, 1043 (2010);
et al.Ashton Breitkreutz,
Interaction Network in Yeast
A Global Protein Kinase and Phosphatase
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at pH 5.0. This complex is likely to interact with
the cytoplasmic region of basal body of the se-
cretion apparatus and to respond to an uniden-
tified pH sensor. The sensor is unlikely to be part
of the translocon because the translocon dele-
tion mutant displayed wild-type levels of ef-
fector secretion upon pH upshift (fig. S7). The
sensor might bethe needle subunititself,which
has been implicated in signaling the trans-
locator to effector switch in Shigella (11) and
Yop secretion by Yersinia (12). Another possi-
bility is that translocon pore assembly changes
the pH gradient within the needle channel and
that the sensor is located toward the base of the
secretion apparatus. Changes in pH from mildly
acidic to neutral can have dramatic effects on pro-
tein folding; for example, some bacterial toxins
refold after their translocation from acidic endo-
somes to the host-cell cytosol in a partially un-
folded state (13). The SPI-2 T3SS pH sensor
might thus undergo a conformational change on
exposure to neutral pH and transduce a dissocia-
tion signal to the SsaL/SsaM/SpiC complex.
References and Notes
1. J. E. Galán, H. Wolf-Watz, Nature 444, 567 (2006).
2. C. Rappl, J. Deiwick, M. Hensel, FEMS Microbiol. Lett.
226, 363 (2003).
3. D. Chakravortty, M. Rohde, L. Jäger, J. Deiwick,
M. Hensel, EMBO J. 24, 2043 (2005).
4. X.-J. Yu, M. Liu, D. W. Holden, Mol. Microbiol. 54, 604
5. M. J. Pallen, S. A. Beatson, C. M. Bailey, BMC Microbiol.
5, 9 (2005).
6. B. K. Coombes, N. F. Brown, Y. Valdez, J. H. Brumell,
B. B. Finlay, J. Biol. Chem. 279, 49804 (2004).
7. Materials and methods are available as supporting
material on Science Online.
8. F. D. Schubot et al., J. Mol. Biol. 346, 1147 (2005).
9. D. Drecktrah, L. A. Knodler, D. Howe, O. Steele-Mortimer,
Traffic 8, 212 (2007).
10. C. R. Beuzón, G. Banks, J. Deiwick, M. Hensel,
D. W. Holden, Mol. Microbiol. 33, 806 (1999).
11. R. Kenjale et al., J. Biol. Chem. 280, 42929 (2005).
12. J. Torruellas, M. W. Jackson, J. W. Pennock, G. V. Plano,
Mol. Microbiol. 57, 1719 (2005).
13. J. A. Young, R. J. Collier, Annu. Rev. Biochem. 76, 243
14. We thank J. Mota, C. Tang, and members of the
Holden laboratory for comments on the manuscript.
This research was supported by grants G0800148
and 074553/Z/04/Z to D.W.H. from the Medical Research
Council and Wellcome Trust.
Supporting Online Material
Materials and Methods
Figs. S1 to S8
2 March 2010; accepted 25 March 2010
Published online 15 April 2010;
Include this information when citing this paper.
A Global Protein Kinase
and Phosphatase Interaction
Network in Yeast
Ashton Breitkreutz,1* Hyungwon Choi,2* Jeffrey R. Sharom,1,3* Lorrie Boucher,1* Victor Neduva,4*
Brett Larsen,1Zhen-Yuan Lin,1Bobby-Joe Breitkreutz,1Chris Stark,1Guomin Liu,1Jessica Ahn,1
Danielle Dewar-Darch,1Teresa Reguly,1Xiaojing Tang,1Ricardo Almeida,4Zhaohui Steve Qin,5
Tony Pawson,1,3Anne-Claude Gingras,1,3† Alexey I. Nesvizhskii,2,6† Mike Tyers1,3,4†
The interactions of protein kinases and phosphatases with their regulatory subunits and
substrates underpin cellular regulation. We identified a kinase and phosphatase interaction
(KPI) network of 1844 interactions in budding yeast by mass spectrometric analysis of protein
complexes. The KPI network contained many dense local regions of interactions that suggested new
functions. Notably, the cell cycle phosphatase Cdc14 associated with multiple kinases that revealed
roles for Cdc14 in mitogen-activated protein kinase signaling, the DNA damage response, and
metabolism, whereas interactions of the target of rapamycin complex 1 (TORC1) uncovered new
effector kinases in nitrogen and carbon metabolism. An extensive backbone of kinase-kinase
interactions cross-connects the proteome and may serve to coordinate diverse cellular responses.
ulation of protein interactions, enzyme activity,
or protein localization (1). However, the protein
interactions of kinases, phosphatases, and their
regulatory subunits and substrates remain sparse-
rotein phosphorylation mediates cellular re-
sponses to growth factors, environmental
signals, and internal processes by the reg-
ly mapped, particularly in high-throughput (HTP)
datasets [fig. S1 (2)]. To chart the budding yeast
kinase and phosphatase interaction (KPI) net-
work, we systematically characterized protein
kinase and phosphatase complexes by rapid
magnetic bead capture, on-bead protein digestion,
and mass spectrometric identification of asso-
ciated proteins, using different epitope tags and
expression systems [fig. S2; (2)]. One hundred
thirty protein kinases, 24 lipid and metabolic
kinases, 47 kinase regulatory subunits, 38 protein
phosphatases, 32 phosphatase regulatory sub-
units, and 5 metabolic phosphatases were ana-
lyzed (tables S1 and S2).
We eliminated nonspecific interactions using a
statistical model called Significance Analysis of
Interactome (SAINT). In contrast to simple thresh-
old models, SAINTassigns the number of peptide
identifications for each interactor to a probability
distribution, which is then used to estimate the
likelihood of a true interaction (2). We validated
SAINT on multiple independent purifications
for several kinases and expression levels (fig.
S3 and tables S3 to S5). A final KPI dataset of
1844 interactions between 887 protein partners
was generated from more than 38,000 unfiltered
identifications at a stringent SAINT threshold of
P > 0.85 (fig. S4 and tables S1 and S2). High-
confidence interactions were recovered for 120
protein kinases (fig. S5; see fig. S6 and table S6
for validation). For a number of kinases, we dem-
onstrated that associated proteins were substrates
in vitro (figs. S7 and S8 and table S7). Our dataset
doubled the number of KPIs obtained in previous
low-throughput (LTP) studies and performed as
well as LTP data against an unbiased HTP high-
confidence (HTP-HC) benchmark dataset [fig. S1
(2)]. Clustering of all kinases and phosphatases by
their interaction profiles revealed locally dense re-
gions in the KPI network (Fig. 1A and fig. S9).
The Cdc14 phosphatase formed one of the
largest single hubs in the network with 53 inter-
action partners, including 23 kinases and 5 phos-
phatases (Fig. 1B, fig. S6, and table S6). Cdc14
antagonizes mitotic cyclin-dependent kinase (CDK)
activity and is activated by the mitotic exit net-
work (MEN) upon completion of anaphase (3).
Many Cdc14 interactors were shared with its
anchor protein Net1 and the nicotinamide ade-
nine dinucleotide (NAD+)–dependent histone de-
acetylase Sir2 that together with Cdc14 form the
nucleolar RENT complex (4). New connections
between Cdc14 and other mitotic regulators
included the CDK-inhibitory kinase Swe1, the
cytokinesis checkpoint protein Boi1 (5), and two
activators of cytokinesis, Cbk1 and Ace2 (6).
Cdc14, Net1, and Sir2 each interacted with the
DNA damage checkpoint kinases Chk1 and Dun1.
In support of a role for Cdc14 in the DNA dam-
age response, we found that ectopic expression
of Cdc14 caused sensitivity to the DNA-damaging
agent methylmethane sulfonate (MMS), while
a strain defective for Cdc14 function was sen-
sitive to the ribonucleotide reductase inhibitor
hydroxyurea (Fig. 1C). Interactions between the
1Centre for Systems Biology, Samuel Lunenfeld Research In-
stitute, 600 University Avenue, Toronto, Ontario, M5G 1X5,
Canada.2Department of Pathology, University of Michigan,
Ann Arbor, MI 48109, USA.
Ontario, M5S 1A8, Canada.4Wellcome Trust Centre for Cell
Biology and School of Biological Sciences, University of
Edinburgh, Mayfield Road, Edinburgh, EH9 3JR Scotland, UK.
5Department of Biostatistics, University of Michigan, Ann Arbor,
MI 48109, USA.6Center for Computational Medicine and Bio-
informatics, University of Michigan, Ann Arbor, MI 48109, USA.
*These authors contributed equally to this work.
†To whom correspondence should be addressed. E-mail:
email@example.com (A.C.G.), firstname.lastname@example.org (A.I.N.),
email@example.com, firstname.lastname@example.org (M.T.)
3Department of Molecular
VOL 328 21 MAY 2010
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RENT and the nutrient-sensing TOR complex 1
(TORC1) were supported by the finding that in-
creased Cdc14 activity caused rapamycin sensi-
tivity, whereas reduced Cdc14 function caused
rapamycin resistance (Fig. 1D), suggesting that
Cdc14 may antagonize TOR signaling. Cdc14
also interacted with the energy-sensing adeno-
sine 5´-monophosphate (AMP)–activated ki-
and is an upstream inhibitor of TOR activity in
metazoans (7). Deregulation of Cdc14 caused a
severe defect in growth on glycerol medium and
sensitivity to 2-deoxyglucose (Fig. 1D).
Cdc14 exhibited connections with three dif-
ferent mitogen-activated protein kinase (MAPK)
modules. Interaction of the pheromone MAPK
pathway kinases Fus3 and Ste7 with Cdc14 was
supported by the finding that constitutive expres-
sion of Cdc14 caused partial pheromone resistance
(fig. S10). Cdc14 interacted with the high osmo-
larity glycerol (HOG) pathway MAPK kinase Pbs2;
consistently, constitutive expression of Cdc14
caused sensitivity to osmotic stress (Fig. 1E). The
HOG pathway is also known to stimulate mitotic
exit (8). The upstream cell wall integrity (CWI)
MAPK kinase Bck1 interacted with Cdc14; a
cdc14-3 strain was sensitive to the cell wall stress
agent calcofluor white (Fig. 1E). These CWI in-
teractions extended along each pathway because
the conditional MEN alleles mob1-77 and cdc15-2
exhibited specific synthetic lethal interactions with
either slt2D or bck1D mutations; this lethality was
alleviated by growth on iso-osmotic medium but
not by a catalytically inactive mutant of Slt2 (Fig.
1F and fig. S10). These data reveal Cdc14 as a
nexus for cell cycle, checkpoint, metabolic, and
stress signals (fig. S10).
The TORC1 and TORC2 kinase complexes
are conserved from yeast to human and control
macromolecular synthesis and polarized morpho-
genesis, respectively; TORC1 is sensitive to the
macrolide rapamycin, whereas TORC2 is not (9).
In the KPI dataset, TORC1 and TORC2 formed
a highly connected subnetwork of 28 interaction
partners, including 13 kinases and 4 phosphatases
(figs. S6 and S11 and table S6). These connec-
tions established new links between TORC1 and
the mitochondrial retrograde (RTG) signaling
pathway (10), which induces genes required for
glutamate production (fig. S12). Multiple TORC1
subunits exhibited previously undocumented in-
teractions with the kinases Fmp48, Nnk1, Npr1,
and Ksp1 (Fig. 2A and fig. S11).
Fmp48 is a kinase of unknown function that is
associated with mitochondrial subcellular frac-
tions (11). Consistent with interactions among
Fig. 1. Cdc14 phosphatase network. (A) Hierarchical two-dimensional clustering
of bait interaction profiles in the KPI dataset. See fig. S9 for full clustergram.
kinase-associated proteins in yellow, and other proteins in gray. Red connecting
lines indicate KPI interactions, gray lines LTP interactions, and gray dashed lines
indicates RENT complex and known associated proteins.RAM,regulationof Ace2p
activity and cellular morphogenesis. (C) Sensitivity of a GAL1-CDC14 strain to
0.03% methyl methanesulfonate (MMS) when induced by 0.02% galactose (see
at 33°C. (D) Sensitivity of a GAL1-CDC14 strain to either rapamycin (5 ng/ml) or
to rapamycin (20 ng/ml) and sensitivity to 2-deoxyglucose (DG, 100 mg/ml) at
33°C. (E) Sensitivity of a GAL1-CDC14 strain to 1 M sorbitol when induced by
ml) at 33°C. (F) Representative tetrads bearing combinations of slt2D, bck1D,
pbs2D and tor1D served as negative controls.
21 MAY 2010VOL 328
on May 20, 2010
Fmp48, TORC1, and the RTG inhibitor Mks1,
elevated expression of FMP48 caused a growth
defect on nonfermentable glycerol medium and
rapamycin resistance on a fermentable carbon
source (Fig.2B). Overexpression of FMP48 caused
abnormal mitochondrial morphology (Fig. 2C)
and repression of genes encoding tricarboxylic acid
cycle enzymes, electron transport chain compo-
activity was specifically increased by rapamycin
treatment (Fig.2E), suggesting that Fmp48 relays
TORC1 signals to the RTG pathway and mito-
The uncharacterized kinase Ykl171w, renamed
Nnk1 for nitrogen network kinase, associated
with all TORC1 subunits (fig. S11) and with
Gdh2, the NAD+-dependent glutamate dehydro-
genase that catalyzes deamination of glutamate
to a-ketoglutarate and ammonia (12). Gdh2 was
phosphorylated by Nnk1 complexes in vitro (Fig.
2F), and a gdh2D strain was resistant to rapamycin
when grown on glutamate as the sole nitrogen
source (Fig. 2G), whereas overexpression of
NNK1 conferred hypersensitivity to rapamycin
(Fig. 2H). Nnk1 also interacted with the TORC1
effector Ure2, which regulates the nitrogen catab-
olite response by sequestering the transcription
factor Gln3 in the cytoplasm (12). Overexpres-
sion of NNK1 induced rapid nuclear accumula-
tion of Gln3 (Fig. 2I) and increased transcription
of Gln3 target genes (Fig. 2J), suggesting that
Nnk1 activity antagonizes the Ure2-Gln3 inter-
Fig. 2. TORC1 kinase network. (A) Partial network of new TORC1-associated
rapamycin resistance. (C) Overexpression of GAL1-FMP48 causes abnormal
mitochondrial morphology as visualized with an Ilv3GFPmitochondrial matrix
fusion protein (GFP, green fluorescent protein). DIC, differential interference
contrast. (D) Genome-wide expression profiles of GAL1-FMP48 and GAL1-MKS1
stress-responsive (green), and Gln3/Gcn4-responsive (blue) genes are marked. (E)
Fmp48FLAGor Sch9FLAGcomplexes were immunopurified from cells grown in the
presence or absence of rapamycin (200 ng/ml) for 30 min, then incubated with
[33P]-g-ATP, and radiolabeled species were resolved by SDS–polyacrylamide gel
(F) Immunopurified Nnk1FLAGcomplexes were incubated with [33P]-g-ATP, then
denatured, and radiolabeled Gdh2 species were repurified with antibody to
hemagglutinin (HA). (G) A gdh2D strain is rapamycin resistant when glutamate is
the sole nitrogen source. (H) Expression of GAL1-NNK1 in 2% galactose confers
1 hour causes nuclear accumulation of Gln3GFP. (J) Expression of GAL1-NNK1 in
0.2% galactose for 1.5 hours specifically induces Gln3 target genes. Color bar
indicates fold increase (red) or decrease (green) relative to empty vector control.
VOL 32821 MAY 2010
on May 20, 2010
action. The expansive TORC1 network also in- Download full-text
cluded other nutrient-sensing kinases (Npr1, Snf1,
Gcn2, and Ksp1; fig. S11) (13), transcription-
kinase (Sky1), and a ribosome biogenesis kinase
of TOR in cell growth.
In a global protein interaction network con-
structed from the KPI, LTP, and HTP-HC data-
sets (2), kinase-kinase (K-K) interactions were
significantly enriched compared to all other ki-
nase interaction partners (P < 3 × 10−6) and col-
lectively formed a highly interconnected K-K
network (Fig. 3A, figs. S13 and S14, and table
S8). Consistent with a trans-kinase phosphoryl-
ation network (14), we assigned 607 phospho-
rylation sites on 98 kinases (fig. S15 and table
S9). This K-K network was extremely robust to
fragmentation by hub deletion (Fig. 3B) and was
far less modular than previous less-complete K-K
networks [fig. S16 (2)]. Within the global net-
work, kinases had a significantly higher centrality
compared to nonkinase nodes [P < 10−16(2)],
suggesting that kinases might unify cellular reg-
ulation. To test this idea, we identified dense clus-
ters of interactions (cliques or complexes) in the
global interaction network, then determined the
extent of clique cross-connection by kinase inter-
actions. More than 80% of the proteome was
interlinked by kinases in this manner (fig. S17), a
significantly larger fraction than in random net-
works [P < 10−8(2)]. The potential for kinases to
co-regulate otherwise separate functions was fur-
ther revealed by the diversity of Gene Ontology
(GO) biological processes associated with kinase
interaction partners (Fig. 3C and fig. S18). The
multifunctionality of kinases, as defined by asso-
ciated GO terms, was markedly increased by the
KPI dataset (Fig. 3D).
Cellular processes are controlled by a multi-
tude of low-affinity interactions, as often mediated
by short linear motifs embedded in disordered pro-
tein regions (15, 16). The KPI network is highly
enriched for disordered regions as compared to
the entire proteome [P < 10−16(2)]. This physical
organization may allow the cell to overcome sto-
chastic limitations in signal propagation, integra-
tion, and downstream responses (16). In human
cells, kinase-mediated signaling can readily propa-
gate across pathways (17) and may dictate complex
decisions through a broadly distributed network
of effectors (18, 19). Moreover, phosphorylation-
based feedback loops often enable cooperative
responses, tuning of network outputs, and entrained
states (20–22). The densely connected and non-
modular architecture of the KPI network suggests
that the interaction of many such circuits will un-
derpin cellular information flow (23).
References and Notes
1. T. Pawson, Curr. Opin. Cell Biol. 19, 112 (2007).
2. Supporting material is available on Science Online.
3. F. Stegmeier, A. Amon, Annu. Rev. Genet. 38, 203 (2004).
4. A. F. Straight et al., Cell 97, 245 (1999).
5. M. Mendoza et al., Nat. Cell Biol. 11, 477 (2009).
6. B. Nelson et al., Mol. Biol. Cell 14, 3782 (2003).
7. D. G. Hardie, Nat. Rev. Mol. Cell Biol. 8, 774 (2007).
8. V. Reiser, K. E. D’Aquino, L. S. Ee, A. Amon, Mol. Biol. Cell
17, 3136 (2006).
9. S. Wullschleger, R. Loewith, M. N. Hall, Cell 124, 471 (2006).
10. Z. Liu, R. A. Butow, Annu. Rev. Genet. 40, 159 (2006).
11. J. Reinders, R. P. Zahedi, N. Pfanner, C. Meisinger,
A. Sickmann, J. Proteome Res. 5, 1543 (2006).
12. B. Magasanik, Proc. Natl. Acad. Sci. U.S.A. 102, 16537
13. A. Huber et al., Genes Dev. 23, 1929 (2009).
14. A. Chi et al., Proc. Natl. Acad. Sci. U.S.A. 104, 2193
15. V. Neduva et al., PLoS Biol. 3, e405 (2005).
16. T. J. Gibson, Trends Biochem. Sci. 34, 471 (2009).
17. W. M. Old et al., Mol. Cell 34, 115 (2009).
18. H. Daub et al., Mol. Cell 31, 438 (2008).
19. A. von Kriegsheim et al., Nat. Cell Biol. 11, 1458
20. J. E. Ferrell Jr., Curr. Opin. Cell Biol. 14, 140 (2002).
21. F. Li, T. Long, Y. Lu, Q. Ouyang, C. Tang, Proc. Natl.
Acad. Sci. U.S.A. 101, 4781 (2004).
22. Q. A. Justman, Z. Serber, J. E. Ferrell Jr., H. El-Samad,
K. M. Shokat, Science 324, 509 (2009).
23. P. Nurse, Nature 454, 424 (2008).
24. We thank B. Raught, A. Amon, L. Harrington, J. Bader,
M. Costanzo, B. Andrews, C. Boone, R. Aebersold,
B. Bodenmiller, I. Sadowski, and F. Sicheri for discussions;
J. P. Zhang and D. Fermin for technical support; and
M. Snyder, Y. Ohsumi, S. Piatti, S. Hahn, H. Reizman,
S. Biggins, T. Petes, M. P. Longhese, and D. Mao for
reagents. Supported by grants from the Canadian Institutes
of Health Research to A.C.G. (MOP-84314), T.P. (MOP-
57793), and M.T. (MOP-12246); the Ontario Research Fund
to T.P. and A.C.G. (REO#-044); the National Institutes of
Health to M.T. (R01RR024031 from the National Center for
Research Resources) and A.I.N. (CA-126239); a Terry Fox
Foundation Research Studentship from the National Cancer
Institute of Canada to J.R.S.; Federation of European
Biochemical Societies and Marie Curie Fellowships to V.N.;
Canada Research Chairs in Functional Genomics and
Bioinformatics (to M.T.) and in Functional Proteomics
(to A.C.G.); the Lea Reichmann Chair in Cancer Proteomics
to A.C.G.; and a Scottish Universities Life Sciences
Alliance Research Professorship and a Royal Society
Wolfson Research Merit Award to M.T.
Supporting Online Material
Materials and Methods
Figs. S1 to S27
Tables S1 to S15
19 May 2009; accepted 7 April 2010
characteristic path length
main component size
fraction of deleted nodes
LTP+HTP−HC KPI Combined
0.000.05 0.10 0.150.200.25
nucleic acid metabolism
Fig. 3. A kinase-kinase (K-K) network connects the proteome. (A) Combined
K-K interaction network derived from the KPI, LTP, and HTP-HC datasets.
Interactions from known kinase regulatory subunits and paralogs were col-
lapsed into single nodes (table S8). The reduced network contains 156
interactions between 75 kinases, 66 of which contain documented phos-
phorylation sites (table S9). Colors indicate fraction of GO Super-Slim bio-
logical processes assigned by interaction partners of each kinase (2). (B)
Nodes in the combined K-K network were deleted in decreasing degree order.
Characteristic path length and largest residual connected component were
normalized to initial values. K-K networks derived from KPI and LTP+HTP-HC
datasets were used as controls. (C) Clustering of GO Slim biological processes
associated with kinase interaction partners. Full clustergram is shown in fig.
S17. (D) Multifunctionality of kinase associations. Ratio indicates number of
GO Slim biological processes per kinase normalized to all processes (2).
21 MAY 2010VOL 328
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