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Supplementary figure legends Figure S1. Schematic representation of the high-throughput flow cytometry screen. S2 cells were transfected with individual dsRNAs in 96-well plates and analyzed for six distinct phenotypes by flow cytometry. To estimate the average size of cells in G1 and G2, we first gated the cell populations according to DNA content, and then analyzed the forward scatter, which is proportional to cell size1. For example, Drosophila cells that are delayed in the G1 phase accelerate the G2 and S phases, and conversely, cells with retarded G2 progression traverse faster through G1/S2. We used flow cytometry as a primary screen because this compensatory mechanism restricts the number of cell cycle regulators that could be identified by screening for effectors of cell division, growth or proliferation using cell counting or viability assays (see also Fig. S9). Figure S2. Overview micrographs of S2 cells after dsRNAs targeting known regulators of G1, G2/M and S, cytokinesis and DNA replication shown in main text Fig 1a. Cells were stained for DNA (blue), F-actin (red) and -tubulin (green). Figure S3. Role of protein phosphorylation in the regulation of the Drosophila cell cycle. Drosophila S2 cells were transfected with a dsRNA library containing 259 kinases and 88 phosphatases3 and the percentage of S2 cells in G1 (a) or G2 (b) phase of the cell cycle was analyzed after 4d treatment with indicated dsRNA against a kinase (left, green diamonds) or a phosphatase (right, blue diamonds). The data is from at least two independent experiments with 100 000 events acquired per sample, and it was scored by automated analysis for the fraction of cells in the G1 and G2 phases. Error bars indicate one standard error (n = 2 to 4). Samples which significantly differ from the mean (Table S3) are indicated with red diamonds. Samples with large error bars such as MAPk-Ak2 that were confirmed significant by re-screening are also indicated. This screen identified 13 kinases and one phosphatase that resulted in increased G1 content. In addition to the kinase components of the pathways described in the main text, we also identified several kinases that we cannot currently place into distinct pathways (see Table S3). These include the kinases DOA/CLK24 and CHED-related (CG7597)5, which have previously been linked to cell viability in Drosophila, and to megakaryocyte differentiation in the mouse, respectively. Figure S4. Dendrogram representing the homologies between kinases (a) and phosphatases (b). Since the eukaryotic cell cycle arose early in evolution, it is a highly robust and redundant system6,7, and it is therefore possible that the most important components of the regulatory system are the ones being the most redundant. It is thus conceivable that at least in some cases multiple kinases and phosphates have overlapping functions, or that loss of one phosphatase or kinase leads to compensation of its function by another. One common evolutionary mechanism that gives rise to redundant or partially overlapping gene functions is gene duplication8. To identify related kinases or phosphatases that are essential for cell cycle progression and have redundant function, we generated a dendrogram tree of the kinases and phosphatases. The dendrogram trees were generated with Neighbor-Joining method using ClustalW version 1.81 with full dynamic programming option9 and BLOSUM-62 comparison matrices. Dendrograms were visualized with TreeView. Black boxes indicate the homolog pools, blue boxes indicate pools that induced cell cycle effect and contain close homologs, and green boxes indicate pools which include also more distant homologs. The dsRNAs corresponding to the ‘branches’ of the tree were pooled systematically and tested for their function (see main Fig. 1c). The apparent lack of redundancy observed may be in part due to relatively low number of paralogs in Drosophila. For example, Drosophila has only 263 kinases10 compared to 518 in humans11, and the additional human kinases represent to a large part extension of the kinase families already found in Drosophila. Figure S5. Comparison of our results with those of Bettencourt-Dias et al.12. a, A venndiagram of the overlap between our analysis and an earlier study of dsRNAs targeting protein kinases. In an earlier RNAi study targeting protein kinases, Bettencourt-Dias et al. (2004)12 reported that a surprisingly large fraction (35%) of Drosophila kinases regulate the cell cycle of S2 cells. However, in our hands, dsRNAs targeting only approximately 19% of kinases (including also kinases found in the DGC library) resulted in a cell cycle phenotype, and we failed to detect 51 of the 80 kinases reported by Bettencourt-Dias et al. (2004). Most of the kinases we did not identify were scored positive by Bettencourt-Dias et al. (2004) with microscopic methods using low statistical stringency (90% confidence; two assays) – whereas we found 55% (23/42) of kinases identified by Bettencourt-Dias et al. (2004) using flow cytometry, only 10% (6/60) of the kinases identified by microscopic methods alone scored positive in our analysis. The kinases we identified here that were not found by Bettencourt-Dias et al.12 include AKT1, MEKK1/4 (see Fig. S15), CG7177 and a known cell cycle regulatory kinase, Pan Gu as well as Fps85D and CG1973, which were validated using both microscopy and MTS assay (see Fig. S9, Table S6). b, Probability distribution for the number of statistical false positive phenotypes resulting from analysis of dsRNAs targeting 228 kinases in seven different phenotypes using two independent assays with 90% confidence (from binomial distribution; because one dsRNA can have multiple phenotypes, the number of genes is lower (by less than 1); the criteria are similar to those used in microscopic analysis of Bettencourt-Dias et al.). c, Mitotic index of S2 cells after treatment with dsRNAs targeting kinases identified only by Bettencourt-Dias et al. Approximately 5500 cells were counted in each case. Note that the control polo and rok dsRNAs identified in our flow cytometry analysis result in the highest increase in mitotic index, whereas the other dsRNAs have a much weaker effect. Black and red vertical lines indicate mean and 2.5SD of control samples, respectively. Samples scoring as hits (>2.5SD) are indicated with an asterisk. These results suggest that flow cytometry can detect major mitotic arrest or defect phenotypes, but fails to detect weaker increases in mitotic index or types of mitotic defects that do not lead to mitotic catastrophe or failure of cytokinesis. The fact that we do not identify significant changes in mitotic index of the majority of the kinases only identified by Bettencourt-Dias et al. is also consistent with the false positive analysis in (b), and the sequence problems delineated in Table S4. After correcting for clone problems in the library used by Bettencourt-Dias et al. and for statistical false positives (b) due to the relatively low statistical stringency (90% confidence in two independent assays) in the microscopic analyses, our results are in a relatively good agreement (Table S4; compare to Table S7). Figure S6. G1 (red) and G2 (blue) content of S2 cells treated with dsRNAs targeting genes in Drosophila Gene Collection 1 and 2. Error bars indicate one standard error from triplicate wells. Names are indicated for the genes corresponding to the dsRNAs with strongest phenotypes, and all samples that significantly (see Table S3) differ from the mean are indicated by darker color. Control samples (green) that were included in screening plates have been moved to the right for clarity. Cases where two genes are targeted by a single dsRNAs, both names are indicated and separated by a plus-sign (see Methods). Figure S7. Correlation between cell size at G1 (y-axis) and G2 (x-axis). Regression line (middle) was calculated using control samples alone. The thinner lines indicate 3 standard deviations from the regression line. Samples that resulted in an increase in G1 and G2 content (see Fig. S6) are in red and green, respectively. Control samples are in light blue. Note that in general, the cell size at G1 correlated well (R2 = 0.67) with cell size in G2 across the 564 control samples, and most dsRNAs that induced an increase in cell size at G1 or G2 also resulted in an increased cell size at the other gap phase. This strong correlation suggests that Drosophila cells do not have a "strong" cell size checkpoint (see Ref. 13) that would force cells to a particular size at a defined cell cycle stage. This view is supported also by the observation that most treatments that resulted in increased G1 size and content, or increased G2 size and content, also induced a smaller yet significant increase in cell size at the other gap phase. The less pronounced increase of cell size in the other cell cycle phases is consistent with the existence of a "weak" size checkpoint13, or other mechanism14 which over time drives cells towards similar size. Figure S8. Cell death and defects in cytokinesis. a, Fraction of cells with less than 2N DNA. This population consists of apoptotic and dead cells, and of cells that have undergone replication without DNA synthesis. In this group of genes there were multiple proteasome and ribosomal subunits. Many of the genes identified have previously been linked to mitosis and sister chromatid segregation, and cell death caused by loss of these proteins is likely due to mitotic catastrophe. The basal rate of cell death is very low under the conditions analyzed, and consistently, we could not identify some known positive regulators of cell death, such as caspases15. b, Micrographs of cells targeted with dsRNAs inducing cell death. Known cell viability regulators (RpL10Ab, Rpt3, Pp1-87B) and novel effectors (CG1315, CG2556, Fps85D, CG1973, CG10979, Tub56D) stained for F-actin (red), -tubulin (green) and DNA (blue). Note that in addition to causing cell death, some dsRNAs induce a mitotic (Pp1-87B; red arrowhead), flat cell (Rpt3; arrow) or multinucleate (Tub56D; white arrowhead) phenotype. For quantitative information, see Table S6. c, Fraction of cells with more than 4N DNA. Cytokinesis defects would in general only be detectable by flow cytometry when two successive rounds of replication occur that bring the DNA content up to 8N. Such effects were observed with known cytokinesis regulators, such as Scraps (Fig. 1a), Fascetto (feo), Pavarotti, Rho1 GTPase, and Incenp (inner centromere protein; Ref. 16; see Table S3), as well as some novel regulators such as Sugarless (sgl), a protein required for synthesis of hyaluronic acid, chondroitin and heparan sulphate. In Drosophila, sgl has been described before as a gene required for Wnt and FGF signaling17. However, consistently with our observations, cytokinesis defects have also been found in C.elegans lacking chondroitin sulphate18. Some dsRNAs also resulted in a decreased number of cells with more than 4N DNA content. Many of these represent dsRNAs targeting ribosomal proteins, which lead to accumulation of cells in the G1 phase, and consequent decrease in other cell cycle phases. However, also known cytokinesis regulators, such as Rho1 GTPase activator protein (RhoGAP1A), which is a negative regulator of Rho1 were among this group of dsRNAs (Table S3). The effect of Rho1GAP dsRNA suggests that modulating the levels of endogenous Rho1 activity can speed up or decrease the frequency of defects in cytokinesis. Samples which significantly differ from the mean (see Table S3) are indicated by red color. The control samples (green) included in the screen are shown separately on the right for clarity. d, Micrographs of cells targeted with dsRNAs inducing over-4N DNA content. Examples of known (Rho1, feo, Incenp) and novel (sgl, CG31856, SH3PX1, Tub56D, CSN8) genes whose loss causes a multinucleate phenotype are shown (red = whole cell protein stain, blue = DNA). Increase in fraction of cells with over-4N DNA content was often accompanied by cell death (Tub56D, CSN8), and also genes whose loss resulted in elevated over-4N DNA content in single nuclei (e.g. CyclinA) were identified in the flow cytometry screen. Note clearly abnormal nuclei in Incenp, CSN8 and CycA dsRNA treated cells (arrowheads). Figure S9. Validation of cell viability phenotypes. Genes identified in the original flow cytometry assay were re-tested using the corresponding dsRNAs in automated microscopic cell count assay and MTS metabolic activity assay. The panels show dsRNAs identified in flow cytometry screen ranked by phenotypic score. Those dsRNAs which scored positive also in microscopic cell count assay (blue) and/or MTS assay (red) are indicated. a, dsRNAs causing G1 phenotype. b, dsRNAs with G1 but not cell death phenotype. c, dsRNAs causing cell death. d, dsRNAs causing both G1 arrest and cell death. Note that cell viability analysis either by using metabolic activity assay (MTS) or automated microscopic cell number counts validates most of the dsRNAs identified using flow cytometry which induce cell death with or without G1 arrest. However, these methods are less sensitive than flow cytometry despite the lower statistical stringency used (2 - 2.5 SD compared to 5SD in flow cytometry, see Table S6), as the genes that do not score as hits (indicated in panel d) are dominantly ribosomal proteins, the loss of which should cause decrease in cell growth or viability. In addition, because Drosophila cells that are delayed in the G1 phase accelerate the G2 and S phases, these assays fail to effectively detect dsRNAs which cause G1 arrest alone (b). e, f, Gene ontology classifications of the genes validated using the cell viability and cell count assays. Note that these methods primarily detect genes required for cell viability, and fail to effectively find direct cell cycle effectors. Figure S10. Analysis of yeast-two hybrid and genetic interactions (edges) between genes (colored nodes) identified in different RNAi screens. The number of genes analyzed, the interactions expected by chance (expected interactions), and the ratio of observed interactions to expected interactions are also indicated. Both all and high-confidence yeast two hybrid interactions are analyzed (from Ref. 19), and self-interactions are not counted. The genetic interactions are from FlyGRID. Colors of the nodes indicate the gene ontology classifications (right) of the identified genes. Note that not all genes are identified by FlyGrid, the actual number of genes analyzed is indicated separately and that in JAK/STAT screens some genes have been omitted by expert analysis (Table S7). For random set of genes, the mean of three different randomly selected sets is shown. Figure S11. Western-blot analysis of efficiency of RNAi targeting six different genes. The lanes in each case were from same filters with the same exposure time. The loading was controlled by visual inspection of the filters stained for total protein by Poinceau S, and by analysis of multiple other protein species in filters probed with different antibodies. Representative loading controls shown (from the same filters): Cdk1: a 37 kDa protein recognized by anti-p27 antibody (Santa-Cruz Biotechnology sc-528); MEK3: p38 MAPK (sc-15715); CycB: -tubulin (Sigma F2168); CycE: CycD (sc-25765); Cul1: CycD (sc-25765); p38MAPK, a 75 kDa protein recognized by the p38 antibody (sc15715). In each case, loading control shown was consistent with other protein species analyzed by Poinceau S staining and/or western blotting. For re-probing the filters were stripped as described in Supplementary Methods. Figure S12. Analysis of 'hit-rate' in selected protein complexes/pathways. a, chromosome passenger complex/checkpoint, APC/cyclosome, Separase and Cohesin. b, Condensin. c, DNA replication initiation. d, RNA polymerase II. Thick black outlines indicate genes identified in our screen. Dotted outlines indicate mild phenotype (> 3SD) but below the 5SD cut-off used, the dash-dot line in MCM4 indicates that MCM4 had an opposite phenotype to those of MCM5 and MCM7. Underlined genes have been identified in other RNAi screens analyzing cell viability or cytokinesis (see Table S9). Genes which were not analyzed are in gray, and asterisk indicates genes that are components of the complexes in other organisms but appear not to be conserved in Drosophila. Color code: red = mutant lethal in Drosophila; pink = mutant with phenotype that is not lethal; green = mutant viable, for type of mutations, see Table S9; white = mutant not characterized. Bold typeface = yeast deletion mutant lethal. See Table S9 for phenotype strength of these complexes and references. Note that only two components of cyclosome/APC complex were identified here. In addition, genes involved in DNA replication (ORC and MCM complexes), and in chromosome maintenance and segregation (cohesin, condensin and separase complexes) were underrepresented apparently due to a weak RNAi phenotype (Fig. 3a, and Refs: 2022 ). The weak RNAi phenotype could be explained by high protein levels/stability, redundant function, and/or the fact that defects in mitotic or DNA replication fidelity often do not have appreciable effect on overall cell cycle distribution as measured by flow cytometry21 (see Table S4). Figure S13. Hierarchical clustering of all genes identified in the DGC screen based on their scores in all six phenotypes. Nine major sub-branches are named based on the predominant phenotype and/or known activities of genes included in them. Note that consistently with the absence of Cyclin A-Cdk2 complexes in Drosophila, Cyclin A was not found in the cluster containing known regulators of the G1 phase of the cell cycle. However, this cluster contained dsRNAs targeting three genes with an E2f -like phenotype, CG10686, CG31642, CG6169, that have previously not been linked to the cell cycle. CG10686 is a novel protein containing a cyclin-like domain, whereas CG31642 is a protein with a ZZ-type zinc finger domain found in many proteins involved in ubiquitinylation, or associated with chromatin and cytoskeleton23. CG6169 is a homolog of mammalian mRNA decapping enzyme (hDcp2) involved in degradation of mRNAs24, suggesting that degradation of some mRNAs regulates G1 progression. Note also that ribosomal proteins clustered into a defined ‘translation’ cluster, composed of dsRNAs that resulted in increased G1 content and apoptosis, and a decrease or no change in cell size. Loss of the majority of the remaining large ribosomal proteins caused a more prominent cell death phenotype, and the corresponding genes clustered into a separate 'apoptotic large ribosomal subunits' cluster. Figure S14. Expression pattern of the identified genes during Drosophila life cycle. The gene expression data was downloaded from http://genome.med.yale.edu/Lifecycle/ (Ref. 25). Out of 488 genes that we identified, 219 were present in this data set. These genes were clustered using the Cluster version 3.0 and visualized with the T reeView version 1.0.10. (E) embryo, (L) larvae, (P) pupae, (Am) adult male, (Af) adult female. The expression patterns of Cyclin E, E2f and eIF-3p66 are indicated with an arrow. The legend shows expression levels in logarithmic scale. For most of the genes we identified, the expression was highest during early embryonic development. Figure S15. Pathways regulating the cell cycle. a, The E2f pathway. b, Six components of the COP9 signalosome (circle) and the associated cullin proteins 1 and 4 (ovals) resulted in an increased G1 content (red text). COP9 is a 450-550 kDa complex consisting of eight proteins which has been linked to both positive and negative regulation of the cell cycle26. It catalyzes the removal of the ubiquitin-like protein Nedd8 from other proteins, including Cul-126. dsRNAs targeting other components of the COP9 either did not significantly affect cell cycle (black) or were not present in the library (gray). dsRNA targeting CSN8 resulted in a decrease in G1 (green), an opposite phenotype to that of dsRNAs targeting the other COP9 subunits. Box indicates percentage of S2 cells in G1 following treatment with indicated dsRNAs together with control or Dacapo dsRNA. Note that Dacapo dsRNA rescued the G1 phenotype caused by CSN1b, CSN2 and CSN5 dsRNAs, and that this effect was specific, as Dacapo loss failed to significantly affect the G1 increase phenotype caused by dsRNA targeting pangolin. c, Metabolic regulatory FRAP/TOR pathway. d, JAK/STAT pathway. e, Wnt/Wingless (Wg) pathway components. Note that loss of three known negative regulators of the Wnt pathway, CK13,27,reptin28 and pontin28, resulted in an increase in the G1 population. Loss of these proteins is known to result in stabilization of -catenin, which binds to the pangolin (Pan/dTCF) transcription factor and converts it from a repressor to an activator29. Because loss of -catenin (arm) did not affect the cell cycle, and dsRNA targeting Pan itself resulted in a G1 phenotype, the activator form of Pan is not required for the cell cycle effect. However, loss of one positive component of the Wnt pathway, Dishevelled, resulted in decreased G1 content and cell death. The effects of Dsh on the cell cycle may be independent of Pan and occur via the planar polarity pathway30. Reptin and pontin can also act via Myc31. f, p38MAPK pathway. Inset: Western blot analysis of p38. Note that dsRNAs targeting MEKK1/4 and MEK3 results in slower migration of p38. 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