Positive Genetic Interactions Research Articles

CRISPR/Cas9 model of prostate cancer identifies Kmt2c deficiency as a metastatic driver by Odam/Cabs1 gene cluster expression

Metastatic prostate cancer (PCa) poses a significant therapeutic challenge with high mortality rates. Utilizing CRISPR-Cas9 in vivo, we target five potential tumor suppressor genes (Pten, Trp53, Rb1, Stk11, and RnaseL) in the mouse prostate, reaching humane endpoint after eight weeks without metastasis. By further depleting three epigenetic factors (Kmt2c, Kmt2d, and Zbtb16), lung metastases are present in all mice. While whole genome sequencing reveals few mutations in coding sequence, RNA sequencing shows significant dysregulation, especially in a conserved genomic region at chr5qE1 regulated by KMT2C. Depleting Odam and Cabs1 in this region prevents metastasis. Notably, the gene expression signatures, resulting from our study, predict progression-free and overall survival and distinguish primary and metastatic human prostate cancer. This study emphasizes positive genetic interactions between classical tumor suppressor genes and epigenetic modulators in metastatic PCa progression, offering insights into potential treatments.

A synthetic genetic array screen for interactions with the RNA helicase DED1 during cell stress in budding yeast.

During cellular stress it is essential for cells to alter their gene expression to adapt and survive. Gene expression is regulated at multiple levels, but translation regulation is both a method for rapid changes to the proteome and, as one of the most energy-intensive cellular processes, a way to efficiently redirect cellular resources during stress conditions. Despite this ideal positioning, many of the specifics of how translation is regulated, positively or negatively, during various types of cellular stress remain poorly understood. To further assess this regulation, we examined the essential translation factor Ded1, an RNA helicase that has been previously shown to play important roles in the translational response to cellular stress. In particular, ded1 mutants display an increased resistance to growth inhibition and translation repression induced by the TOR pathway inhibitor, rapamycin, suggesting that normal stress responses are partially defective in these mutants. To gain further insight into Ded1 translational regulation during stress, synthetic genetic array analysis was conducted in the presence of rapamycin with a ded1 mutant and a library of nonessential genes in Saccharomyces cerevisiae to identify positive and negative genetic interactions in an unbiased manner. Here, we report the results of this screen and subsequent network mapping and Gene Ontology-term analysis. Hundreds of candidate interactions were identified, which fell into expected categories, such as ribosomal proteins and amino acid biosynthesis, as well as unexpected ones, including membrane trafficking, sporulation, and protein glycosylation. Therefore, these results provide several specific directions for further comprehensive studies.

Genetic landscape of T cells identifies synthetic lethality for T-ALL

To capture the global gene network regulating the differentiation of immature T cells in an unbiased manner, large-scale forward genetic screens in zebrafish were conducted and combined with genetic interaction analysis. After ENU mutagenesis, genetic lesions associated with failure of T cell development were identified by meiotic recombination mapping, positional cloning, and whole genome sequencing. Recessive genetic variants in 33 genes were identified and confirmed as causative by additional experiments. The mutations affected T cell development but did not perturb the development of an unrelated cell type, growth hormone-expressing somatotrophs, providing an important measure of cell-type specificity of the genetic variants. The structure of the genetic network encompassing the identified components was established by a subsequent genetic interaction analysis, which identified many instances of positive (alleviating) and negative (synthetic) genetic interactions. Several examples of synthetic lethality were subsequently phenocopied using combinations of small molecule inhibitors. These drugs not only interfered with normal T cell development, but also elicited remission in a model of T cell acute lymphoblastic leukaemia. Our findings illustrate how genetic interaction data obtained in the context of entire organisms can be exploited for targeted interference with specific cell types and their malignant derivatives.

Exploring a Local Genetic Interaction Network Using Evolutionary Replay Experiments

Understanding how genes interact is a central challenge in biology. Experimental evolution provides a useful, but underutilized, tool for identifying genetic interactions, particularly those that involve non-loss-of-function mutations or mutations in essential genes. We previously identified a strong positive genetic interaction between specific mutations in KEL1 (P344T) and HSL7 (A695fs) that arose in an experimentally evolved Saccharomyces cerevisiae population. Because this genetic interaction is not phenocopied by gene deletion, it was previously unknown. Using “evolutionary replay” experiments, we identified additional mutations that have positive genetic interactions with the kel1-P344T mutation. We replayed the evolution of this population 672 times from six timepoints. We identified 30 populations where the kel1-P344T mutation reached high frequency. We performed whole-genome sequencing on these populations to identify genes in which mutations arose specifically in the kel1-P344T background. We reconstructed mutations in the ancestral and kel1-P344T backgrounds to validate positive genetic interactions. We identify several genetic interactors with KEL1, we validate these interactions by reconstruction experiments, and we show these interactions are not recapitulated by loss-of-function mutations. Our results demonstrate the power of experimental evolution to identify genetic interactions that are positive, allele specific, and not readily detected by other methods, shedding light on an underexplored region of the yeast genetic interaction network.

Sgs1 Binding to Rad51 Stimulates Homology-Directed DNA Repair in Saccharomyces cerevisiae.

Accurate repair of DNA breaks is essential to maintain genome integrity and cellular fitness. Sgs1, the sole member of the RecQ family of DNA helicases in Saccharomyces cerevisiae, is important for both early and late stages of homology-dependent repair. Its large number of physical and genetic interactions with DNA recombination, repair, and replication factors has established Sgs1 as a key player in the maintenance of genome integrity. To determine the significance of Sgs1 binding to the strand-exchange factor Rad51, we have identified a single amino acid change at the C-terminal of the helicase core of Sgs1 that disrupts Rad51 binding. In contrast to an SGS1 deletion or a helicase-defective sgs1 allele, this new separation-of-function allele, sgs1-FD, does not cause DNA damage hypersensitivity or genome instability, but exhibits negative and positive genetic interactions with sae2Δ, mre11Δ, exo1Δ, srs2Δ, rrm3Δ, and pol32Δ that are distinct from those of known sgs1 mutants. Our findings suggest that the Sgs1-Rad51 interaction stimulates homologous recombination (HR). However, unlike sgs1 mutations, which impair the resection of DNA double-strand ends, negative genetic interactions of the sgs1-FD allele are not suppressed by YKU70 deletion. We propose that the Sgs1-Rad51 interaction stimulates HR by facilitating the formation of the presynaptic Rad51 filament, possibly by Sgs1 competing with single-stranded DNA for replication protein A binding during resection.

Mutations that prevent methylation of cohesin render sensitivity to DNA damage in S. pombe.

ABSTRACTThe canonical role of cohesin is to mediate sister chromatid cohesion. In addition, cohesin plays important roles in processes such as DNA repair and regulation of gene expression. Mounting evidence suggests that various post-translational modifications, including phosphorylation, acetylation and sumoylation regulate cohesin functions. Our mass spectrometry analysis of cohesin purified from Schizosaccharomyces pombe cells revealed that the cohesin subunit Psm1 is methylated on two evolutionarily conserved lysine residues, K536 and K1200. We found that mutations that prevent methylation of Psm1 K536 and K1200 render sensitivity to DNA-damaging agents and show positive genetic interactions with mutations in genes encoding the Mus81–Eme1 endonuclease. Yeast two-hybrid and co-immunoprecipitation assays showed that there were interactions between subunits of the cohesin and Mus81–Eme1 complexes. We conclude that cohesin is methylated and that mutations that prevent methylation of Psm1 K536 and K1200 show synthetic phenotypes with mutants defective in the homologous recombination DNA repair pathway.

Elp3 and Dph3 of Schizosaccharomyces pombe mediate cellular stress responses through tRNALysUUU modifications

Efficient protein synthesis in eukaryotes requires diphthamide modification of translation elongation factor eEF2 and wobble uridine modifications of tRNAs. In higher eukaryotes, these processes are important for preventing neurological and developmental defects and cancer. In this study, we used Schizosaccharomyces pombe as a model to analyse mutants defective in eEF2 modification (dph1Δ), in tRNA modifications (elp3Δ), or both (dph3Δ) for sensitivity to cytotoxic agents and thermal stress. The dph3Δ and elp3Δ mutants were sensitive to a range of drugs and had growth defects at low temperature. dph3Δ was epistatic with dph1Δ for sensitivity to hydroxyurea and methyl methanesulfonate, and with elp3Δ for methyl methanesulfonate and growth at 16 °C. The dph1Δ and dph3Δ deletions rescued growth defects of elp3Δ in response to thiabendazole and at 37 °C. Elevated tRNALysUUU levels suppressed the elp3Δ phenotypes and some of the dph3Δ phenotypes, indicating that lack of tRNALysUUU modifications were responsible. Furthermore, we found positive genetic interactions of elp3Δ and dph3Δ with sty1Δ and atf1Δ, indicating that Elp3/Dph3-dependent tRNA modifications are important for efficient biosynthesis of key factors required for accurate responses to cytotoxic stress conditions.

Functional mapping of yeast genomes by saturated transposition.

Yeast is a powerful model for systems genetics. We present a versatile, time- and labor-efficient method to functionally explore the Saccharomyces cerevisiae genome using saturated transposon mutagenesis coupled to high-throughput sequencing. SAturated Transposon Analysis in Yeast (SATAY) allows one-step mapping of all genetic loci in which transposons can insert without disrupting essential functions. SATAY is particularly suited to discover loci important for growth under various conditions. SATAY (1) reveals positive and negative genetic interactions in single and multiple mutant strains, (2) can identify drug targets, (3) detects not only essential genes, but also essential protein domains, (4) generates both null and other informative alleles. In a SATAY screen for rapamycin-resistant mutants, we identify Pib2 (PhosphoInositide-Binding 2) as a master regulator of TORC1. We describe two antagonistic TORC1-activating and -inhibiting activities located on opposite ends of Pib2. Thus, SATAY allows to easily explore the yeast genome at unprecedented resolution and throughput.

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.

A high-resolution gene expression atlas of epistasis between gene-specific transcription factors exposes potential mechanisms for genetic interactions.

BackgroundGenetic interactions, or non-additive effects between genes, play a crucial role in many cellular processes and disease. Which mechanisms underlie these genetic interactions has hardly been characterized. Understanding the molecular basis of genetic interactions is crucial in deciphering pathway organization and understanding the relationship between genotype, phenotype and disease.ResultsTo investigate the nature of genetic interactions between gene-specific transcription factors (GSTFs) in Saccharomyces cerevisiae, we systematically analyzed 72 GSTF pairs by gene expression profiling double and single deletion mutants. These pairs were selected through previously published growth-based genetic interactions as well as through similarity in DNA binding properties. The result is a high-resolution atlas of gene expression-based genetic interactions that provides systems-level insight into GSTF epistasis. The atlas confirms known genetic interactions and exposes new ones. Importantly, the data can be used to investigate mechanisms that underlie individual genetic interactions. Two molecular mechanisms are proposed, “buffering by induced dependency” and “alleviation by derepression”.ConclusionsThese mechanisms indicate how negative genetic interactions can occur between seemingly unrelated parallel pathways and how positive genetic interactions can indirectly expose parallel rather than same-pathway relationships. The focus on GSTFs is important for understanding the transcription regulatory network of yeast as it uncovers details behind many redundancy relationships, some of which are completely new. In addition, the study provides general insight into the complex nature of epistasis and proposes mechanistic models for genetic interactions, the majority of which do not fall into easily recognizable within- or between-pathway relationships.Electronic supplementary materialThe online version of this article (doi:10.1186/s12915-015-0222-5) contains supplementary material, which is available to authorized users.

Minimal metabolic pathway structure is consistent with associated biomolecular interactions.

Pathways are a universal paradigm for functionally describing cellular processes. Even though advances in high-throughput data generation have transformed biology, the core of our biological understanding, and hence data interpretation, is still predicated on human-defined pathways. Here, we introduce an unbiased, pathway structure for genome-scale metabolic networks defined based on principles of parsimony that do not mimic canonical human-defined textbook pathways. Instead, these minimal pathways better describe multiple independent pathway-associated biomolecular interaction datasets suggesting a functional organization for metabolism based on parsimonious use of cellular components. We use the inherent predictive capability of these pathways to experimentally discover novel transcriptional regulatory interactions in Escherichia coli metabolism for three transcription factors, effectively doubling the known regulatory roles for Nac and MntR. This study suggests an underlying and fundamental principle in the evolutionary selection of pathway structures; namely, that pathways may be minimal, independent, and segregated.

Constructing module maps for integrated analysis of heterogeneous biological networks

Improved methods for integrated analysis of heterogeneous large-scale omic data are direly needed. Here, we take a network-based approach to this challenge. Given two networks, representing different types of gene interactions, we construct a map of linked modules, where modules are genes strongly connected in the first network and links represent strong inter-module connections in the second. We develop novel algorithms that considerably outperform prior art on simulated and real data from three distinct domains. First, by analyzing protein–protein interactions and negative genetic interactions in yeast, we discover epistatic relations among protein complexes. Second, we analyze protein–protein interactions and DNA damage-specific positive genetic interactions in yeast and reveal functional rewiring among protein complexes, suggesting novel mechanisms of DNA damage response. Finally, using transcriptomes of non–small-cell lung cancer patients, we analyze networks of global co-expression and disease-dependent differential co-expression and identify a sharp drop in correlation between two modules of immune activation processes, with possible microRNA control. Our study demonstrates that module maps are a powerful tool for deeper analysis of heterogeneous high-throughput omic data.

The rRNA methyltransferase Bud23 shows functional interaction with components of the SSU processome and RNase MRP

Bud23 is responsible for the conserved methylation of G1575 of 18S rRNA, in the P-site of the small subunit of the ribosome. bud23Δ mutants have severely reduced small subunit levels and show a general failure in cleavage at site A2 during rRNA processing. Site A2 is the primary cleavage site for separating the precursors of 18S and 25S rRNAs. Here, we have taken a genetic approach to identify the functional environment of BUD23. We found mutations in UTP2 and UTP14, encoding components of the SSU processome, as spontaneous suppressors of a bud23Δ mutant. The suppressors improved growth and subunit balance and restored cleavage at site A2. In a directed screen of 50 ribosomal trans-acting factors, we identified strong positive and negative genetic interactions with components of the SSU processome and strong negative interactions with components of RNase MRP. RNase MRP is responsible for cleavage at site A3 in pre-rRNA, an alternative cleavage site for separating the precursor rRNAs. The strong negative genetic interaction between RNase MRP mutants and bud23Δ is likely due to the combined defects in cleavage at A2 and A3. Our results suggest that Bud23 plays a role at the time of A2 cleavage, earlier than previously thought. The genetic interaction with the SSU processome suggests that Bud23 could be involved in triggering disassembly of the SSU processome, or of particular subcomplexes of the processome.

A quantitative analysis of monochromaticity in genetic interaction networks

BackgroundA genetic interaction refers to the deviation of phenotypes from the expected when perturbing two genes simultaneously. Studying genetic interactions help clarify relationships between genes, such as compensation and masking, and identify gene groups of functional modules. Recently, several genome-scale experiments for measuring quantitative (positive and negative) genetic interactions have been conducted. The results revealed that genes in the same module usually interact with each other in a consistent way (pure positive or negative); this phenomenon was designated as monochromaticity. Monochromaticity might be the underlying principle that can be utilized to unveil the modularity of cellular networks. However, no appropriate quantitative measurement for this phenomenon has been proposed.ResultsIn this study, we propose the monochromatic index (MCI), which is able to quantitatively evaluate the monochromaticity of potential functional modules of genes, and the MCI was used to study genetic landscapes in different cellular subsystems. We demonstrated that MCI not only amend the deficiencies of MP-score but also properly incorporate the background effect. The results showed that not only within-complex but also between-complex connections present significant monochromatic tendency. Furthermore, we also found that significantly higher proportion of protein complexes are connected by negative genetic interactions in metabolic network, while transcription and translation system adopts relatively even number of positive and negative genetic interactions to link protein complexes.ConclusionIn summary, we demonstrate that MCI improves deficiencies suffered by MP-score, and can be used to evaluate monochromaticity in a quantitative manner. In addition, it also helps to unveil features of genetic landscapes in different cellular subsystems. Moreover, MCI can be easily applied to data produced by different types of genetic interaction methodologies such as Synthetic Genetic Array (SGA), and epistatic miniarray profile (E-MAP).

Putting genetic interactions in context through a global modular decomposition

Genetic interactions provide a powerful perspective into gene function, but our knowledge of the specific mechanisms that give rise to these interactions is still relatively limited. The availability of a global genetic interaction map in Saccharomyces cerevisiae, covering ∼30% of all possible double mutant combinations, provides an unprecedented opportunity for an unbiased assessment of the native structure within genetic interaction networks and how it relates to gene function and modular organization. Toward this end, we developed a data mining approach to exhaustively discover all block structures within this network, which allowed for its complete modular decomposition. The resulting modular structures revealed the importance of the context of individual genetic interactions in their interpretation and revealed distinct trends among genetic interaction hubs as well as insights into the evolution of duplicate genes. Block membership also revealed a surprising degree of multifunctionality across the yeast genome and enabled a novel association of VIP1 and IPK1 with DNA replication and repair, which is supported by experimental evidence. Our modular decomposition also provided a basis for testing the between-pathway model of negative genetic interactions and within-pathway model of positive genetic interactions. While we find that most modular structures involving negative genetic interactions fit the between-pathway model, we found that current models for positive genetic interactions fail to explain 80% of the modular structures detected. We also find differences between the modular structures of essential and nonessential genes.

An integrated approach to characterize genetic interaction networks in yeast metabolism

Intense experimental and theoretical efforts have been made to globally map genetic interactions, yet we still do not understand how gene-gene interactions arise from the operation of biomolecular networks. To bridge the gap between empirical and computational studies, we: i) quantitatively measure genetic interactions between ~185,000 metabolic gene pairs in Saccharomyces cerevisiae, ii) superpose the data on a detailed systems biology model of metabolism, and iii) introduce a machine-learning method to reconcile empirical interaction data with model predictions. We systematically investigate the relative impacts of functional modularity and metabolic flux coupling on the distribution of negative and positive genetic interactions. We also provide a mechanistic explanation for the link between the degree of genetic interaction, pleiotropy, and gene dispensability. Last, we demonstrate the feasibility of automated metabolic model refinement by correcting misannotations in NAD biosynthesis and confirming them by in vivo experiments.

Protein Complexes are Central in the Yeast Genetic Landscape

If perturbing two genes together has a stronger or weaker effect than expected, they are said to genetically interact. Genetic interactions are important because they help map gene function, and functionally related genes have similar genetic interaction patterns. Mapping quantitative (positive and negative) genetic interactions on a global scale has recently become possible. This data clearly shows groups of genes connected by predominantly positive or negative interactions, termed monochromatic groups. These groups often correspond to functional modules, like biological processes or complexes, or connections between modules. However it is not yet known how these patterns globally relate to known functional modules. Here we systematically study the monochromatic nature of known biological processes using the largest quantitative genetic interaction data set available, which includes fitness measurements for ∼5.4 million gene pairs in the yeast Saccharomyces cerevisiae. We find that only 10% of biological processes, as defined by Gene Ontology annotations, and less than 1% of inter-process connections are monochromatic. Further, we show that protein complexes are responsible for a surprisingly large fraction of these patterns. This suggests that complexes play a central role in shaping the monochromatic landscape of biological processes. Altogether this work shows that both positive and negative monochromatic patterns are found in known biological processes and in their connections and that protein complexes play an important role in these patterns. The monochromatic processes, complexes and connections we find chart a hierarchical and modular map of sensitive and redundant biological systems in the yeast cell that will be useful for gene function prediction and comparison across phenotypes and organisms. Furthermore the analysis methods we develop are applicable to other species for which genetic interactions will progressively become more available.

Quantitative analysis of fitness and genetic interactions in yeast on a genome scale.

Global quantitative analysis of genetic interactions is a powerful approach for deciphering the roles of genes and mapping functional relationships among pathways. Using colony size as a proxy for fitness, we developed a method for measuring fitness-based genetic interactions from high-density arrays of yeast double mutants generated by synthetic genetic array (SGA) analysis. We identified several experimental sources of systematic variation and developed normalization strategies to obtain accurate single- and double-mutant fitness measurements, which rival the accuracy of other high-resolution studies. We applied the SGA score to examine the relationship between physical and genetic interaction networks, and we found that positive genetic interactions connect across functionally distinct protein complexes revealing a network of genetic suppression among loss-of-function alleles.

Exploring the Monochromatic Landscape in Yeast using Genetic Interactions and Known Processes Reveals the Importance of Protein Complexes

Background If perturbing two genes together has a stronger effect than expected based on both single perturbation effects, these two genes are said to genetically interact. Recently experimental methods have enabled to obtain quantitative positive and negative genetic interactions [1] where negative interactions indicate buffering between genes and positive interactions suggest that the genes are part of the same process. In such genetic interaction networks, some groups of genes interact in a monochromatic manner: interactions between them are mostly positive or mostly negative. It has been proposed that these groups are functionally related and enriched in protein complexes [2]. Nevertheless boundaries and system level relationships are still difficult to define from the network. Instead of starting from the network, our new approach starts from known biological processes. We propose to evaluate the current model and study the monochromatic purity of biological processes using the most comprehensive quantitative genetic interaction data set currently available in the budding yeast that includes measurements for 5.4 million pairs of genes [1]. Method We first study the monochromatic purity of biological processes and of the connections between processes. We use Gene Ontology (GO) to define biological processes in yeast. We assess the monochromatic purity of a process with a score based on the relative ratio of positive to negative interactions within that process. We generate random networks and compute zscores to identify statistically significant scores. We perform a statistical test (Fisher) to assess to which extent the connections between different processes are monochromatic. Then we study different features that could explain the monochromaticity (paralogs, protein complexes, ...). Performing the identification of monochromatic purity after removing the features indicates to which extent this feature is important for the monochromaticity. Results We show that 10% of the biological processes are monochromatic and identify 1% of the connections between processes as monochromatic. Among the different features we studied to explain this monochromaticity, protein complexes have a strong effect. In addition we show that 63% of the interactions are attributed to complexes whereas we expect only 49% given the number of genes present in complexes. Conclusion This work is the first systematic study of the monochromatic purity of biological processes and connections between them. It reveals that protein complexes play a disproportionately important role in the yeast genetic landscape. This result gives new insight into the organization of biological processes and can be used for complex member prediction.

Avoiding transcription factor competition at promoter level increases the chances of obtaining oscillation

BackgroundThe ultimate goal of synthetic biology is the conception and construction of genetic circuits that are reliable with respect to their designed function (e.g. oscillators, switches). This task remains still to be attained due to the inherent synergy of the biological building blocks and to an insufficient feedback between experiments and mathematical models. Nevertheless, the progress in these directions has been substantial.ResultsIt has been emphasized in the literature that the architecture of a genetic oscillator must include positive (activating) and negative (inhibiting) genetic interactions in order to yield robust oscillations. Our results point out that the oscillatory capacity is not only affected by the interaction polarity but by how it is implemented at promoter level. For a chosen oscillator architecture, we show by means of numerical simulations that the existence or lack of competition between activator and inhibitor at promoter level affects the probability of producing oscillations and also leaves characteristic fingerprints on the associated period/amplitude features.ConclusionsIn comparison with non-competitive binding at promoters, competition drastically reduces the region of the parameters space characterized by oscillatory solutions. Moreover, while competition leads to pulse-like oscillations with long-tail distribution in period and amplitude for various parameters or noisy conditions, the non-competitive scenario shows a characteristic frequency and confined amplitude values. Our study also situates the competition mechanism in the context of existing genetic oscillators, with emphasis on the Atkinson oscillator.