Conserved rules govern genetic interaction degree across species

Identifying actionable synthetically lethal cancer gene pairs using mutual exclusivity

Mutually exclusive loss-of-function alterations in gene pairs are those that occur together less frequently than may be expected and may denote a synthetically lethal relationship (SSL) between the genes. SSLs can be exploited therapeutically to selectively kill cancer cells. Here, we analysed mutation, copy number variation, and methylation levels in samples from The Cancer Genome Atlas, using the hypergeometric and the Poisson binomial tests to identify mutually exclusive inactivated genes. We focused on gene pairs where one is an inactivated tumour suppressor and the other a gene whose protein product can be inhibited by known drugs. This provided an abundance of potential targeted therapeutics and repositioning opportunities for several cancers. These data are available on the MexDrugs website, https://bioinformaticslab.sussex.ac.uk/mexdrugs.

The distribution of fitness effects during adaptive walks using a simple genetic network

The tempo and mode of adaptation depends on the availability of beneficial alleles. Genetic interactions arising from gene networks can restrict this availability. However, the extent to which networks affect adaptation remains largely unknown. Current models of evolution consider additive genotype-phenotype relationships while often ignoring the contribution of gene interactions to phenotypic variance. In this study, we model a quantitative trait as the product of a simple gene regulatory network, the negative autoregulation motif. Using forward-time genetic simulations, we measure adaptive walks towards a phenotypic optimum in both additive and network models. A key expectation from adaptive walk theory is that the distribution of fitness effects of new beneficial mutations is exponential. We found that both models instead harbored distributions with fewer large-effect beneficial alleles than expected. The network model also had a complex and bimodal distribution of fitness effects among all mutations, with a considerable density at deleterious selection coefficients. This behavior is reminiscent of the cost of complexity, where correlations among traits constrain adaptation. Our results suggest that the interactions emerging from genetic networks can generate complex and multimodal distributions of fitness effects.

Meta-analysis of dispensable essential genes and their interactions with bypass suppressors

Genes have been historically classified as essential or non-essential based on their requirement for viability. However, genomic mutations can sometimes bypass the requirement for an essential gene, challenging the binary classification of gene essentiality. Such dispensable essential genes represent a valuable model for understanding the incomplete penetrance of loss-of-function mutations often observed in natural populations. Here, we compiled data from multiple studies on essential gene dispensability in Saccharomyces cerevisiae to comprehensively characterize these genes. In analyses spanning different evolutionary timescales, dispensable essential genes exhibited distinct phylogenetic properties compared with other essential and non-essential genes. Integration of interactions with suppressor genes that can bypass the gene essentiality revealed the high functional modularity of the bypass suppression network. Furthermore, dispensable essential and bypass suppressor gene pairs reflected simultaneous changes in the mutational landscape of S. cerevisiae strains. Importantly, species in which dispensable essential genes were non-essential tended to carry bypass suppressor mutations in their genomes. Overall, our study offers a comprehensive view of dispensable essential genes and illustrates how their interactions with bypass suppressors reflect evolutionary outcomes.

Data-driven identification of inherent features of eukaryotic stress-responsive genes

Living organisms are continuously challenged by changes in their environment that can propagate to stresses at the cellular level, such as rapid changes in osmolarity or oxygen tension. To survive these sudden changes, cells have developed stress-responsive mechanisms that tune cellular processes. The response of Saccharomyces cerevisiae to osmostress includes a massive reprogramming of gene expression. Identifying the inherent features of stress-responsive genes is of significant interest for understanding the basic principles underlying the rewiring of gene expression upon stress. Here, we generated a comprehensive catalog of osmostress-responsive genes from 5 independent RNA-seq experiments. We explored 30 features of yeast genes and found that 25 (83%) were distinct in osmostress-responsive genes. We then identified 13 non-redundant minimal osmostress gene traits and used statistical modeling to rank the most stress-predictive features. Intriguingly, the most relevant features of osmostress-responsive genes are the number of transcription factors targeting them and gene conservation. Using data on HeLa samples, we showed that the same features that define yeast osmostress-responsive genes can predict osmostress-responsive genes in humans, but with changes in the rank-ordering of feature-importance. Our study provides a holistic understanding of the basic principles of the regulation of stress-responsive gene expression across eukaryotes.

Genome Comparisons of the Fission Yeasts Reveal Ancient Collinear Loci Maintained by Natural Selection

Fission yeasts have a unique life history and exhibit distinct evolutionary patterns from other yeasts. Besides, the species demonstrate stable genome structures despite the relatively fast evolution of their genomic sequences. To reveal what could be the reason for that, comparative genomic analyses were carried out. Our results provided evidence that the structural and sequence evolution of the fission yeasts were correlated. Moreover, we revealed ancestral locally collinear blocks (aLCBs), which could have been inherited from their last common ancestor. These aLCBs proved to be the most conserved regions of the genomes as the aLCBs contain almost eight genes/blocks on average in the same orientation and order across the species. Gene order of the aLCBs is mainly fission-yeast-specific but supports the idea of filamentous ancestors. Nevertheless, the sequences and gene structures within the aLCBs are as mutable as any sequences in other parts of the genomes. Although genes of certain Gene Ontology (GO) categories tend to cluster at the aLCBs, those GO enrichments are not related to biological functions or high co-expression rates, they are, rather, determined by the density of essential genes and Rec12 cleavage sites. These data and our simulations indicated that aLCBs might not only be remnants of ancestral gene order but are also maintained by natural selection.

Systematic analysis of bypass suppression of essential genes

Essential genes tend to be highly conserved across eukaryotes, but, in some cases, their critical roles can be bypassed through genetic rewiring. From a systematic analysis of 728 different essential yeast genes, we discovered that 124 (17%) were dispensable essential genes. Through whole-genome sequencing and detailed genetic analysis, we investigated the genetic interactions and genome alterations underlying bypass suppression. Dispensable essential genes often had paralogs, were enriched for genes encoding membrane-associated proteins, and were depleted for members of protein complexes. Functionally related genes frequently drove the bypass suppression interactions. These gene properties were predictive of essential gene dispensability and of specific suppressors among hundreds of genes on aneuploid chromosomes. Our findings identify yeast's core essential gene set and reveal that the properties of dispensable essential genes are conserved from yeast to human cells, correlating with human genes that display cell line-specific essentiality in the Cancer Dependency Map (DepMap) project.

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.

Environmental robustness of the global yeast genetic interaction network

Phenotypes associated with genetic variants can be altered by interactions with other genetic variants (GxG), with the environment (GxE), or both (GxGxE). Yeast genetic interactions have been mapped on a global scale, but the environmental influence on the plasticity of genetic networks has not been examined systematically. To assess environmental rewiring of genetic networks, we examined 14 diverse conditions and scored 30,000 functionally representative yeast gene pairs for dynamic, differential interactions. Different conditions revealed novel differential interactions, which often uncovered functional connections between distantly related gene pairs. However, the majority of observed genetic interactions remained unchanged in different conditions, suggesting that the global yeast genetic interaction network is robust to environmental perturbation and captures the fundamental functional architecture of a eukaryotic cell.

Analysing the yeast complexome-the Complex Portal rising to the challenge

The EMBL-EBI Complex Portal is a knowledgebase of macromolecular complexes providing persistent stable identifiers. Entries are linked to literature evidence and provide details of complex membership, function, structure and complex-specific Gene Ontology annotations. Data are freely available and downloadable in HUPO-PSI community standards and missing entries can be requested for curation. In collaboration with Saccharomyces Genome Database and UniProt, the yeast complexome, a compendium of all known heteromeric assemblies from the model organism Saccharomyces cerevisiae, was curated. This expansion of knowledge and scope has led to a 50% increase in curated complexes compared to the previously published dataset, CYC2008. The yeast complexome is used as a reference resource for the analysis of complexes from large-scale experiments. Our analysis showed that genes coding for proteins in complexes tend to have more genetic interactions, are co-expressed with more genes, are more multifunctional, localize more often in the nucleus, and are more often involved in nucleic acid-related metabolic processes and processes where large machineries are the predominant functional drivers. A comparison to genetic interactions showed that about 40% of expanded co-complex pairs also have genetic interactions, suggesting strong functional links between complex members.

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.

PhenoModifier: a genetic modifier database for elucidating the genetic basis of human phenotypic variation

From clinical observations to large-scale sequencing studies, the phenotypic impact of genetic modifiers is evident. To better understand the full spectrum of the genetic contribution to human disease, concerted efforts are needed to construct a useful modifier resource for interpreting the information from sequencing data. Here, we present the PhenoModifier (https://www.biosino.org/PhenoModifier), a manually curated database that provides a comprehensive overview of human genetic modifiers. By manually curating over ten thousand published articles, 3078 records of modifier information were entered into the current version of PhenoModifier, related to 288 different disorders, 2126 genetic modifier variants and 843 distinct modifier genes. To help users probe further into the mechanism of their interested modifier genes, we extended the yeast genetic interaction data and yeast quantitative trait loci to the human and we also integrated GWAS data into the PhenoModifier to assist users in evaluating all possible phenotypes associated with a modifier allele. As the first comprehensive resource of human genetic modifiers, PhenoModifier provides a more complete spectrum of genetic factors contributing to human phenotypic variation. The portal has a broad scientific and clinical scope, spanning activities relevant to variant interpretation for research purposes as well as clinical decision making.

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.

Identifying Drivers of Parallel Evolution: A Regression Model Approach

Parallel evolution, defined as identical changes arising in independent populations, is often attributed to similar selective pressures favoring the fixation of identical genetic changes. However, some level of parallel evolution is also expected if mutation rates are heterogeneous across regions of the genome. Theory suggests that mutation and selection can have equal impacts on patterns of parallel evolution; however, empirical studies have yet to jointly quantify the importance of these two processes. Here, we introduce several statistical models to examine the contributions of mutation and selection heterogeneity to shaping parallel evolutionary changes at the gene-level. Using this framework, we analyze published data from forty experimentally evolved Saccharomyces cerevisiae populations. We can partition the effects of a number of genomic variables into those affecting patterns of parallel evolution via effects on the rate of arising mutations, and those affecting the retention versus loss of the arising mutations (i.e., selection). Our results suggest that gene-to-gene heterogeneity in both mutation and selection, associated with gene length, recombination rate, and number of protein domains drive parallel evolution at both synonymous and nonsynonymous sites. While there are still a number of parallel changes that are not well described, we show that allowing for heterogeneous rates of mutation and selection can provide improved predictions of the prevalence and degree of parallel evolution.

Synthetic Lethal Networks for Precision Oncology: Promises and Pitfalls

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

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

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

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.

Global study of holistic morphological effectors in the budding yeast Saccharomyces cerevisiae

Background

The size of the phenotypic effect of a gene has been thoroughly investigated in terms of fitness and specific morphological traits in the budding yeast Saccharomyces cerevisiae, but little is known about gross morphological abnormalities.

Results

We identified 1126 holistic morphological effectors that cause severe gross morphological abnormality when deleted, and 2241 specific morphological effectors with weak holistic effects but distinctive effects on yeast morphology. Holistic effectors fell into many gene function categories and acted as network hubs, affecting a large number of morphological traits, interacting with a large number of genes, and facilitating high protein expression. Holistic morphological abnormality was useful for estimating the importance of a gene to morphology. The contribution of gene importance to fitness and morphology could be used to efficiently classify genes into functional groups.

Conclusion

Holistic morphological abnormality can be used as a reproducible and reliable gene feature for high-dimensional morphological phenotyping. It can be used in many functional genomic applications.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4526-z) contains supplementary material, which is available to authorized users.

Computational Approaches to Identify Genetic Interactions for Cancer Therapeutics

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

Constraints and consequences of the emergence of amino acid repeats in eukaryotic proteins

Proteins with amino acid homorepeats have the potential to be detrimental to cells and are often associated with human diseases. Why, then, are homorepeats prevalent in eukaryotic proteomes? In yeast, homorepeats are enriched in proteins that are essential and pleiotropic and that buffer environmental insults. The presence of homorepeats increases the functional versatility of proteins by mediating protein interactions and facilitating spatial organization in a repeat-dependent manner. During evolution, homorepeats are preferentially retained in proteins with stringent proteostasis, which might minimize repeat-associated detrimental effects such as unregulated phase separation and protein aggregation. Their presence facilitates rapid protein divergence through accumulation of amino acid substitutions, which often affect linear motifs and post-translational-modification sites. These substitutions may result in rewiring protein interaction and signaling networks. Thus, homorepeats are distinct modules that are often retained in stringently regulated proteins. Their presence facilitates rapid exploration of the genotype-phenotype landscape of a population, thereby contributing to adaptation and fitness.

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

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

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

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

Dominance from the perspective of gene-gene and gene-chemical interactions

In this study, we used genetic interaction (GI) and gene-chemical interaction (GCI) data to compare mutations with different dominance phenotypes. Our analysis focused primarily on Saccharomyces cerevisiae, where haploinsufficient genes (HI; genes with dominant loss-of-function mutations) were found to be participating in gene expression processes, namely, the translation and regulation of gene transcription. Non-ribosomal HI genes (mainly regulators of gene transcription) were found to have more GIs and GCIs than haplosufficient (HS) genes. Several properties seem to lead to the enrichment of interactions, most notably, the following: importance, pleiotropy, gene expression level and gene expression variation. Importantly, after these properties were appropriately considered in the analysis, the correlation between dominance and GI/GCI degrees was still observed. Strikingly, for the GCIs of heterozygous strains, haploinsufficiency was the only property significantly correlated with the number of GCIs. We found ribosomal HI genes to be depleted in GIs/GCIs. This finding can be explained by their high variation in gene expression under different genetic backgrounds and environmental conditions. We observed the same distributions of GIs among non-ribosomal HI, ribosomal HI and HS genes in three other species: Schizosaccharomyces pombe, Drosophila melanogaster and Homo sapiens. One potentially interesting exception was the lack of significant differences in the degree of GIs between non-ribosomal HI and HS genes in Schizosaccharomyces pombe.

Cell-based screens and phenomics with fission yeast

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

An Effective Big Data Supervised Imbalanced Classification Approach for Ortholog Detection in Related Yeast Species

Orthology detection requires more effective scaling algorithms. In this paper, a set of gene pair features based on similarity measures (alignment scores, sequence length, gene membership to conserved regions, and physicochemical profiles) are combined in a supervised pairwise ortholog detection approach to improve effectiveness considering low ortholog ratios in relation to the possible pairwise comparison between two genomes. In this scenario, big data supervised classifiers managing imbalance between ortholog and nonortholog pair classes allow for an effective scaling solution built from two genomes and extended to other genome pairs. The supervised approach was compared with RBH, RSD, and OMA algorithms by using the following yeast genome pairs: Saccharomyces cerevisiae-Kluyveromyces lactis, Saccharomyces cerevisiae-Candida glabrata, and Saccharomyces cerevisiae-Schizosaccharomyces pombe as benchmark datasets. Because of the large amount of imbalanced data, the building and testing of the supervised model were only possible by using big data supervised classifiers managing imbalance. Evaluation metrics taking low ortholog ratios into account were applied. From the effectiveness perspective, MapReduce Random Oversampling combined with Spark SVM outperformed RBH, RSD, and OMA, probably because of the consideration of gene pair features beyond alignment similarities combined with the advances in big data supervised classification.

Prediction of Genetic Interactions Using Machine Learning and Network Properties

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

Quantitative and Systems-Based Approaches for Deciphering Bacterial Membrane Interactome and Gene Function

High-throughput genomic and proteomic methods provide a concise description of the molecular constituents of a cell, whereas systems biology strives to understand the way these components function as a whole. Recent developments, such as genome editing technologies and protein epitope-tagging coupled with high-sensitivity mass-spectrometry, allow systemic studies to be performed at an unprecedented scale. Available methods can be successfully applied to various goals, both expanding fundamental knowledge and solving applied problems. In this review, we discuss the present state and future of bacterial cell envelope interactomics, with a specific focus on host-pathogen interactions and drug target discovery. Both experimental and computational methods will be outlined together with examples of their practical implementation.

All or nothing: protein complexes flip essentiality between distantly related eukaryotes

In the budding yeast Saccharomyces cerevisiae, the subunits of any given protein complex are either mostly essential or mostly nonessential, suggesting that essentiality is a property of molecular machines rather than individual components. There are exceptions to this rule, however, that is, nonessential genes in largely essential complexes and essential genes in largely nonessential complexes. Here, we provide explanations for these exceptions, showing that redundancy within complexes, as revealed by genetic interactions, can explain many of the former cases, whereas “moonlighting,” as revealed by membership of multiple complexes, can explain the latter. Surprisingly, we find that redundancy within complexes cannot usually be explained by gene duplication, suggesting alternate buffering mechanisms. In the distantly related Schizosaccharomyces pombe, we observe the same phenomenon of modular essentiality, suggesting that it may be a general feature of eukaryotes. Furthermore, we show that complexes flip essentiality in a cohesive fashion between the two species, that is, they tend to change from mostly essential to mostly nonessential, or vice versa, but not to mixed patterns. We show that these flips in essentiality can be explained by differing lifestyles of the two yeasts. Collectively, our results support a previously proposed model where proteins are essential because of their involvement in essential functional modules rather than because of specific topological features such as degree or centrality.

Genetic interaction networks: better understand to better predict

A genetic interaction (GI) between two genes generally indicates that the phenotype of a double mutant differs from what is expected from each individual mutant. In the last decade, genome scale studies of quantitative GIs were completed using mainly synthetic genetic array technology and RNA interference in yeast and Caenorhabditis elegans. These studies raised questions regarding the functional interpretation of GIs, the relationship of genetic and molecular interaction networks, the usefulness of GI networks to infer gene function and co-functionality, the evolutionary conservation of GI, etc. While GIs have been used for decades to dissect signaling pathways in genetic models, their functional interpretations are still not trivial. The existence of a GI between two genes does not necessarily imply that these two genes code for interacting proteins or that the two genes are even expressed in the same cell. In fact, a GI only implies that the two genes share a functional relationship. These two genes may be involved in the same biological process or pathway; or they may also be involved in compensatory pathways with unrelated apparent function. Considering the powerful opportunity to better understand gene function, genetic relationship, robustness and evolution, provided by a genome-wide mapping of GIs, several in silico approaches have been employed to predict GIs in unicellular and multicellular organisms. Most of these methods used weighted data integration. In this article, we will review the later knowledge acquired on GI networks in metazoans by looking more closely into their relationship with pathways, biological processes and molecular complexes but also into their modularity and organization. We will also review the different in silico methods developed to predict GIs and will discuss how the knowledge acquired on GI networks can be used to design predictive tools with higher performances.

Characterization and prediction of haploinsufficiency using systems-level gene properties in yeast

Variation in gene copy number can significantly affect organism fitness. When one allele is missing in a diploid, the phenotype can be compromised because of haploinsufficiency. In this work, we identified associations between Saccharomyces cerevisiae gene properties and genome-scale haploinsufficiency phenotypes from previous work. We compared the haploinsufficiency profiles against 23 gene properties and found that genes with higher level of connectivity (degree) in a protein–protein interaction network, higher genetic interaction degree, greater gene sequence conservation, and higher protein expression were significantly more likely to be haploinsufficient. Additionally, haploinsufficiency showed negative relationships with cell cycle regulation and promoter sequence conservation.

Epigenetic epistatic interactions constrain the evolution of gene expression

Harmful epistatic (genetic) interactions not only occur between mutations, but also when genes change in expression. Gene expression dynamics in yeast suggests that this ‘epigenetic' epistasis constrains evolution, with the tight regulation of network hubs promoting a robust, ‘canalized' phenotype.

Yeast genes with many negative genetic interaction partners tend to have expression that is stable between cells, across conditions, and through evolution.

This low expression variation is linked to the use of alternative promoter architectures.

The stable expression of genetic interaction network hubs suggests that epigenetic epistasis confers a constraint on evolution.

Reduced activity of two genes in combination often has a more detrimental effect than expected. Such epistatic interactions not only occur when genes are mutated but also due to variation in gene expression, including among isogenic individuals in a controlled environment. We hypothesized that these ‘epigenetic' epistatic interactions could place important constraints on the evolution of gene expression. Consistent with this, we show here that yeast genes with many epistatic interaction partners typically show low expression variation among isogenic individuals and low variation across different conditions. In addition, their expression tends to remain stable in response to the accumulation of mutations and only diverges slowly between strains and species. Yeast promoter architectures, the retention of gene duplicates, and the divergence of expression between humans and chimps are also consistent with selective pressure to reduce the likelihood of harmful epigenetic epistatic interactions. Based on these and previous analyses, we propose that the tight regulation of epistatic interaction network hubs makes an important contribution to the maintenance of a robust, ‘canalized' phenotype. Moreover, that epigenetic epistatic interactions may contribute substantially to fitness defects when single genes are deleted.

Negative functional interaction between cell integrity MAPK pathway and Rho1 GTPase in fission yeast

Rho1 GTPase is the main activator of cell wall glucan biosynthesis and regulates actin cytoskeleton in fungi, including Schizosaccharomyces pombe. We have obtained a fission yeast thermosensitive mutant strain carrying the rho1-596 allele, which displays reduced Rho1 GTPase activity. This strain has severe cell wall defects and a thermosensitive growth, which is partially suppressed by osmotic stabilization. In a global screening for rho1-596 multicopy suppresors the pmp1+ gene was identified. Pmp1 is a dual specificity phosphatase that negatively regulates the Pmk1 mitogen-activated protein kinase (MAPK) cell integrity pathway. Accordingly, elimination of Pmk1 MAPK partially rescued rho1-596 thermosensitivity, corroborating the unexpected antagonistic functional relationship of these genes. We found that rho1-596 cells displayed increased basal activation of the cell integrity MAPK pathway and therefore were hypersensitive to MgCl2 and FK506. Moreover, the absence of calcineurin was lethal for rho1-596. We found a higher level of calcineurin activity in rho1-596 than in wild-type cells, and overexpression of constitutively active calcineurin partially rescued rho1-596 thermosensitivity. All together our results suggest that loss of Rho1 function causes an increase in the cell integrity MAPK activity, which is detrimental to the cells and turns calcineurin activity essential.

Quantitative genetic-interaction mapping in mammalian cells

Mapping genetic interactions (GIs) by simultaneously perturbing pairs of genes is a powerful tool for understanding complex biological phenomena. Here we describe an experimental platform for generating quantitative GI maps in mammalian cells using a combinatorial RNA interference strategy. We performed ∼11,000 pairwise knockdowns in mouse fibroblasts, focusing on 130 factors involved in chromatin regulation to create a GI map. Comparison of the GI and protein-protein interaction (PPI) data revealed that pairs of genes exhibiting positive GIs and/or similar genetic profiles were predictive of the corresponding proteins being physically associated. The mammalian GI map identified pathways and complexes but also resolved functionally distinct submodules within larger protein complexes. By integrating GI and PPI data, we created a functional map of chromatin complexes in mouse fibroblasts, revealing that the PAF complex is a central player in the mammalian chromatin landscape.

Genotype to phenotype: lessons from model organisms for human genetics

To what extent can variation in phenotypic traits such as disease risk be accurately predicted in individuals? In this Review, I highlight recent studies in model organisms that are relevant both to the challenge of accurately predicting phenotypic variation from individual genome sequences ('whole-genome reverse genetics') and for understanding why, in many cases, this may be impossible. These studies argue that only by combining genetic knowledge with in vivo measurements of biological states will it be possible to make accurate genetic predictions for individual humans.

A yeast phenomic model for the gene interaction network modulating CFTR-ΔF508 protein biogenesis

Background

The overall influence of gene interaction in human disease is unknown. In cystic fibrosis (CF) a single allele of the cystic fibrosis transmembrane conductance regulator (CFTR-ΔF508) accounts for most of the disease. In cell models, CFTR-ΔF508 exhibits defective protein biogenesis and degradation rather than proper trafficking to the plasma membrane where CFTR normally functions. Numerous genes function in the biogenesis of CFTR and influence the fate of CFTR-ΔF508. However it is not known whether genetic variation in such genes contributes to disease severity in patients. Nor is there an easy way to study how numerous gene interactions involving CFTR-ΔF would manifest phenotypically.

Methods

To gain insight into the function and evolutionary conservation of a gene interaction network that regulates biogenesis of a misfolded ABC transporter, we employed yeast genetics to develop a 'phenomic' model, in which the CFTR-ΔF508-equivalent residue of a yeast homolog is mutated (Yor1-ΔF670), and where the genome is scanned quantitatively for interaction. We first confirmed that Yor1-ΔF undergoes protein misfolding and has reduced half-life, analogous to CFTR-ΔF. Gene interaction was then assessed quantitatively by growth curves for approximately 5,000 double mutants, based on alteration in the dose response to growth inhibition by oligomycin, a toxin extruded from the cell at the plasma membrane by Yor1.

Results

From a comparative genomic perspective, yeast gene interactions influencing Yor1-ΔF biogenesis were representative of human homologs previously found to modulate processing of CFTR-ΔF in mammalian cells. Additional evolutionarily conserved pathways were implicated by the study, and a ΔF-specific pro-biogenesis function of the recently discovered ER membrane complex (EMC) was evident from the yeast screen. This novel function was validated biochemically by siRNA of an EMC ortholog in a human cell line expressing CFTR-ΔF508. The precision and accuracy of quantitative high throughput cell array phenotyping (Q-HTCP), which captures tens of thousands of growth curves simultaneously, provided powerful resolution to measure gene interaction on a phenomic scale, based on discrete cell proliferation parameters.

Conclusion

We propose phenomic analysis of Yor1-ΔF as a model for investigating gene interaction networks that can modulate cystic fibrosis disease severity. Although the clinical relevance of the Yor1-ΔF gene interaction network for cystic fibrosis remains to be defined, the model appears to be informative with respect to human cell models of CFTR-ΔF. Moreover, the general strategy of yeast phenomics can be employed in a systematic manner to model gene interaction for other diseases relating to pathologies that result from protein misfolding or potentially any disease involving evolutionarily conserved genetic pathways.