Download Supplementary figure legends

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); p38MAPK, 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, CK13,27,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, p38MAPK pathway. Inset: Western blot
analysis of p38. Note that dsRNAs targeting MEKK1/4 and MEK3 results in slower
migration of p38. Arrows with dotted line indicate potential MAPk-Ak2 targets (Tables
S3, S12; Refs. 32,33). g, Regulators of Cdk1 activity. Color code for all panels: Red,
increase in G1 or decrease in G2; Green, decrease in G1 or increase in G2; Blue,
decreased cell size. Light color indicates a weaker phenotype. An asterisk indicates
functions that are carried out by two or more genes in Drosophila.
REFERENCES
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
Neufeld, T. P., de la Cruz, A. F., Johnston, L. A. & Edgar, B. A. Coordination
of growth and cell division in the Drosophila wing. Cell 93, 1183-93 (1998).
Reis, T. & Edgar, B. A. Negative regulation of dE2F1 by cyclin-dependent
kinases controls cell cycle timing. Cell 117, 253-64 (2004).
Lum, L. et al. Identification of Hedgehog pathway components by RNAi in
Drosophila cultured cells. Science 299, 2039-45 (2003).
Yun, B., Lee, K., Farkas, R., Hitte, C. & Rabinow, L. The LAMMER protein
kinase encoded by the Doa locus of Drosophila is required in both somatic
and germline cells and is expressed as both nuclear and cytoplasmic isoforms
throughout development. Genetics 156, 749-61 (2000).
Lapidot-Lifson, Y. et al. Cloning and antisense oligodeoxynucleotide
inhibition of a human homolog of cdc2 required in hematopoiesis. Proc Natl
Acad Sci U S A 89, 579-83 (1992).
Su, T. T. & Stumpff, J. Promiscuity rules? The dispensability of cyclin E and
Cdk2. Sci STKE 2004, pe11 (2004).
Cross, F. R. Two redundant oscillatory mechanisms in the yeast cell cycle.
Dev Cell 4, 741-52 (2003).
Hurles, M. Gene duplication: the genomic trade in spare parts. PLoS Biol 2,
E206 (2004).
Chenna, R. et al. Multiple sequence alignment with the Clustal series of
programs. Nucleic Acids Res 31, 3497-500 (2003).
Morrison, D. K., Murakami, M. S. & Cleghon, V. Protein kinases and
phosphatases in the Drosophila genome. J Cell Biol 150, F57-62 (2000).
Manning, G., Whyte, D. B., Martinez, R., Hunter, T. & Sudarsanam, S. The
protein kinase complement of the human genome. Science 298, 1912-34
(2002).
Bettencourt-Dias, M. et al. Genome-wide survey of protein kinases required
for cell cycle progression. Nature 432, 980-7 (2004).
Sveiczer, A., Novak, B. & Mitchison, J. M. Size control in growing yeast and
mammalian cells. Theor Biol Med Model 1, 12 (2004).
Conlon, I. & Raff, M. Differences in the way a mammalian cell and yeast
cells coordinate cell growth and cell-cycle progression. J Biol 2, 7 (2003).
Goyal, L. Cell death inhibition: keeping caspases in check. Cell 104, 805-8
(2001).
Sampath, S. C. et al. The chromosomal passenger complex is required for
chromatin-induced microtubule stabilization and spindle assembly. Cell 118,
187-202 (2004).
Lin, X., Buff, E. M., Perrimon, N. & Michelson, A. M. Heparan sulfate
proteoglycans are essential for FGF receptor signaling during Drosophila
embryonic development. Development 126, 3715-23 (1999).
Mizuguchi, S. et al. Chondroitin proteoglycans are involved in cell division of
Caenorhabditis elegans. Nature 423, 443-8 (2003).
Giot, L. et al. A protein interaction map of Drosophila melanogaster. Science
302, 1727-36 (2003).
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
Christensen, T. W. & Tye, B. K. Drosophila MCM10 interacts with members
of the prereplication complex and is required for proper chromosome
condensation. Mol Biol Cell 14, 2206-15 (2003).
Huang, J. Y. & Raff, J. W. The dynamic localisation of the Drosophila
APC/C: evidence for the existence of multiple complexes that perform
distinct functions and are differentially localised. J Cell Sci 115, 2847-56
(2002).
Dej, K. J., Ahn, C. & Orr-Weaver, T. L. Mutations in the Drosophila
condensin subunit dCAP-G: defining the role of condensin for chromosome
condensation in mitosis and gene expression in interphase. Genetics 168, 895906 (2004).
Legge, G. B. et al. ZZ domain of CBP: an unusual zinc finger fold in a protein
interaction module. J Mol Biol 343, 1081-93 (2004).
Wang, Z., Jiao, X., Carr-Schmid, A. & Kiledjian, M. The hDcp2 protein is a
mammalian mRNA decapping enzyme. Proc Natl Acad Sci U S A 99, 12663-8
(2002).
Arbeitman, M. N. et al. Gene expression during the life cycle of Drosophila
melanogaster. Science 297, 2270-5 (2002).
Cope, G. A. & Deshaies, R. J. COP9 signalosome: a multifunctional regulator
of SCF and other cullin-based ubiquitin ligases. Cell 114, 663-71 (2003).
Yanagawa, S. et al. Casein kinase I phosphorylates the Armadillo protein and
induces its degradation in Drosophila. Embo J 21, 1733-42 (2002).
Bauer, A. et al. Pontin52 and reptin52 function as antagonistic regulato rs of
beta-catenin signalling activity. Embo J 19, 6121-30 (2000).
Schweizer, L., Nellen, D. & Basler, K. Requirement for Pangolin/dTCF in
Drosophila Wingless signaling. Proc Natl Acad Sci U S A 100, 5846-51
(2003).
Gong, Y., Mo, C. & Fraser, S. E. Planar cell polarity signalling controls cell
division orientation during zebrafish gastrulation. Nature 430, 689-93 (2004).
Wood, M. A., McMahon, S. B. & Cole, M. D. An ATPase/helicase complex is
an essential cofactor for oncogenic transformation by c-Myc. Mol Cell 5, 32130 (2000).
Manke, I. A. et al. MAPKAP Kinase-2 Is a Cell Cycle Checkpoint Kinase that
Regulates the G(2)/M Transition and S Phase Progression in Response to UV
Irradiation. Mol Cell 17, 37-48 (2005).
Powell, D. W. et al. Proteomic identification of 14-3-3zeta as a mitogenactivated protein kinase-activated protein kinase 2 substrate: role in dimer
formation and ligand binding. Mol Cell Biol 23, 5376-87 (2003).