领导干部思想道德廉政风险及防控措施

Articles nAture methods  |  VOL.8  NO.4  |  APRIL 2011  |  341 the analysis of synthetic genetic interaction networks can reveal how biological systems achieve a high level of complexity with a limited repertoire of components. studies in yeast and bacteria have taken advantage of collections of deletion strains to construct matrices of quantitative interaction profiles and infer gene function. Yet comparable approaches in higher organisms have been difficult to implement in a robust manner. here we report a method to identify genetic interactions in tissue culture cells through rnAi. By performing more than 70,000 pairwise perturbations of signaling factors, we identified >600 interactions affecting different quantitative phenotypes of Drosophila melanogaster cells. computational analysis of this interaction matrix allowed us to reconstruct signaling pathways and identify a conserved regulator of ras-mAPK signaling. large-scale genetic interaction mapping by rnAi is a versatile, scalable approach for revealing gene function and the connectivity of cellular networks. Genetics underlying many phenotypes, including most common diseases, are complex with contributions from multiple loci. Studies in model organisms provide evidence for pervasive genetic inter­ actions with large effects on many phenotypes. Such genetic factors are difficult to identify in classical loss­of­function screens. For example, RNAi screens in cultured cells have revealed components of signaling pathways through pathway­specific reporter assays but revealed little about the interactions between the different com­ ponents. To explore the underlying network connectivity, simul­ taneous perturbations of multiple components are required, for example, through combinatorial drug treatments1 or the genera­ tion of double mutant strains2,3. Here we describe an RNAi­based experimental approach to functionally annotate metazoan genes based on their genetic interaction profiles, independent of path­ way­specific reporters, mutant collections or chemical inhibitors. results double-rnAi analyses RNAi offers the opportunity to simultaneously reduce the expres­ sion of any chosen pair of genes, allowing us to systematically mapping of signaling networks through synthetic genetic interaction analysis by rnAi Thomas Horn1,2,5, Thomas Sandmann1,3,5, Bernd Fischer4,5, Elin Axelsson4, Wolfgang Huber4 & Michael Boutros1 sample large numbers of distinct, biologically relevant conditions. To overcome limitations of RNAi experiments, such as off­target effects, we designed two independent dsRNAs to each target and used robust statistical modeling to identify single­RNAi or double­RNAi phenotypes and synthetic effects. As genetic inter­ actions can manifest themselves by affecting any phenotypic trait, we used automated microscopy to collect multiparametric data, for example, information about cell number, nuclear size and fluorescence intensity after Hoechst staining, instead of restricting the analysis to any individual, preselected pathway4. To validate our approach, we assessed genetic interactions between known components of the Ras­MAPK pathway affecting cell number at varying concentrations of dsRNA (Fig. 1a–c and Supplementary Fig. 1). Depletion of the Drosophila Ras85D gene, which encodes a member of the conserved Ras superfamily of small GTPases, led to a concentration­dependent reduction in cell growth5,6 (Fig. 1a). RNAi­mediated knockdown of CG13197, a gene not connected to Ras signaling, also attenuated cell growth. When we simulta­ neously targeted both Ras85D and CG13197 with different con­ centrations of dsRNAs, we could predict the resulting number of cells based on the single­knockdown phenotypes of the individual genes by a multiplicative model (Fig. 1a,d), as has been observed for double deletion strains in yeast2,7. Deviations between the expected and the experimentally observed double­RNAi effects revealed positive (alleviating) or negative (aggravating for genes with negative single­RNAi effect) genetic interactions (Fig. 1d). The outcome of simultaneously targeting two genes with known functions in the Ras­MAPK signaling pathway deviated strongly from predictions based on single­knockdown phenotypes. Double RNAi of Ras85D and Gap1, a negative regulator of Ras, alleviated the growth inhibition caused by targeting Ras85D alone, revealing a strong positive interaction (Fig. 1b). Double RNAi of Gap1 and Ptp69D, a receptor­linked protein tyrosine phosphatase, in con­ trast, led to a negative genetic interaction (Fig. 1c). Thus, double RNAi can reveal both positive and negative interactions, whose strengths, analogous to interactions between chemical com­ pounds8, are concentration­dependent and reflect the quantitative nature of functional interactions (Supplementary Fig. 1). 1German Cancer Research Center (Deutsches Krebsforschungszentrum), Division Signaling and Functional Genomics and Heidelberg University, Department of Cell and Molecular Biology, Faculty of Medicine Mannheim, Heidelberg, Germany. 2Heidelberg University, Hartmut Hoffmann­Berling International Graduate School for Molecular and Cellular Biology, Heidelberg, Germany. 3Heidelberg University, CellNetworks Cluster of Excellence, Heidelberg, Germany. 4European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany. 5These authors contributed equally to this work. Correspondence should be addressed to M.B. (m.boutros@dkfz.de) or W.H. (whuber@embl.de). Received 23 SeptembeR 2010; accepted 4 FebRuaRy 2011; publiShed online 6 maRch 2011; doi:10.1038/nmeth.1581 © 2 01 1 N at u re A m er ic a, In c. A ll ri g h ts r es er ve d . 342  |  VOL.8  NO.4  |  APRIL 2011  |  nAture methods Articles high-throughput analysis of pairwise interactions We tested all pairwise interactions between 93 genes involved in signal transduction in Drosophila cells, evaluating two non­ overlapping RNAi reagents for each target (192 dsRNA reagents including controls; Fig. 1e, Supplementary Figs. 2 and 3, and Supplementary Table 1)9. Targeted genes included annotated components of the three MAPK pathways (Ras­MAPK, JNK and p38 pathway) and all annotated protein and lipid phosphatases expressed in Drosophila Schneider cells (Supplementary Table 2). We performed experiments in Schneider S2 cells, which we fixed, stained with Hoechst dye and analyzed using high­throughput fluorescence imaging and automated image analysis (Fig. 1e). We selected three nonredundant quantitative features from the images: number of cells per well, mean nuclear area and nuclear fluorescence intensity (Fig. 1e and Supplementary Fig. 4). For example, treatment with dsRNA to the firefly luciferase gene (negative control) led to an average of 48,200 cells per well with a mean nuclear area of 59.1 µm2 per cell. Depletion of Rho1, a small GTPase involved in cytokinesis and cytoskeleton remodeling10, led to significantly larger nuclear area (77.9 µm2 per cell, P < 1 × 10−15, Student’s t­test, n = 16), likely reflecting multinucleate morphology caused by incomplete cytokinesis, with a concomi­ tant decrease in the number of cells (25,400 cells per well, P < 1 × 10−15, Student’s t­test, n = 16). In contrast, pnt depletion also resulted in a decreased number of cells (19,000 cells per well, P < 1 × 10−15, Student’s t­test, n = 16) but a smaller mean nuclear area (44.7 µm2 per cell, P < 1 × 10−15, Student’s t­test, n = 16). We performed two biologically independent experiments yielding 73,728 measurements in total, from which we esti­ mated interaction scores (Fig. 2a, Supplementary Figs. 5–8 and Supplementary Table 3). Phenotypic measurements were highly a b c 2–2 π score e d FlucA B 100%80% 62.5% 75% 100%50%12.5% –2 +10 Aggravating (negative) Alleviating (positive) P he no ty pe In te ra ct io ns 25% Interaction score (πA,B) CG13197 dsRNA (ng) R as 85 D d sR N A ( ng ) 0 50 100 0 50 100 0.0 Gap1 dsRNA (ng) R as 85 D d sR N A ( ng ) 0 50 100 0 50 100 0.5 1.0 1.5 2.0 Gap1 dsRNA (ng) P tp 69 D d sR N A ( ng ) 0 50 100 0 50 100 −1. 5 −1.0 −0.5 0.0 ... N um be r A re a In te ns ity Interaction score (πA,B) + RNAi 1 + RNAi 2 + RNAi 192 Combinatorial RNAi Imaging and image analysis Modeling of genetic interactions Gene A Gene B A1 A2 B1 B2 × Figure 1 | A multiparametric approach to identify genetic interactions through double-RNAi. (a–c) Genetic interaction surfaces of double-RNAi treatments over a range of dsRNA concentrations. Axes indicate the amounts of the respective dsRNAs combined per well. Interaction scores (π scores based on cell number phenotypes) are shown on a color scale ranging from −2 (negative interaction) to 2 (positive interaction). (d) Schematic overview of π score calculation. Single RNAi effects (A and B) are compared to that of a negative control dsRNA targeting firefly luciferase (Fluc). The expected double-RNAi effect is obtained by multiplying the single RNAi effects (arrowhead points to the relative cell number of 50% expected in this example) and compared to the observed double-RNAi phenotype. The π score is the log2 ratio between the observed and the expected value. (e) Schematic overview of the combinatorial RNAi experiment. Each color corresponds to a single dsRNA in the assay plates. To each plate, a different second dsRNA (RNAi 1–192) is added to all wells. This design creates all possible dsRNA combinations (arrows) targeting each pair of genes, A and B, with two dsRNAs (A1 and A2, and B1 and B2). c a b rl phl csw msk Sos Ras85D Dsor1 Cka stg mRNA−cap mop PpV CG3573 Gap1 Rho1 pnt puc drk Pvr mts π− sc or e nu cl ea r ar ea π− sc or e ce ll nu m be r Jra kay shark slpr bsk π− sc or e m ea n in te ns ty −0.5 0.5 −0.2 0.2 −0.2 0.2 Figure 2 | Clustering of genetic interaction profiles predicts gene function. (a) Hierarchical clustering of the genetic interaction profiles based on observed cell number. Known signaling components from the Ras-MAPK pathway (top right) and the JNK pathway (bottom right) are highlighted. mRNA-cap, gene encoding mRNA-capping enzyme. (b,c) Genetic interaction profiles for Ras-MAPK (right; dark gray) and JNK (right; light gray) regulators based on nuclear area per cell (b) or mean signal intensity (c). Genes in b and c are ordered as in a. © 2 01 1 N at u re A m er ic a, In c. A ll ri g h ts r es er ve d . nAture methods  |  VOL.8  NO.4  |  APRIL 2011  |  343 Articles reproducible across technical and biological replicates, with a Pearson correlation coefficient of 0.95 for observed numbers of cells between biological replicates (Supplementary Fig. 9). In each replicate, we measured the double­RNAi phenotype for each pair of genes eight times (Fig. 1e), allowing for stringent statistical analysis. Interactions based on cell number, nuclear area or intensity were clearly distinct (Fig. 2a–c and Supplementary Figs. 6 and 7). For example, depletion of downstream of receptor kinase (drk), a positively acting component of the Ras­MAPK signaling pathway, which provides mitogenic signals in S2 cells6, caused reduced cell growth as well as a reduction in cell size (Fig. 3 and Supplementary Table 3). Rho1 RNAi knockdown, in contrast, caused cyto­ kinesis defects, resulting in large, multinucleated cells (Fig. 3). The observed single­RNAi phenotypes of drk and Rho1 there­ fore differed depending on the phenotype studied: both led to a reduction in cell numbers but caused opposite effects on cell size. As the cytokinesis defects caused by Rho1 RNAi knockdown can­ not manifest themselves in the absence of mitogenic signals from the Ras­MAPK pathway, the drk-Rho1 double­RNAi phenotype (Fig. 3) was similar to the drk single­RNAi situation: Rho1 is epi­ static to drk. Comparing the observed quantitative double­RNAi phenotypes with those expected under a multiplicative model revealed deviations for both phenotypes: although the cells were smaller than expected (resulting in a negative interaction), there were more cells than predicted (a positive interaction). Thus, multiparametric phenotyping revealed context­specific inter­ actions, affecting different phenotypes with different strength and/or direction. Interaction scores were quantitatively reproducible with a Pearson correlation coefficient of 0.62 between biological repli­ cates (Supplementary Fig. 10). We accepted interactions with local false discovery rate (FDR) of 5% for each phenotype and identified 634 interactions in total. Of these, 372 interactions affected the number of cells, 379 led to changes in nuclear area per cell and 337 modulated the nuclear fluorescence intensity (Fig. 4a and Supplementary Table 3). We validated a subset of interactions that affected the number of detected nuclei using an independent, enzymatic assay for cell viability. The interactions were highly reproducible irrespective of the method used to assess the number of cells per well (Supplementary Fig. 11). Although we observed 135 interaction pairs for all phenotypes, 315 (49.7%) were specific to a single phenotypic readout, high­ lighting the multidimensionality of the genetic interaction space. For validation, we compared the identified genetic interactions with previously reported interactions in Drosophila or between human interologs11. Both the common as well as the pheno­ type­specific subsets of genetic interactions were significantly enriched for annotated interaction pairs (Fisher’s exact test, P­values between 10−14 and 0.05; details and P values are available in Supplementary Fig. 12). For example, we observed a genetic interaction affecting cell number between drk and the inositol 5′­phosphatase synaptojanin (synj), whose orthologs GRB2 and SYNJ1/2 associate with each other in human cells12 In addi­ tion, components of the JNK and Ras/MAPK pathways known to associate physically were significantly more likely to interact genetically (P < 0.01, Fisher’s exact test; Supplementary Fig. 13). The numbers of detected positive and negative interactions were approximately the same (Fig. 4b and Supplementary Fig. 14). In particular, we observed a high frequency of interactions between known components of the Ras­MAPK signaling pathways, reflect­ ing the focused design of this dataset. Only 15 genes lacked any interactions affecting the number of detected cells at an experiment­wide FDR of 5% (Fig. 4b). For example, RNAi knockdown of the protein phosphatase CG10376 by itself caused a reduction in cell number (Fig. 4c). The observed pairwise phenotypes of CG10376 with all other genes, including those with strong positive or negative single­RNAi phenotypes, were consistent with predictions from combining single gene effects and did not indicate the presence of a genetic interac­ tion (Fig. 4c). In contrast, depletion of Gap1 alleviated the RNAi phenotypes of many other components of the Ras­MAPK path­ way (Fig. 4d), often restoring approximately normal growth. Genetic interaction profiles In addition to revealing individual pairwise genetic interactions, we obtained an informative genetic interaction profile, a vector of interactions with all targeted loci, for each gene. Unsupervised clustering of these profiles reconstructed known global and local relationships between the assayed genes without requiring any Figure 3 | Phenotype-specific genetic interactions. (a,b) Single-RNAi and double-RNAi effects of targeting Rho1 and/or drk on nuclear area (a) or cell number (b). Observed and expected phenotypes are indicated as percentage relative to the negative control treatment (Fluc dsRNA). (c) Schematic representation of drk and/or Rho1 RNAi effects on nuclear area and cell number. Images are fluorescence microscopic images of S2 cells after RNAi treatment, stained with Hoechst and antibodies to α-tubulin. Scale bars, 10 µm. c drk Rho1 drk Rho1 drk Rho1 drk Rho1 DNA α-tubulin a b 100 Nuclear area (percentage of negative control) 500 Cell number (percentage of negative control) 150 100% 81% 131% 106% (expected) 83% (measured) 100% 34% 53% 18% (expected) 34% (measured) 50 1000 Fluc drk Rho1 drk and Rho1 drk and Rho1 © 2 01 1 N at u re A m er ic a, In c. A ll ri g h ts r es er ve d . 344  |  VOL.8  NO.4  |  APRIL 2011  |  nAture methods Articles additional information (Fig. 2a and Supplementary Figs. 5–7). We observed clear separation of components of the Ras­MAPK pathway from other signaling components, such as regulators of JNK signaling (Fig. 2a). Notably, the JNK phosphatase puckered (puc) was grouped together with positive regulators of the Ras­ MAPK pathway. In addition to its role as a negative feedback regu­ lator of the JNK pathway, puc has also been shown to be required for maximum activity of Rolled (rl) in Drosophila tissue culture cells13, and this signaling cross­talk is reflected in our dataset. cka is a modulator of ras-mAPK signaling To assign genes to known signaling pathways, we trained a clas­ sifier on the combined, multiparametric interaction profiles of annotated components of the Ras­MAPK and JNK pathways and estimated its predictive power through cross­validation (Fig. 4e). The classifier correctly identified most of the known positive regulators of Ras­MAPK signaling in the dataset (for example, the PP2A catalytic subunit microtubule star (mts) or the tyrosine phosphatase myopic (mop)) (Fig. 4e,f) and clearly separated them from negative regulators. Classifications of the latter overlapped partially with components involved in JNK signaling, reflecting the antagonistic relationship between the two pathways13. In addi­ tion, we observed several unexpected functional relationships. For instance, connector of kinase to AP­1 (encoded by Cka), previously described as a scaffold protein in the JNK signaling pathway14, was predicted with high confidence to act as a positive regulator of Ras­MAPK signaling. We therefore characterized and validated the role of Cka in this pathway through independent biochemical and genetic experiments. Similar to known regu­ lators of Ras­MAPK signaling, Cka genetically interacted with individual components of both the Ras­MAPK and JNK path­ ways, and its correlation profile was highly similar to that of Ras85D (Fig. 5a and Supplementary Fig. 15). As knockdown of Ras85D or rl, depletion of Cka led to a significant reduction in mRNA levels of sprouty (sty)15 (Fig. 5b; P < 0.05, Student’s t­test, n = 6) and affected other genes downstream of the Ras­MAPK cascade (Supplementary Fig. 16). In contrast, neither RNAi knockdown of bsk nor slpr, the Drosophila JNK and JNKKK, caused any substantial change in sprouty (sty) expression. Next, we investigated whether RNAi to Cka affected the activ­ ity of the Ras­MAPK cascade. Knockdown of Cka led to reduced basal Rolled­ERK phosphorylation levels in Drosophila S2 cells (Fig. 5c and Supplementary Fig. 17). A similar attenuation of Ras­MAPK pathway activity was observed in human cells upon RNAi knockdown of Cka’s orthologs, Striatin (STRN) or Striatin3 (STRN3) (Fig. 5d and Supplementary Figs. 18 and 19). Drosophila GCKIII, a modifier of Ras/MAPK signaling13, as well as its binding partner, the PP2A catalytic subunit Microtubule star (mts), immunoprecipitated along with Cka (Fig. 5e and Supplementary Figs. 20 and 21). Cka also has a role downstream of the epidermal growth factor receptor (Egfr) in vivo, as reducing Cka gene dosage in the background of the gain­of­function allele EgfrElpB1 partially suppressed the formation of ectopic wing vein material (P < 1 × 10−10, Fisher’s exact test; Fig. 5f). Drosop