The results of two large-scale, RNAi-based screens in cultured Drosophila cells have identified genes that are potential regulators of the cell cycle 60 61 . We set out to provide independent confirmation of the identified regulators, and to identify potential false negatives from the previous screens. To identify a subset of the negative genes that were likely to be enriched for cell cycle regulators, we performed a virtual protein-protein interaction screen to find proteins that interact with known or suspected cell cycle regulators. The bait proteins that we used for the virtual screen were proteins identified as potential cell cycle regulators in the two published RNAi-based screens as well as all genes annotated with a Gene Ontology (GO) biological process 62 of "cell cycle" (see Methods). These 642 bait proteins were used to query DroID, the Drosophila Interactions Database 63 64 to identify 5,008 potential protein interaction partners (Additional File 1). We filtered this data (see Methods) to obtain a higher confidence set that consisted of 1,843 interaction partners for the 642 bait proteins (Figure 1A and Additional File 1). We hypothesized that the interaction partners of the baits would be enriched for cell cycle regulators relative to random proteins. In support of this, analysis of both the filtered and unfiltered protein network showed that the bait proteins interact with each other much more than would be expected for a random group of proteins (p-value < 10^-82) (Additional File 2A). The high level of connectivity between bait proteins is also evident from the size of the maximally connected component subnetwork for the baits, which was found to be significantly larger than for equally sized random sets of proteins (p-value < 10^-18) (Additional File 2B). This analysis demonstrates that within the protein interaction data that we screened, cell cycle regulators frequently interact with each other. It also supports the hypothesis that proteins that interact with the bait proteins that we used in the virtual screen may be enriched for novel cell cycle regulators that were false negatives in the previous screens. Figure 1B shows a subset of the interaction map data involving 6 members of the COP9 signalosome protein complex that was identified as a regulator of the G1/S transition in one of the previous screens 60 . As expected for a protein complex, there are a number of interactions that connect COP9 signalosome subunits to each other in the map. There are also a number of interactions between COP9 signalosome subunits and non-complex members. These interactors potentially function in conjunction with the COP9 signalosome to regulate cell cycle progression and represent possible false negatives from previous screens.

We used a previously described library of dsDNA templates 3 65 that allowed for the generation of dsRNAs targeting 596 of the 642 bait protein genes and 1,612 of the 1,843 interaction partner genes. We also generated dsRNAs targeting a random set of 550 genes encoding proteins that were not known to interact with the cell cycle baits or their interactors (Methods). We treated Drosophila S2R+ cells with dsRNAs targeting a total of 2,758 genes and determined cell cycle profiles by flow cytometry. Cell cycle profiles were used to determine the percentage of cells with G1, G2/M, greater than G2/M, or less than G1 DNA content (Additional File 3). dsRNAs that induced a significant increase (>3 standard deviations from the mean) in the percentage of cells in any of these four categories were considered as hits. Examples of cell cycle phenotypes are shown in Figure 2A. Overall, the screen identified 371 unique genes as hits (Additional File 4). A global view of the data reveals that the majority of the strong phenotypes were observed for dsRNAs targeting the putative cell cycle proteins that we used as baits or their interaction partners (Figure 2B). As expected, targeting of the baits resulted in the highest rate (26.0%) of cell cycle defects (Figure 3A). The hit rate for bait interaction partners was 11.8%, significantly higher than the hit rate for the set of random non-interactors (4.5%) (Figure 3A). This was also true for interactors that were derived just from baits that were hits in previous screens without regard to their Gene Ontology annotation. For those, 12.3% (160/1303) were hits, suggesting that prior knowledge of the Gene Ontology annotation of the baits was not necessary to enrich for hits over random genes. Additionally, genes from the group of interactors that were hits interacted with a greater number of baits than did the interactors that were non-hits (p = < .0001) (Additional File 5). We also found that the quality of the protein interaction data affected the hit rate in our RNAi screens. For example, interactors connected to baits by higher confidence interactions 66 were more likely to be hits than those connected by low confidence interactions (Additional File 6). Interestingly, the hit rate for non-interactors was similar to the hit rate observed in undirected genome-wide screens 4 60 . These results show that protein interaction map data can be used to identify a set of genes that is enriched for regulators of a cellular process like the cell cycle. Moreover, the identification of phenotypes for a substantial number of genes that were negative in previous screens shows that the interaction map-guided approach can help to identify putative false negatives from the hits reported in individual screens (e.g., see Figure 3B).

Comparison between the results of previous screens and the current screen revealed a significant overlap in the genes that were identified as hits (Figure 4A). Of the 375 hits from previous screens that were re-screened in the current study, 138 or 36.8% were identified as hits in the current study. However, our screen and the previous cell cycle RNAi screens each identified substantial numbers of unique hits. We consider three possible explanations for this lack of overlap. First, it is possible that the screens have different levels of sensitivity and that one screen captured a larger fraction of the true positives than the other. This seems unlikely given that each screen (e.g., ours and the genome-wide screen) had similar numbers of hits (371 vs. 361) among the genes screened in common (Figure 4A). Second, the lack of overlap may be due to high but roughly equal rates of experimental false negatives in each screen. Such false negatives, for example, could result from inefficient knock down of gene expression by specific dsRNAs. A third possibility is that many of the novel hits in each screen are the result of off-target effects. This possibility must be given serious consideration since the previous screens did not control for off-target effects and, in at least the genome-wide screen, full-length cDNAs were used to produce dsRNA 60 . dsRNA generated from full-length cDNAs are more likely to lead to off-target effects than dsRNA from smaller regions of each transcript 35 . We performed GO enrichment analysis 67 on the hits from each dataset (Additional File 7). As expected, hits from each dataset were significantly enriched for cell cycle regulators. Moreover, the hits in common between our screen and the previous genome-wide screen were significantly more enriched for cell cycle regulators than the unique hits, consistent with the idea that the overlapping data is enriched for true positives. However, the hits that were unique to each screen were also significantly enriched for cell cycle regulators (p values < 10^-4 and <10^-16, respectively), indicating that each screen detected true positives that were false negatives in the other screen (Additional File 7). Interestingly, our set of novel hits was more enriched for cell cycle regulators than the novel hits from the previous genome-wide screen, possibly because we used smaller dsRNAs that are less prone to off-target effects.

To confirm the putative novel cell cycle regulators that we identified and to determine the rate at which they may be due to off-target effects, we generated validation dsRNAs for 256 genes. The validation dsRNAs targeted regions of the transcripts not overlapping or only minimally overlapping (<12 nucleotides) with those targeted by the dsRNA used in the initial screen. Overall, 155 (60.5%) of the hits from the primary screen were validated by testing an additional dsRNA (Figure 4B; Additional File 3; Additional File 4). This validation rate is similar to that of other validated RNAi-based screens for regulators of other biological processes in cultured Drosophila cells 68 69 70 . The validation rate for hits identified in both the current screen and a previous screen was significantly higher than for genes identified only in the current screen (72.8% versus 50.7%) further suggesting that genes identified in two, independent screens are more likely to be true positives than genes identified in only a single screen. This conclusion is also supported by a repeat screen in which we re-screened 4 plates of dsRNAs from the original screen a second time (Additional File 3). Of the 52 hits identified in the initial screen, 36 were identified again in the repeat screen. Hits identified in both the initial screen and the repeat screen were more likely to be confirmed by a validation dsRNA (24/33 versus 2/7). Combined, these results suggest that one major reason for the low level of overlap among different RNAi screens that probe the same biological process is the prevalence of off-target effects, which can be as high as 40-50% in any given screen. This is consistent with other recent studies and further highlights the importance of validating genes identified in RNAi screens, as previously recommended 34 35 71 .

Our results also suggest that false negatives are prevalent in individual RNAi screens. Among the validation dsRNAs that we tested, 142 targeted genes were hits in our initial screen but not in previous screens. 72 (50.7%) of these validation dsRNAs confirmed the results of the primary screen (Additional File 3). This finding indicates that these genes were false negatives in the previous screens. Combining the complete set of validated genes from the current study with genes that were identified both in the current screen and a previous screen (regardless of validation in current screen) allows us to define a set of 210 genes that are high confidence regulators of the cell cycle (Additional File 4).

Among the 155 validated hits from the current study, 46 were neither classified as cell cycle genes (based on Gene Ontology identifier GO:0007049 62 ) nor were they hits in one of the previous RNAi-based screens 60 61 . These genes are therefore novel cell cycle regulators that were identified in the current study. Thirty-three of the identified genes displayed a subG1 phenotype, 11 displayed a G1 phenotype, 1 (zipper) displayed a greater than G2/M phenotype, and 1 displayed both a G1 and sub-G1 phenotype (Additional File 4). The most common ontology associated with the 33 genes that displayed a sub-G1 phenotype was mRNA processing (11/33). This finding suggests that mRNA processing plays a critical role in maintaining viability in these cells. A similar dependence on mRNA processing for cell viability has been observed in other RNAi-based screens in Drosophila cells as well as in studies of S. cerevisiae 72 73 , C. elegans 2 74 and human cells 75 . The single gene with a greater than G2/M DNA content phenotype was the Drosophila non-muscle Myosin II gene zipper. Zipper has been identified in two previous large-scale RNAi-based screens for cytokinesis regulators 65 76 and has an established role in the process of cytokinesis (reviewed in 77 ). Among the 11 novel G1 regulators there were two genes, Rae1 and l(2)dtl, encoding proteins with WD40 repeat domains 78 79 . Neither gene had been annotated with any GO terms for molecular function or biological process. A literature search, however, revealed a previous study that focused on Rae1 and cell cycle regulation 79 . Interestingly, this study also identified a G1 phenotype for Rae1 following RNAi in cultured Drosophila cells 79 . In the case of l(2)dtl, while the Drosophila gene had not been annotated as a cell cycle regulator, data from both Drosophila and human cells suggest that the l(2)dtl protein (L2DTL) is a targeting subunit of the CUL4/DDB1 ubiquitin ligase complex that targets critical cell cycle regulators for degradation 80 . RNAi targeting the human ortholog has been shown to cause growth arrest and an increase in both p53 protein and the DNA replication licensing factor CDT1 81 82 . Additionally, human l(2)dtl has been shown to oscillate during the cell cycle with peak expression occurring at the G1/S transition, consistent with a role in regulating G1/S 83 . The remaining genes that displayed a G1 phenotype contained multiple members of three structurally related protein complexes (see below).

An examination of genes in the list of 210 high confidence cell cycle regulators that displayed a G1 phenotype revealed the presence of multiple members of three structurally related protein complexes: the eukaryotic translation initiation factor 3 complex, the COP9 signalosome, and the proteasome lid. These three complexes have been referred to as "the zomes" based on their related structures. Each complex is composed of protein subunits that contain a domain named PCI (proteasome-COP9-eukaryotic initiation factor) and protein subunits that contain a domain named MPN (Mpr1-Pad1 N-terminus) 84 85 . We sought to identify mechanisms that underlie the G1 phenotype observed following RNAi targeting each of these complexes. Progression of cells through the G1 phase of the cell cycle requires activation of the Cyclin dependent kinase 2 (Cdk2) and Cyclin E (CycE) complex 86 87 88 89 90 91 92 . Drosophila Dacapo (Dap), a member of the p21^CIP1/p27^KIP1 family of Cdk inhibitors, can block progression from G1 into S phase by specifically inhibiting Cdk2/CycE 93 94 . To determine if Dap mediates the G1 arrest induced by RNAi targeting the zomes, cells were treated with dsRNA targeting members of each zome complex alone or in combination with dsRNA targeting the Dap transcript (Figure 5 and Additional File 8). We observed that Dap knockdown completely rescued the G1 arrest induced by RNAi targeting members of the COP9 signalosome, consistent with a similar demonstration by Bjorklund et al. 60 for CSN1b, CSN2 and CSN5. Knockdown of Dap also completely rescued the G1 arrest caused by targeting subunits of the proteasome lid. The COP9 signalosome and proteasome lid complexes both play a role in ubiquitin-mediated proteolysis of Dap/p21/p27 in human and Drosophila cells 95 96 97 . RNAi targeting the proteasome lid or COP9 signalosome, therefore, likely stabilizes Dap leading to increased Cdk2 inhibition and delayed progression from G1 into S phase. Interestingly, dsRNA targeting Dap transcripts did not have a significant effect on the G1 arrest induced by knocking down members of the eIF3 complex (Figure 5 and Additional File 8). These results suggest that the G1 arrest induced by knocking down eIF3 subunits is not mediated by Dap, or not solely by Dap, and that the underlying mechanism is distinct from that which mediates the COP9 signalosome and proteasome lid phenotypes.

There are several lines of evidence that have previously implicated the eIF3 complex in regulation of the G1/S transition in other organisms. In the yeast S. cerevisiae, a cell division cycle mutant, cdc63, that arrests in G1 encodes a component of the eIF3 complex 98 99 100 101 and is the ortholog of Drosophila eIF3-S9 that was identified in this study. Temperature sensitive mutants of TIF34, another yeast eIF3 component, also arrest in G1 at the restrictive temperature 102 103 . The Drosophila TIF34 ortholog was not screened in this study but was a negative in the previous genome-wide screen 60 . In human tissue culture cells, overexpression of 5 different eIF3 proteins each resulted in an increase in the percentage of cells in S phase and an increased rate of cell proliferation suggesting that the human eIF3 complex also regulates G1/S and cell cycle progression 104 . Together with our results, these studies indicate an evolutionarily conserved role for the eIF3 protein complex in regulating the G1/S transition. The mechanism by which the eIF3 complex regulates the G1/S transition of the cell cycle however is not known, but appears to be independent of Dap regulation.

Activation of CycE transcription, increased CycE protein expression, and the activation of Cdk2 by CycE are all required for cells to progress from G1 into S phase 87 105 . eIF3 is a large, multi-subunit complex that has been shown to play a key role in regulating mRNA translation and thus gene expression 106 . One possible mechanism by which eIF3 could be required for G1/S is that eIF3 may be required for CycE translation. To explore this possibility, we treated cultured cells with dsRNA targeting eIF3 complex subunits and determined the effect that this had on CycE expression levels. As expected, treating cells with dsRNA targeting CycE transcripts results in a significant reduction in CycE protein levels (Figure 6A). However, treatment of cells with dsRNA targeting eIF3 subunits had no significant effect on CycE protein levels (Figure 6A). This result suggests that the increase in cells with G1 DNA content following RNAi targeting eIF3 subunits is not the result of reduced expression of CycE.

Activation of Cdk2 during G1 results in phosphorylation of proteins that mediate entry of cells into S phase. We examined the possibility that RNAi targeting eIF3 subunits could affect Cdk2 expression or activation of Cdk2/CycE kinase activity. We immunoprecipitated CycE complexes from cells that had been treated with dsRNAs targeting eIF3 subunits and examined their ability to phosphorylate histone H1, a model Cdk2 substrate. We observed a significant decrease in CycE-associated kinase activity for complexes purified from cells treated with dsRNA targeting eIF3 subunits in comparison to cells treated with control dsRNA (Figure 6B). Surprisingly, simultaneously targeting Dap rescued CycE-associated kinase activity in these cells even though it did not rescue their ability to efficiently progress from G1 into S phase (Figure 6C and Figure 5). Thus, reduction in CycE-associated kinase activity is not sufficient for the G1 phenotype observed following knockdown of eIF3 subunits. These results suggest that eIF3 is required for a G1 to S rate-limiting process that is independent of Cdk2/CycE activation.

The bait proteins used in the virtual protein-protein interaction screen were taken from the published hits that were identified in two large-scale, RNAi-based screens for cell cycle regulators in cultured Drosophila cells 60 61 . Only genes that gave a G1 or G2/M phenotype were taken from the Bjorklund et al. 60 study while all hits were taken from the Bettencourt-Dias et al. 61 study. In addition, the baits included all genes with a Gene Ontology that contained the biological process term "cell cycle". These genes were obtained as a batch download from the Flybase database 116 on 09/26/07. The complete list of 642 baits was used to search DroID, the Drosophila Interactions Database 63 using the dynamic graphing tool, IM Browser 117 . Seven datasets of protein-protein interactions (PPI) from DroID were searched, including data from three large-scale yeast two-hybrid studies 44 118 119 , and published data curated by other PPI databases. The remaining three datasets contained interactions predicted for Drosophila proteins based on experimental interaction data for S. cerevisiae, C. elegans, and human. The relatively high rate of false positives reported for large-scale protein interaction map data 120 121 suggests that not all of the interaction partners that were identified by querying DroID are true interactors. In some cases false positives result from so-called sticky proteins that have many interaction partners in the interaction map 122 . In an effort to reduce the number of false positives, all bait proteins with >30 interactions had their interaction partners removed. Any removed interactors that were identified in more than one independent study were then added back. Finally, all interaction partners for Cdk1 and Cdk2 were added back because of the central role that these proteins play in cell cycle regulation. The results of this analysis identified the set of proteins that we referred to as interactors. By using somewhat arbitrary criteria for selecting interactions (e.g., interactions with baits that had <30 interactors, and inclusion of all Cdk1 and Cdk2 interactors) we effectively included some low confidence interactions. This enabled us to test whether high or low confidence interactions are more useful in the network-guided RNAi screen (Additional File 6). The set of non-interactors was randomly selected from genes in DroID that were in the overall protein interaction map in DroID, but that did not connect with any of the baits or their direct interactors. It is interesting to note that protein interaction data for organisms other than Drosophila performed as well as, or in some cases better than, protein interaction data that is available for Drosophila proteins (Additional File 6 and Additional File 9). Any screening approach that takes advantage of protein interaction data should therefore consider not just interactions available for a particular organism, but all available interaction data.

Templates available in the Drosophila RNAi Library Release 1.0 and 2.0 (Open Biosystems) were used to generate dsRNA 3 65 . To determine the gene that was targeted by each template in the library we performed BLAST analysis of the predicted amplicon sequences that were provided by the distributor. In some cases, there was no amplicon information provided. For these positions in the library, we obtained amplicon information from the library developer in order to determine the targeted gene 3 65 . Templates were PCR amplified using the library universal primer (5' TAA TAC GAC TCA CTA TAG GGA GAC CAC GGG CGG GT 3') to generate fresh template. 1.5 μl of fresh template was used in in vitro transcription (IVT) reactions to generate dsRNAs targeting baits, interactors, and non-interactors. Control templates targeting the GFP gene were generated by amplification of GFP template DNA using a T7-containing primer pair (5'-GAA TTA ATA CGA CTC ACT ATA GGG AGA TGC CAT CTT CCT TGA AGT CA-3', and 5'-GAA TTA ATA CGA CTC ACT ATA GGG AGA TGA TGT TAA CGG CCA CAA GTT-3'). Templates were arrayed into 96-well PCR plates with 1 to 3 GFP templates/plate and 6 μl IVT reactions were performed using either MegaScript (Ambion) or Ampliscribe (Epicentre Biotechnologies) T7 kits according to the manufacturer's instructions. dsRNA was purified from IVT reactions using MultiScreen_HTS-96-well filter plates (Millipore) in conjunction with a Biomek NX MC Laboratory Automation Workstation. 95 μl of nuclease-free water was added to each IVT reaction before transfer onto the filter plate. Reactions were passed through the filter plate by application of vacuum pressure for 35 minutes. dsRNA was eluted from the filter into 110 μl of nuclease-free water by shaking at 1100 rpm for 10 minutes. Purified dsRNAs were analyzed by spectrophotometry and stored at -80°C.

Drosophila S2R+ cells were obtained from the Drosophila Genomics Resource Center (Indiana University) and cultured in Schneider's media (Invitrogen) supplemented with 10% Fetal Bovine Serum and 0.1 mg/ml Gentamicin (GIBCO). For induction of RNAi, cells were re-suspended in Schneider's media at a density of 4 × 10⁵ cells/ml and 75 μl of cell suspension was added per well to duplicate 96-well cell culture plates that had been spotted with 5 μl of dsRNA per well. Cells were incubated for 1.5 hours before addition of 150 μl of Schneider's media containing 10% fetal bovine serum and 0.1 mg/ml gentamicin. After a 6-day incubation period, media was removed from cells and cellular DNA was stained by adding Schneider's media containing 2 μl/ml Vybrant DyeCycle Orange stain (Invitrogen). Cells were dislodged from the growth surface by pipetting and incubated at room temperature in the dark for 1 hour. Cell cycle profiles were obtained by analyzing cells on a FACSArray flow cytometer (BD Biosciences). The percentage of cells with G1, G2/M, greater than G2/M and subG1 DNA content was determined using the BD FACSArray system software with user-defined gates for each measured parameter.

In order to normalize the data across plates, a global average (g) for each measured parameter was calculated using all non-GFP well values and a plate average (p) was calculated for each measured parameter using all non-GFP well values for each plate. Raw data on each plate was normalized by multiplying the raw data by the factor g/p. Normalized averages for each dsRNA were determined by calculating the average of the normalized duplicate values. A mean of the normalized values from wells treated with GFP dsRNA was calculated. This mean was used to calculate standard deviations from the mean of normalized values for each non-GFP dsRNA. A dsRNA was considered a hit (i.e., affecting cell cycle progression), if the percentage of cells in a phase was >3 standard deviations from the mean for that phase. Gene Ontology analysis of hits was done using the GO term enrichment tool DAVID 67 and data in the GO database release 2010-10-30 62 .

For 258 of the genes that scored as hits in the primary screen, we generated templates for preparing a minimally overlapping (0-11nt) validation dsRNA. Amplicon sequences from the Open Biosystems library were compared with amplicon sequences from the Drosophila RNAi Screening Center (DRSC) RNAi library 25 . If amplicons showed overlap of <12 nucleotides, primer pairs for generating the DRSC amplicon were ordered. If no DRSC amplicon with <12 nucleotide overlap was available, primer pairs for generating a minimally overlapping amplicon were manually designed. Templates for generating dsRNA were prepared using the DRSC or manually designed primer pairs and a 2-step PCR approach. The first step PCR reaction was performed with primers containing a GC rich anchor 5'GGGCGGGT3' and the products of this reaction were amplified in the second step PCR reaction using the T7-containing universal primer (above). dsRNA generation, cell treatment, data acquisition and analysis were all performed as described above for the primary screen.

Drosophila S2R+ cells were re-suspended in Schneider's Media at a density of 4 × 10⁵ cells/ml and 75 μl of cell suspension was added to 96-well cell culture plates that had been spotted in triplicate with 5 μl of dsRNA targeting individual zomes subunits and 5 μl of dsRNA targeting GFP or Dacapo. Cellular DNA was stained and flow cytometry was performed as described above.

Drosophila S2R+ cells were re-suspended in Schneider's media at a density of 6 × 10⁶ cells/ml and 1.5 ml of cell suspension was added to 25 cm² cell culture flasks. 20 μg of dsRNA was added to cells and they were incubated at 25°C for 1.5 hours before addition of 3 ml Schneider's media containing 10% FBS and 0.1 mg/ml gentamicin. Following a 6-day incubation, cells were resuspended in 1× RIPA lysis buffer (Cell Signaling) and placed on ice for 30 minutes. Cells were further disrupted by 10 needle strokes through a 21-gauge needle. Clarified cell lysates were collected, run on SDS-PAGE, then immunoblotted using anti-Cyclin E antibody 1:1000 (Santa Cruz SC-33748), anti-beta-tubulin 1:5000 (E7 antibody) or anti-alpha-tubulin 1:10000 (Sigma B-5-1-2).

Drosophila S2R+ cells were re-suspended in Schneider's media at a density of 2.3 × 10⁶ cells/ml and 1.5 ml of cell suspension was added to 25 cm² cell culture flasks. 20 μg of dsRNA was added to cells and they were incubated at 25°C for 1.5 hours before addition of 3 ml Schneider's media containing 10% FBS and 0.1 mg/ml gentamicin. Following a 6-day incubation, cells were dislodged from the growth surface and resuspended in lysis buffer (0.2% NP-40, 200 mM Tris-HCl, 200 mM NaCl, 100 mM Na₂EDTA, 20 mM EGTA, 200 mM NaF, Na₃VO₄, 100 mM glycerol phosphate, 40 mM PMSF, 1× Protease Inhibitor Cocktail 123 ) and incubated on ice for 30 minutes. Clarified lysates were collected and stored at -80°C. For immunoprecipitation, 10 μl of anti-Cyclin E antibody (Santa Cruz sc-33748) was added to 400 μg of protein extract and incubated 30 minutes at room temperature with agitation. 20 μl of Protein A-agarose (Santa Cruz sc-2001) was added to immunoprecipitations and tubes were placed at 4°C with agitation overnight. Immunoprecipitations were pelleted and washed twice with kinase assay buffer (10 mM MgCl₂, 10 mM DTT, 50 mM Hepes pH7.5). After the second wash, pellets were resuspended in 13 μl kinase assay buffer. To initiate the kinase assay reaction, 0.2 μg of histone H1 in kinase assay buffer and 5 μl of [γ³²P]-ATP (1.0 μCi/μl) was added. Kinase reactions were incubated 30 minutes at room temperature before being terminated by the addition of gel loading buffer.