresses and yeast deletion mutants exhibited conditional growth defects. We report here abundant expression of Group I PMP3 homologues (PMP3(i)hs) during normal vegetative growth in both prokaryotic and eukaryotic cells, at a level comparable to housekeeping genes, implicating the regular cellular functions. Expression of eukaryotic PMP3(i)hs was dramatically upregulated in response to membrane potential (Vm) variability (Vm_var ), whereas PMP3(i)hs deletion-knockdown led to Vm changes with conditional growth defects. Bacterial PMP3(i)h yqaE deletion led to a shift of salt sensitivity; Vm_var alternations with exogenous K⁺ addition downregulated prokaryotic PMP3(i)hs, suggesting [K⁺ ]-Vm_var axis being a significant feedback element in prokaryotic ionic homeostasis. Remarkably, the eukaryotic homologues functionally suppressed the conditional growth defects in bacterial deletion mutant, demonstrating the conserved cross-kingdom membrane functions by PMP3(i)hs. These data demonstrated a direct reciprocal relationship between PMP3(i)hs expression and Vm differentials in both prokaryotic and eukaryotic cells. Cumulative with PMP3(i)hs ubiquitous abundance, their lipid-binding selectivity and membrane protein colocalization, we propose [PMP3(i)hs]-Vm_var axis as a key element in membrane homeostasis.

Interaction between NSMCE4A and GPS1 links the SMC5/6 complex to the COP9 signalosome

Background

The SMC5/6 complex, cohesin and condensin are the three mammalian members of the structural maintenance of chromosomes (SMC) family, large ring-like protein complexes that are essential for genome maintenance. The SMC5/6 complex is the least characterized complex in mammals; however, it is known to be involved in homologous recombination repair (HRR) and chromosome segregation.

Results

In this study, a yeast two-hybrid screen was used to help elucidate novel interactions of the kleisin subunit of the SMC5/6 complex, NSMCE4A. This approach discovered an interaction between NSMCE4A and GPS1, a COP9 signalosome (CSN) component, and this interaction was further confirmed by co-immunoprecipitation. Additionally, GPS1 and components of SMC5/6 complex colocalize during interphase and mitosis. CSN is a cullin deNEDDylase and is an important factor for HRR. Depletion of GPS1, which has been shown to negatively impact DNA end resection during HRR, caused an increase in SMC5/6 levels at sites of laser-induced DNA damage. Furthermore, inhibition of the dennedylation function of CSN increased SMC5/6 levels at sites of laser-induced DNA damage.

Conclusion

Taken together, these data demonstrate for the first time that the SMC5/6 and CSN complexes interact and provides evidence that the CSN complex influences SMC5/6 functions during cell cycle progression and response to DNA damage.

Posttranslational Arginylation Enzyme Arginyltransferase1 Shows Genetic Interactions With Specific Cellular Pathways in vivo

Arginyltransferase1 (ATE1) is a conserved enzyme in eukaryotes mediating posttranslational arginylation, the addition of an extra arginine to an existing protein. In mammals, the dysregulations of the ATE1 gene (ate1) is shown to be involved in cardiovascular abnormalities, cancer, and aging-related diseases. Although biochemical evidence suggested that arginylation may be involved in stress response and/or protein degradation, the physiological role of ATE1 in vivo has never been systematically determined. This gap of knowledge leads to difficulties for interpreting the involvements of ATE1 in diseases pathogenesis. Since ate1 is highly conserved between human and the unicellular organism Schizosaccharomyces pombe (S. pombe), we take advantage of the gene-knockout library of S. pombe, to investigate the genetic interactions between ate1 and other genes in a systematic and unbiased manner. By this approach, we found that ate1 has a surprisingly small and focused impact size. Among the 3659 tested genes, which covers nearly 75% of the genome of S. pombe, less than 5% of them displayed significant genetic interactions with ate1. Furthermore, these ate1-interacting partners can be grouped into a few discrete clustered categories based on their functions or their physical interactions. These categories include translation/transcription regulation, biosynthesis/metabolism of biomolecules (including histidine), cell morphology and cellular dynamics, response to oxidative or metabolic stress, ribosomal structure and function, and mitochondrial function. Unexpectedly, inconsistent to popular belief, very few genes in the global ubiquitination or degradation pathways showed interactions with ate1. Our results suggested that ATE1 specifically regulates a handful of cellular processes in vivo, which will provide critical mechanistic leads for studying the involvements of ATE1 in normal physiologies as well as in diseased conditions.

Genetic interaction networks mediate individual statin drug response in Saccharomyces cerevisiae

Eukaryotic genetic interaction networks (GINs) are extensively described in the Saccharomyces cerevisiae S288C model using deletion libraries, yet being limited to this one genetic background, not informative to individual drug response. Here we created deletion libraries in three additional genetic backgrounds. Statin response was probed with five queries against four genetic backgrounds. The 20 resultant GINs representing drug-gene and gene-gene interactions were not conserved by functional enrichment, hierarchical clustering, and topology-based community partitioning. An unfolded protein response (UPR) community exhibited genetic background variation including different betweenness genes that were network bottlenecks, and we experimentally validated this UPR community via measurements of the UPR that were differentially activated and regulated in statin-resistant strains relative to the statin-sensitive S288C background. These network analyses by topology and function provide insight into the complexity of drug response influenced by genetic background.

Changes in gene expression predictably shift and switch genetic interactions

Non-additive interactions between mutations occur extensively and also change across conditions, making genetic prediction a difficult challenge. To better understand the plasticity of genetic interactions (epistasis), we combine mutations in a single protein performing a single function (a transcriptional repressor inhibiting a target gene). Even in this minimal system, genetic interactions switch from positive (suppressive) to negative (enhancing) as the expression of the gene changes. These seemingly complicated changes can be predicted using a mathematical model that propagates the effects of mutations on protein folding to the cellular phenotype. More generally, changes in gene expression should be expected to alter the effects of mutations and how they interact whenever the relationship between expression and a phenotype is nonlinear, which is the case for most genes. These results have important implications for understanding genotype-phenotype maps and illustrate how changes in genetic interactions can often—but not always—be predicted by hierarchical mechanistic models.

Non-additive genetic interactions are plastic and can complicate genetic prediction. Here, using deep mutagenesis of the lambda repressor, Li et al. reveal that changes in gene expression can alter the strength and direction of genetic interactions between mutations in many genes and develop mathematical models for predicting them.

Humanized yeast genetic interaction mapping predicts synthetic lethal interactions of FBXW7 in breast cancer

Background

Synthetic lethal interactions (SLIs) that occur between gene pairs are exploited for cancer therapeutics. Studies in the model eukaryote yeast have identified ~ 550,000 negative genetic interactions that have been extensively studied, leading to characterization of novel pathways and gene functions. This resource can be used to predict SLIs that can be relevant to cancer therapeutics.

Methods

We used patient data to identify genes that are down-regulated in breast cancer. InParanoid orthology mapping was performed to identify yeast orthologs of the down-regulated genes and predict their corresponding SLIs in humans. The predicted network graphs were drawn with Cytoscape. CancerRXgene database was used to predict drug response.

Results

Harnessing the vast available knowledge of yeast genetics, we generated a Humanized Yeast Genetic Interaction Network (HYGIN) for 1009 human genes with 10,419 interactions. Through the addition of patient-data from The Cancer Genome Atlas (TCGA), we generated a breast cancer specific subnetwork. Specifically, by comparing 1009 genes in HYGIN to genes that were down-regulated in breast cancer, we identified 15 breast cancer genes with 130 potential SLIs. Interestingly, 32 of the 130 predicted SLIs occurred with FBXW7, a well-known tumor suppressor that functions as a substrate-recognition protein within a SKP/CUL1/F-Box ubiquitin ligase complex for proteasome degradation. Efforts to validate these SLIs using chemical genetic data predicted that patients with loss of FBXW7 may respond to treatment with drugs like Selumitinib or Cabozantinib.

Conclusions

This study provides a patient-data driven interpretation of yeast SLI data. HYGIN represents a novel strategy to uncover therapeutically relevant cancer drug targets and the yeast SLI data offers a major opportunity to mine these interactions.

Electronic supplementary material

The online version of this article (10.1186/s12920-019-0554-z) contains supplementary material, which is available to authorized users.

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.

Improved discovery of genetic interactions using CRISPRiSeq across multiple environments

Large-scale genetic interaction (GI) screens in yeast have been invaluable for our understanding of molecular systems biology and for characterizing novel gene function. Owing in part to the high costs and long experiment times required, a preponderance of GI data has been generated in a single environmental condition. However, an unknown fraction of GIs may be specific to other conditions. Here, we developed a pooled-growth CRISPRi-based sequencing assay for GIs, CRISPRiSeq, which increases throughput such that GIs can be easily assayed across multiple growth conditions. We assayed the fitness of approximately 17,000 strains encompassing approximately 7700 pairwise interactions in five conditions and found that the additional conditions increased the number of GIs detected nearly threefold over the number detected in rich media alone. In addition, we found that condition-specific GIs are prevalent and improved the power to functionally classify genes. Finally, we found new links during respiratory growth between members of the Ras nutrient-sensing pathway and both the COG complex and a gene of unknown function. Our results highlight the potential of conditional GI screens to improve our understanding of cellular genetic networks.

Drug combinations: a strategy to extend the life of antibiotics in the 21st century

Antimicrobial resistance threatens a resurgence of life-threatening bacterial infections and the potential demise of many aspects of modern medicine. Despite intensive drug discovery efforts, no new classes of antibiotics have been developed into new medicines for decades, in large part owing to the stringent chemical, biological and pharmacological requisites for effective antibiotic drugs. Combinations of antibiotics and of antibiotics with non-antibiotic activity-enhancing compounds offer a productive strategy to address the widespread emergence of antibiotic-resistant strains. In this Review, we outline a theoretical and practical framework for the development of effective antibiotic combinations.

How Surrogate and Chemical Genetics in Model Organisms Can Suggest Therapies for Human Genetic Diseases

Genetic diseases are both inherited and acquired. Many genetic diseases fall under the paradigm of orphan diseases, a disease found in < 1 in 2000 persons. With rapid and cost-effective genome sequencing becoming the norm, many causal mutations for genetic diseases are being rapidly determined. In this regard, model organisms are playing an important role in validating if specific mutations identified in patients drive the observed phenotype. An emerging challenge for model organism researchers is the application of genetic and chemical genetic platforms to discover drug targets and drugs/drug-like molecules for potential treatment options for patients with genetic disease. This review provides an overview of how model organisms have contributed to our understanding of genetic disease, with a focus on the roles of yeast and zebrafish in gene discovery and the identification of compounds that could potentially treat human genetic diseases.

Identification of an oncogenic network with prognostic and therapeutic value in prostate cancer

Identifying critical pathways governing disease progression is essential for accurate prognosis and effective therapy. We developed a broadly applicable and novel systems-level gene discovery strategy. This approach focused on constitutively active androgen receptor (AR) splice variant-driven pathways as representative of an intractable mechanism of prostate cancer (PC) therapeutic resistance. We performed a meta-analysis of human prostate samples using weighted gene co-expression network analysis combined with experimental AR variant transcriptome analyses. An AR variant-driven gene module that is upregulated during human PC progression was identified. We filtered this module by identifying genes that functionally interacted with AR variants using a high-throughput synthetic genetic array screen in Schizosaccharomyces pombe This strategy identified seven AR variant-regulated genes that also enhance AR activity and drive cancer progression. Expression of the seven genes predicted poor disease-free survival in large independent PC patient cohorts. Pharmacologic inhibition of interacting members of the gene set potently and synergistically decreased PC cell proliferation. This unbiased and novel gene discovery strategy identified a clinically relevant, oncogenic, interacting gene hub with strong prognostic and therapeutic potential in PC.

Integrating genetic and protein-protein interaction networks maps a functional wiring diagram of a cell

Systematic experimental approaches have led to construction of comprehensive genetic and protein-protein interaction networks for the budding yeast, Saccharomyces cerevisiae. Genetic interactions capture functional relationships between genes using phenotypic readouts, while protein-protein interactions identify physical connections between gene products. These complementary, and largely non-overlapping, networks provide a global view of the functional architecture of a cell, revealing general organizing principles, many of which appear to be evolutionarily conserved. Here, we focus on insights derived from the integration of large-scale genetic and protein-protein interaction networks, highlighting principles that apply to both unicellular and more complex systems, including human cells. Network integration reveals fundamental connections involving key functional modules of eukaryotic cells, defining a core network of cellular function, which could be elaborated to explore cell-type specificity in metazoans.

Genetic Interaction Network as an Important Determinant of Gene Order in Genome Evolution

Although it is generally accepted that eukaryotic gene order is not random, the basic principles of gene arrangement on a chromosome remain poorly understood. Here, we extended existing population genetics theories that were based on two-locus models and proposed a hypothesis that genetic interaction networks drive the evolution of eukaryotic gene order. We predicted that genes with positive epistasis would move toward each other in evolution, during which a negative correlation between epistasis and gene distance formed. We tested and confirmed our prediction with computational simulations and empirical data analyses. Importantly, we demonstrated that gene order in the budding yeast could be successfully predicted from the genetic interaction network. Taken together, our study reveals the role of the genetic interaction network in the evolution of gene order, extends our understanding of the encoding principles in genomes, and potentially offers new strategies to improve synthetic biology.

Harnessing synthetic lethality to predict the response to cancer treatment

While synthetic lethality (SL) holds promise in developing effective cancer therapies, SL candidates found via experimental screens often have limited translational value. Here we present a data-driven approach, ISLE (identification of clinically relevant synthetic lethality), that mines TCGA cohort to identify the most likely clinically relevant SL interactions (cSLi) from a given candidate set of lab-screened SLi. We first validate ISLE via a benchmark of large-scale drug response screens and by predicting drug efficacy in mouse xenograft models. We then experimentally test a select set of predicted cSLi via new screening experiments, validating their predicted context-specific sensitivity in hypoxic vs normoxic conditions and demonstrating cSLi's utility in predicting synergistic drug combinations. We show that cSLi can successfully predict patients' drug treatment response and provide patient stratification signatures. ISLE thus complements existing actionable mutation-based methods for precision cancer therapy, offering an opportunity to expand its scope to the whole genome.

Testing the Domino Theory of Gene Loss in Buchnera aphidicola: The Relevance of Epistatic Interactions

The domino theory of gene loss states that when some particular gene loses its function and cripples a cellular function, selection will relax in all functionally related genes, which may allow for the non-functionalization and loss of these genes. Here we study the role of epistasis in determining the pattern of gene losses in a set of genes participating in cell envelope biogenesis in the endosymbiotic bacteria Buchnera aphidicola. We provide statistical evidence indicating pairs of genes in B. aphidicola showing correlated gene loss tend to have orthologs in Escherichia coli known to have alleviating epistasis. In contrast, pairs of genes in B. aphidicola not showing correlated gene loss tend to have orthologs in E. coli known to have aggravating epistasis. These results suggest that during the process of genome reduction in B. aphidicola by gene loss, positive or alleviating epistasis facilitates correlated gene losses while negative or aggravating epistasis impairs correlated gene losses. We interpret this as evidence that the reduced proteome of B. aphidicola contains less pathway redundancy and more compensatory interactions, mimicking the situation of E. coli when grown under environmental constrains.

Low doses of DNA damaging agents extend Saccharomyces cerevisiae chronological lifespan by promoting entry into quiescence

A variety of mild stresses have been shown to extend lifespan in diverse species through hormesis, which is a beneficial response to a stress or toxin that would cause a negative response at a higher exposure. Whether particular stresses induce hormesis can vary with genotype for a given species, and the underlying mechanisms of lifespan extension are only partly understood in most cases. We show that low doses of the DNA damaging or replication stress agents hydroxyurea, methyl methanesulfonate, 4-nitroquinoline 1-oxide, or Zeocin (a phleomycin derivative) lengthened chronological lifespan in Saccharomyces cerevisiae if cells were exposed during growth, but not if they were exposed during stationary phase. Treatment with these agents did not change mitochondrial activity, increase resistance to acetic acid, ethanol, or heat stress, and three of four treatments did not increase resistance to hydrogen peroxide. Stationary phase yeast populations consist of both quiescent and nonquiescent cells, and all four treatments increased the proportion of quiescent cells. Several mutant strains with deletions in genes that influence quiescence prevented Zeocin treatment from extending lifespan and from increasing the proportion of quiescent stationary phase cells. These data indicate that mild DNA damage stress can extend lifespan by promoting quiescence in the absence of mitohormesis or improved general stress responses that have been frequently associated with improved longevity in other cases of hormesis. Further study of the underlying mechanism may yield new insights into quiescence regulation that will be relevant to healthy aging.

Cell polarity protein Spa2 coordinates Chs2 incorporation at the division site in budding yeast

Deposition of additional plasma membrane and cargoes during cytokinesis in eukaryotic cells must be coordinated with actomyosin ring contraction, plasma membrane ingression and extracellular matrix remodelling. The process by which the secretory pathway promotes specific incorporation of key factors into the cytokinetic machinery is poorly understood. Here, we show that cell polarity protein Spa2 interacts with actomyosin ring components during cytokinesis. Spa2 directly binds to cytokinetic factors Cyk3 and Hof1. The lethal effects of deleting the SPA2 gene in the absence of either Cyk3 or Hof1 can be suppressed by expression of the hypermorphic allele of the essential chitin synthase II (Chs2), a transmembrane protein transported on secretory vesicles that makes the primary septum during cytokinesis. Spa2 also interacts directly with the chitin synthase Chs2. Interestingly, artificial incorporation of Chs2 into the cytokinetic machinery allows the localisation of Spa2 at the site of division. In addition, increased Spa2 protein levels promote Chs2 incorporation at the site of division and primary septum formation. Our data indicate that Spa2 is recruited to the cleavage site to co-operate with the secretory vesicle system and particular actomyosin ring components to promote the incorporation of Chs2 into the so-called 'ingression progression complexes' during cytokinesis in budding yeast.

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.

Using genetic buffering relationships identified in fission yeast to reveal susceptibilities in cells lacking hamartin or tuberin function

Tuberous sclerosis complex is an autosomal dominant disorder characterized by benign tumors arising from the abnormal activation of mTOR signaling in cells lacking TSC1 (hamartin) or TSC2 (tuberin) activity. To expand the genetic framework surrounding this group of growth regulators, we utilized the model eukaryote Schizosaccharomyces pombe to uncover and characterize genes that buffer the phenotypic effects of mutations in the orthologous tsc1 or tsc2 loci. Our study identified two genes: fft3 (encoding a DNA helicase) and ypa1 (encoding a peptidyle-prolyl cis/trans isomerase). While the deletion of fft3 or ypa1 has little effect in wild-type fission yeast cells, their loss in tsc1Δ or tsc2Δ backgrounds results in severe growth inhibition. These data suggest that the inhibition of Ypa1p or Fft3p might represent an 'Achilles' heel' of cells defective in hamartin/tuberin function. Furthermore, we demonstrate that the interaction between tsc1/tsc2 and ypa1 can be rescued through treatment with the mTOR inhibitor, torin-1, and that ypa1Δ cells are resistant to the glycolytic inhibitor, 2-deoxyglucose. This identifies ypa1 as a novel upstream regulator of mTOR and suggests that the effects of ypa1 loss, together with mTOR activation, combine to result in a cellular maladaptation in energy metabolism that is profoundly inhibitory to growth.

The fission yeast nucleoporin Alm1 is required for proteasomal degradation of kinetochore components

TPR nucleoporins form the nuclear pore complex basket. The fission yeast TPR Alm1 is required for localization of the proteasome to the nuclear envelope, which is in turn required for kinetochore homeostasis and proper chromosome segregation.

Kinetochores (KTs) are large multiprotein complexes that constitute the interface between centromeric chromatin and the mitotic spindle during chromosome segregation. In spite of their essential role, little is known about how centromeres and KTs are assembled and how their precise stoichiometry is regulated. In this study, we show that the nuclear pore basket component Alm1 is required to maintain both the proteasome and its anchor, Cut8, at the nuclear envelope, which in turn regulates proteostasis of certain inner KT components. Consistently, alm1-deleted cells show increased levels of KT proteins, including CENP-C^Cnp3, spindle assembly checkpoint activation, and chromosome segregation defects. Our data demonstrate a novel function of the nucleoporin Alm1 in proteasome localization required for KT homeostasis.

A Lysine Desert Protects a Novel Domain in the Slx5-Slx8 SUMO Targeted Ub Ligase To Maintain Sumoylation Levels in Saccharomyces cerevisiae

Protein modification by the small ubiquitin-like modifier (SUMO) plays important roles in genome maintenance. In Saccharomyces cerevisiae, proper regulation of sumoylation is known to be essential for viability in certain DNA repair mutants. Here, we find the opposite result; proper regulation of sumoylation is lethal in certain DNA repair mutants. Yeast cells lacking the repair factors TDP1 and WSS1 are synthetically lethal due to their redundant roles in removing Top1-DNA covalent complexes (Top1ccs). A screen for suppressors of tdp1∆ wss1∆ synthetic lethality isolated mutations in genes known to control global sumoylation levels including ULP1, ULP2, SIZ2, and SLX5 The results suggest that alternative pathways of repair become available when sumoylation levels are altered. Curiously, both suppressor mutations that were isolated in the Slx5 subunit of the SUMO-targeted Ub ligase created new lysine residues. These "slx5-K" mutations localize to a 398 amino acid domain that is completely free of lysine, and they result in the auto-ubiquitination and partial proteolysis of Slx5. The decrease in Slx5-K protein leads to the accumulation of high molecular weight SUMO conjugates, and the residual Ub ligase activity is needed to suppress inviability presumably by targeting polysumoylated Top1ccs. This "lysine desert" is found in the subset of large fungal Slx5 proteins, but not its smaller orthologs such as RNF4. The lysine desert solves a problem that Ub ligases encounter when evolving novel functional domains.

'Big data' approaches for novel anti-cancer drug discovery

INTRODUCTION

The development of improved cancer therapies is frequently cited as an urgent unmet medical need. Recent advances in platform technologies and the increasing availability of biological 'big data' are providing an unparalleled opportunity to systematically identify the key genes and pathways involved in tumorigenesis. The discoveries made using these new technologies may lead to novel therapeutic interventions. Areas covered: The authors discuss the current approaches that use 'big data' to identify cancer drivers. These approaches include the analysis of genomic sequencing data, pathway data, multi-platform data, identifying genetic interactions such as synthetic lethality and using cell line data. They review how big data is being used to identify novel drug targets. The authors then provide an overview of the available data repositories and tools being used at the forefront of cancer drug discovery. Expert opinion: Targeted therapies based on the genomic events driving the tumour will eventually inform treatment protocols. However, using a tailored approach to treat all tumour patients may require developing a large repertoire of targeted drugs.

A systematic screen for morphological abnormalities during fission yeast sexual reproduction identifies a mechanism of actin aster formation for cell fusion

In non-motile fungi, sexual reproduction relies on strong morphogenetic changes in response to pheromone signaling. We report here on a systematic screen for morphological abnormalities of the mating process in fission yeast Schizosaccharomyces pombe. We derived a homothallic (self-fertile) collection of viable deletions, which, upon visual screening, revealed a plethora of phenotypes affecting all stages of the mating process, including cell polarization, cell fusion and sporulation. Cell fusion relies on the formation of the fusion focus, an aster-like F-actin structure that is marked by strong local accumulation of the myosin V Myo52, which concentrates secretion at the fusion site. A secondary screen for fusion-defective mutants identified the myosin V Myo51-associated coiled-coil proteins Rng8 and Rng9 as critical for the coalescence of the fusion focus. Indeed, rng8Δ and rng9Δ mutant cells exhibit multiple stable dots at the cell-cell contact site, instead of the single focus observed in wildtype. Rng8 and Rng9 accumulate on the fusion focus, dependent on Myo51 and tropomyosin Cdc8. A tropomyosin mutant allele, which compromises Rng8/9 localization but not actin binding, similarly leads to multiple stable dots instead of a single focus. By contrast, myo51 deletion does not strongly affect fusion focus coalescence. We propose that focusing of the actin filaments in the fusion aster primarily relies on Rng8/9-dependent cross-linking of tropomyosin-actin filaments.

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

We generated a global genetic interaction network for Saccharomyces cerevisiae, constructing more than 23 million double mutants, identifying about 550,000 negative and about 350,000 positive genetic interactions. This comprehensive network maps genetic interactions for essential gene pairs, highlighting essential genes as densely connected hubs. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections among gene pairs, rather than shared functionality. The global network illustrates how coherent sets of genetic interactions connect protein complex and pathway modules to map a functional wiring diagram of the cell.

Big data mining powers fungal research: recent advances in fission yeast systems biology approaches

Biology research has entered into big data era. Systems biology approaches therefore become the powerful tools to obtain the whole landscape of how cell separate, grow, and resist the stresses. Fission yeast Schizosaccharomyces pombe is wonderful unicellular eukaryote model, especially studying its division and metabolism can facilitate to understanding the molecular mechanism of cancer and discovering anticancer agents. In this perspective, we discuss the recent advanced fission yeast systems biology tools, mainly focus on metabolomics profiling and metabolic modeling, protein-protein interactome and genetic interaction network, DNA sequencing and applications, and high-throughput phenotypic screening. We therefore hope this review can be useful for interested fungal researchers as well as bioformaticians.

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.

A genetic interaction map of cell cycle regulators

A combination of genome-scale RNA interference screening and genetic interaction analysis using process-directed phenotypes is used to assign components to specific pathways and complexes for modulators of mitosis and cytokinesis in Drosophila S2 cells.

Cell-based RNA interference (RNAi) is a powerful approach to screen for modulators of many cellular processes. However, resulting candidate gene lists from cell-based assays comprise diverse effectors, both direct and indirect, and further dissecting their functions can be challenging. Here we screened a genome-wide RNAi library for modulators of mitosis and cytokinesis in Drosophila S2 cells. The screen identified many previously known genes as well as modulators that have previously not been connected to cell cycle control. We then characterized ∼300 candidate modifiers further by genetic interaction analysis using double RNAi and a multiparametric, imaging-based assay. We found that analyzing cell cycle–relevant phenotypes increased the sensitivity for associating novel gene function. Genetic interaction maps based on mitotic index and nuclear size grouped candidates into known regulatory complexes of mitosis or cytokinesis, respectively, and predicted previously uncharacterized components of known processes. For example, we confirmed a role for the Drosophila CCR4 mRNA processing complex component l(2)NC136 during the mitotic exit. Our results show that the combination of genome-scale RNAi screening and genetic interaction analysis using process-directed phenotypes provides a powerful two-step approach to assigning components to specific pathways and complexes.

Global regulation of heterochromatin spreading by Leo1

Heterochromatin plays important roles in eukaryotic genome regulation. However, the repressive nature of heterochromatin combined with its propensity to self-propagate necessitates robust mechanisms to contain heterochromatin within defined boundaries and thus prevent silencing of expressed genes. Here we show that loss of the PAF complex (PAFc) component Leo1 compromises chromatin boundaries, resulting in invasion of heterochromatin into flanking euchromatin domains. Similar effects are seen upon deletion of other PAFc components, but not other factors with related functions in transcription-associated chromatin modification, indicating a specific role for PAFc in heterochromatin regulation. Loss of Leo1 results in reduced levels of H4K16 acetylation at boundary regions, while tethering of the H4K16 acetyltransferase Mst1 to boundary chromatin suppresses heterochromatin spreading in leo1Δ cells, suggesting that Leo1 antagonises heterochromatin spreading by promoting H4K16 acetylation. Our findings reveal a previously undescribed role for PAFc in regulating global heterochromatin distribution.

Systematic identification and correction of annotation errors in the genetic interaction map of Saccharomyces cerevisiae

The yeast mutant collections are a fundamental tool in deciphering genomic organization and function. Over the last decade, they have been used for the systematic exploration of ∼6 000 000 double gene mutants, identifying and cataloging genetic interactions among them. Here we studied the extent to which these data are prone to neighboring gene effects (NGEs), a phenomenon by which the deletion of a gene affects the expression of adjacent genes along the genome. Analyzing ∼90,000 negative genetic interactions observed to date, we found that more than 10% of them are incorrectly annotated due to NGEs. We developed a novel algorithm, GINGER, to identify and correct erroneous interaction annotations. We validated the algorithm using a comparative analysis of interactions from Schizosaccharomyces pombe. We further showed that our predictions are significantly more concordant with diverse biological data compared to their mis-annotated counterparts. Our work uncovered about 9500 new genetic interactions in yeast.

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.

Evolutionary rescue by compensatory mutations is constrained by genomic and environmental backgrounds

Since deleterious mutations may be rescued by secondary mutations during evolution, compensatory evolution could identify genetic solutions leading to therapeutic targets. Here, we tested this hypothesis and examined whether these solutions would be universal or would need to be adapted to one's genetic and environmental makeups. We performed experimental evolutionary rescue in a yeast disease model for the Wiskott–Aldrich syndrome in two genetic backgrounds and carbon sources. We found that multiple aspects of the evolutionary rescue outcome depend on the genotype, the environment, or a combination thereof. Specifically, the compensatory mutation rate and type, the molecular rescue mechanism, the genetic target, and the associated fitness cost varied across contexts. The course of compensatory evolution is therefore highly contingent on the initial conditions in which the deleterious mutation occurs. In addition, these results reveal biologically favored therapeutic targets for the Wiskott–Aldrich syndrome, including the target of an unrelated clinically approved drug. Our results experimentally illustrate the importance of epistasis and environmental evolutionary constraints that shape the adaptive landscape and evolutionary rate of molecular networks.

Wss1 metalloprotease partners with Cdc48/Doa1 in processing genotoxic SUMO conjugates

Sumoylation during genotoxic stress regulates the composition of DNA repair complexes. The yeast metalloprotease Wss1 clears chromatin-bound sumoylated proteins. Wss1 and its mammalian analog, DVC1/Spartan, belong to minigluzincins family of proteases. Wss1 proteolytic activity is regulated by a cysteine switch mechanism activated by chemical stress and/or DNA binding. Wss1 is required for cell survival following UV irradiation, the smt3-331 mutation and Camptothecin-induced formation of covalent topoisomerase 1 complexes (Top1cc). Wss1 forms a SUMO-specific ternary complex with the AAA ATPase Cdc48 and an adaptor, Doa1. Upon DNA damage Wss1/Cdc48/Doa1 is recruited to sumoylated targets and catalyzes SUMO chain extension through a newly recognized SUMO ligase activity. Activation of Wss1 results in metalloprotease self-cleavage and proteolysis of associated proteins. In cells lacking Tdp1, clearance of topoisomerase covalent complexes becomes SUMO and Wss1-dependent. Upon genotoxic stress, Wss1 is vacuolar, suggesting a link between genotoxic stress and autophagy involving the Doa1 adapter.

Low-Rank and Sparse Matrix Decomposition for Genetic Interaction Data

Background. Epistatic miniarray profile (EMAP) studies have enabled the mapping of large-scale genetic interaction networks and generated large amounts of data in model organisms. One approach to analyze EMAP data is to identify gene modules with densely interacting genes. In addition, genetic interaction score (S score) reflects the degree of synergizing or mitigating effect of two mutants, which is also informative. Statistical approaches that exploit both modularity and the pairwise interactions may provide more insight into the underlying biology. However, the high missing rate in EMAP data hinders the development of such approaches. To address the above problem, we adopted the matrix decomposition methodology “low-rank and sparse decomposition” (LRSDec) to decompose EMAP data matrix into low-rank part and sparse part. Results. LRSDec has been demonstrated as an effective technique for analyzing EMAP data. We applied a synthetic dataset and an EMAP dataset studying RNA-related processes in Saccharomyces cerevisiae. Global views of the genetic cross talk between different RNA-related protein complexes and processes have been structured, and novel functions of genes have been predicted.

Cancer type-dependent genetic interactions between cancer driver alterations indicate plasticity of epistasis across cell types

Cancers, like many diseases, are normally caused by combinations of genetic alterations rather than by changes affecting single genes. It is well established that the genetic alterations that drive cancer often interact epistatically, having greater or weaker consequences in combination than expected from their individual effects. In a stringent statistical analysis of data from > 3,000 tumors, we find that the co-occurrence and mutual exclusivity relationships between cancer driver alterations change quite extensively in different types of cancer. This cannot be accounted for by variation in tumor heterogeneity or unrecognized cancer subtypes. Rather, it suggests that how genomic alterations interact cooperatively or partially redundantly to driver cancer changes in different types of cancers. This re-wiring of epistasis across cell types is likely to be a basic feature of genetic architecture, with important implications for understanding the evolution of multicellularity and human genetic diseases. In addition, if this plasticity of epistasis across cell types is also true for synthetic lethal interactions, a synthetic lethal strategy to kill cancer cells may frequently work in one type of cancer but prove ineffective in another.