Hierarchical modularity and the evolution of genetic interactomes across species

Fish Evo-Devo: Moving Toward Species-Specific and Knowledge-Based Interactome

A knowledge-based interactome maps interactions among proteins and molecules within a cell using experimental data, computational predictions, and literature mining. These interactomes are vital for understanding cellular functions, pathways, and the evolutionary conservation of protein interactions. They reveal how interactions regulate growth, differentiation, and development. Transitioning to functionally validated interactomes is crucial in evolutionary developmental biology (Evo-Devo), especially for non-model species, to uncover unique regulatory networks, evolutionary novelties, and reliable gene interaction models. This enhances our understanding of complex trait evolution across species. The European Evo-Devo 2024 conference in Helsinki hosted the first fish-specific Evo-Devo symposium, highlighting the growing interest in fish models. Advances in genome annotation, genome editing, imaging, and molecular screening are expanding fish Evo-Devo research. High-throughput molecular data have enabled the deduction of gene regulatory networks. The next steps involve creating species-specific interactomes, validating them functionally, and integrating additional molecular data to deepen the understanding of complex regulatory interactions in fish Evo-Devo. This short review aims to address the logical steps for this transition, as well as the necessities and limitations of this journey.

PhpCNF-Y transcription factor infiltrates heterochromatin to generate cryptic intron-containing transcripts crucial for small RNA production

The assembly of repressive heterochromatin in eukaryotic genomes is crucial for silencing lineage-inappropriate genes and repetitive DNA elements. Paradoxically, transcription of repetitive elements within constitutive heterochromatin domains is required for RNA-based mechanisms, such as the RNAi pathway, to target heterochromatin assembly proteins. However, the mechanism by which heterochromatic repeats are transcribed has been unclear. Using fission yeast, we show that the conserved trimeric transcription factor (TF) PhpCNF-Y complex can infiltrate constitutive heterochromatin via its histone-fold domains to transcribe repeat elements. PhpCNF-Y collaborates with a Zn-finger containing TF to bind repeat promoter regions with CCAAT boxes. Mutating either the TFs or the CCAAT binding site disrupts the transcription of heterochromatic repeats. Although repeat elements are transcribed from both strands, PhpCNF-Y-dependent transcripts originate from only one strand. These TF-driven transcripts contain multiple cryptic introns which are required for the generation of small interfering RNAs (siRNAs) via a mechanism involving the spliceosome and RNAi machinery. Our analyses show that siRNA production by this TF-mediated transcription pathway is critical for heterochromatin nucleation at target repeat loci. This study reveals a mechanism by which heterochromatic repeats are transcribed, initiating their own silencing by triggering a primary cascade that produces siRNAs necessary for heterochromatin nucleation.

Clr4SUV39H1 ubiquitination and non-coding RNA mediate transcriptional silencing of heterochromatin via Swi6 phase separation

Transcriptional silencing by RNAi paradoxically relies on transcription, but how the transition from transcription to silencing is achieved has remained unclear. The Cryptic Loci Regulator complex (CLRC) in Schizosaccharomyces pombe is a cullin-ring E3 ligase required for silencing that is recruited by RNAi. We found that the E2 ubiquitin conjugating enzyme Ubc4 interacts with CLRC and mono-ubiquitinates the histone H3K9 methyltransferase Clr4SUV39H1, promoting the transition from co-transcriptional gene silencing (H3K9me2) to transcriptional gene silencing (H3K9me3). Ubiquitination of Clr4 occurs in an intrinsically disordered region (Clr4IDR), which undergoes liquid droplet formation in vitro, along with Swi6HP1 the effector of transcriptional gene silencing. Our data suggests that phase separation is exquisitely sensitive to non-coding RNA (ncRNA) which promotes self-association of Clr4, chromatin association, and di-, but not tri- methylation instead. Ubc4-CLRC also targets the transcriptional co-activator Bdf2BRD4, down-regulating centromeric transcription and small RNA (sRNA) production. The deubiquitinase Ubp3 counteracts both activities.

Divergence time shapes gene reuse during repeated adaptation

When diverse lineages repeatedly adapt to similar environmental challenges, the extent to which the same genes are involved (gene reuse) varies across systems. We propose that divergence time among lineages is a key factor driving this variability: as lineages diverge, the extent of gene reuse should decrease due to reductions in allele sharing, functional differentiation among genes, and restructuring of genome architecture. Indeed, we show that many genomic studies of repeated adaptation find that more recently diverged lineages exhibit higher gene reuse during repeated adaptation, but the relationship becomes less clear at older divergence time scales. Thus, future research should explore the factors shaping gene reuse and their interplay across broad divergence time scales for a deeper understanding of evolutionary repeatability.

Genetic Interactions in Various Environmental Conditions in Caenorhabditis elegans

Although it is well known that epistasis plays an important role in many evolutionary processes (e.g., speciation, evolution of sex), our knowledge on the frequency and prevalent sign of epistatic interactions is mainly limited to unicellular organisms or cell cultures of multicellular organisms. This is even more pronounced in regard to how the environment can influence genetic interactions. To broaden our knowledge in that respect we studied gene-gene interactions in a whole multicellular organism, Caenorhabditis elegans. We screened over one thousand gene interactions, each one in standard laboratory conditions, and under three different stressors: heat shock, oxidative stress, and genotoxic stress. Depending on the condition, between 7% and 22% of gene pairs showed significant genetic interactions and an overall sign of epistasis changed depending on the condition. Sign epistasis was quite common, but reciprocal sign epistasis was extremally rare. One interaction was common to all conditions, whereas 78% of interactions were specific to only one environment. Although epistatic interactions are quite common, their impact on evolutionary processes will strongly depend on environmental factors.

Membrane binding of endocytic myosin-1s is inhibited by a class of ankyrin repeat proteins

Myosin-1s are monomeric actin-based motors that function at membranes. Myo1 is the single myosin-1 isoform in Schizosaccharomyces pombe that works redundantly with Wsp1-Vrp1 to activate the Arp2/3 complex for endocytosis. Here, we identified Ank1 as an uncharacterized cytoplasmic Myo1 binding partner. We found that in ank1Δ cells, Myo1 dramatically redistributed from endocytic patches to decorate the entire plasma membrane and endocytosis was defective. Biochemical analysis and structural predictions suggested that the Ank1 ankyrin repeats bind the Myo1 lever arm and the Ank1 acidic tail binds the Myo1 TH1 domain to prevent TH1-dependent Myo1 membrane binding. Indeed, Ank1 overexpression precluded Myo1 membrane localization and recombinant Ank1 reduced purified Myo1 liposome binding in vitro. Based on biochemical and cell biological analyses, we propose budding yeast Ank1 and human OSTF1 are functional Ank1 orthologs and that cytoplasmic sequestration by small ankyrin repeat proteins is a conserved mechanism regulating myosin-1s in endocytosis.

Genetic interactions under the microscope

Traditional genetic interaction screens profile phenotypes at aggregate level, missing interactions that may influence the distribution of single cells in specific states. Here, Heigwer and colleagues use an imaging approach to generate a large-scale high-resolution genetic interaction map in Drosophila cells and demonstrate its utility for understanding gene function.

Membrane binding of endocytic myosin-1s is inhibited by a class of ankyrin repeat proteins

Myosin-1s are monomeric actin-based motors that function at membranes. Myo1 is the single myosin-1 isoform in Schizosaccharomyces pombe that works redundantly with Wsp1-Vrp1 to activate the Arp2/3 complex for endocytosis. Here, we identified Ank1 as an uncharacterized cytoplasmic Myo1 binding partner. We found that in ank1Δ cells, Myo1 dramatically redistributed from endocytic patches to decorate the entire plasma membrane and endocytosis was defective. Biochemical analysis and structural predictions suggested that the Ank1 ankyrin repeats bind the Myo1 lever arm and the Ank1 acidic tail binds the Myo1 TH1 domain to prevent TH1-dependent Myo1 membrane binding. Indeed, Ank1 over-expression precluded Myo1 membrane localization and recombinant Ank1 blocked purified Myo1 liposome binding in vitro. Based on biochemical and cell biology analyses, we propose budding yeast Ank1 and human OSTF1 are functional Ank1 orthologs and that cytoplasmic sequestration by small ankyrin repeat proteins is a conserved mechanism regulating myosin-1s in endocytosis.

Summary

Fission yeast long-tailed myosin-1 binds Ank1. Ank1 ankyrin repeats associate with the Myo1 lever arm and Ank1 acidic tail binds the Myo1 TH1 domain to inhibit Myo1 membrane binding. Ank1 orthologs exists in budding yeast (Ank1) and humans (OSTF1).

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.

Exploring Genetic Interactions with Telomere Protection Gene pot1 in Fission Yeast

The regulation of telomere length has a significant impact on cancer risk and aging in humans. Circular chromosomes are found in humans and are often unstable during mitosis, resulting in genome instability. Some types of cancer have a high frequency of a circular chromosome. Fission yeast is a good model for studying the formation and stability of circular chromosomes as deletion of pot1 (encoding a telomere protection protein) results in rapid telomere degradation and chromosome fusion. Pot1 binds to single-stranded telomere DNA and is conserved from fission yeast to humans. Loss of pot1 leads to viable strains in which all three fission yeast chromosomes become circular. In this review, I will introduce pot1 genetic interactions as these inform on processes such as the degradation of uncapped telomeres, chromosome fusion, and maintenance of circular chromosomes. Therefore, exploring genes that genetically interact with pot1 contributes to finding new genes and/or new functions of genes related to the maintenance of telomeres and/or circular chromosomes.

Proteome effects of genome-wide single gene perturbations

Protein abundance is controlled at the transcriptional, translational and post-translational levels, and its regulatory principles are starting to emerge. Investigating these principles requires large-scale proteomics data and cannot just be done with transcriptional outcomes that are commonly used as a proxy for protein abundance. Here, we determine proteome changes resulting from the individual knockout of 3308 nonessential genes in the yeast Schizosaccharomyces pombe. We use similarity clustering of global proteome changes to infer gene functionality that can be extended to other species, such as humans or baker's yeast. Furthermore, we analyze a selected set of deletion mutants by paired transcriptome and proteome measurements and show that upregulation of proteins under stable transcript expression utilizes optimal codons.

BIONIC: biological network integration using convolutions

Biological networks constructed from varied data can be used to map cellular function, but each data type has limitations. Network integration promises to address these limitations by combining and automatically weighting input information to obtain a more accurate and comprehensive representation of the underlying biology. We developed a deep learning-based network integration algorithm that incorporates a graph convolutional network framework. Our method, BIONIC (Biological Network Integration using Convolutions), learns features that contain substantially more functional information compared to existing approaches. BIONIC has unsupervised and semisupervised learning modes, making use of available gene function annotations. BIONIC is scalable in both size and quantity of the input networks, making it feasible to integrate numerous networks on the scale of the human genome. To demonstrate the use of BIONIC in identifying new biology, we predicted and experimentally validated essential gene chemical-genetic interactions from nonessential gene profiles in yeast.

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.

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

MOTIVATION

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

RESULTS

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

AVAILABILITY AND IMPLEMENTATION

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

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Elastic network modeling of cellular networks unveils sensor and effector genes that control information flow

The high-level organization of the cell is embedded in indirect relationships that connect distinct cellular processes. Existing computational approaches for detecting indirect relationships between genes typically consist of propagating abstract information through network representations of the cell. However, the selection of genes to serve as the source of propagation is inherently biased by prior knowledge. Here, we sought to derive an unbiased view of the high-level organization of the cell by identifying the genes that propagate and receive information most effectively in the cell, and the indirect relationships between these genes. To this aim, we adapted a perturbation-response scanning strategy initially developed for identifying allosteric interactions within proteins. We deployed this strategy onto an elastic network model of the yeast genetic interaction profile similarity network. This network revealed a superior propensity for information propagation relative to simulated networks with similar topology. Perturbation-response scanning identified the major distributors and receivers of information in the network, named effector and sensor genes, respectively. Effectors formed dense clusters centrally integrated into the network, whereas sensors formed loosely connected antenna-shaped clusters and contained genes with previously characterized involvement in signal transduction. We propose that indirect relationships between effector and sensor clusters represent major paths of information flow between distinct cellular processes. Genetic similarity networks for fission yeast and human displayed similarly strong propensities for information propagation and clusters of effector and sensor genes, suggesting that the global architecture enabling indirect relationships is evolutionarily conserved across species. Our results demonstrate that elastic network modeling of cellular networks constitutes a promising strategy to probe the high-level organization and cooperativity in the cell.

Chl1 coordinates with H3K9 methyltransferase Clr4 to reduce the accumulation of RNA-DNA hybrids and maintain genome stability

A genome-wide analysis in Schizosaccharomyces pombe indicated that double-deletion mutants of Chl1 and histone H3K9 methyltransferase complex factors are synthetically sick. Here, we show that loss of Chl1 increases the accumulation of RNA-DNA hybrids at pericentromeric dg and dh repeats in the absence of the H3K9 methyltransferase Clr4, which leads to genome instability, including more severe defects in chromosome segregation and increased chromatin accessibility. Localization of Chl1 at pericentromeric regions depends on a subunit of replication protein A (RPA), Ssb1. In wild-type (WT) cells, transcriptionally repressed heterochromatin prevents the formation of RNA-DNA hybrids. When Clr4 is deleted, dg and dh repeats are highly transcribed. Then Ssb1 associates with the displaced single-stranded DNA (ssDNA) and recruits Chl1 to resolve the RNA-DNA hybrids. Together, our data suggest that Chl1 coordinates with Clr4 to eliminate RNA-DNA hybrids, which contributes to the maintenance of genome integrity.

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Double mutant of Chl1 and Chl1 leads to the accumulation of RNA-DNA hybrids

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RNA-DNA hybrids at pericentromeric regions affect genome stability and cell viability

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Ssb1 recruits Chl1 to unwind RNA-DNA hybrids in the absence of Clr4

Genetic interaction of the histone chaperone hip1 + with double strand break repair genes in Schizosaccharomyces pombe

Schizosaccharomyces pombe hip1 + (human HIRA) is a histone chaperone and transcription factor involved in establishment of the centromeric chromatin and chromosome segregation, regulation of histone transcription, and cellular response to stress. We carried out a double mutant genetic screen of Δhip1 and mutations in double strand break repair pathway. We find that hip1 + functions after the MRN complex which initiates resection of blunt double strand break ends but before recruitment of the DNA damage repair machinery. Further, deletion of hip1 + partially suppresses sensitivity to DNA damaging agents of mutations in genes involved in Break Induced Replication (BIR), one mechanism of rescue of stalled or collapses replication forks ( rad51 + , cdc27 + ). Δhip1 also suppresses mutations in two checkpoint genes ( cds1 + , rad3 + ) on hydroxyurea a drug that stalls replication forks. Our results show that hip1 + forms complex interactions with the DNA double strand break repair genes and may be involved in facilitating communication between damage sensors and downstream factors.

Response to leucine in Schizosaccharomyces pombe (fission yeast)

Leucine (Leu) is a branched-chain, essential amino acid in animals, including humans. Fungi, including the fission yeast Schizosaccharomyces pombe, can biosynthesize Leu, but deletion of any of the genes in this biosynthesis leads to Leu auxotrophy. In this yeast, although a mutation in the Leu biosynthetic pathway, leu1-32, is clearly inconvenient for this species, it has increased its usefulness as a model organism in laboratories worldwide. Leu auxotrophy produces intracellular responses and phenotypes different from those of the prototrophic strains, depending on the growing environment, which necessitates a certain degree of caution in the analysis and interpretation of the experimental results. Under amino acid starvation, the amino acid-auxotrophic yeast induces cellular responses, which are conserved in higher organisms without the ability of synthesizing amino acids. This mini-review focuses on the roles of Leu in S. pombe and discusses biosynthetic pathways, contribution to experimental convenience using a plasmid specific for Leu auxotrophic yeast, signaling pathways, and phenotypes caused by Leu starvation. An accurate understanding of the intracellular responses brought about by Leu auxotrophy can contribute to research in various fields using this model organism and to the understanding of intracellular responses in higher organisms that cannot synthesize Leu.

The fission yeast FLCN/FNIP complex augments TORC1 repression or activation in response to amino acid (AA) availability

The target of Rapamycin complex1 (TORC1) senses and integrates several environmental signals, including amino acid (AA) availability, to regulate cell growth. Folliculin (FLCN) is a tumor suppressor (TS) protein in renal cell carcinoma, which paradoxically activates TORC1 in response to AA supplementation. Few tractable systems for modeling FLCN as a TS are available. Here, we characterize the FLCN-containing complex in Schizosaccharomyces pombe (called BFC) and show that BFC augments TORC1 repression and activation in response to AA starvation and supplementation, respectively. BFC co-immunoprecipitates V-ATPase, a TORC1 modulator, and regulates its activity in an AA-dependent manner. BFC genetic and proteomic networks identify the conserved peptide transmembrane transporter Ptr2 and the phosphoribosylformylglycinamidine synthase Ade3 as new AA-dependent regulators of TORC1. Overall, these data ascribe an additional repressive function to Folliculin in TORC1 regulation and reveal S. pombe as an excellent system for modeling the AA-dependent, FLCN-mediated repression of TORC1 in eukaryotes.

Mapping the multiscale structure of biological systems

Biological systems are by nature multiscale, consisting of subsystems that factor into progressively smaller units in a deeply hierarchical structure. At any level of the hierarchy, an ever-increasing diversity of technologies can be applied to characterize the corresponding biological units and their relations, resulting in large networks of physical or functional proximities-e.g., proximities of amino acids within a protein, of proteins within a complex, or of cell types within a tissue. Here, we review general concepts and progress in using network proximity measures as a basis for creation of multiscale hierarchical maps of biological systems. We discuss the functionalization of these maps to create predictive models, including those useful in translation of genotype to phenotype, along with strategies for model visualization and challenges faced by multiscale modeling in the near future. Collectively, these approaches enable a unified hierarchical approach to biological data, with application from the molecular to the macroscopic.

Genetic interaction network has a very limited impact on the evolutionary trajectories in continuous culture-grown populations of yeast

Background

The impact of genetic interaction networks on evolution is a fundamental issue. Previous studies have demonstrated that the topology of the network is determined by the properties of the cellular machinery. Functionally related genes frequently interact with one another, and they establish modules, e.g., modules of protein complexes and biochemical pathways. In this study, we experimentally tested the hypothesis that compensatory evolutionary modifications, such as mutations and transcriptional changes, occur frequently in genes from perturbed modules of interacting genes.

Results

Using Saccharomyces cerevisiae haploid deletion mutants as a model, we investigated two modules lacking COG7 or NUP133, which are evolutionarily conserved genes with many interactions. We performed laboratory evolution experiments with these strains in two genetic backgrounds (with or without additional deletion of MSH2), subjecting them to continuous culture in a non-limiting minimal medium. Next, the evolved yeast populations were characterized through whole-genome sequencing and transcriptome analyses. No obvious compensatory changes resulting from inactivation of genes already included in modules were identified. The supposedly compensatory inactivation of genes in the evolved strains was only rarely observed to be in accordance with the established fitness effect of the genetic interaction network. In fact, a substantial majority of the gene inactivations were predicted to be neutral in the experimental conditions used to determine the interaction network. Similarly, transcriptome changes during continuous culture mostly signified adaptation to growth conditions rather than compensation of the absence of the COG7, NUP133 or MSH2 genes. However, we noticed that for genes whose inactivation was deleterious an upregulation of transcription was more common than downregulation.

Conclusions

Our findings demonstrate that the genetic interactions and the modular structure of the network described by others have very limited effects on the evolutionary trajectory following gene deletion of module elements in our experimental conditions and has no significant impact on short-term compensatory evolution. However, we observed likely compensatory evolution in functionally related (albeit non-interacting) genes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12862-021-01830-9.

Repression of a large number of genes requires interplay between homologous recombination and HIRA

During homologous recombination, Dbl2 protein is required for localisation of Fbh1, an F-box helicase that efficiently dismantles Rad51-DNA filaments. RNA-seq analysis of dbl2Δ transcriptome showed that the dbl2 deletion results in upregulation of more than 500 loci in Schizosaccharomyces pombe. Compared with the loci with no change in expression, the misregulated loci in dbl2Δ are closer to long terminal and long tandem repeats. Furthermore, the misregulated loci overlap with antisense transcripts, retrotransposons, meiotic genes and genes located in subtelomeric regions. A comparison of the expression profiles revealed that Dbl2 represses the same type of genes as the HIRA histone chaperone complex. Although dbl2 deletion does not alleviate centromeric or telomeric silencing, it suppresses the silencing defect at the outer centromere caused by deletion of hip1 and slm9 genes encoding subunits of the HIRA complex. Moreover, our analyses revealed that cells lacking dbl2 show a slight increase of nucleosomes at transcription start sites and increased levels of methylated histone H3 (H3K9me2) at centromeres, subtelomeres, rDNA regions and long terminal repeats. Finally, we show that other proteins involved in homologous recombination, such as Fbh1, Rad51, Mus81 and Rad54, participate in the same gene repression pathway.

Cellular quiescence in budding yeast

Cellular quiescence, the temporary and reversible exit from proliferative growth, is the predominant state of all cells. However, our understanding of the biological processes and molecular mechanisms that underlie cell quiescence remains incomplete. As with the mitotic cell cycle, budding and fission yeast are preeminent model systems for studying cellular quiescence owing to their rich experimental toolboxes and the evolutionary conservation across eukaryotes of pathways and processes that control quiescence. Here, we review current knowledge of cell quiescence in budding yeast and how it pertains to cellular quiescence in other organisms, including multicellular animals. Quiescence entails large-scale remodeling of virtually every cellular process, organelle, gene expression, and metabolic state that is executed dynamically as cells undergo the initiation, maintenance, and exit from quiescence. We review these major transitions, our current understanding of their molecular bases, and highlight unresolved questions. We summarize the primary methods employed for quiescence studies in yeast and discuss their relative merits. Understanding cell quiescence has important consequences for human disease as quiescent single-celled microbes are notoriously difficult to kill and quiescent human cells play important roles in diseases such as cancer. We argue that research on cellular quiescence will be accelerated through the adoption of common criteria, and methods, for defining cell quiescence. An integrated approach to studying cell quiescence, and a focus on the behavior of individual cells, will yield new insights into the pathways and processes that underlie cell quiescence leading to a more complete understanding of the life cycle of cells. TAKE AWAY: Quiescent cells are viable cells that have reversibly exited the cell cycle Quiescence is induced in response to a variety of nutrient starvation signals Quiescence is executed dynamically through three phases: initiation, maintenance, and exit Quiescence entails large-scale remodeling of gene expression, organelles, and metabolism Single-cell approaches are required to address heterogeneity among quiescent cells.

Matrix (factorization) reloaded: flexible methods for imputing genetic interactions with cross-species and side information

MOTIVATION

Mapping genetic interactions (GIs) can reveal important insights into cellular function and has potential translational applications. There has been great progress in developing high-throughput experimental systems for measuring GIs (e.g. with double knockouts) as well as in defining computational methods for inferring (imputing) unknown interactions. However, existing computational methods for imputation have largely been developed for and applied in baker's yeast, even as experimental systems have begun to allow measurements in other contexts. Importantly, existing methods face a number of limitations in requiring specific side information and with respect to computational cost. Further, few have addressed how GIs can be imputed when data are scarce.

RESULTS

In this article, we address these limitations by presenting a new imputation framework, called Extensible Matrix Factorization (EMF). EMF is a framework of composable models that flexibly exploit cross-species information in the form of GI data across multiple species, and arbitrary side information in the form of kernels (e.g. from protein-protein interaction networks). We perform a rigorous set of experiments on these models in matched GI datasets from baker's and fission yeast. These include the first such experiments on genome-scale GI datasets in multiple species in the same study. We find that EMF models that exploit side and cross-species information improve imputation, especially in data-scarce settings. Further, we show that EMF outperforms the state-of-the-art deep learning method, even when using strictly less data, and incurs orders of magnitude less computational cost.

AVAILABILITY

Implementations of models and experiments are available at: https://github.com/lrgr/EMF.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genetic interaction mapping informs integrative structure determination of protein complexes

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

The phosphatase inhibitor Sds23 promotes symmetric spindle positioning in fission yeast

A hallmark of cell division in eukaryotic cells is the formation and elongation of a microtubule (MT)-based mitotic spindle. Proper positioning of the spindle is critical to ensure equal segregation of the genetic material to the resulting daughter cells. Both the timing of spindle elongation and constriction of the actomyosin contractile ring must be precisely coordinated to prevent missegregation or damage to the genetic material during cellular division. Here, we show that Sds23, an inhibitor of protein phosphatases, contributes to proper positioning of elongating spindles in fission yeast cells. We found that sds23∆ mutant cells exhibit asymmetric spindles that initially elongate asymmetrically toward one end of the dividing cell. Spindle asymmetry in sds23∆ cells results from a defect that is distinct from previously identified mechanisms, including MT protrusions and enlarged vacuoles. Combined with our previous work, this study demonstrates that Sds23, an inhibitor of PP2A-family protein phosphatases, promotes proper positioning of both the bipolar spindle and cytokinetic ring during fission yeast cell division. These two steps ensure the overall symmetry and fidelity of the cell division process.

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.

Resurrection from lethal knockouts: Bypass of gene essentiality

Understanding genotype-phenotype relationships is a central pursuit in biology. Gene knockout generates a complete loss-of-function genotype and is a commonly used approach for probing gene functions. The most severe phenotypic consequence of gene knockout is lethality. Genes with a lethal knockout phenotype are called essential genes. Based on genome-wide knockout analyses in yeasts, up to approximately a quarter of genes in a genome can be essential. Like other genotype-phenotype relationships, gene essentiality is subject to background effects and can vary due to gene-gene interactions. In particular, for some essential genes, lethality caused by knockout can be rescued by extragenic suppressors. Such "bypass of essentiality" (BOE) gene-gene interactions have been an understudied type of genetic suppression. A recent systematic analysis revealed that, remarkably, the essentiality of nearly 30% of essential genes in the fission yeast Schizosaccharomyces pombe can be bypassed by BOE interactions. Here, I review the history and recent progress on uncovering and understanding the bypass of gene essentiality.

Integrative analysis of large-scale loss-of-function screens identifies robust cancer-associated genetic interactions

Genetic interactions, including synthetic lethal effects, can now be systematically identified in cancer cell lines using high-throughput genetic perturbation screens. Despite this advance, few genetic interactions have been reproduced across multiple studies and many appear highly context-specific. Here, by developing a new computational approach, we identified 220 robust driver-gene associated genetic interactions that can be reproduced across independent experiments and across non-overlapping cell line panels. Analysis of these interactions demonstrated that: (i) oncogene addiction effects are more robust than oncogene-related synthetic lethal effects; and (ii) robust genetic interactions are enriched among gene pairs whose protein products physically interact. Exploiting the latter observation, we used a protein-protein interaction network to identify robust synthetic lethal effects associated with passenger gene alterations and validated two new synthetic lethal effects. Our results suggest that protein-protein interaction networks can be used to prioritise therapeutic targets that will be more robust to tumour heterogeneity.

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.

The functional study of human proteins using humanized yeast

The functional and optimal expression of genes is crucial for survival of all living organisms. Numerous experiments and efforts have been performed to reveal the mechanisms required for the functional and optimal expression of human genes. The yeast Saccharomyces cerevisiae has evolved independently of humans for billions of years. Nevertheless, S. cerevisiae has many conserved genes and expression mechanisms that are similar to those in humans. Yeast is the most commonly used model organism for studying the function and expression mechanisms of human genes because it has a relatively simple genome structure, which is easy to manipulate. Many previous studies have focused on understanding the functions and mechanisms of human proteins using orthologous genes and biological systems of yeast. In this review, we mainly introduce two recent studies that replaced human genes and nucleosomes with those of yeast. Here, we suggest that, although yeast is a relatively small eukaryotic cell, its humanization is useful for the direct study of human proteins. In addition, yeast can be used as a model organism in a broader range of studies, including drug screening.

Mutations in a Single Signaling Pathway Allow Cell Growth in Heavy Water

Life is completely dependent on water. To analyze the role of water as a solvent in biology, we replaced water with heavy water (D₂O) and investigated the biological effects by a wide range of techniques, using Schizosaccharomyces pombe as model organism. We show that high concentrations of D₂O lead to altered glucose metabolism and growth retardation. After prolonged incubation in D₂O, cells displayed gross morphological changes, thickened cell walls, and aberrant cytoskeletal organization. By transcriptomics and genetic screens, we show that the solvent replacement activates two signaling pathways: (1) the heat-shock response pathway and (2) the cell integrity pathway. Although the heat-shock response system upregulates various chaperones and other stress-relieving enzymes, we find that the activation of this pathway does not offer any fitness advantage to the cells under the solvent-replaced conditions. However, limiting the D₂O-triggered activation of the cell integrity pathway allows cell growth when H₂O is completely replaced with D₂O. The isolated D₂O-tolerant strains may aid biological production of deuterated biomolecules.

A Quantitative Genetic Interaction Map of HIV Infection

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

The fission yeast FHIT homolog affects checkpoint control of proliferation and is regulated by mitochondrial electron transport

Genetic analysis has strongly implicated human FHIT (Fragile Histidine Triad) as a tumor suppressor gene, being mutated in a large proportion of early‐stage cancers. The functions of the FHIT protein have, however, remained elusive. Here, we investigated aph1⁺, the fission yeast homolog of FHIT, for functions related to checkpoint control and oxidative metabolism. In sublethal concentrations of DNA damaging agents, aph1Δ mutants grew with a substantially shorter lag phase. In aph1Δ mutants carrying a hypomorphic allele of cds1 (the fission yeast homolog of Chk2), in addition, increased chromosome fragmentation and missegregation were found. We also found that under hypoxia or impaired electron transport function, the Aph1 protein level was strongly depressed. Previously, FHIT has been linked to regulation of the human 9‐1‐1 checkpoint complex constituted by Hus1, Rad1, and Rad9. In Schizosaccharomyces pombe, the levels of all three 9‐1‐1 proteins are all downregulated by hypoxia in similarity with Aph1. Moreover, deletion of the aph1⁺ gene reduced the Rad1 protein level, indicating a direct relationship between these two proteins. We conclude that the fission yeast FHIT homolog has a role in modulating DNA damage checkpoint function, possibly through an effect on the 9‐1‐1 complex, and that this effect may be critical under conditions of limiting oxidative metabolism and reoxygenation.

A Gaussian process-based definition reveals new and bona fide genetic interactions compared to a multiplicative model in the Gram-negative Escherichia coli

MOTIVATION

A digenic genetic interaction (GI) is observed when mutations in two genes within the same organism yield a phenotype that is different from the expected, given each mutation's individual effects. While multiplicative scoring is widely applied to define GIs, revealing underlying gene functions, it remains unclear if it is the most suitable choice for scoring GIs in Escherichia coli. Here, we assess many different definitions, including the multiplicative model, for mapping functional links between genes and pathways in E.coli.

RESULTS

Using our published E.coli GI datasets, we show computationally that a machine learning Gaussian process (GP)-based definition better identifies functional associations among genes than a multiplicative model, which we have experimentally confirmed on a set of gene pairs. Overall, the GP definition improves the detection of GIs, biological reasoning of epistatic connectivity, as well as the quality of GI maps in E.coli, and, potentially, other microbes.

AVAILABILITY AND IMPLEMENTATION

The source code and parameters used to generate the machine learning models in WEKA software were provided in the Supplementary information.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

PS-MCL: parallel shotgun coarsened Markov clustering of protein interaction networks

Background

How can we obtain fast and high-quality clusters in genome scale bio-networks? Graph clustering is a powerful tool applied on bio-networks to solve various biological problems such as protein complexes detection, disease module detection, and gene function prediction. Especially, MCL (Markov Clustering) has been spotlighted due to its superior performance on bio-networks. MCL, however, is skewed towards finding a large number of very small clusters (size 1-3) and fails to detect many larger clusters (size 10+). To resolve this fragmentation problem, MLR-MCL (Multi-level Regularized MCL) has been developed. MLR-MCL still suffers from the fragmentation and, in cases, unrealistically large clusters are generated.

Results

In this paper, we propose PS-MCL (Parallel Shotgun Coarsened MCL), a parallel graph clustering method outperforming MLR-MCL in terms of running time and cluster quality. PS-MCL adopts an efficient coarsening scheme, called SC (Shotgun Coarsening), to improve graph coarsening in MLR-MCL. SC allows merging multiple nodes at a time, which leads to improvement in quality, time and space usage. Also, PS-MCL parallelizes main operations used in MLR-MCL which includes matrix multiplication.

Conclusions

Experiments show that PS-MCL dramatically alleviates the fragmentation problem, and outperforms MLR-MCL in quality and running time. We also show that the running time of PS-MCL is effectively reduced with parallelization.

Identification of proteins associated with splicing factors Ntr1, Ntr2, Brr2 and Gpl1 in the fission yeast Schizosaccharomyces pombe

The spliceosome is a complex molecular machine assembled from many components, which catalyzes the removal of introns from mRNA precursors. Our previous study revealed that the Nrl1 (NRDE-2 like 1) protein associates with spliceosome proteins and regulates pre-mRNA splicing and homologous recombination-dependent R-loop formation in the fission yeast Schizosaccharomyces pombe. Here, we identify proteins associated with splicing factors Ntr1, Ntr2, Brr2 and Gpl1, a poorly characterized G-patch domain-containing protein required for efficient splicing. This work provides new evidence that Nrl1 and splicing factors physically interact and reveals additional insights into the protein interaction network of the spliceosome. We discuss implications of these findings in the light of recent progress in our understanding of how Nrl1 and splicing factors ensure genome stability.

Quantitative Operating Principles of Yeast Metabolism during Adaptation to Heat Stress

Microorganisms evolved adaptive responses to survive stressful challenges in ever-changing environments. Understanding the relationships between the physiological/metabolic adjustments allowing cellular stress adaptation and gene expression changes being used by organisms to achieve such adjustments may significantly impact our ability to understand and/or guide evolution. Here, we studied those relationships during adaptation to various stress challenges in Saccharomyces cerevisiae, focusing on heat stress responses. We combined dozens of independent experiments measuring whole-genome gene expression changes during stress responses with a simplified kinetic model of central metabolism. We identified alternative quantitative ranges for a set of physiological variables in the model (production of ATP, trehalose, NADH, etc.) that are specific for adaptation to either heat stress or desiccation/rehydration. Our approach is scalable to other adaptive responses and could assist in developing biotechnological applications to manipulate cells for medical, biotechnological, or synthetic biology purposes.

Context-dependent genetic interactions in cancer

Genetic co-dependencies have been found in many contexts, from processes during the development of organisms to many diseases in man, including cancer. Genetic interactions - and in particular synthetic lethal phenotypes - have provided fundamental insights into the genetic architecture of cells and identified potential new opportunities for therapeutic interventions. However, recent studies also demonstrated that genetic interactions are highly context dependent and synthetic lethal interactions in one tumor context might not be translatable to others. Therefore, to better define and understand contexts will be a key challenge for future studies to fully exploit genetic interaction networks for target identification and cancer therapy. In this review, we summarize recent developments in mapping context-specific genetic interaction networks with a particular focus on conceptual and experimental advances in the past years. We then discuss genetic and environmental contexts that influence genetic interaction networks. Finally, we outline challenges of putting genetic interaction networks into context and give an outlook on future directions.

Constructing Yeast Chimeric Pathways To Boost Lipophilic Terpene Synthesis

Synthetic chimeric biological system offers opportunities to illuminate principles of designing life, and a primary step is constructing synthetic chimeric pathways. Here, we constructed yeast chimeric pathways by transferring the genes from  Saccharomyces cerevisiae pathways into another budding yeast Yarrowia lipolytica for in vivo assembly. We efficiently diversified gene option, combination, localization order, and copy number as expected. Convergence of two yeast pathways, especially mevalonic acid (MVA) pathways, remarkably enhanced synthesis of a lipophilic terpene, lycopene. In the selected champion strain with 50-fold of enhanced lycopene production, the chimeric MVA pathway gathered three S. cerevisiae genes with particular copies and locations. Amazingly, therein we discovered distinct transcriptional up-regulation of three significant pathways correlated with acetyl-CoA supply and tuning of cellular lipid amounts and composition. Modulating these pathways further improved lycopene production to 150-fold, a final 259 mg/L (approximately 80 mg/g DCW). We primarily showed the capacity of boosting the synthesis of lipophilic products with yeast chimeric pathways.

Using BEAN-counter to quantify genetic interactions from multiplexed barcode sequencing experiments

The construction of genome-wide mutant collections has enabled high-throughput, high-dimensional quantitative characterization of gene and chemical function, particularly via genetic and chemical-genetic interaction experiments. As the throughput of such experiments increases with improvements in sequencing technology and sample multiplexing, appropriate tools must be developed to handle the large volume of data produced. Here, we describe how to apply our approach to high-throughput, fitness-based profiling of pooled mutant yeast collections using the BEAN-counter software pipeline (Barcoded Experiment Analysis for Next-generation sequencing) for analysis. The software has also successfully processed data from Schizosaccharomyces pombe, Escherichia coli, and Zymomonas mobilis mutant collections. We provide general recommendations for the design of large-scale, multiplexed barcode sequencing experiments. The procedure outlined here was used to score interactions for ~4 million chemical-by-mutant combinations in our recently published chemical-genetic interaction screen of nearly 14,000 chemical compounds across seven diverse compound collections. Here we selected a representative subset of these data on which to demonstrate our analysis pipeline. BEAN-counter is open source, written in Python, and freely available for academic use. Users should be proficient at the command line; advanced users who wish to analyze larger datasets with hundreds or more conditions should also be familiar with concepts in analysis of high-throughput biological data. BEAN-counter encapsulates the knowledge we have accumulated from, and successfully applied to, our multiplexed, pooled barcode sequencing experiments. This protocol will be useful to those interested in generating their own high-dimensional, quantitative characterizations of gene or chemical function in a high-throughput manner.

The contribution of non-essential Schizosaccharomyces pombe genes to fitness in response to altered nutrient supply and target of rapamycin activity

Nutrient fluctuations in the cellular environment promote changes in cell metabolism and growth to adapt cell proliferation accordingly. The target of rapamycin (TOR) signalling network plays a key role in the coordination of growth and cell proliferation with the nutrient environment and, importantly, nutrient limitation reduces TOR complex 1 (TORC1) signalling. We have performed global quantitative fitness profiling of the collection of Schizosaccharomyces pombe strains from which non-essential genes have been deleted. We identified genes that regulate fitness when cells are grown in a nutrient-rich environment compared with minimal environments, with varying nitrogen sources including ammonium, glutamate and proline. In addition, we have performed the first global screen for genes that regulate fitness when both TORC1 and TORC2 signalling is reduced by Torin1. Analysis of genes whose deletions altered fitness when nutrients were limited, or when TOR signalling was compromised, identified a large number of genes that regulate transmembrane transport, transcription and chromatin organization/regulation and vesicle-mediated transport. The ability to tolerate reduced TOR signalling placed demands upon a large number of biological processes including autophagy, mRNA metabolic processing and nucleocytoplasmic transport. Importantly, novel biological processes and all processes known to be regulated by TOR were identified in our screens. In addition, deletion of 62 genes conserved in humans gave rise to strong sensitivity or resistance to Torin1, and 29 of these 62 genes have novel links to TOR signalling. The identification of chromatin and transcriptional regulation, nutritional uptake and transport pathways in this powerful genetic model now paves the way for a molecular understanding of how cells adapt to the chronic and acute fluctuations in nutrient supply that all eukaryotes experience at some stage, and which is a key feature of cancer cells within solid tumours.

Systematic analysis reveals the prevalence and principles of bypassable gene essentiality

Gene essentiality is a variable phenotypic trait, but to what extent and how essential genes can become dispensable for viability remain unclear. Here, we investigate 'bypass of essentiality (BOE)' - an underexplored type of digenic genetic interaction that renders essential genes dispensable. Through analyzing essential genes on one of the six chromosome arms of the fission yeast Schizosaccharomyces pombe, we find that, remarkably, as many as 27% of them can be converted to non-essential genes by BOE interactions. Using this dataset we identify three principles of essentiality bypass: bypassable essential genes tend to have lower importance, tend to exhibit differential essentiality between species, and tend to act with other bypassable genes. In addition, we delineate mechanisms underlying bypassable essentiality, including the previously unappreciated mechanism of dormant redundancy between paralogs. The new insights gained on bypassable essentiality deepen our understanding of genotype-phenotype relationships and will facilitate drug development related to essential genes.

Modular epistasis and the compensatory evolution of gene deletion mutants

Screens for epistatic interactions have long been used to characterize functional relationships corresponding to protein complexes, metabolic pathways, and other functional modules. Although epistasis between adaptive mutations is also common in laboratory evolution experiments, the functional basis for these interactions is less well characterized. Here, we quantify the extent to which gene function (as determined by a genome-wide screen for epistasis among deletion mutants) influences the rate and genetic basis of compensatory adaptation in a set of 37 gene deletion mutants nested within 16 functional modules. We find that functional module has predictive power: mutants with deletions in the same module tend to adapt more similarly, on average, than those with deletions in different modules. At the same time, initial fitness also plays a role: independent of the specific functional modules involved, adaptive mutations tend to be systematically more beneficial in less-fit genetic backgrounds, consistent with a general pattern of diminishing returns epistasis. We measured epistatic interactions between initial gene deletion mutations and the mutations that accumulate during compensatory adaptation and found a general trend towards positive epistasis (i.e. mutations tend to be more beneficial in the background in which they arose). In two functional modules, epistatic interactions between the initial gene deletions and the mutations in their descendant lines caused evolutionary entrenchment, indicating an intimate functional relationship. Our results suggest that genotypes with similar epistatic interactions with gene deletion mutations will also have similar epistatic interactions with adaptive mutations, meaning that genome scale maps of epistasis between gene deletion mutations can be predictive of evolutionary dynamics.

The effects of mutations often depend on the presence or absence of other mutations. This phenomenon, known as epistasis, has been used extensively to infer functional associations between genes. For example, genes that participate in the same functional module will often show a characteristic pattern of positive epistasis where the knockout of one gene will mask the deleterious effects of knockouts in the other genes. In the context of adaptation, epistasis can cause the outcomes of evolution to depend strongly on the initial genotype. Although studies have found that epistasis is common in laboratory populations, we do not know the extent to which the patterns of epistasis that reveal functional associations overlap with the patterns of epistasis that are important in evolution. Here, by comparing evolution in strains with gene deletions in different functional modules, we quantify the effect of functional epistasis on evolutionary outcomes. We find that mutants with deletions in the same module have more similar evolutionary outcomes, on average, than mutants with deletions in different modules. This suggests that screens for epistasis between gene deletion mutations will not only reveal functional interactions between those genes but may also predict evolutionary dynamics.

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.

Genetic analysis reveals functions of atypical polyubiquitin chains

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

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

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

Understanding how chemical compounds affect biological systems is of paramount importance as pharmaceutical companies strive to develop life-saving medicines, governments seek to regulate the safety of consumer products and agrichemicals, and basic scientists continue to study the fundamental inner workings of biological organisms. One powerful approach to characterize the effects of chemical compounds in living cells is chemical-genetic interaction screening. Using this approach, a collection of cells–each with a different defined genetic perturbation–is tested for sensitivity or resistance to the presence of a compound, resulting in a quantitative profile describing the functional effects of that compound on the cells. The work presented here describes our efforts to integrate compounds’ chemical-genetic interaction profiles with reference genetic interaction profiles containing information on gene function to predict the cellular processes perturbed by the compounds. We focused on specifically developing a method that could scale to perform these functional predictions for large collections of thousands of screened compounds and robustly control the false discovery rate. With chemical-genetic and genetic interaction screens now underway in multiple species including human cells, the method described here can be generally applied to enable the characterization of compounds’ effects across the tree of life.