A map of directional genetic interactions in a metazoan cell
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 .
We generated the largest map of multi-phenotype genetic interaction profiles in metazoan cells to date by co-depleting gene pairs by RNAi in cultured Drosophila S2 cells , high-throughput imaging of single-cell phenotypes , and modelling of gene–gene interactions ( Figure 1A ) . We selected 1367 genes implicated in key biological processes , that is , signalling , chromatin biology , cell cycle regulation and protein turnover control ( Supplementary file 1 ) . Each of these 1367 target genes was tested against 72 query genes in all pairwise knockdown combinations ( 2 × 2 dsRNAs ) , following previously established approaches ( Casey et al . , 2008; Horn et al . , 2011; Laufer et al . , 2013 ) ( Figure 1—figure supplement 1 ) . The 72 query genes were selected from an initial single-gene screen on the 1367 genes , to cover a range of phenotypes , processes and protein complexes ( Figure 1—figure supplement 2 and Supplementary file 2 ) . After 5 days , cells were fixed and stained for DNA , α-tubulin , and Ser9-phosphorylated histone 3 , a mitosis marker . Cells were imaged by automated whole-well fluorescence microscopy , and phenotypic features were extracted using an image analysis pipeline ( see ‘Materials and methods’ ) . On average , 15 , 962 cells were imaged and analysed per well . Single-cell measurements were aggregated into 328 cell population features per experiment such as cell number , mitotic index , nuclear and cellular area , and other descriptors of shape and morphology ( Supplementary file 3 ) . 162 features were highly reproducible between replicates , with Pearson correlation >0 . 6 ( Figure 1B–C ) . Using a step-wise feature selection algorithm , we determined a subset of 21 features ( Supplementary file 4 ) that non-redundantly captured the range of phenotypes . The algorithm ensured that each feature contained new information not yet covered by the already selected features ( Figure 1D and ‘Materials and methods’ ) ( Laufer et al . , 2013 ) . 10 . 7554/eLife . 05464 . 003Figure 1 . Combinatorial RNAi to map multi-phenotype genetic interactions . ( A ) Workflow for multi-phenotype genetic interaction analysis by RNAi . ( B ) Reproducibility of phenotypic measurements . Plot shows replicate measurements for 1293 target genes at the beginning and end of the screening campaign . No batch effects on phenotypes were observed . ( C ) Each point in the plot corresponds to one of the phenotypic features . The y-axis shows the Pearson correlation coefficient of the feature's values between two replicates . Along the x-axis , features are ordered by their correlation coefficient . ( D ) Selection of non-redundant features proceeded step-wise , starting with cell number , mitotic index and cell area . In the left panel , the x-axis shows the information gain ( as measured by the correlation of the residuals between replicates ) for the selected features . Features are ordered as selected . In the right panel , the x-axis shows the fraction of positively correlated residual features remaining , which is used as a stop criterion ( Laufer et al . , 2013 ) . ( E ) Representative image regions are shown for negative control ( Ctrl ) , imaginal discs arrested ( ida ) , string ( stg ) and actin-related protein 2/3 complex , subunit 1 ( Arpc1 ) . Bar charts display measured quantitative features . ( F ) Two independent dsRNA reagents per gene were used to assess on-target specificity . The plot shows the correlation coefficient ( r ) between the two reagents across all phenotypic features and 72 query dsRNAs . Only genes with r > 0 . 7 ( red line ) were included in further analyses . DOI: http://dx . doi . org/10 . 7554/eLife . 05464 . 00310 . 7554/eLife . 05464 . 004Figure 1—figure supplement 1 . Experimental design . Re-analysis of the multi-parametric genetic interaction data set of Horn et al . ( 2010 ) , a square matrix of all pairwise combinations of 93 genes . Matrix columns ( playing the role of query genes ) were ordered from left to right according to their ability to explain the data in the remaining columns . The explained variance is shown on the y-axis as a function of the number of query genes . The graph illustrates that already 17 suitably selected query genes are sufficient to explain 90% of the variance in the data . DOI: http://dx . doi . org/10 . 7554/eLife . 05464 . 00410 . 7554/eLife . 05464 . 005Figure 1—figure supplement 2 . Selection of query genes . Pairwise scatter plots of the first five principal components of the feature space ( multivariate phenotypes ) of the single-dsRNA effects against all target genes are shown . The genes selected as query genes are coloured in red . DOI: http://dx . doi . org/10 . 7554/eLife . 05464 . 00510 . 7554/eLife . 05464 . 006Figure 1—figure supplement 3 . Comparison of phenotypes . The measured single gene phenotypes were compared between this study and the study by Rohn et al . ( 2011 ) on the overlapping set of genes . The y-axis shows single gene phenotypes of this study . The x-axis shows the categorical phenotypes of Rohn . Statistical significance was computed by anova ( F-test ) . DOI: http://dx . doi . org/10 . 7554/eLife . 05464 . 006 Of the 1367 single gene knockdowns , 499 genes showed a statistically significant phenotype in at least one feature ( false discovery rate , FDR 1% ) . Collectively , the phenotypes provided a broad overview of biological processes that impact cell proliferation and growth , cell shape and cell cycle regulation . For example , depletion of ida , a subunit of the anaphase-promoting complex ( APC/C ) , led to an increased mitotic index as previously described ( Bentley et al . , 2002 ) ( Figure 1E ) . In contrast , cell number and mitotic index decreased upon depletion of stg , the Drosophila CDC25 ortholog required for mitotic progression , which resulted in enlarged cells with large nuclei ( Edgar and O'Farrell , 1990; Cui and Doe , 1995 ) ( Figure 1E ) . Knockdown of Arpc1 , a core member of the actin-related protein Arp2/3 complex , led to elongated , spindle-like cell morphologies detected by an increase in cell eccentricity ( Machesky et al . , 1994 ) ( Figure 1E ) . Thus , the measured phenotypes reflect known biological functions . All 98 , 424 pairwise knockdown combinations of these 1367 target genes and the 72 query genes were tested four times each ( 2 × 2 dsRNAs ) . We filtered out genes with suboptimal dsRNAs ( low efficiency or specificity ) by assessing the phenotypic concordance between the two dsRNAs targeting different regions of the same gene ( referred to as sequence-independent dsRNAs ) . For more than 94% ( 1293 of 1367 ) of genes , phenotypic profiles were highly correlated ( Figure 1F , Supplementary file 5 ) , and these genes were used in further analysis . Genetic interactions were calculated using a multiplicative model ( Horn et al . , 2011 ) . 12361 gene pairs ( 13% ) showed significant statistical genetic interactions in at least one phenotype ( FDR 1% , moderated t-test ) . Compared to an analysis of the cell number phenotype alone , which detected 2357 genetic interactions , the number of observed genetic interactions was more than fivefold higher in the multi-phenotype analysis ( Figure 2A ) . Interactions derived from different phenotypes were non-redundant , for example , genetic interactions affecting the number of cells displayed only weak correlation with those affecting mitotic index ( correlation coefficient: 0 . 33 ) or cell diameter ( correlation coefficient: −0 . 32 ) ( Figure 2B ) . For example , depletion of Rho1 mRNA alone interfered with cytokinesis , resulting in large , multinucleated cells and reduced cell number ( Figure 2C ) . In contrast , RNAi against Dlic , the dynein light intermediate chain , led to a moderate reduction in cell number but did not affect nuclear size . As predicted from the single-RNAi phenotypes , combined targeting of Rho1 and Dlic reduced cell numbers further , with no evidence of a genetic interaction for this phenotypic feature . In contrast , their co-depletion resulted in a negative genetic interaction on cell and nuclear sizes , indicating that Dlic is epistatic to Rho1 . Similarly , co-depleting Not3 ( a member of the CCR4-NOT deadenylase complex ) and the growth regulator hippo ( hpo ) did not reveal a significant genetic interaction affecting total cell number . Yet , the same RNAi combination reversed the mild decrease in mitotic index observed upon RNAi of each single gene , and increased the fraction of mitotic nuclei ( Figure 2D ) . Thus , genetic interactions affect multiple phenotypes , with each phenotype contributing independent information . 10 . 7554/eLife . 05464 . 007Figure 2 . Genetic interactions across multiple phenotypes . ( A ) On top , the fraction of genetic interactions over all gene pairs ( at FDR 0 . 01 ) for each feature is shown . Blue: negative interactions , yellow: positive interactions . The lower panel shows the cumulative distribution of genetic interactions . The kth bar shows the fraction of gene pairs that show a genetic interaction in at least one of the first k features . ( B ) Genetic interactions for different phenotypes were non-redundant . Plots compare genetic interactions affecting cell number , mitotic index and length of major cell axis . ( C and D ) Many genetic interactions were phenotype-specific . Interactions at a FDR of 0 . 01 for a moderated t-test ( See ‘Materials and methods’ ) are marked by ( ** ) . Images in ( C ) show cells after RNAi of Rho1 ( left ) , Dlic ( center ) and both together ( right ) . Bar charts show single-gene effects , effects expected without a genetic interaction , and effects observed by the double-RNAi . Images in ( D ) show cell centres identified by image analysis after RNAi of Not3 ( left ) , hpo ( center ) and both ( right ) . Black and grey colours indicate whether a cell was pH3-positive or negative ( mitotic state ) . DOI: http://dx . doi . org/10 . 7554/eLife . 05464 . 007 To obtain an initial overview of the patterns of genetic interactions , we clustered the interaction profiles of each of the 1293 genes across the 21 phenotypes ( Figure 3—figure supplement 1 shows nine representative phenotypic features ) . We constructed a correlation graph that represents the similarity between interaction profiles across all phenotypes for each gene pair ( Figure 3A ) . The information generated by the multi-phenotyping dissected numerous biological processes in detail . For example , DNA-dependent RNA Pol II core components clustered together and were connected to general transcription factors , mediator and SAGA components , which are known to regulate Pol II transcriptional activity . Also , the SWI/SNF chromatin remodelling complex members Bap60 , brm , mor , osa , Snr1 clustered together , representing the osa-containing complex state ( Moshkin et al . , 2012 ) . 10 . 7554/eLife . 05464 . 008Figure 3 . A landscape of statistical genetic interactions . ( A ) Correlation network of genetic interaction profiles . Pearson correlation coefficients were computed for each pair of genes using the profile of all genetic interactions of these two genes to all other genes in all phenotypic features . An edge is drawn in the graph for each gene pair with correlation coefficient ≥0 . 6 . Genes were placed by a graph layout algorithm ( Fruchtermann–Reingold ) . Genes with similar genetic interaction profiles are proximal . The colour code depicts different biological processes that were separated by the correlation network . ( B ) Subgraphs of the correlation graph shown in Figure 2 highlight the wiring within and between the annotated complexes . DOI: http://dx . doi . org/10 . 7554/eLife . 05464 . 00810 . 7554/eLife . 05464 . 009Figure 3—figure supplement 1 . Heatmap of multi-parametric genetic interactions . Heatmap of all interaction coefficients between 1293 target genes and 72 query genes for nine of the phenotypic features . Blue colour indicates negative genetic interactions and yellow are positive genetic interactions . DOI: http://dx . doi . org/10 . 7554/eLife . 05464 . 00910 . 7554/eLife . 05464 . 010Figure 3—figure supplement 2 . Genetic interaction profiles and protein complexes . Distribution of pairwise Pearson correlation coefficients across 21 phenotypes for gene pairs detected by co-purification and mass spectrometry according to the DPiM dataset ( Guruharsha et al . , 2011 ) ( red line ) and for all other gene pairs ( blue line ) . DOI: http://dx . doi . org/10 . 7554/eLife . 05464 . 010 Tor signalling ( i . e . , Tor , Rheb , S6k , dm , Pdk1 ) was connected to a cluster containing the translational apparatus , as expected ( Figure 3B ) . Beside genes belonging to the RNA Pol I and Pol III complexes , the translational apparatus cluster contained putative RNA helicases ( CG32344 , CG9630 , kz , pit and Rs1 ) and the RNA-binding methyltransferase CG8545 , suggesting a function related to ribosome biogenesis . Interestingly , Dbp45A , a putative ATP-dependent RNA helicase of the DEAD box protein family , was linked to several genes involved in ribosomal biogenesis as well as with genes involved in DNA damage repair ( Figure 3B ) , suggesting that it might facilitate communication between the translation machinery and DNA repair and apoptosis . The map exposed two tight and well-separated clusters of genes that have functions in DNA damage sensing and repair , and in the regulation and execution of apoptosis ( Figure 3B ) . Intriguingly , the cluster containing p53 , Ice , CG7922 , Ikb1 also contained genes known to regulate the cytoskeleton and vesicle trafficking . This cluster also showed similarities of genetic interaction profiles with hpo and 14-3-3ε , two core members of the Hippo signalling pathway . In the genetic interaction map , all γ-tubulin ring complex ( γTuRC ) members mapped in a dense cluster surrounded by the motor proteins of the dynein/dynactin complexes as well as auxiliary regulators of the mitotic spindle ( Figure 3B ) . Also in close proximity to the mitotic spindle we found the APC/C , which drives the metaphase-to-anaphase transition by priming several cell cycle regulators for ubiquitin-mediated degradation by the proteasome . Interestingly , the dynactin member Arp10 was found to have an ‘APC/C-like’ interaction profile , suggesting a role in supporting APC/C function . Moreover , our map showed that the APC/C is functionally linked to the proteasome via Cdc27 , the motor protein Klp61F , the APC/C activator fzy ( CDC20 ) and the ubiquitin-conjugating enzyme vih . We observed that gene pairs with high correlation coefficients are enriched for experimentally validated protein–protein interactions ( Figure 3—figure supplement 2 ) ( Guruharsha et al . , 2011 ) . Overall , the different protein complexes and functional modules were well separated in the genetic interaction map . While symmetric ( non-directional ) genetic interactions reflect a collaborative role of genes , for instance , between members of a protein complex , this approach does not provide an ordering of the proteins according to their temporal or regulatory activities . We leveraged the multivariate nature of our data to identify the direction of genetic interactions by testing each pair of genes ( A and B ) for one of two directional relationship models: A modifies the phenotype of B , or B modifies the phenotype of A . Moreover , we assigned a sign to the relationship , which indicates whether the modification is of an alleviating or aggravating nature . For each gene pair , we analysed the multivariate phenotypes of both single as well as double perturbations . This is exemplarily shown in Figure 4A . The first column depicts multivariate single ( in blue and orange ) and double knockdown ( in grey ) phenotypes for five different experiments . In a first step , we computed the multivariate genetic interaction scores ( π ) as described above ( Figure 4A , second column ) . A geometric interpretation to visualize the relationship of the genetic interactions to the single gene phenotypes is shown in the third column of Figure 4A . We explain the five exemplary cases below . 10 . 7554/eLife . 05464 . 011Figure 4 . Deriving directional genetic interactions . ( A ) Multiparametric phenotypes are extracted for single and double knockdowns . Genetic interaction scores are computed for each double knockdown experiment . The schematic plots in the third column show the model for identifying directional genetic interactions between gene A and gene B using two exemplary phenotypes . The single knockdown phenotypes of genes A and B and the measured double knockdown phenotypes ( AB ) are depicted as arrows . The expected double knockdown phenotype for non-interacting ( NI ) genes , which is the sum of the single gene effects , is depicted by the symbol NI . Black arrows depict the genetic interaction π . The first row shows the case where genes A and B are not interacting . Below , four types of interaction between the genes A and B are shown: gene A is alleviating to gene B , gene A is aggravating to gene B; and in reverse , B alleviates or aggravates gene A . Whenever the genetic interaction ( black arrows ) is parallel or anti- parallel to one of the single gene effects , a directional genetic interaction is called . ( B–D ) A directional interaction detected between Cdc23 and sti . ( C ) The two orange and two blue arrows show the phenotypes ( nuclei area and cell number ) of the two dsRNAs designed for sti and Cdc23 . The grey dots show the expected double knockdown effect for the two genes . The black arrows , indicating the genetic interaction , are directed opposite to the phenotype of sti , indicating that functional Cdc23 is required for the phenotype of sti . ( D ) Graphical annotation of the detected alleviating epistasis from Cdc23 to sti as shown in ( B–C ) . DOI: http://dx . doi . org/10 . 7554/eLife . 05464 . 01110 . 7554/eLife . 05464 . 012Figure 4—figure supplement 1 . Directed genetic interactions . Example when a direction of the genetic interaction is not determined . The pairwise interaction ( π ) is not parallel to one of the single gene effects . DOI: http://dx . doi . org/10 . 7554/eLife . 05464 . 01210 . 7554/eLife . 05464 . 013Figure 4—figure supplement 2 . Inference of directed genetic interaction of Cdc23 and sti . Testing for directed genetic interactions for the gene pair Cdc23 and sti . Fit of the 21-dimensional π-score vector as linear function of the single gene effects . Each dot shows the π-score and single gene effect in one phenotype . The π-scores are negatively correlated to the single gene effects of sti , indicating that the knockdown phenotypic effect of sti is strongly reduced by a depletion of Cdc23 . DOI: http://dx . doi . org/10 . 7554/eLife . 05464 . 013 First , consider two genes A and B with distinct multivariate phenotypes . In Figure 4A , this situation is depicted by the blue and orange arrows that represent the effects of the single-gene knockdowns . When A and B do not genetically interact , the phenotype of the double-knockdown ( grey arrow; AB ) is simply the combination ( vector sum ) of the two individual arrows ( Figure 4A ) . We call this the non-interacting model ( NI ) . Second , when A and B genetically interact , the multivariate phenotype of the double knockdown ( AB ) is different from the vector sum ( NI ) . This difference , geometrically depicted by the black arrows , defines the genetic interaction ( π ) . If the double knockdown phenotype is an amplified or dampened version of one of the single gene knockdown phenotypes , then the genetic interaction ( black arrow ) is parallel or antiparallel to the phenotype of either one of the single knockdowns ( blue or orange arrow: Figure 4A ) . Together , this geometric information indicates the directionality of the interaction ( i . e . , which gene modifies which ) , and whether it is alleviating ( antiparallel ) or aggravating ( parallel ) . For example , if the double knockdown phenotype of gene A and gene B is similar to the single knockdown of gene A , we call this an alleviating interaction from A to B ( A → B ) . This indicates that a functional gene A is required for the phenotype of gene B ( i . e . , gene A is functionally upstream of gene B ) , while the absence or presence of B has a limited effect on the phenotype of A . Similarly , Figure 4A shows an example of an aggravating interaction from gene A to gene B ( A ⊣ B ) , where loss of gene A's function leads to an even stronger phenotype of gene B , while the phenotype of gene A is unchanged . The two remaining cases , alleviating and aggravating directional interactions from gene B to gene A are shown in Figure 4A ( B → A , B ⊣ A ) . Alternatively , if the double knockdown phenotype ( AB ) is not merely increasing or decreasing the amplitude of one of the single gene phenotypes , no direction is assigned ( Figure 4—figure supplement 1 ) . We systematically determined whether such an epistatic relationship could be identified for each of the 93 , 096 gene pairs by testing if the 21-dimensional genetic interaction vectors were ( anti- ) parallel to either of the two single knockdown vectors ( ‘Materials and methods’ , Figure 4—figure supplement 2 ) . Using a conservative threshold , we found directional epistatic interactions between 1344 pairs of genes . Of these , 1040 were alleviating and 304 aggravating ( Supplementary file 6 ) . This directional genetic interaction network provided a rich snapshot of regulatory , causal and temporal relationships . For instance , we detected an alleviating interaction between the APC/C member Cdc23 and the cytokinesis regulator sti . After knockdown of Cdc23 , we recorded reduced growth and slightly smaller nuclear area ( Figure 4B–C , blue arrows for the two independent dsRNAs ) . Knockdown of sti also reduced growth , but in addition led to a strong increase in nuclear area ( Figure 4B–C , orange arrows ) . The double knockdown of Cdc23 and sti , however , almost completely reproduced the phenotype of Cdc23 alone ( Figure 4C , black arrows for 2 × 2 dsRNA design ) . This directional alleviating interaction between the genes reflects the known , consecutive functions of the encoded proteins . Without Cdc23 , cells arrest in metaphase , leading to the observed reduction in cell number ( Figure 4B , top ) . Without sti , a component of the contractile ring , which executes the abscission of the cytoplasm to form the two daughter cells ( D'Avino et al . , 2004 ) , cells undergo karyokinesis ( division of the nucleus ) and re-replicate their genome without being able to divide their cytoplasm , resulting in giant cells with large nuclei and often multi-nucleated cells ( Figure 4B , middle ) . However , when Cdc23 is depleted together with sti , the increase of nucleus size is avoided ( Figure 4B , bottom ) since depletion of Cdc23 arrests cells in metaphase thereby preventing re-replication ( Zielke et al . , 2008 ) , explaining the detected directional interaction ( Figure 4D ) . Next , we studied the directional genetic interactions of key regulators of the cell cycle ( Figure 5A ) . The mitosis master regulator polo was observed as alleviating epistatic to components that are required for the initial mitotic phases , including the γTuRC , which serves as the structural basis of the mitotic spindle . In contrast , the ubiquitin-conjugating enzyme vih showed directional interactions to components that are active at later stages . Depletion of γTuRC members aggravated the cellular phenotype of the spindle assembly checkpoint ( SAC ) . Dynein/dynactin motor proteins mediated a chain of directional epistatic interactions between the SAC and the APC/C . Moreover , whereas SAC knockdown had an aggravating effect on the phenotype of condensin/cohesion , the latter relied on a functional APC/C . The phenotypes of cytokinesis regulators including sti , pav , tum , Rho1 or scra were strongly dependent on APC/C as well as on dynein/dynactin motor proteins . Our data also showed that vih , the ubiquitin-ligase APC/C , as well as cytokinesis executers regulated the phenotype of the SCF ubiquitin-ligase core component Skp2 . In contrast , Elongin-B , another ubiquitin-ligase core component , itself regulated the phenotype of the SAC , condensin/cohesin components and cytokinesis regulators . This unbiased mapping and automated inference of directional epistatic interactions , made possible by the multivariate phenotyping approach , revealed a detailed circuit diagram of regulatory , temporal and causal relationships of complexes that function during the M-phase of the cell cycle ( Figure 5B ) . 10 . 7554/eLife . 05464 . 014Figure 5 . An epistatic network of mitotic genes . ( A ) Graph showing data-derived epistatic interactions of genes that are key regulators of cell cycle events , including polo , and members of the γTuRC , SAC , APC/C , the dynein/dynactin complex , sister chromatid cohesion complexes and cytokinesis genes . Blue arrows show alleviating epistatic interactions and red arrows show aggravating epistatic interactions . Exemplarily , the underlying data is shown for epistatic interactions . ( B ) Epistatic network of complexes that regulate mitotic events , as derived from the epistatic interactions between the members of the complexes . DOI: http://dx . doi . org/10 . 7554/eLife . 05464 . 014 Inspection of our epistatic map highlighted an unexpected link between chromatin remodelling and RAS signalling . We found genetic interactions between the SWI/SNF complex with both the SWI/SNF-associated chromatin remodeller dalao ( Papoulas et al . , 2001 ) and the Ras inhibitor RasGap1 ( Figure 6A ) . Moreover , dalao and RasGap1 also both interacted with other Ras signalling pathway components . Directional epistasis was evident in several of these genetic interactions . While dalao alleviated the knockdown phenotype of SWI/SNF , RasGap1 aggravated it ( Figure 6A–B ) . RasGap1 is a known negative regulator of Ras signalling ( Gaul et al . , 1992 ) , and consistent with that , its single knockdown increased cell proliferation . We found that additional knockdown of SWI/SNF complex members ( mor , brm , Bap60 , Snr1 or osa ) compensated the RasGAP1-dependent increased proliferation ( Figure 6C ) , suggesting that constitutive active Ras signalling requires SWI/SNF function . In addition , cell area and eccentricity were increased after the double knockdown compared to the single-knockdown ( Figure 6C ) . Next , we tested whether mutations in SWI/SNF complex components could suppress a Ras gain-of-function rough eye phenotype in the Drosophila eye ( Zipursky and Rubin , 1994 ) . As shown in Figure 6D , the phenotype caused by the expression of constitutively active Ras85DV12 in the eye was rescued by mutant alleles of the SWI/SNF members osa , Snr1 or brm ( Figure 6D–E ) . 10 . 7554/eLife . 05464 . 015Figure 6 . Genetic interactions between Ras signalling and chromatin remodelling . ( A ) Edges show the detected genetic interactions in the interaction screen of members of the SWI/SNF complex with RasGAP1 and members of the Ras pathway . An edge is drawn if a significant interaction ( FDR 0 . 01 ) was detected for at least one phenotype . Edges with attributed epistatic interactions are shown in blue ( alleviating epistasis ) and red ( aggravating epistasis ) . Black edges indicate gene pairs for which a genetic interaction was detected , but directionality could not be assigned . ( B ) Scheme of the Ras/MAP kinase pathway . RasGAP1 inhibits the activity of the pathway by catalysing the intrinsic GTPase activity of Ras . ( C ) Exemplary the cell number , area of nuclei and eccentricity phenotypes are shown that guide the directionality estimate . Blue arrows indicate the RasGap1 phenotype , the SWI/SNF phenotype is depicted by orange arrows and the black arrows show the genetic interaction between SWI/SNF and RasGap1 . ( D ) In vivo validation of genetic interaction of SWI/SNF and constitutively active Ras85D . From left to right: Normal eye in wild type flies ( Oregon-R , +/+ ) . Rough eye phenotype upon expression of Ras85DV12 under the sevenless promotor ( sev-Ras85DV12/+ ) . Strong rescue after heterozygous removal of the SWI/SNF complex members osa ( sev-Ras85DV12/osa2 ) , Snr1 ( sev-Ras85DV12/Snr101319 ) and brm ( sev-Ras85DV12/brm2 ) . ( E ) Quantification of the rescue of the rough eye phenotype . Black colour: expression of Ras85DV12 in the eye of otherwise wild-type flies results in a rough eye phenotype . Grey to white colour scale shows the strength of the rescue of the rough eye phenotype in flies expressing Ras85DV12 in the eye and with heterozygous mutants of SWI/SNF complex members osa ( sev-Ras85DV12/osa2 ) , Snr1 ( sev-Ras85DV12/Snr101319 ) and brm ( sev-Ras85DV12/brm2 ) . ( F ) Cell viability of isogenic HCT116 cells 96 hr after siRNA against members of the SWI/SNF complex ( SMARCA4 , SMARCB1 , and ARID1A ) . Right: parental HCT116 KRAS mutant cells ( KRASG13D/+ ) ; left: isogenic cell line HCT116 KRAS wt ( KRASKO/+ ) . The experiment was performed in four biological replicates . DOI: http://dx . doi . org/10 . 7554/eLife . 05464 . 01510 . 7554/eLife . 05464 . 016Figure 6—figure supplement 1 . Ras-induced cell growth requires SWI/SNF complex function . Cell viability of parental HCT116 KRAS mutant cells ( KRASG13D/+ ) and isogenic HCT116 KRAS wild type cells ( KRASKO/+ ) . DOI: http://dx . doi . org/10 . 7554/eLife . 05464 . 016 Next , we tested whether human KRAS-mutant cancer cells might specifically depend on the SWI/SNF complex . We investigated this by knockdown of SWI/SNF complex members in the colon cancer cell line HCT116 , which harbours an activating KRAS G13D mutation ( KRASG13D/+ ) , and in a derived cell line in which the mutant KRAS allele was deleted ( KRASKO/+ ) . Whereas there was no difference in cell growth between the two isogenic cell lines ( Figure 6—figure supplement 1 ) , knockdown of individual SWI/SNF members SMARCA4 , SMARCB1 and ARID1A caused a significant decrease in viability selectively in the context of constitutively active KRAS ( Figure 6F ) . Together , this indicates that SWI/SNF complex members genetically interact with activated Ras signalling . The detection of recurrent mutations in genes and pathways in cancers ( Tamborero et al . , 2013 ) has resulted in a growing catalogue of known cancer mutations , however , in any individual tumour , only a subset of these mutations is present , and how they cooperatively produce the cancer phenotype is often not well understood . We aimed to use our directional interaction network to predict relationships between recurrent cancer mutations . For example , we found an alleviating directional interaction between Pten ( PTEN ) and gig ( TSC2 ) ( Figure 7A ) , as previously described ( Song et al . , 2012 ) . We also identified upstream and downstream regulators: for instance , our data predict that the ARP2/3 complex member Arp3 , a regulator of the actin cytoskeleton , aggravates the phenotype of the Ras GTP exchange factor Sos ( Figure 7B ) . Another example is given by the epistatic interactions of Myb ( MYB ) , an essential member of the DREAM complex . This complex regulates cell-cycle dependent gene expression ( Sadasivam and DeCaprio , 2013 ) and is recurrently mutated in cancer . Upstream of Myb we detected its known regulators mip120 and mip130 ( Figure 7C ) ( Beall et al . , 2004 ) . Moreover , our data indicate functions of central mitosis regulators ( polo , Elongin-B , fzy ) upstream of Myb , while SMC4 , which is involved in chromosomal maintenance , and many cytokinetic genes are downstream . The DREAM subcomplex members Myb , mip120 , mip130 have been shown to co-ordinately regulate transcription of mitotic genes ( Georlette et al . , 2007 ) . Furthermore , we found that the phenotype of the recurrently mutated transcriptional repressor spen ( SPEN ) is aggravated by nonC , which is involved in nonsense-mediated mRNA decay ( Figure 7D ) ( Oswald et al . , 2005 ) . 10 . 7554/eLife . 05464 . 017Figure 7 . Recurrently mutated genes . ( A–E ) Subnetworks showing detected directional genetic interactions of recurrently mutated cancer genes . Recurrently mutated genes as reported by Tamborero et al . ( 2013 ) are coloured dark . Red arrows depict aggravating interactions and blue arrows depict alleviating interactions . DOI: http://dx . doi . org/10 . 7554/eLife . 05464 . 017 We detected complex epistatic network structures . For example , the transcriptional co-regulators jumu and CtBP and the nuclear pore complex component Nup75 formed an epistatic network composed of an alleviating and two aggravating interactions ( Figure 7E ) . The recurrently mutated DNA damage response gene RecQ4 ( WRN ) aggravates jumu , while Nup75 aggravates Sin3A ( SIN3A/B ) . This results in a network structure containing a loop . Taken together , this demonstrates that our method of mapping epistatic interactions can predict network structures of regulatory activity .
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 .
Schneider S2 cells ( D . Mel-2; ThermoFisher , Waltham , MA ) were grown in serum-free medium ( Express Five SFM; ThermoFisher ) supplemented with 20 mM GlutaMAX ( ThermoFisher ) and 1% penicillin-streptomycin ( ThermoFisher ) as described previously ( Horn et al . , 2011 ) . Two dsRNAs of length 200–225 nt and non-overlapping in sequence were designed for each target gene using NEXT-RNAi software ( Horn et al . , 2010 ) and annotation from FlyBase release r5 . 24 ( Supplementary file 1 ) . The algorithm was designed to avoid perfect matches of length ≥19 nt or overall sequence similarity ( BLAST E-value >10−10 ) with non-target transcripts , more than six tandem tri-nucleotide repeats of the type CAN , low complexity regions and UTRs . The library was synthesized in 96-well plates by two-step PCR approach followed by in vitro transcription ( Steinbrink and Boutros , 2008 ) . dsRNA products were cleaned using filter plates ( MultiScreen HTS PCR 96-Well , Millipore ) with columns of Bio-Gel P-30 ( Bio-Rad ) . Concentrations were measured using a NanoDrop 8000 spectrophotometer ( Thermo Scientific ) and individually adjusted for each dsRNA using a Beckman Coulter Biomek FX Span-8 . For combinatorial RNAi , a template-query experimental design was used as previously described ( Horn et al . , 2011 ) . 100 ng ( 2 μl ) of each template ( target ) dsRNA were pipetted into 384-well clear-bottom microscopy plates ( BD Falcon ) . 100 ng ( 2 μl ) of one of the query dsRNAs were added to all wells of the plates except for column 2 ( which contained control reagents ) using a NanoDrop II dispenser ( Innovadyne ) . In each well , 6500 cells were seeded in 30 μl culture medium with 0 . 15 μl of 0 . 4 mg ml−1 DDAB per well and incubated for 5 days at 25°C before fixation and staining . Cells were stained using a Beckman Coulter Biomek FX liquid handling workstation with 384-well tip head . Of the 40 μl assay volume , 15 μl were removed , and cells were fixed and permeabilised for 60 min at room temperature by addition of 40 μl of 6% PFA ( Sigma ) , 0 . 3% TX-100 ( AppliChem ) solution in PBS ( Sigma ) . Cells were washed by removing 40 μl supernatant and adding 50 μl PBS . 50 μl of the supernatant were removed and 10 μl of 4% BSA ( GERBU ) , 0 . 1% TX-100 ( in PBS ) containing monoclonal phospho-Histone H3 antibody ( conjugated to Alexa Fluor 647 , Cell Signaling , #3458BC ) and monoclonal anti-α-Tubulin antibody ( conjugated to FITC , Sigma F2168 ) , both diluted 1:750 , were added and incubated overnight at 8°C ( protected from light ) . The next day 10 μl supernatant were removed , and DNA was stained by addition of 40 μl Hoechst 33 , 342 ( ThermoFisher ) diluted 1:2000 in PBS and incubation for 30 min at room temperature . Finally , cells were washed once by removing 40 μl supernatant and adding 50 μl PBS and washed twice more by removing 50 μl supernatant and adding 50 μl PBS . Plates were sealed with aluminium sealing tape ( Corning ) and imaged directly or stored at 8°C until imaging . Fluorescence images were acquired on an INCell Analyzer 2000 ( GE Healthcare ) . Whole-well , two-channel images were acquired using the 4× objective ( Hoechst/DNA and Alexa647/phospho-Histone H3 ) . Three-channel images ( additionally including FITC/α-Tubulin ) of higher resolution were acquired using the 10× objective . Image analysis was adapted from previously described methods ( Carpenter et al . , 2006; Fuchs et al . , 2010; Held et al . , 2010; Pau et al . , 2010 ) , using the R package EBImage ( Pau et al . , 2010 ) . Nuclei were segmented by adaptive thresholding , identified separately in the DAPI and pH3 channels and matched . From the 4× images , 84 features per nucleus were extracted , including number of nuclei in each channel , mean , standard deviation and other distribution statistics of area and fluorescence intensity in both channels . Local cell density was estimated with a kernel density estimator using a Gaussian kernel on eight different scales . In the 10× images , the cellular boundary was estimated from fluorescence levels in the α-tubulin channel by extension from nuclei by the propagation algorithm ( Jones et al . , 2005 ) . 244 features per cell were extracted from the 10× images , including distribution moments , shape and Haralick texture features ( Haralick et al . , 1973 ) . To obtain features per experiment ( cell population ) , averages of per-cell features were computed separately for mitotic and non-mitotic cells . This yielded 328 features , including number of nuclei , local cell density , mean , standard deviation and other distribution statistics of area and fluorescence intensity in all channels . A full list of features is provided in Supplementary file 3 . All feature data were subjected to a variance-stabilising transformation ( Huber et al . , 2002 ) that interpolates between the logarithm function for large values and a linear function around 0 . The transformation reduced heteroskedasticity and improved the fit of the multiplicative model , which was verified by the concentration of residuals around 0 . Many of the extracted features have obvious or intuitive interpretations , such as mean cell area , eccentricity , intensity . Our measurements for these phenotypes correlate with previously published single gene morphological phenotypes ( Rohn et al . , 2011 ) for cell size and eccentricity ( Figure 1—figure supplement 3 ) . Other features are more abstract . For instance , we included the quantile features to be able to detect changes that affect only a small proportion of cells: for example , the elongation phenotype often only affects a small fraction of the cells . Other features are mathematical descriptors , or numerical ‘fingerprints’ , of texture and intensity patterns; the use of these is well-established in cellular image analysis ( Boland and Murphy , 2001 ) , and while they are not directly interpreted as a specific process or function , they sensitively pick up complex changes in the cellular network caused by the genetic perturbations . Together , we computed a large number of phenotypic features , which were partially redundant . We then aimed at selecting an informative , non-redundant subset of these features for further analysis by an automated procedure . First , we eliminated those features that were not sufficiently reproducible between replicates ( cor <0 . 6 ) ( Figure 1C ) . We then started the feature selection with three manually chosen features based on their interpretability and to facilitate comparability with previous work: number of cells , fraction of mitotic cells and cell area ( Horn et al . , 2011 ) . Subsequently , for each further feature , we fit a linear regression that modelled the feature's values over all experiments as a function of the already selected features . The residuals of this fit were used to measure new information content not yet covered by the already selected features ( Figure 1D ) . Among candidate features , we selected the one with the maximum correlation coefficient of residuals between two replicates . This procedure was iterated , and in each iteration we computed the fraction of features with positive correlation coefficients . The iteration was stopped when this fraction no longer exceeded 0 . 5 ( Figure 1D ) . This choice of cut-off was motivated by the fact that for a set of uninformative features , the fraction is expected to be 0 . 5 . The 21 selected features are listed in Supplementary file 4 . Pairwise interaction scores ( π-scores ) were computed for each pair of dsRNA reagents and each of the 21 selected features as described before ( Horn et al . , 2011 ) . To summarize the four measurements for each gene pair , they were tested against the null hypothesis that their mean was zero . p-values were computed by the moderated t-test implemented in the R package limma ( Smyth , 2004 ) and adjusted for multiple testing by the method of Benjamini-Hochberg ( Benjamini and Hochberg , 1995 ) . Figure 2A shows the number of interactions ( positive and negative interactions ) for each feature and the cumulative number of interactions . For all gene pairs with a significant interaction ( FDR 0 . 01 ) in at least one phenotype , we aimed to compute the direction of phenotypic regulation . For each gene pair the π-score-vector over all 21 features was fit by a linear combination of the main effect vectors of the two genes . By analysis of variance we then quantified how much of the π-score was explained by each of the two genes . We called an epistatic interaction from gene A to B if the fraction of sum of squares explained by gene A was smaller than a lower threshold and if the fraction of sum of squares explained by gene B was larger than a higher threshold . The two thresholds were chosen as the 10% and 95% quantile over all gene pairs . In addition to the direction of phenotypic regulation , the coefficient of the fit defined the sign , which could be alleviating ( negative coefficient ) or aggravating ( positive coefficient ) . The following Drosophila stocks were obtained from the Bloomington stock center: osa2/TM6B , Tb1 ( #3616 ) ; P ( PZ ) Snr101319 ry506/TM3 , ryRK Sb1 Ser1 ( #11529 ) and brm2 es ca1/TM6B , Sb1 Tb1 ca1 ( #3619 ) . These flies were crossed to Q1/CyO , P{ry[+t7 . 2] = sevRas85DV12}FK1 ( also called Q1/CyO , P ( sev-Ras1V12 ) FK1 , Bloomington stock #3795 ) . Progeny containing CyO , P{ry[+t7 . 2] = sevRas85DV12}FK1 and either heterozygous mutant candidate genes or wild-type ( w1118 ) background were scored for rough eye phenotypes . Crosses were reared at 25°C and phenotypes were scored independently by two experienced researchers . Flies containing CyO , P{ry[+t7 . 2] = sevRas85DV12}FK1 and either heterozygous mutant candidate genes or hetrogyzous wild-type ( w1118 ) background were selected . Since all experimental flies were red-eyed thus red-eyed Oregon-R ( +/+ ) flies were used as control . Drosophila heads were prepared for SEM by serial dehydration in 20% , 50% , 70% , 90% , 100% ethanol , 30 min each step . Heads were further dehydrated with critical point dehydration using Leica Critical Point Dryer CPD300 . Heads were then treated with 15 nm Gold particle sputtering using Leica EM MED020 . Scanning electron images were acquired with Zeiss Leo1530 SEM at 3 KV and WD = 5 mm . HCT116 PAR007 and HCT116 HD104-008 ( KRAS mtG12 KO ) cell lines were ordered from HORIZON . Cells were cultured in McCoy's medium ( Gibco ) supplemented with 10% FCS and penicillin-streptomycin at 37°C in 5% CO2 . Cells were transfected in a 384-well format using Lipofectamine RNAiMAX ( ThermoFisher ) diluted in serum-free McCoy's medium . Genes were targeted using a pool of four siRNAs ( Thermo Fisher Scientific ) with a total final concentration of 50 μM . Cell viability was measured using CellTiter-Glo Luminescent Viability Assay ( Promega ) using a Mithras LB 940 Multimode Microplate Reader ( Berthold ) . Each biological replicate was performed in technical quadruplicates . The experiment was conducted in four independent biological replicates ( Table 1 ) . 10 . 7554/eLife . 05464 . 018Table 1 . siRNAsDOI: http://dx . doi . org/10 . 7554/eLife . 05464 . 018GeneTarget sequenceCat #CTRL/controlSIGENOME NON TARGET POOLNO2D-00120614-20ARID1AGCAACGACAUGAUUCCUAUMU-017263-01 ( siGENOME ) D-017263-01ARID1AGAAUAGGGCCUGAGGGAAAMU-017263-01 ( siGENOME ) D-017263-02ARID1AAGAUGUGGGUGGACCGUUAMU-017263-01 ( siGENOME ) D-017263-03ARID1AUAGUAUGGCUGGCAUGAUCMU-017263-01 ( siGENOME ) D-017263-04SMARCA4GAAAGGAGCUGCCCGAGUAMU-010431-00 ( siGENOME ) D-010431-01SMARCA4CCAAGGAUUUCAAGGAAUAMU-010431-00 ( siGENOME ) D-010431-02SMARCA4GAAAGUGGCUCAGAAGAAGMU-010431-00 ( siGENOME ) D-010431-03SMARCA4AGACAGCCCUCAAUGCUAAMU-010431-00 ( siGENOME ) D-010431-04SMARCB1GAAACUACCUCCGUAUGUUMU-010536-01 ( siGENOME ) D-010536-01SMARCB1CCACAACCAUCAACAGGAAMU-010536-01 ( siGENOME ) D-010536-02SMARCB1GUGACGAUCUGGAUUUGAAMU-010536-01 ( siGENOME ) D-010536-03SMARCB1AGACCUACGCCUUCAGCGAMU-010536-01 ( siGENOME ) D-010536-04 A documented software package is available as the R/Bioconductor package DmelSGI . ( http://bioconductor . org ) The package contains all software that is used for analysis and contains a vignette with the documented source code . The code vignette is provided as Source code 1 .
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 .
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 .