You will be presented with the abstract, introduction, and discussion section from a biomedical research article. Your task is to create a summary that a layperson can understand, capturing the essence of the research article. Keep essential scientific terms, but ensure that the language remains clear and the concepts are explained in an uncomplicated manner.

Title: A map of directional genetic interactions in a metazoan cell}
Article:
Abstract:
Gene–gene interactions shape complex phenotypes and modify the effects of mutations during development and disease. The effects of statistical gene–gene interactions on phenotypes have been used to assign genes to functional modules. However, directional, epistatic interactions, which reflect regulatory relationships between genes, have been challenging to map at large-scale. Here, we used combinatorial RNA interference and automated single-cell phenotyping to generate a large genetic interaction map for 21 phenotypic features of Drosophila cells. We devised a method that combines genetic interactions on multiple phenotypes to reveal directional relationships. This network reconstructed the sequence of protein activities in mitosis. Moreover, it revealed that the Ras pathway interacts with the SWI/SNF chromatin-remodelling complex, an interaction that we show is conserved in human cancer cells. Our study presents a powerful approach for reconstructing directional regulatory networks and provides a resource for the interpretation of functional consequences of genetic alterations.
Introduction:
Genes display epistatic( genetic) interactions, whereby the presence of one genetic variant can mask, alleviate or amplify the phenotypic effect of other variants( Bateson, 1907). Genetic interactions have profound effects during normal development and in disease( Moore, 2003; Ashworth et al., 2011). Two genes show a genetic interaction if the phenotype of allele combinations( or double perturbations) is different from the expected combination of the individual phenotypes; this statistical definition of genetic interactions was introduced by Fisher( 1918). Mathematically, Fisher defined a genetic interaction as an interaction term between genetic covariates in a( statistical) linear model for a phenotype. Genetic interactions occur, for example, when a cellular process is controlled by two parallel pathways: loss of gene products in one pathway are buffered, and only when the both pathways are disturbed, a phenotype is observed. Conversely, the phenotype of closely collaborating proteins, for example, in a protein complex, is often not further enhanced when more than one of its gene products are depleted( Dixon et al., 2009). Systematic screens for genetic interactions have been performed in yeast( Tong et al., 2004; Schuldiner et al., 2005; Costanzo et al., 2010; Ryan et al., 2012), Escherichia coli( Nichols et al., 2011; Babu et al., 2014), Caenorhabditis elegans( Lehner et al., 2006) and metazoan cells( Bakal et al., 2008; Horn et al., 2011; Bassik et al., 2013; Laufer et al., 2013; Roguev et al., 2013). These approaches have successfully unravelled symmetric relationships, such as pathway and complex co-membership( Baryshnikova et al., 2013). However, the observation of a statistical genetic interaction implies no directionality between the genes: it does not take into account a possible order of relationships, for example, their temporal relation in a signalling cascade. Directionality is present if one gene product positively or negatively regulates the activity of the other, if its function temporally precedes that of the other, or if its function is a necessary requirement for the action of the other. Such directional genetic interactions were described by Bateson( 1907), and observing epistatic interactions between genes has been a powerful method to organise them into functional pathways( Phillips, 2008). In special cases, for example, in which one of the two interrogated genes has no phenotype itself, it has been possible to infer directionality by comparing the gene–gene phenotype to the single gene phenotype( Drees et al., 2005; St Onge et al., 2007). However, in many cases genes that show genetic interactions have phenotypes themselves, and more general methods to predict directionalities have been lacking. We report the largest map of multi-phenotype genetic interaction profiles in metazoan cells to date. Our map comprises 1367 Drosophila genes implicated in cellular processes including signalling, chromatin and cell cycle regulation. We quantitatively scored 21 phenotypes in cultured cells using automated imaging. First, we inferred gene functions based on statistical genetic interactions, predicted protein complexes and clustered processes by similarity. Moreover, in a novel analytical approach, we employ the multivariate nature of the phenotypes to compute directional genetic interactions, and we show that they reveal the logical and temporal dependencies between functional modules. For example, we were able to reconstruct the temporal order in which protein complexes are active during mitosis. Furthermore, we demonstrate an epistatic relationship between Ras/MAPK signalling and SWI/SNF regulators of chromatin remodelling, which we confirmed in vivo. We demonstrate that directional genetic interactions can be identified at a large scale and mapped to other genomic data sets to identify regulatory relationships.
Discussion:
The identification and mechanistic dissection of genetic interactions is a current frontier in molecular genetics. A systematic understanding of genetic interactions is an important basis for linking genotypes to phenotypes, to dissect the genetic dependencies of cellular processes and may also have profound implications for disease treatment. Identifying genetic dependencies can facilitate finding combinatorial drug treatments and avoiding resistance mechanisms, which are important in cancer( Nijman and Friend, 2013). The identification of genetic interactions in population-based observational studies, however, is limited by sample size and statistical power( Zuk et al., 2012) both in model organisms( Mackay, 2014) and for human diseases( Civelek and Lusis, 2014). Even though algorithmic and statistical approaches have been tuned over the last 10 years to address this issue, the number of detected instances of epistasis in genome-wide association studies remains small( Lincoln et al., 2005; Gregersen et al., 2006; Wei et al., 2014). Large-scale reverse-genetic combinatorial perturbation experiments offer an avenue to systematically map genetic interactions to infer gene functions and their interdependencies. Analyses of genetic interaction screens so far have been mainly based on the ‘statistical’ definition by Fisher( 1918). A statistical genetic interaction exists between genes if the effect of their combined perturbation differs from what is expected from the single perturbations. The obtained information on gene–gene interactions is symmetric and does not reveal a potential directionality. The experimental and computational approach presented in this study aimed at capturing interactions, first irrespective of direction( symmetric), and second, adding directionality. Directional genetic interactions provide an additional layer of information above symmetric, statistical genetic interactions. Directional genetic interactions reflect causal, temporal or regulatory relationships between phenotypes and their underlying biological processes. Our method requires the observation of multiple phenotypes and is an algorithmic formalisation of the epistasis concept of Bateson( 1907). Bateson mapped epistatic alleles when studying the inheritance of coat colour of mice using multiple colour phenotypes. The formalisation of mapping epistatic interactions as done in our study is straightforward to implement and should also be applicable to future studies. Our map encompasses approximately 1300 genes for which we derived genetic interaction profiles, the largest genetic interaction map generated for a metazoan cell to date. The study demonstrates the feasibility of large-scale, automated discovery of directional epistatic interactions. There are also other approaches to annotate relationships between genes. For instance, Kelley and Ideker( 2005) detected different interaction types by searching for network motifs and integrated information from genetic, physical and reaction networks. Drees et al.( 2005) and St Onge et al.( 2007) derived partial orderings of single and double mutant viability phenotypes. This method can derive directional interactions in special cases, for example, when one of the genes does not have a phenotype itself. More recently, Vinayagam et al.( 2014) annotated protein–protein interactions with positive and negative signs, which they obtained from the correlations between the genes' individual knockdown phenotypes. Liberali et al.( 2014) tested whether one gene's single knockdown phenotypes are contained in those of another gene, and used such subset relationships to derive hierarchies between genes. In contrast to these previous approaches, our study directly captures epistatic relationships from multivariate phenotypes observed after di-genic perturbations, often revealing effects that cannot be constructed from single-gene perturbations, or from single phenotypes alone. van Wageningen et al.( 2010) recorded gene expression profiles from single and double yeast deletion strains and reported frequent instances of mixed epistasis. It would be interesting to study how our approach can be extended to detect mixed directional relationships. Another avenue for future work is the combination of directional genetic interaction mapping with Bayesian modelling( Battle et al., 2010) to further improve the inference of regulatory networks. For the gene set presented here, we predicted the temporal and causal relationships between cell cycle regulators during mitosis without using prior knowledge. To highlight the functional relevance of the genetic interactions revealed in the study, we first showed that Ras-mediated signalling in fly development in vivo can be suppressed by loss-of function mutations in multiple SWI/SNF components. This is concordant with results from Herr et al.( 2010), who reported attenuated EGFR/Ras/MAPK signalling activity after reduced brm activity in the background of the dominant active EGFR mutation ElpB1. In addition, we confirmed the genetic interactions of SWI/SNF components with constitutively active KRAS in human colon cancer cells. Recent cancer genome sequencing studies have revealed many recurrent mutations that are individually rare( Chin et al., 2011). A current challenge is to order these genetic variants by their functional and phenotypic effects. We have demonstrated a method to detect which recurrently mutated genes modify each other's phenotypes. Such information could be instrumental for understanding the joint effect of multiple mutated genes, for example, in the same tumour, and may contribute to understanding tumour evolution and drug sensitivity. We have demonstrated inference of directional genetic interactions from multiple phenotypes as a new approach to shed light on the patterns underlying complex genotype-phenotype relationships. This approach could provide a basis for the understanding of epistatic and functional dependencies between disease-associated genetic variants. Future studies could include additional phenotypes, optimized experimental designs and improved computational methods for the discovery of directional interactions.
Genes encode instructions that control our physical characteristics , known as traits . Although some traits are controlled by the activity of a single gene , most traits are influenced by the activities of multiple genes . The genes that influence a particular trait may work independently of each other . However , it is also possible for the genes to interact so that one gene may mask or amplify the effect of another gene . Although gene interactions were first described almost 100 years ago , it has been difficult to identify them and work out the direction of these interactions ( i . e . , does gene A affect gene B , or vice versa ? ) . Fischer , Sandmann et al . have now studied the interactions between the genes involved in 21 different traits of fruit fly cells . A technique called RNA interference was used to lower the expression of the genes in different combinations , which made it possible to analyze any changes in the traits that occurred when particular genes were not working properly . Fischer , Sandmann et al . took hundreds of thousands images of the cells and analyzed the changes in cell shape , cell size , cell division and other traits . Next , they developed a method to infer the directions of the interactions between individual pairs of genes from the data and then made a map of the genetic interactions for the traits . This map was able to reconstruct the known order of activity of genes during cell division and other cell processes . Furthermore , it revealed previously unknown interactions between genes . For example , genes involved in the Ras signaling pathway—which promotes cell growth and is frequently mutated in human tumors—interacted with genes that encode a group of proteins called the SWI/SNF complex . This complex alters how DNA is packaged in cells to control the expression of genes , and these gene interactions may play an important role in the control of cell growth by Ras signaling . The approach developed by Fischer , Sandmann et al . can shed light on the interactions between genes that produce complex traits of cells . In future , this approach might be helpful to find out which genetic differences between individuals alter the effectiveness of drug treatments , and the impact of using combinations of drugs to treat diseases .