The chemical genomic portrait of yeast: uncovering a phenotype for all genes

NAD(P)HX repair deficiency causes central metabolic perturbations in yeast and human cells

NADHX and NADPHX are hydrated and redox inactive forms of the NADH and NADPH cofactors, known to inhibit several dehydrogenases in vitro. A metabolite repair system that is conserved in all domains of life and that comprises the two enzymes NAD(P)HX dehydratase and NAD(P)HX epimerase, allows reconversion of both the S- and R-epimers of NADHX and NADPHX to the normal cofactors. An inherited deficiency in this system has recently been shown to cause severe neurometabolic disease in children. Although evidence for the presence of NAD(P)HX has been obtained in plant and human cells, little is known about the mechanism of formation of these derivatives in vivo and their potential effects on cell metabolism. Here, we show that NAD(P)HX dehydratase deficiency in yeast leads to an important, temperature-dependent NADHX accumulation in quiescent cells with a concomitant depletion of intracellular NAD⁺ and serine pools. We demonstrate that NADHX potently inhibits the first step of the serine synthesis pathway in yeast. Human cells deficient in the NAD(P)HX dehydratase also accumulated NADHX and showed decreased viability. In addition, those cells consumed more glucose and produced more lactate, potentially indicating impaired mitochondrial function. Our results provide first insights into how NADHX accumulation affects cellular functions and pave the way for a better understanding of the mechanism(s) underlying the rapid and severe neurodegeneration leading to early death in NADHX repair-deficient children.

A simple and inexpensive quantitative technique for determining chemical sensitivity in Saccharomyces cerevisiae

Chemical sensitivity, growth inhibition in response to a chemical, is a powerful phenotype that can reveal insight into diverse cellular processes. Chemical sensitivity assays are used in nearly every model system, however the yeast Saccharomyces cerevisiae provides a particularly powerful platform for discovery and mechanistic insight from chemical sensitivity assays. Here we describe a simple and inexpensive approach to determine chemical sensitivity quantitatively in yeast in the form of half maximal inhibitory concentration (IC50) using common laboratory equipment. We demonstrate the utility of this method using chemicals commonly used to monitor changes in membrane traffic. When compared to traditional agar-based plating methods, this method is more sensitive and can detect defects not apparent using other protocols. Additionally, this method reduces the experimental protocol from five days to 18 hours for the toxic amino acid canavanine. Furthermore, this method provides reliable results using lower amounts of chemicals. Finally, this method is easily adapted to additional chemicals as demonstrated with an engineered system that activates the spindle assembly checkpoint in response to rapamycin with differing efficiencies. This approach provides researchers with a cost-effective method to perform chemical genetic profiling without specialized equipment.

Recent advances in the genomic and molecular biology of Giardia

Giardia duodenalis is the most common gastrointestinal protozoan parasite of humans and a significant contributor to the global burden of both diarrheal disease and post-infectious chronic disorders. Robust tools for analyzing gene function in this parasite have been developed and a range of genetic tools are now available. These together with public databases have provided insights on the function of different genes in Giardia. In this review we provide a current perspective on different molecular aspects of Giardia related to genomics, regulation of encystation, trophozoite transcriptional responses to physiological and xenobiotic (drug-induced) stress, and mechanisms of drug resistance. We also examine recent insights that have contributed to gain knowledge in the study of VSPs, antigenic variation, epigenetics, DNA repair and in the direct manipulation of gene function in Giardia, with a particular focus on the inducible Cre/loxP system.

Machine learning and image-based profiling in drug discovery

The increase in imaging throughput, new analytical frameworks and high-performance computational resources open new avenues for data-rich phenotypic profiling of small molecules in drug discovery. Image-based profiling assays assessing single-cell phenotypes have been used to explore mechanisms of action, target efficacy and toxicity of small molecules. Technological advances to generate large data sets together with new machine learning approaches for the analysis of high-dimensional profiling data create opportunities to improve many steps in drug discovery. In this review, we will discuss how recent studies applied machine learning approaches in functional profiling workflows with a focus on chemical genetics. While their utility in image-based screening and profiling is predictably evident, examples of novel insights beyond the status quo based on the applications of machine learning approaches are just beginning to emerge. To enable discoveries, future studies also need to develop methodologies that lower the entry barriers to high-throughput profiling experiments by streamlining image-based profiling assays and providing applications for advanced learning technologies such as easy to deploy deep neural networks.

FgBud3, a Rho4-Interacting Guanine Nucleotide Exchange Factor, Is Involved in Polarity Growth, Cell Division and Pathogenicity of Fusarium graminearum

Rho GTPases are signaling macromolecules that are associated with developmental progression and pathogenesis of Fusarium graminearum. Generally, enzymatic activities of Rho GTPases are regulated by Rho GTPase guanine nucleotide exchange factors (RhoGEFs). In this study, we identified a putative RhoGEF encoding gene (FgBUD3) in F. graminearum database and proceeded further by using a functional genetic approach to generate FgBUD3 targeted gene deletion mutant. Phenotypic analysis results showed that the deletion of FgBUD3 caused severe reduction in growth of FgBUD3 mutant generated during this study. We also observed that the deletion of FgBUD3 completely abolished sexual reproduction and triggered the production of abnormal asexual spores with nearly no septum in ΔFgbud3 strain. Further results obtained from infection assays conducted during this research revealed that the FgBUD3 defective mutant lost its pathogenicity on wheat and hence, suggests FgBud3 plays an essential role in the pathogenicity of F. graminearum. Additional, results derived from yeast two-hybrid assays revealed that FgBud3 strongly interacted with FgRho4 compared to the interaction with FgRho2, FgRho3, and FgCdc42. Moreover, we found that FgBud3 interacted with both GTP-bound and GDP-bound form of FgRho4. From these results, we subsequently concluded that, the Rho4-interacting GEF protein FgBud3 crucially promotes vegetative growth, asexual and sexual development, cell division and pathogenicity in F. graminearum.

TOR complex 2-regulated protein kinase Ypk1 controls sterol distribution by inhibiting StARkin domain-containing proteins located at plasma membrane-endoplasmic reticulum contact sites

In our proteome-wide screen, Ysp2 (also known as Lam2/Ltc4) was identified as a likely physiologically relevant target of the TOR complex 2 (TORC2)-dependent protein kinase Ypk1 in the yeast Saccharomyces cerevisiae. Ysp2 was subsequently shown to be one of a new family of sterol-binding proteins located at plasma membrane (PM)-endoplasmic reticulum (ER) contact sites. Here we document that Ysp2 and its paralogue Lam4/Ltc3 are authentic Ypk1 substrates in vivo and show using genetic and biochemical criteria that Ypk1-mediated phosphorylation inhibits the ability of these proteins to promote retrograde transport of sterols from the PM to the ER. Furthermore, we provide evidence that a change in PM sterol homeostasis promotes cell survival under membrane-perturbing conditions known to activate TORC2-Ypk1 signaling. These observations define the underlying molecular basis of a new regulatory mechanism for cellular response to plasma membrane stress.

Mitochondrial dysfunction reduces yeast replicative lifespan by elevating RAS-dependent ROS production by the ER-localized NADPH oxidase Yno1

Mitochondrial dysfunction leads to the accumulation of reactive oxygen species (ROS) which is associated with cellular dysfunction, disease etiology, and senescence. Here, we used the eukaryotic model Saccharomyces cerevisiae, commonly studied for cellular aging, to demonstrate how defective mitochondrial function affects yeast replicative lifespan (RLS). We show that RLS of respiratory-deficient cells decreases significantly, indicating that the maintenance of RLS requires active respiration. The shortening of RLS due to mitochondrial dysfunction was not related to the accumulation of extrachromosomal ribosomal DNA circles, a well-known cause of aging in yeast. Instead, intracellular ROS and oxidatively damaged proteins increased in respiratory-deficient mutants. We show that, while the protein kinase A activity is not elevated, ROS generation in respiratory-deficient cells depends on RAS signaling pathway. The ER-localized NADPH oxidase Yno1 also played a role in producing ROS. Our data suggest that a severe defect in mitochondrial respiration accelerates cellular aging by disturbing protein homeostasis in yeast.

Epigallocatechin-3-O-gallate, the main green tea component, is toxic to Saccharomyces cerevisiae cells lacking the Fet3/Ftr1

Epigallocatechin-3-O-gallate (EGCG), the main green tea component, is intensively studied for its anti-oxidant, anti-inflammatory, anti-microbial and anti-cancer effects. In the present study, a screen on a Saccharomyces cerevisiae gene deletion library was performed to identify conditions under which EGCG had deleterious rather than beneficial effects. Two genes were identified whose deletion resulted in sensitivity to EGCG: FET3 and FTR1, encoding the components of the Fet3/Ftr1 high-affinity iron uptake system, also involved in Cu(I)/Cu(II) balance on the surface of yeast cells. The presence of EGCG in the growth medium induced the production of Cu(I), with deleterious effects on fet3Δ and ftr1Δ cells. Additionally, when combined, physiological surpluses of Cu(II) and EGCG acted in synergy not only against fet3Δ and ftr1Δ, but also against wild type cells, by generating surplus Cu(I) in the growth medium. The results imply that caution should be taken when combining EGCG-rich beverages/nutraceuticals with copper-rich foods.

High-dimensional single-cell phenotyping reveals extensive haploinsufficiency

Haploinsufficiency, a dominant phenotype caused by a heterozygous loss-of-function mutation, has been rarely observed. However, high-dimensional single-cell phenotyping of yeast morphological characteristics revealed haploinsufficiency phenotypes for more than half of 1,112 essential genes under optimal growth conditions. Additionally, 40% of the essential genes with no obvious phenotype under optimal growth conditions displayed haploinsufficiency under severe growth conditions. Haploinsufficiency was detected more frequently in essential genes than in nonessential genes. Similar haploinsufficiency phenotypes were observed mostly in mutants with heterozygous deletion of functionally related genes, suggesting that haploinsufficiency phenotypes were caused by functional defects of the genes. A global view of the gene network was presented based on the similarities of the haploinsufficiency phenotypes. Our dataset contains rich information regarding essential gene functions, providing evidence that single-cell phenotyping is a powerful approach, even in the heterozygous condition, for analyzing complex biological systems.

Diploid organisms harboring a wild-type gene and a loss-of-function mutation are called heterozygotes. They are expected to have weak or no individual phenotypes because the mutation is compensated for by the intact allele. The dominant inheritance of phenotypes in heterozygotes is an exceptional phenomenon called haploinsufficiency. Haploinsufficiency was thought to be a rare occurrence; however, a sensitive technique called high-dimensional single-cell phenotyping challenges this perspective. Investigations of single-cell phenotypes revealed that a large extent of the essential genes in yeast exhibit haploinsufficiency. Our analyses also provided crucial information on gene functional networks based on haploinsufficiency phenotypes. This work shows that high-dimensional single-cell phenotyping is a useful tool that can be used to better understand complex biological systems.

Database for High Throughput Screening Hits (dHITS): a simple tool to retrieve gene specific phenotypes from systematic screens done in yeast

In the last decade several collections of Saccharomyces cerevisiae yeast strains have been created. In these collections every gene is modified in a similar manner such as by a deletion or the addition of a protein tag. Such libraries have enabled a diversity of systematic screens, giving rise to large amounts of information regarding gene functions. However, often papers describing such screens focus on a single gene or a small set of genes and all other loci affecting the phenotype of choice (‘hits’) are only mentioned in tables that are provided as supplementary material and are often hard to retrieve or search. To help unify and make such data accessible, we have created a Database of High Throughput Screening Hits (dHITS). The dHITS database enables information to be obtained about screens in which genes of interest were found as well as the other genes that came up in that screen – all in a readily accessible and downloadable format. The ability to query large lists of genes at the same time provides a platform to easily analyse hits obtained from transcriptional analyses or other screens. We hope that this platform will serve as a tool to facilitate investigation of protein functions to the yeast community.

Integrative Approaches to Understanding the Pathogenic Role of Genetic Variation in Rheumatic Diseases

The use of high-throughput omics may help to understand the contribution of genetic variants to the pathogenesis of rheumatic diseases. We discuss the concept of missing heritability: that genetic variants do not explain the heritability of rheumatoid arthritis and related rheumatologic conditions. In addition to an overview of how integrative data analysis can lead to novel insights into mechanisms of rheumatic diseases, we describe statistical approaches to prioritizing genetic variants for future functional analyses. We illustrate how analyses of large datasets provide hope for improved approaches to the diagnosis, treatment, and prevention of rheumatic diseases.

Multi-environment fitness landscapes of a tRNA gene

A fitness landscape (FL) describes the genotype-fitness relationship in a given environment. To explain and predict evolution, it is imperative to measure the FL in multiple environments because the natural environment changes frequently. Using a high-throughput method that combines precise gene replacement with next-generation sequencing, we determine the in vivo FL of a yeast tRNA gene comprising over 23,000 genotypes in four environments. Although genotype-by-environment interaction (G×E) is abundantly detected, its pattern is so simple that we can transform an existing FL to that in a new environment with fitness measures of only a few genotypes in the new environment. Under each environment, we observe prevalent, negatively biased epistasis between mutations (G×G). Epistasis-by-environment interaction (G×G×E) is also prevalent, but trends in epistasis difference between environments are predictable. Our study thus reveals simple rules underlying seemingly complex FLs, opening the door to understanding and predicting FLs in general.

Detection of functional protein domains by unbiased genome-wide forward genetic screening

Establishing genetic and chemo-genetic interactions has played key roles in elucidating mechanisms by which certain chemicals perturb cellular functions. In contrast to gene disruption/depletion strategies to identify mechanisms of drug resistance, searching for point-mutational genetic suppressors that can identify separation- or gain-of-function mutations has been limited. Here, by demonstrating its utility in identifying chemical-genetic suppressors of sensitivity to the DNA topoisomerase I poison camptothecin or the poly(ADP-ribose) polymerase inhibitor olaparib, we detail an approach allowing systematic, large-scale detection of spontaneous or chemically-induced suppressor mutations in yeast or haploid mammalian cells in a short timeframe, and with potential applications in other haploid systems. In addition to applications in molecular biology research, this protocol can be used to identify drug targets and predict drug-resistance mechanisms. Mapping suppressor mutations on the primary or tertiary structures of protein suppressor hits provides insights into functionally relevant protein domains. Importantly, we show that olaparib resistance is linked to missense mutations in the DNA binding regions of PARP1, but not in its catalytic domain. This provides experimental support to the concept of PARP1 trapping on DNA as the prime source of toxicity to PARP inhibitors, and points to a novel olaparib resistance mechanism with potential therapeutic implications.

Gracob: a novel graph-based constant-column biclustering method for mining growth phenotype data

Motivation

Growth phenotype profiling of genome-wide gene-deletion strains over stress conditions can offer a clear picture that the essentiality of genes depends on environmental conditions. Systematically identifying groups of genes from such high-throughput data that share similar patterns of conditional essentiality and dispensability under various environmental conditions can elucidate how genetic interactions of the growth phenotype are regulated in response to the environment.

Results

We first demonstrate that detecting such ‘co-fit’ gene groups can be cast as a less well-studied problem in biclustering, i.e. constant-column biclustering. Despite significant advances in biclustering techniques, very few were designed for mining in growth phenotype data. Here, we propose Gracob, a novel, efficient graph-based method that casts and solves the constant-column biclustering problem as a maximal clique finding problem in a multipartite graph. We compared Gracob with a large collection of widely used biclustering methods that cover different types of algorithms designed to detect different types of biclusters. Gracob showed superior performance on finding co-fit genes over all the existing methods on both a variety of synthetic data sets with a wide range of settings, and three real growth phenotype datasets for E. coli, proteobacteria and yeast.

Availability and Implementation

Our program is freely available for download at http://sfb.kaust.edu.sa/Pages/Software.aspx.

Supplementary information

Supplementary data are available at Bioinformatics online.

Genome-wide association across Saccharomyces cerevisiae strains reveals substantial variation in underlying gene requirements for toxin tolerance

Cellulosic plant biomass is a promising sustainable resource for generating alternative biofuels and biochemicals with microbial factories. But a remaining bottleneck is engineering microbes that are tolerant of toxins generated during biomass processing, because mechanisms of toxin defense are only beginning to emerge. Here, we exploited natural diversity in 165 Saccharomyces cerevisiae strains isolated from diverse geographical and ecological niches, to identify mechanisms of hydrolysate-toxin tolerance. We performed genome-wide association (GWA) analysis to identify genetic variants underlying toxin tolerance, and gene knockouts and allele-swap experiments to validate the involvement of implicated genes. In the process of this work, we uncovered a surprising difference in genetic architecture depending on strain background: in all but one case, knockout of implicated genes had a significant effect on toxin tolerance in one strain, but no significant effect in another strain. In fact, whether or not the gene was involved in tolerance in each strain background had a bigger contribution to strain-specific variation than allelic differences. Our results suggest a major difference in the underlying network of causal genes in different strains, suggesting that mechanisms of hydrolysate tolerance are very dependent on the genetic background. These results could have significant implications for interpreting GWA results and raise important considerations for engineering strategies for industrial strain improvement.

Understanding the genetic architecture of complex traits is important for elucidating the genotype-phenotype relationship. Many studies have sought genetic variants that underlie phenotypic variation across individuals, both to implicate causal variants and to inform on architecture. Here we used genome-wide association analysis to identify genes and processes involved in tolerance of toxins found in plant-biomass hydrolysate, an important substrate for sustainable biofuel production. We found substantial variation in whether or not individual genes were important for tolerance across genetic backgrounds. Whether or not a gene was important in a given strain background explained more variation than the alleleic differences in the gene. These results suggest substantial variation in gene contributions, and perhaps underlying mechanisms, of toxin tolerance.

Gene-gene and gene-environment interactions in complex traits in yeast

One of the fundamental questions in biology is how the genotype regulates the phenotype. An increasing number of studies indicate that, in most cases, the effect of a genetic locus on the phenotype is context-dependent, i.e. it is influenced by the genetic background and the environment in which the phenotype is measured. Still, the majority of the studies, in both model organisms and humans, that map the genetic regulation of phenotypic variation in complex traits primarily identify additive loci with independent effects. This does not reflect an absence of the contribution of genetic interactions to phenotypic variation, but instead is a consequence of the technical limitations in mapping gene-gene interactions (GGI) and gene-environment interactions (GEI). Yeast, with its detailed molecular understanding, diverse population genomics and ease of genetic manipulation, is a unique and powerful resource to study the contributions of GGI and GEI in the regulation of phenotypic variation. Here we review studies in yeast that have identified GGI and GEI that regulate phenotypic variation, and discuss the contribution of these findings in explaining missing heritability of complex traits, and how observations from these GGI and GEI studies enhance our understanding of the mechanisms underlying genetic robustness and adaptability that shape the architecture of the genotype-phenotype map.

Hidden Complexity of Yeast Adaptation under Simple Evolutionary Conditions

Few studies have "quantitatively" probed how adaptive mutations result in increased fitness. Even in simple microbial evolution experiments, with full knowledge of the underlying mutations and specific growth conditions, it is challenging to determine where within a growth-saturation cycle those fitness gains occur. A common implicit assumption is that most benefits derive from an increased exponential growth rate. Here, we instead show that, in batch serial transfer experiments, adaptive mutants' fitness gains can be dominated by benefits that are accrued in one growth cycle, but not realized until the next growth cycle. For thousands of evolved clones (most with only a single mutation), we systematically varied the lengths of fermentation, respiration, and stationary phases to assess how their fitness, as measured by barcode sequencing, depends on these phases of the growth-saturation-dilution cycles. These data revealed that, whereas all adaptive lineages gained similar and modest benefits from fermentation, most of the benefits for the highest fitness mutants came instead from the time spent in respiration. From monoculture and high-resolution pairwise fitness competition experiments for a dozen of these clones, we determined that the benefits "accrued" during respiration are only largely "realized" later as a shorter duration of lag phase in the following growth cycle. These results reveal hidden complexities of the adaptive process even under ostensibly simple evolutionary conditions, in which fitness gains can accrue during time spent in a growth phase with little cell division, and reveal that the memory of those gains can be realized in the subsequent growth cycle.

Recent advances in the mechanism of selenoamino acids toxicity in eukaryotic cells

Selenium is an essential trace element due to its incorporation into selenoproteins with important biological functions. However, at high doses it is toxic. Selenium toxicity is generally attributed to the induction of oxidative stress. However, it has become apparent that the mode of action of seleno-compounds varies, depending on its chemical form and speciation. Recent studies in various eukaryotic systems, in particular the model organism Saccharomyces cerevisiae, provide new insights on the cytotoxic mechanisms of selenomethionine and selenocysteine. This review first summarizes current knowledge on reactive oxygen species (ROS)-induced genotoxicity of inorganic selenium species. Then, we discuss recent advances on our understanding of the molecular mechanisms of selenocysteine and selenomethionine cytotoxicity. We present evidences indicating that both oxidative stress and ROS-independent mechanisms contribute to selenoamino acids cytotoxicity. These latter mechanisms include disruption of protein homeostasis by selenocysteine misincorporation in proteins and/or reaction of selenols with protein thiols.

Mapping Causal Variants with Single-Nucleotide Resolution Reveals Biochemical Drivers of Phenotypic Change

Understanding the sequence determinants that give rise to diversity among individuals and species is the central challenge of genetics. However, despite ever greater numbers of sequenced genomes, most genome-wide association studies cannot distinguish causal variants from linked passenger mutations spanning many genes. We report that this inherent challenge can be overcome in model organisms. By pushing the advantages of inbred crossing to its practical limit in Saccharomyces cerevisiae, we improved the statistical resolution of linkage analysis to single nucleotides. This "super-resolution" approach allowed us to map 370 causal variants across 26 quantitative traits. Missense, synonymous, and cis-regulatory mutations collectively gave rise to phenotypic diversity, providing mechanistic insight into the basis of evolutionary divergence. Our data also systematically unmasked complex genetic architectures, revealing that multiple closely linked driver mutations frequently act on the same quantitative trait. Single-nucleotide mapping thus complements traditional deletion and overexpression screening paradigms and opens new frontiers in quantitative genetics.

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.

Natural Variation in SER1 and ENA6 Underlie Condition-Specific Growth Defects in Saccharomyces cerevisiae

Despite their ubiquitous use in laboratory strains, naturally occurring loss-of-function mutations in genes encoding core metabolic enzymes are relatively rare in wild isolates of Saccharomyces cerevisiae. Here, we identify a naturally occurring serine auxotrophy in a sake brewing strain from Japan. Through a cross with a honey wine (white tecc) brewing strain from Ethiopia, we map the minimal medium growth defect to SER1, which encodes 3-phosphoserine aminotransferase and is orthologous to the human disease gene, PSAT1. To investigate the impact of this polymorphism under conditions of abundant external nutrients, we examine growth in rich medium alone or with additional stresses, including the drugs caffeine and rapamycin and relatively high concentrations of copper, salt, and ethanol. Consistent with studies that found widespread effects of different auxotrophies on RNA expression patterns in rich media, we find that the SER1 loss-of-function allele dominates the quantitative trait locus (QTL) landscape under many of these conditions, with a notable exacerbation of the effect in the presence of rapamycin and caffeine. We also identify a major-effect QTL associated with growth on salt that maps to the gene encoding the sodium exporter, ENA6. We demonstrate that the salt phenotype is largely driven by variation in the ENA6 promoter, which harbors a deletion that removes binding sites for the Mig1 and Nrg1 transcriptional repressors. Thus, our results identify natural variation associated with both coding and regulatory regions of the genome that underlie strong growth phenotypes.

Phenotype inference in an Escherichia coli strain panel

Understanding how genetic variation contributes to phenotypic differences is a fundamental question in biology. Combining high-throughput gene function assays with mechanistic models of the impact of genetic variants is a promising alternative to genome-wide association studies. Here we have assembled a large panel of 696 Escherichia coli strains, which we have genotyped and measured their phenotypic profile across 214 growth conditions. We integrated variant effect predictors to derive gene-level probabilities of loss of function for every gene across all strains. Finally, we combined these probabilities with information on conditional gene essentiality in the reference K-12 strain to compute the growth defects of each strain. Not only could we reliably predict these defects in up to 38% of tested conditions, but we could also directly identify the causal variants that were validated through complementation assays. Our work demonstrates the power of forward predictive models and the possibility of precision genetic interventions.

Was the Watchmaker Blind? Or Was She One-Eyed?

The question whether evolution is blind is usually presented as a choice between no goals at all ('the blind watchmaker') and long-term goals which would be external to the organism, for example in the form of special creation or intelligent design. The arguments either way do not address the question whether there are short-term goals within rather than external to organisms. Organisms and their interacting populations have evolved mechanisms by which they can harness blind stochasticity and so generate rapid functional responses to environmental challenges. They can achieve this by re-organising their genomes and/or their regulatory networks. Epigenetic as well as DNA changes are involved. Evolution may have no foresight, but it is at least partially directed by organisms themselves and by the populations of which they form part. Similar arguments support partial direction in the evolution of behavior.

Drug toxicity profiling of a Saccharomyces cerevisiae deubiquitinase deletion panel shows that acetaminophen mimics tyrosine

Post-translational protein modification by addition or removal of the small polypeptide ubiquitin is involved in a range of critical cellular processes, like proteasomal protein degradation, DNA repair, gene expression, internalization of membrane proteins, and drug sensitivity. We recently identified genes important for acetaminophen (APAP) toxicity in a comprehensive screen and our findings suggested that a small set of yeast strains carrying deletions of ubiquitin-related genes can be informative for drug toxicity profiling. In yeast, approximately 20 different deubiquitinating enzymes (DUBs) have been identified, of which only one is essential for viability. We investigated whether the toxicity profile of DUB deletion yeast strains would be informative about the toxicological mode of action of APAP. A set of DUB deletion strains was tested for sensitivity and resistance to a diverse series of compounds, including APAP, quinine, ibuprofen, rapamycin, cycloheximide, cadmium, peroxide and amino acids and a cluster analysis was performed. Most DUB deletion strains showed an altered growth pattern when exposed to these compounds by being either more sensitive or more resistant than WT. Toxicity profiling of the DUB strains revealed a remarkable overlap between the amino acid tyrosine and acetaminophen (APAP), but not its stereoisomer AMAP. Furthermore, co-exposure of cells to both APAP and tyrosine showed an enhancement of the cellular growth inhibition, suggesting that APAP and tyrosine have a similar mode of action.

A High-Resolution Genome-Wide CRISPR/Cas9 Viability Screen Reveals Structural Features and Contextual Diversity of the Human Cell-Essential Proteome

To interrogate genes essential for cell growth, proliferation and survival in human cells, we carried out a genome-wide clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 screen in a B-cell lymphoma line using a custom extended-knockout (EKO) library of 278,754 single-guide RNAs (sgRNAs) that targeted 19,084 RefSeq genes, 20,852 alternatively spliced exons, and 3,872 hypothetical genes. A new statistical analysis tool called robust analytics and normalization for knockout screens (RANKS) identified 2,280 essential genes, 234 of which were unique. Individual essential genes were validated experimentally and linked to ribosome biogenesis and stress responses. Essential genes exhibited a bimodal distribution across 10 different cell lines, consistent with a continuous variation in essentiality as a function of cell type. Genes essential in more lines had more severe fitness defects and encoded the evolutionarily conserved structural cores of protein complexes, whereas genes essential in fewer lines formed context-specific modules and encoded subunits at the periphery of essential complexes. The essentiality of individual protein residues across the proteome correlated with evolutionary conservation, structural burial, modular domains, and protein interaction interfaces. Many alternatively spliced exons in essential genes were dispensable and were enriched for disordered regions. Fitness defects were observed for 44 newly evolved hypothetical reading frames. These results illuminate the contextual nature and evolution of essential gene functions in human cells.

Deciphering the Genic Basis of Yeast Fitness Variation by Simultaneous Forward and Reverse Genetics

The budding yeast Saccharomyces cerevisiae is the best studied eukaryote in molecular and cell biology, but its utility for understanding the genetic basis of phenotypic variation in natural populations is limited by inefficient association mapping due to strong and complex population structure. To overcome this challenge, we generated genome sequences for 85 strains and performed a comprehensive population genomic survey of a total of 190 diverse strains. We identified considerable variation in population structure among chromosomes and identified 181 genes that are absent from the reference genome. Many of these nonreference genes are expressed and we functionally confirmed that two of these genes confer increased resistance to antifungals. Next, we simultaneously measured the growth rates of over 4,500 laboratory strains, each of which lacks a nonessential gene, and 81 natural strains across multiple environments using unique DNA barcode present in each strain. By combining the genome-wide reverse genetic information gained from the gene deletion strains with a genome-wide association analysis from the natural strains, we identified genomic regions associated with fitness variation in natural populations. To experimentally validate a subset of these associations, we used reciprocal hemizygosity tests, finding that while the combined forward and reverse genetic approaches can identify a single causal gene, the phenotypic consequences of natural genetic variation often follow a complicated pattern. The resources and approach provided outline an efficient and reliable route to association mapping in yeast and significantly enhance its value as a model for understanding the genetic mechanisms underlying phenotypic variation and evolution in natural populations.

Genetic variation in Dip5, an amino acid permease, and Pdr5, a multiple drug transporter, regulates glyphosate resistance in S. cerevisiae

S. cerevisiae from different environments are subject to a wide range of selective pressures, whether intentional or by happenstance. Chemicals classified by their application, such as herbicides, fungicides and antibiotics, can affect non-target organisms. First marketed as RoundUp™, glyphosate is the most widely used herbicide. In plants, glyphosate inhibits EPSPS, of the shikimate pathway, which is present in many organisms but lacking in mammals. The shikimate pathway produces chorismate which is the precursor to all the aromatic amino acids, para-aminobenzoic acid, and Coenzyme Q10. Crops engineered to be resistant to glyphosate contain a homolog of EPSPS that is not bound by glyphosate. Here, we show that S. cerevisiae has a wide-range of glyphosate resistance. Sequence comparison between the target proteins, i.e., the plant EPSPS and the yeast orthologous protein Aro1, predicted that yeast would be resistant to glyphosate. However, the growth variation seen in the subset of yeast tested was not due to polymorphisms within Aro1, instead, it was caused by genetic variation in an ABC multiple drug transporter, Pdr5, and an amino acid permease, Dip5. Using genetic variation as a probe into glyphosate response, we uncovered mechanisms that contribute to the transportation of glyphosate in and out of the cell. Taking advantage of the natural genetic variation within yeast and measuring growth under different conditions that would change the use of the shikimate pathway, we uncovered a general transport mechanism of glyphosate into eukaryotic cells.

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.

TRMT1-Catalyzed tRNA Modifications Are Required for Redox Homeostasis To Ensure Proper Cellular Proliferation and Oxidative Stress Survival

Mutations in the tRNA methyltransferase 1 (TRMT1) gene have been identified as the cause of certain forms of autosomal-recessive intellectual disability (ID). However, the molecular pathology underlying ID-associated TRMT1 mutations is unknown, since the biological role of the encoded TRMT1 protein remains to be determined. Here, we have elucidated the molecular targets and function of TRMT1 to uncover the cellular effects of ID-causing TRMT1 mutations. Using human cells that have been rendered deficient in TRMT1, we show that TRMT1 is responsible for catalyzing the dimethylguanosine (m2,2G) base modification in both nucleus- and mitochondrion-encoded tRNAs. TRMT1-deficient cells exhibit decreased proliferation rates, alterations in global protein synthesis, and perturbations in redox homeostasis, including increased endogenous ROS levels and hypersensitivity to oxidizing agents. Notably, ID-causing TRMT1 variants are unable to catalyze the formation of m2,2G due to defects in RNA binding and cannot rescue oxidative stress sensitivity. Our results uncover a biological role for TRMT1-catalyzed tRNA modification in redox metabolism and show that individuals with TRMT1-associated ID are likely to have major perturbations in cellular homeostasis due to the lack of m2,2G modifications.

Emerging and evolving concepts in gene essentiality

Gene essentiality is a founding concept of genetics with important implications in both fundamental and applied research. Multiple screens have been performed over the years in bacteria, yeasts, animals and more recently in human cells to identify essential genes. A mounting body of evidence suggests that gene essentiality, rather than being a static and binary property, is both context dependent and evolvable in all kingdoms of life. This concept of a non-absolute nature of gene essentiality changes our fundamental understanding of essential biological processes and could directly affect future treatment strategies for cancer and infectious diseases.

Taxonomically Restricted Genes with Essential Functions Frequently Play Roles in Chromosome Segregation in Caenorhabditis elegans and Saccharomyces cerevisiae

Genes encoding essential components of core cellular processes are typically highly conserved across eukaryotes. However, a small proportion of essential genes are highly taxonomically restricted; there appear to be no similar genes outside the genomes of highly related species. What are the functions of these poorly characterized taxonomically restricted genes (TRGs)? Systematic screens in Saccharomyces cerevisiae and Caenorhabditis elegans previously identified yeast or nematode TRGs that are essential for viability and we find that these genes share many molecular features, despite having no significant sequence similarity. Specifically, we find that those TRGs with essential phenotypes have an expression profile more similar to highly conserved genes, they have more protein–protein interactions and more protein disorder. Surprisingly, many TRGs play central roles in chromosome segregation; a core eukaryotic process. We thus find that genes that appear to be highly evolutionarily restricted do not necessarily play roles in species-specific biological functions but frequently play essential roles in core eukaryotic processes.

Dual targeting of the cancer antioxidant network with 1,4-naphthoquinone fused Gold(i) N-heterocyclic carbene complexes

To achieve a systems-based approach to targeting the antioxidant pathway, 1,4-naphthoquinone annulated N-heterocyclic carbene (NHC) [bis(1,3-dimesityl-4,5-naphthoquino-imidazol-2-ylidene)-gold(i)] [silver(i) dichloride] (1), [bis(1,3-dimesityl-4,5-naphthoquino-imidazol-2-ylidene)-gold(i)] chloride (2), and 1,3-dimesityl-4,5-naphthoquino-imidazol-2-ylidene)-gold(i) chloride (3)) were designed, synthesized, and tested for biological activity in a series of human cancer cell lines. The solution phase of complexes 1-3 were assigned using several spectroscopy techniques, including NMR spectroscopic analysis. Complexes 1 and 3 were further characterized by single crystal X-ray diffraction analysis. Electrochemical and spectroelectrochemical studies revealed that quinone reductions are reversible and that the electrochemically generated semiquinone and quinone dianions are stable under these conditions. Complex 1, containing two NHC-quinone moieties (to accentuate exogenous ROS via redox cycling) centered around a Au(i) center (to inactivate thioredoxin reductase (TrxR) irreversibly), was found to inhibit cancer cell proliferation to a much greater extent than the individual components (i.e., Au(i)-NHC alone or naphthoquinone alone). Treatment of A549 lung cancer cells with 1 produced a 27-fold increase in exogenous reactive oxygen species (ROS) which was found to localize to the mitochondria. The inhibition of TrxR, an essential mediator of ROS homeostasis, was achieved in the same cell line at low administrated concentrations of 1. TrxR inhibition by 1 was similar to that of auranofin, a gold(i) containing complex known to inhibit TrxR irreversibly. Complex 1 was found to induce cell death via an apoptotic mechanism as confirmed by annexin-V staining. Complex 1 was demonstrated to be efficacious in zebrafish bearing A549 xenografts. These results provide support for the suggestion that a dual targeting approach that involves reducing ROS tolerance while concurrently increasing ROS production can perturb antioxidant homeostasis, enhance cancer cell death in vitro, and reduce tumor burden in vivo, as inferred from preliminary zebra fish model studies.

Evolution viewed from physics, physiology and medicine

Stochasticity is harnessed by organisms to generate functionality. Randomness does not, therefore, necessarily imply lack of function or 'blind chance' at higher levels. In this respect, biology must resemble physics in generating order from disorder. This fact is contrary to Schrödinger's idea of biology generating phenotypic order from molecular-level order, which inspired the central dogma of molecular biology. The order originates at higher levels, which constrain the components at lower levels. We now know that this includes the genome, which is controlled by patterns of transcription factors and various epigenetic and reorganization mechanisms. These processes can occur in response to environmental stress, so that the genome becomes 'a highly sensitive organ of the cell' (McClintock). Organisms have evolved to be able to cope with many variations at the molecular level. Organisms also make use of physical processes in evolution and development when it is possible to arrive at functional development without the necessity to store all information in DNA sequences. This view of development and evolution differs radically from that of neo-Darwinism with its emphasis on blind chance as the origin of variation. Blind chance is necessary, but the origin of functional variation is not at the molecular level. These observations derive from and reinforce the principle of biological relativity, which holds that there is no privileged level of causation. They also have important implications for medical science.

Chemical genetics in drug discovery

Chemical-genetic approaches are based on measuring the cellular outcome of combining genetic and chemical perturbations in large-numbers in tandem. In these approaches the contribution of every gene to the fitness of an organism is measured upon exposure to different chemicals. Current technological advances enable the application of chemical genetics to almost any organism and at an unprecedented throughput. Here we review the underlying concepts behind chemical genetics, present its different vignettes and illustrate how such approaches can propel drug discovery.

Genome-scale modeling of yeast: chronology, applications and critical perspectives

Over the last 15 years, several genome-scale metabolic models (GSMMs) were developed for different yeast species, aiding both the elucidation of new biological processes and the shift toward a bio-based economy, through the design of in silico inspired cell factories. Here, an historical perspective of the GSMMs built over time for several yeast species is presented and the main inheritance patterns among the metabolic reconstructions are highlighted. We additionally provide a critical perspective on the overall genome-scale modeling procedure, underlining incomplete model validation and evaluation approaches and the quest for the integration of regulatory and kinetic information into yeast GSMMs. A summary of experimentally validated model-based metabolic engineering applications of yeast species is further emphasized, while the main challenges and future perspectives for the field are finally addressed.

Functional Profiling of a Plasmodium Genome Reveals an Abundance of Essential Genes

The genomes of malaria parasites contain many genes of unknown function. To assist drug development through the identification of essential genes and pathways, we have measured competitive growth rates in mice of 2,578 barcoded Plasmodium berghei knockout mutants, representing >50% of the genome, and created a phenotype database. At a single stage of its complex life cycle, P. berghei requires two-thirds of genes for optimal growth, the highest proportion reported from any organism and a probable consequence of functional optimization necessitated by genomic reductions during the evolution of parasitism. In contrast, extreme functional redundancy has evolved among expanded gene families operating at the parasite-host interface. The level of genetic redundancy in a single-celled organism may thus reflect the degree of environmental variation it experiences. In the case of Plasmodium parasites, this helps rationalize both the relative successes of drugs and the greater difficulty of making an effective vaccine.

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Two-thirds of Plasmodium berghei genes contribute to normal blood stage growth

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The core genome of malaria parasites is highly optimized for rapid host colonization

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Essential parasite genes and pathways are identified for drug target prioritization

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Low functional redundancy reflects the constant environment encountered by a parasite

Synthetic essentiality: Targeting tumor suppressor deficiencies in cancer

In this review, we summarize recent work exploring a novel conceptual approach termed "synthetic essentiality" as a means for targeting specific tumor suppressor gene deficiencies in cancer. With the aid of extensive publically available cancer genome and clinical databases, "synthetic essentiality" could be utilized to identify synthetic essential genes, which might be occasionally deleted in some cancers but almost always retained in the context of a specific tumor suppressor deficiency. Synthetic essentiality expands the existing concepts for therapeutic strategies, including oncogene addiction, tumor maintenance, synthetic, and collateral lethality, to provide a framework for the discovery of cancer-specific vulnerabilities. Enabled by ever-expanding large-scale genome datasets and genome-scale functional screens, the "synthetic essentiality" framework provides an avenue for the identification of context-specific therapeutic targets and development of patient responder hypotheses for novel and existing therapies.

No Evidence That Protein Noise-Induced Epigenetic Epistasis Constrains Gene Expression Evolution

Changes in gene expression can affect phenotypes and therefore both its level and stochastic variability are frequently under selection. It has recently been proposed that epistatic interactions influence gene expression evolution: gene pairs where simultaneous knockout is more deleterious than expected should evolve reduced expression noise to avoid concurrent low expression of both proteins. In apparent support, yeast genes with many epistatic partners have low expression variation both among isogenic individuals and between species. However, the specific predictions and basic assumptions of this verbal model remain untested. Using bioinformatics analysis, we first demonstrate that the model's predictions are unsupported by available large-scale data. Based on quantitative biochemical modeling, we then show that epistasis between expression reductions (epigenetic epistasis) is not expected to aggravate the fitness cost of stochastic expression, which is in sharp contrast to the verbal argument. This nonintuitive result can be readily explained by the typical diminishing return of fitness on gene activity and by the fact that expression noise not only decreases but also increases the abundance of proteins. Overall, we conclude that stochastic variation in epistatic partners is unlikely to drive noise minimization or constrain gene expression divergence on a genomic scale.

Network-assisted target identification for haploinsufficiency and homozygous profiling screens

Chemical genomic screens have recently emerged as a systematic approach to drug discovery on a genome-wide scale. Drug target identification and elucidation of the mechanism of action (MoA) of hits from these noisy high-throughput screens remain difficult. Here, we present GIT (Genetic Interaction Network-Assisted Target Identification), a network analysis method for drug target identification in haploinsufficiency profiling (HIP) and homozygous profiling (HOP) screens. With the drug-induced phenotypic fitness defect of the deletion of a gene, GIT also incorporates the fitness defects of the gene's neighbors in the genetic interaction network. On three genome-scale yeast chemical genomic screens, GIT substantially outperforms previous scoring methods on target identification on HIP and HOP assays, respectively. Finally, we showed that by combining HIP and HOP assays, GIT further boosts target identification and reveals potential drug's mechanism of action.

Extensive Copy Number Variation in Fermentation-Related Genes Among Saccharomyces cerevisiae Wine Strains

Due to the importance of Saccharomyces cerevisiae in wine-making, the genomic variation of wine yeast strains has been extensively studied. One of the major insights stemming from these studies is that wine yeast strains harbor low levels of genetic diversity in the form of single nucleotide polymorphisms (SNPs). Genomic structural variants, such as copy number (CN) variants, are another major type of variation segregating in natural populations. To test whether genetic diversity in CN variation is also low across wine yeast strains, we examined genome-wide levels of CN variation in 132 whole-genome sequences of S. cerevisiae wine strains. We found an average of 97.8 CN variable regions (CNVRs) affecting ∼4% of the genome per strain. Using two different measures of CN diversity, we found that gene families involved in fermentation-related processes such as copper resistance (CUP), flocculation (FLO), and glucose metabolism (HXT), as well as the SNO gene family whose members are expressed before or during the diauxic shift, showed substantial CN diversity across the 132 strains examined. Importantly, these same gene families have been shown, through comparative transcriptomic and functional assays, to be associated with adaptation to the wine fermentation environment. Our results suggest that CN variation is a substantial contributor to the genomic diversity of wine yeast strains, and identify several candidate loci whose levels of CN variation may affect the adaptation and performance of wine yeast strains during fermentation.

Fitness effects of mutation: testing genetic redundancy in Arabidopsis thaliana

Screens of organisms with disruptive mutations in a single gene often fail to detect phenotypic consequences for the majority of mutants. One explanation for this phenomenon is that the presence of paralogous loci provides genetic redundancy. However, it is also possible that the assayed traits are affected by few loci, that effects could be subtle or that phenotypic effects are restricted to certain environments. We assayed a set of T-DNA insertion mutant lines of Arabidopsis thaliana to determine the frequency with which mutation affected fitness-related phenotypes. We found that between 8% and 42% of the assayed lines had altered fitness from the wild type. Furthermore, many of these lines exhibited fitness greater than the wild type. In a second experiment, we grew a subset of the lines in multiple environments and found whether a T-DNA insert increased or decreased fitness traits depended on the assay environment. Overall, our evidence contradicts the hypothesis that genetic redundancy is a common phenomenon in A. thaliana for fitness traits. Evidence for redundancy from prior screens of knockout mutants may often be an artefact of the design of the phenotypic assays which have focused on less complex phenotypes than fitness and have used single environments. Finally, our study adds to evidence that beneficial mutations may represent a significant component of the mutational spectrum of A. thaliana.

Yeast response and tolerance to benzoic acid involves the Gcn4- and Stp1-regulated multidrug/multixenobiotic resistance transporter Tpo1

The action of benzoic acid in the food and beverage industries is compromised by the ability of spoilage yeasts to cope with this food preservative. Benzoic acid occurs naturally in many plants and is an intermediate compound in the biosynthesis of many secondary metabolites. The understanding of the mechanisms underlying the response and resistance to benzoic acid stress in the eukaryotic model yeast is thus crucial to design more suitable strategies to deal with this toxic lipophilic weak acid. In this study, the Saccharomyces cerevisiae multidrug transporter Tpo1 was demonstrated to confer resistance to benzoic acid. TPO1 transcript levels were shown to be up-regulated in yeast cells suddenly exposed to this stress agent. This up-regulation is under the control of the Gcn4 and Stp1 transcription factors, involved in the response to amino acid availability, but not under the regulation of the multidrug resistance transcription factors Pdr1 and Pdr3 that have binding sites in TPO1 promoter region. Benzoic acid stress was further shown to affect the intracellular pool of amino acids and polyamines. The observed decrease in the concentration of these nitrogenous compounds, registered upon benzoic acid stress exposure, was not found to be dependent on Tpo1, although the limitation of yeast cells on nitrogenous compounds was found to activate Tpo1 expression. Altogether, the results described in this study suggest that Tpo1 is one of the key players standing in the crossroad between benzoic acid stress response and tolerance and the control of the intracellular concentration of nitrogenous compounds. Also, results can be useful to guide the design of more efficient preservation strategies and the biotechnological synthesis of benzoic acid or benzoic acid-derived compounds.

The effect of acetaminophen on ubiquitin homeostasis in Saccharomyces cerevisiae

Acetaminophen (APAP), although considered a safe drug, is one of the major causes of acute liver failure by overdose, and therapeutic chronic use can cause serious health problems. Although the reactive APAP metabolite N-acetyl-p-benzoquinoneimine (NAPQI) is clearly linked to liver toxicity, toxicity of APAP is also found without drug metabolism of APAP to NAPQI. To get more insight into mechanisms of APAP toxicity, a genome-wide screen in Saccharomyces cerevisiae for APAP-resistant deletion strains was performed. In this screen we identified genes related to the DNA damage response. Next, we investigated the link between genotype and APAP-induced toxicity or resistance by performing a more detailed screen with a library containing mutants of 1522 genes related to nuclear processes, like DNA repair and chromatin remodelling. We identified 233 strains that had an altered growth rate relative to wild type, of which 107 showed increased resistance to APAP and 126 showed increased sensitivity. Gene Ontology analysis identified ubiquitin homeostasis, regulation of transcription of RNA polymerase II genes, and the mitochondria-to-nucleus signalling pathway to be associated with APAP resistance, while histone exchange and modification, and vesicular transport were connected to APAP sensitivity. Indeed, we observed a link between ubiquitin levels and APAP resistance, whereby ubiquitin deficiency conferred resistance to APAP toxicity while ubiquitin overexpression resulted in sensitivity. The toxicity profile of various chemicals, APAP, and its positional isomer AMAP on a series of deletion strains with ubiquitin deficiency showed a unique resistance pattern for APAP. Furthermore, exposure to APAP increased the level of free ubiquitin and influenced the ubiquitination of proteins. Together, these results uncover a role for ubiquitin homeostasis in APAP-induced toxicity.

Growth condition dependency is the major cause of non-responsiveness upon genetic perturbation

Investigating the role and interplay between individual proteins in biological processes is often performed by assessing the functional consequences of gene inactivation or removal. Depending on the sensitivity of the assay used for determining phenotype, between 66% (growth) and 53% (gene expression) of Saccharomyces cerevisiae gene deletion strains show no defect when analyzed under a single condition. Although it is well known that this non-responsive behavior is caused by different types of redundancy mechanisms or by growth condition/cell type dependency, it is not known what the relative contribution of these different causes is. Understanding the underlying causes of and their relative contribution to non-responsive behavior upon genetic perturbation is extremely important for designing efficient strategies aimed at elucidating gene function and unraveling complex cellular systems. Here, we provide a systematic classification of the underlying causes of and their relative contribution to non-responsive behavior upon gene deletion. The overall contribution of redundancy to non-responsive behavior is estimated at 29%, of which approximately 17% is due to homology-based redundancy and 12% is due to pathway-based redundancy. The major determinant of non-responsiveness is condition dependency (71%). For approximately 14% of protein complexes, just-in-time assembly can be put forward as a potential mechanistic explanation for how proteins can be regulated in a condition dependent manner. Taken together, the results underscore the large contribution of growth condition requirement to non-responsive behavior, which needs to be taken into account for strategies aimed at determining gene function. The classification provided here, can also be further harnessed in systematic analyses of complex cellular systems.

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.