journal of proteome research 2010 breitkopf

8/8/2019 Journal of Proteome Research 2010 Breitkopf

1/39

Subscriber access provided by KU LEUVEN - BIOMEDICAL LIB

Journal of Proteome Research is published by the American Chemical Society. 1155Sixteenth Street N.W., Washington, DC 20036Published by American Chemical Society. Copyright American Chemical Society.

However, no copyright claim is made to original U.S. Government works, or worksproduced by employees of any Commonwealth realm Crown government in the courseof their duties.

Article

Proteomics Analysis of Cellular Imatinib Targets

and their Candidate Downstream EffectorsSusanne B Breitkopf, Felix S Oppermann, Gyrgy Kri, Markus Grammel, and Henrik Daub

J. Proteome Res., Just Accepted Manuscript DOI: 10.1021/pr1008527 Publication Date (Web): 27 September 2010

Downloaded from http://pubs.acs.org on October 8, 2010

Just Accepted

Just Accepted manuscripts have been peer-reviewed and accepted for publication. They are postedonline prior to technical editing, formatting for publication and author proofing. The American Chemical

Society provides Just Accepted as a free service to the research community to expedite thedissemination of scientific material as soon as possible after acceptance. Just Accepted manuscriptsappear in full in PDF format accompanied by an HTML abstract. Just Accepted manuscripts have been

fully peer reviewed, but should not be considered the official version of record. They are accessible to allreaders and citable by the Digital Object Identifier (DOI). Just Accepted is an optional service offered

to authors. Therefore, the Just Accepted Web site may not include all articles that will be publishedin the journal. After a manuscript is technically edited and formatted, it will be removed from the Just

Accepted Web site and published as an ASAP article. Note that technical editing may introduce minorchanges to the manuscript text and/or graphics which could affect content, and all legal disclaimers

and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errorsor consequences arising from the use of information contained in these Just Accepted manuscripts.


2/39

1

Proteomics Analysis of Cellular Imatinib Targets and their Candidate

Downstream Effectors

Susanne B. Breitkopf1,,*, Felix S. Oppermann1,,*, Gyrgy Kri2,3,

Markus Grammel1, & Henrik Daub1,4,#

1Department of Molecular Biology, Max Planck Institute of Biochemistry, Am Klopferspitz 18,

82152 Martinsried, Germany. 2Vichem Chemie Ltd., Herman Ott u. 15., Budapest, 1022,

Hungary. 3Pathobiochemistry Research Group of the Hungarian Academy of Science,

Semmelweis University, Puskin u. 9., Budapest, 1088, Hungary. 4Kinaxo Biotechnologies

GmbH, Am Klopferspitz 19, 82152 Martinsried, Germany.

Present address: Beth Israel Deaconess Medical Center, Harvard Medical School, 3 Blackfan

Circle, Boston, MA 02115

Present address: Kinaxo Biotechnologies GmbH, Am Klopferspitz 19, 82152 Martinsried,

Germany.

Present address: Laboratory of Chemical Biology and Microbial Pathogenesis, The Rockefeller

University, 1230 York Avenue, New York, NY 10065

*These author contributed equally to this work.

#Correspondence: [email protected]

Running title: Quantitative kinase drug proteomics

Keywords: Kinase, inhibitor, imatinib, CML, SILAC, affinity purification, target

ge 1 of 38

ACS Paragon Plus Environment

Journal of Proteome Research


3/39

2

Summary

Inhibition of de-regulated protein kinases by small molecule drugs has evolved into a major

therapeutic strategy for the treatment of human malignancies. Knowledge about direct cellular

targets of kinase-selective drugs and the identification of druggable downstream mediators of

oncogenic signaling are relevant for both initial therapy selection and the nomination of

alternative targets in case molecular resistance emerges. To address these issues, we

performed a proof-of-concept proteomics study designed to monitor drug effects on the

pharmacologically tractable subproteome isolated by affinity purification with immobilized, non-

selective kinase inhibitors. We applied this strategy to chronic myeloid leukemia cells that

express the transforming Bcr-Abl fusion kinase. We used SILAC to measure how cellular

treatment with the Bcr-Abl inhibitor imatinib affects protein binding to a generic kinase inhibitor

resin and further quantified site-specific phosphorylations on resin-retained proteins. Our

integrated approach indicated additional imatinib target candidates, such as flavine adenine

dinucleotide synthetase, as well as repressed phosphorylation events on downstream effectors

not yet implicated in imatinib-regulated signaling. These included activity-regulating

phosphorylations on the kinases Btk, Fer and focal adhesion kinase which may qualify them as

alternative target candidates in Bcr-Abl-driven oncogenesis. Our approach is rather generic and

may have various applications in kinase drug discovery.

Page 2




4/39

3

Introduction

Protein kinases are critical regulators in human cancer and play major roles in tumour cell

proliferation, migration and survival.1 Aberrant kinase activity has been identified as a major

factor contributing to disease progression in various human malignancies.2 The targeted

inhibition of protein kinases has therefore emerged as a major therapeutic approach and fuelled

the development of various kinase-selective drugs, such as cell-permeable small molecule

inhibitors, with the potential to address currently unmet medical needs in cancer therapy.3, 4

Imatinib (also known as imatinib mesylate, Gleevec, Glivec or STI571) was one of the first small

molecule inhibitors developed for the targeted inactivation of kinases in human cancer. Imatinib

efficiently blocks the activities of several tyrosine kinases including Abl, Kit and the platelet-

derived growth factor receptor and has demonstrated impressing clinical efficacy in human

malignancies such as chronic myeloid leukemia (CML).5 In most cases CML pathogenesis

results from the Philadelphia (Ph) chromosome translocation which generates the causative

BCR-ABL oncogene.6 By selective interference with the de-regulated Bcr-Abl kinase activity

imatinib treatment results in impressive and long-lasting responses in chronic-phase CML

patients.5 However, CML patients in advanced disease states such as the accelerated and blast

crisis phases typically relapse and acquire resistance to imatinib within several months.7 In the

majority of these cases, resistance formation is due to mutations in the kinase domain-encoding

region of the BCR-ABL oncogene, which selectively interfere with imatinib binding without

abrogating the catalytic activity of Abl tyrosine kinase.7-9 Molecular resistance of the targeted

Bcr-Abl oncoprotein in relapsed CML patients demonstrates its continued requirement for

disease progression. Structural data revealed that imatinib selectively interacts with an inactive

conformation of the Abl kinase, which is destabilized by many imatinib resistance-conferring

mutations.10 These mechanistic insights provided a rational basis for the development of

second-generation inhibitors, such as the small molecule drugs bosutinib and dasatinib, which

ge 3 of 38




5/39

4

target the active kinase conformation and thereby overcome imatinib resistance in many Abl

kinase variants.7-9 However, similar to imatinib, these second-generation drugs lack inhibitory

activity against the frequently occurring Thr-315 to Ile mutation in the Abl kinase domain, which

directly interferes with drug binding irrespective of the kinase conformation. Drug development

and clinical efforts are ongoing with the goal to address all possible drug-resistant Abl kinase

mutants.9 Alternative to targeted inhibition of the mutated, causative oncoprotein, therapeutic

intervention might also be directed against essential downstream mediators of Bcr-Abl. The Bcr-

Abl fusion protein possesses constitutive tyrosine kinase activity and assembles multi-protein

complexes that trigger proliferative and anti-apoptotic signaling as well as regulation of the actin

cytoskeleton.11, 12 Previous investigations have mostly been hypothesis-driven and placed

known signal transducing modules in Bcr-Abl signaling, such as the Ras/mitogen-activated

protein kinase (MAPK) cascades, phosphatidylinositol-3 kinase/Akt signaling and the signal

transducer and activator of transcription (STAT) pathway,7 and their concomitant activation is

implicated in the malignant transformation in Bcr-Abl-expressing leukemia cells. In addition to

these well documented pathways, Bcr-Abl might engage additional signal transducers that are

regulated by reversible phosphorylation events and have not been revealed by previous studies.

Recent developments in proteomics, including the availability of rapid, sensitive and highly

accurate hybrid ion trap-orbitrap mass spectrometers, improved phosphopeptide fractionation

procedures and breakthroughs in MS data processing and quantification, make MS-based

phosphoproteomics the method-of-choice for unbiased signal transduction analyses.13-20

Quantitative phosphorylation analyses enabled by stable isotope labeling by amino acids in cell

culture (SILAC) has been used to identify imatinib-induced tyrosine phosphorylation changes in

K562 cells21 and, more recently, for a global survey of phosphoproteome regulation upon

dasatinib treatment of the same cell line.22 The identification of downstream protein kinases with

essential roles in Bcr-Abl signal transmission would be of particular interest, as such knowledge

Page 4




6/39

5

might define alternative, small molecule-tractable targets in case of relapse due to drug-

insensitive Abl kinase mutants. In addition to phosphorylating their cellular substrates, protein

kinases regulate each other by reversible phosphorylation events and, moreover, many protein

kinases undergo autophosphorylation upon cellular activation.23 Thus, comprehensive

monitoring of imatinib-induced phosphorylation changes on protein kinases might allow for the

identification of druggable downstream targets in Bcr-Abl signaling, and thus contribute to the

nomination of new candidates for therapeutic intervention.

To analyze small molecule-tractable protein kinases with high analytical sensitivity we and

others have previously developed affinity chromatography procedures that employ combinations

of immobilized kinase inhibitors for selective kinase pre-fractionation from total cell extracts.24-26

This approach combined with SILAC enabled us to quantify the cell cycle regulation of more

than 200 protein kinases and to detect more than 1,000 distinct phosphorylation events on

these key signaling enzymes.24 A similar kinase enrichment strategy reported by Bantscheff et

al. was used to identify cellular targets of the clinical Bcr-Abl kinase inhibitors imatinib, dasatinib

and bosutinib as well as downstream signaling elements upon treatment of K562 cells with

these drugs.26 To further expand the knowledge obtained in this previous work, in particular to

identify additional kinase candidates downstream of direct imatinib targets, we revisited the

imatinib paradigm in our present study. Using imatinib-treated K562 CML cells as a model

system, we here present proof-of-concept for an integrated proteomics strategy that

quantitatively assesses both direct drug targets and their downstream signal transducers, and

report previously unknown target protein candidates falling in both categories.

ge 5 of 38




7/39

6

Experimental Section

Cell Culture. For all SILAC experiments, the human CML cell line K562 (ATCC, # CCL-243)

was cultured in suspension in RPMI1640 medium (Invitrogen) containing 10% dialyzed fetal

bovine serum (Invitrogen), 1% (10,000 units/ml) penicillin/(10 mg/ml) streptomycin (Invitrogen)

and either 45 mg/l unlabeled L-arginine and 76 mg/l unlabeled L-lysine (Arg 0, Lys0) or equimolar

amounts of L-[U-13C6]arginine and L-[2H4]lysine (Arg

6, Lys4) or L-[U-13C6,15N4] and L-[U-

13C6,15N2]lysine (Arg

10, Lys8) (Cambridge Isotope Laboratories or Sigma). K562 cells were grown

in SILAC medium for five cell doublings on 15 cm dishes and then transferred to spinner flasks

into 500 ml fresh SILAC medium at a cell density of 0.25 x 106 cells/ml. After two further rounds

of cell division, K562 cells were either treated with 1 M or 10 M imatinib mesylate (ACC

Corporation) or control-incubated with DMSO for 90 min before harvesting by centrifugation.

Our cell culture strategy yielded total cell numbers of 5 x 108 per labeling condition. Harvested

K562 cells were washed once with ice-cold PBS, snap-frozen in liquid nitrogen and stored at -

80C until cell lysis.

Cell Lysis and Kinase Enrichment. Kinase inhibitor resins containing the immobilizedcompounds VI16832, purvalanol B, bisindolylmaleimide X, AX14596 and SU6668 were

essentially prepared as described previously,24, 25, 27 with the only differences that 2 volumes of

1.5 mM (instead of previously 0.75 mM)24 VI16832 solution and 2 volumes of 5 mM (instead of

10 mM)25 bisindolylmaleimide X were coupled to 1 volume of aspirated epoxy-activated

Sepharose 6B for immobilization. For each of the two replicate experiments, we prepared a

mixed kinase inhibitor resin containing 0,5 ml of the VI16832 and purvalanol B resins and 0,33ml of the bisindolylmaleimide X, AX14596 and SU6668 resins. Frozen cell pellets from

differentially encoded and treated K562 cell populations were solubilized with 9 ml of lysis buffer

containing 50 mM Hepes-NaOH, pH 7.5, 150 mM NaCl, 0.5% Triton X-100, 1 mM EDTA, 1 mM

Page 6




8/39

7

EGTA, 1mM phenylmethylsulfonyl fluoride, 10 mM NaF, 2.5 mM Na3VO4, 50 ng/ml calyculin A

(Alexis Biochemicals, San Diego, CA), 10 g/ml aprotinin, 10 g/ml leupeptin, and 1%

phosphatase inhibitor mixtures 1 and 2 (Sigma) for 1 h at 4C. Cell debris was removed by

centrifugation (20 min at 13,000 rpm) and by further filtering through 0.22-m mixed esters of

cellulose membranes (Millipore). Protein concentration was measured using the BCA assay

(Pierce). 55 mg of each of the three differentially labeled K562 lysates were adjusted to a final

NaCl concentration of 1 M and a final volume of 10 ml. SILAC-encoded samples were then

subjected to parallel in vitroassociations with 0.7 ml mixed kinase inhibitor resin for 2 h at 4C in

the dark. Beads were then washed three times with 10 ml lysis buffer adjusted to 1 M NaCl and

twice with lysis buffer containing 150 mM NaCl. For elution of bound proteins, mixed kinase

inhibitor beads were repeatedly incubated for 10 min with 1.4 ml elution buffer (20 mM Tris-HCl

pH 7.5, 5 mM DTT, 0.5% SDS) at 50C. Aliquots of the resulting elution fractions were analyzed

by SDS-PAGE and silver staining. Protein-containing elution fractions were pooled and

lyophilized, and then resuspended with water in one tenths of the initial volume prior to protein

precipitation according to the protocol by Wessel & Flgge.28

Sample Preparation for Mass Spectrometry. In each of the two replicate analyses, 25% of

the kinase-enriched fraction was solubilized in 20 mM HEPES buffer (pH 7.5) containing 7 M

urea, 2 M thiourea, 1% n-octylglucoside and then reduced, alkylated and sequentially digested

with the endoprotease Lys-C (Wako) and modified trypsin (sequencing grade, Promega) as

described previously.13 The resulting peptide samples were then separated by strong cation

exchange chromatography on an KTA explorer system into a flow-through and 6 elution

fractions using a 1 ml Resource S column (GE Healthcare) according to a published

protocol.13

ge 7 of 38




9/39

8

The remaining protein pellets were dissolved in 1.5x LDS-Buffer and separated on a 10%

NuPage Bis-Tris gel (Invitrogen) according to the manufacturers instructions. Proteins were

stained using the Collodial Blue staining kit (Invitrogen). In both SILAC experiments, the gel was

cut into 16 slices followed by in-gel digestion with trypsin.29 20% of the resulting peptide

mixtures were mixed with an equal volume of 1% TFA, 5% ACN and then loaded on C 18

StageTips.30 After washing twice with buffer containing 0.5% acetic acid and 0.1% TFA bound

peptides were eluted with buffer containing 0.5% acetic acid and 80% ACN and then

concentrated in a Speed-Vac prior to further analysis.

The larger part of 80% of each fraction from the tryptic in-gel digests were subjected to

phosphopeptide enrichment using titanium dioxide (TiO2) microspheres.31, 32 The TiO2 beads

(GL Science, Tokyo, Japan) were first equilibrated by consecutive incubations with 20 mM

NH4OH in20% acetonitrile (ACN), pH 10.5, washing buffer (0.1%TFA, 50% ACN) and loading

buffer (5 g/liter 2,5-dihydrobenzoicacid in 55% ACN). Trypsin digests from adjacent gel slides

were combined to a total of 8 peptide samples for further phosphopeptide enrichment. Each of

them was adjusted to a final concentration of 30% ACN, 2 M urea and incubated with 5 mg

equilibrated TiO2 beads for 30 min at room temperature on a rotating wheel. Afterwards, beads

were washed once with 100 l of loading buffer, three times with 1.5 ml of washing buffer, and

phosphopeptides wereeluted by incubating twice with 30 l of 20 mM NH4OH in20% ACN, pH

10.5. Elution fractions were combined and passed through a C8 StageTip followed by a 30-l

rinse with 80% ACN, 0.5%acetic acid. After adjusting to a pH of 6, samples were concentrated

to 3 l and mixed with an equal volume of 4% ACN, 0.2% TFA. We further performed

phosphopeptide purifications with TiO2 microspheres from the seven SCX chromatography

fractions of in-solution digested, kinase-enriched samples in each of the two replicate

experiments. Additionally, total peptide extractions with C18 StageTips were done with 20%

aliquots of the SCX fractions in experiment 2.

Page 8




10/39

9

Mass Spectrometry Analysis. MS analyses were done as described previously13, 24. Briefly,

peptide separations were done on 15-cm analytical columns (75-m inner diameter) in-house

packed with 3-m C18 beads (Reprosil-AQ Pur, Dr. Maisch) using a nanoflow high pressure

liquid chromatography system (Agilent Technologies 1100). Peptides were eluted with 2 h

gradients from 5% to 40% ACN in 0.5% acetic acid and directly electrosprayed into a LTQ-

Orbitrap mass spectrometer (Thermo Fisher Scientific) by a nanoelectrospray ion source

(Proxeon Biosystems). The LTQ-Orbitrap was operated in the data-dependent mode to

automatically switch between full scan MS in the orbitrapanalyzer (with resolution r= 60,000 at

m/z400) and the fragmentationof the five most intense multiply-charged peptide ions by either

MS/MS or multi-stageactivation in the LTQ part of the instrument, the latter being triggered upon

neutral losses of 97.97, 48.99, or 32.66 m/z.33 For all full scan measurements in the orbitrap

detector a lock-mass strategy was used for internal calibration as described.34Typical mass

spectrometric conditions were: spray voltage, 2.4 kV; no sheath and auxiliary gas flow; heated

capillary temperature, 150C; normalized collision energy 35% for MSA in LTQ. The ion

selection threshold was 500 counts for MS2. An activation q = 0.25 and activation time of 30 ms

were used.

Peptide Identification, Quantification, and Data Analysis. All MS raw files from both

biological replicate analyses were collectively processed with the MaxQuant software suite

(version 1.0.13.12), which performs peak list generation, SILAC-based quantification, estimation

of false discovery rates, peptide to protein group assembly, and data filtration and presentation

as described.20 Data were searched against a concatenated forward and reversed version of the

human International Protein Index (IPI) database version 3.37 containing 69141 protein entries

and 175 frequently detected contaminants (such as porcine trypsin, human keratins and Lys-C)

using the Mascot search engine (Matrix Science; version 2.2.04). Cysteine

carbamidomethylation was set as a fixed modification and methionine oxidation, protein N-

ge 9 of 38




11/39

10

acetylation, loss of ammonia from N-terminal glutamine as well as phosphorylation of serine,

threonine and tyrosine residues were allowed as variable modifications. Spectra resulting from

isotopically labeled peptides, as revealed by presearch MaxQuant analysis of SILAC partners,

were searched with the fixed modifications Arg6 and Lys4 or Arg10 and Lys8, respectively,

whereas spectra for which a SILAC state could not be assigned before database searching

were searched with Arg6, Arg10, Lys4 and Lys8 as variable modifications. The accepted mass

tolerance was set to 7 p.p.m for precursor ions and to 0.5 Da for fragment ions. The minimum

required peptide length was 6 amino acids and up to three missed cleavage sites and three

isotopically labeled amino acids were permitted. The accepted FDR was 1% for both protein

and peptide identifications, and the cut-off for the posterior error probability (PEP) of peptides

was set to 10%. Phosphorylation site assignments were performed by a modified version of the

PTM scoring algorithm13 implemented in MaxQuant. Phosphorylation site assignments were

classified as class I sites in case of a localization probability of at least 0.75 and a score

difference of at least 5 to the second most likely assignment.

Network Analysis. All IPI identifiers of all quantified protein groups were matched to respective

Ensembl entries using BioMart and then uploaded to the Search Tool for the Retrieval of

Interacting Genes/Proteins (STRING) database (version 8.2).35 We retrieved interactions that

were of at least high confidence (score 0.7) based exclusively on experimental and database

knowledge while excluding all other prediction methods implemented in STRING (such as

textmining and coexpression). The resulting networks were visualized using Cytoscape36.

Additionally, we randomly selected subsets of the IPI database that contained the same number

of entries as present in our experimental data. This was repeated five times to determine the

average numbers of network nodes and edges in random protein selections by STRING

analysis.

Page 10




12/39

11

Data Availability.All raw data files from this study have been up-loaded to the Tranche file-

sharing system (ProteomeCommons.org, hash: 1jgzdG97a2b6CKjEqKrhQr2xSu6uyff7dft5qy

nd/cBqaOTO37Re2Ys7GHbDiOX5J+J8FQjtVQZ799wYWADqviLesoIAAAAAAABHsA==).

Furthermore, annotated phosphopeptide spectra for all identified class I sites have been

deposited (hash: G3KHiKwaE7x1r164vN4p1uMmbNrRDbVozsgmlmLi1qsAnXFPV2mcHTeyJ

JpQIqNtygeQbnZe3WtmmrsknUeyVmXkMh8AAAAAAAfdqQ==).

ge 11 of 38




13/39

12

Results and Discussion

Experimental Strategy. As judged by initial immunoblot analysis of total K562 cell lysates,

maximal repression of cellular tyrosine phosphorylation was evident after 45 min treatment with

10 M imatinib (data not shown). We further reasoned that serine/threonine phosphorylation

events located downstream in imatinib-regulated signaling might exhibit slower

dephosphorylation kinetics than direct tyrosine kinase substrates and therefore opted for 90 min

treatment in subsequent SILAC experiments. We also decided against longer stimulation times

of several hours used in earlier studies12, 26, as such treatment schemes might bear an

increased risk of accumulating secondary changes far away from the initial sites of imatinib

action. To enable sensitive and unbiased detection of imatinib effects on the kinase inhibitor-

tractable sub-proteome, we implemented SILAC for K562 leukemia cells grown in spinner

flasks. We differently encoded three populations of K562 cells by culturing them in medium

containing either normal arginine and lysine (Arg0 and Lys0) or combinations of heavier isotopic

variants of the two amino acids (Arg6 and Lys4, or Arg10 and Lys8) (Figure 1). Differently labeled

cells were treated for 90 min with either 1 M or 10 M imatinib, or control-incubated with

solvent prior to cell lysis. We subjected each of the resulting lysates to a separate in vitro

association with a mixture of five kinase inhibitor resins. This affinity purification strategy was

designed for comprehensive enrichment of drug-interacting protein kinases along with their

associating factors, and we used our previously established incubation conditions to ensure

preservation of cellular protein phosphorylation states.24, 25 Bound proteins were eluted from the

resin mixtures, and we pooled the kinase-enriched fractions from differentially encoded and

treated K562 cells prior to further sample processing. Three fourth of the combined material was

resolved by gel electrophoresis and in-gel digested with trypsin, followed by StageTip

extractions of total peptide samples and phosphopeptide purification with TiO2 beads. The

remaining inhibitor resin-enriched material was digested with trypsin in-solution prior to SCX

Page 12




14/39

13

chromatography and TiO2 enrichment of phosphorylated peptides (Figure 1). Thus, our

combined pre-fractionation strategy exploited the different fractionation principles of gel-based

and gel-free approaches for more comprehensive phosphopeptide coverage than possible with

either approach alone.24 All peptide and phosphopeptide fractions were analyzed by nanoscale

liquid chromatography-tandem MS (LC-MS/MS) analysis on a linear ion trap/orbitrap (LTQ-

Orbitrap) hybrid mass spectrometer. Moreover, to assess the biological reproducibility of

quantitative MS data, we repeated the whole analysis in a replicate experiment with a modified

SILAC scheme for the different treatment conditions. All resulting raw data files were then

collectively processed with the MaxQuant software suite for the integrated analysis of both site-

specific phosphorylation changes and protein binding to the kinase inhibitor resin upon imatinib

treatment. Our goal was to demonstrate proof-of-concept for a proteomics approach designed to

identify both regulated effector proteins, which are downstream of imatinib-inhibited kinases and

therefore exhibit repressed site-specific phosphorylations without changes in protein

abundance, and possible direct cellular imatinib targets, which are prevented from resin

interactions by bound imatinib.

Analysis of the Kinase Inhibitor-enriched Sub-proteome. In total, we identified more than

2,000 inhibitor resin-retained proteins with an accepted false-discovery rate of less than 1%.

Protein ratios for imatinib versus control-treated K562 cells could be determined for 1,275

distinct proteins, of which 683 were quantified in both biological replicate experiments (Figure

2A, Supplementary Table 1). Due to the kinase enrichment strategy we obtained such

quantitative data for more than 170 members of the protein kinase superfamily, which indicate

substantial enrichment considering that the kinome accounts for only 1.7% of the human

genome. Our affinity purifications with a mixture of broadly selective, ATP competitive inhibitors

fractionated for other likely direct binders that did not belong to the protein kinase superfamily,

ge 13 of 38




15/39

14

for example other types of nucleotide-utilizing enzymes. We detected many proteins falling into

such categories, including various dehydrogenases and lipid kinases.

In addition to protein quantification based on unphosphorylated peptides, phosphopeptide

enrichment allowed for quantification of more than 13,500 identified phosphopeptides, which

harbored 1,842 distinct phosphorylation sites that could be localized to specific serine, threonine

or tyrosine residues with high confidence (class I sites with a localization probability 0.75)

(Supplementary Tables 2 and 3). For 898 class I phosphorylation site ratios were determined in

both biological replicate experiments, and, notably, with 504 more than half of all repeatedly

quantified sites were detected on protein kinases (Figure 2A). The overlap of phosphorylation

sites quantified in both experiments was higher for protein kinases compared to all other

proteins, which might be due to a certain subset of non-specific binders in the latter group prone

to higher inter-experimental variability. Both experiments combined, we quantified as many as

868 distinct phosphorylation events on K562 cell-derived protein kinases which account for

three times as many phosphorylation sites on protein kinases compared to an earlier study.26

Thus, our current study considerably expands previous knowledge on phospho-modifications in

the expressed K562 cell kinome.

While protein and phosphorylation site ratios were determined for only 23% of all quantified

proteins, this was possible for the majority (64%) of the 213 quantified protein kinases (Figure

2B). About 81% of all identified site-specific phosphorylations were located on serine residues,

whereas phosphorylated threonines and tyrosines accounted for about 13% and 6% of all

phosphorylation sites, respectively (Figure 2C).

Furthermore, cellular interaction partners of direct inhibitor targets were also expected to be

captured by subsequent MS analysis in case such interactions were preserved during cell lysis

and affinity purification. In the present study, we used 1 M NaCl-containing buffer to promote

Page 14




16/39

15

protein kinase selectivity in the enrichment step.37, 38 Although such high salt concentrations can

disrupt hydrophilic protein-protein binding, the majority of protein kinase interactions with other

proteins are apparently not suppressed according to our recent comparison of kinase

enrichment in low and high salt conditions.38 Therefore, we reasoned that the current datasets

can be used to get an impression of the overall relationships within the affinity purified sub-

proteome. We used STRING to retrieve known interactions among all proteins for which

quantitative data was available from both biological replicate experiments. Out of these 900

proteins submitted to STRING 489 reappeared within a complex network, in which we specified

all nodes depending on whether they were identified as phosphoproteins and/or represented

protein kinases (Supplementary Figure 1, Supplementary Table 4). Notably, we detected as

many as 3435edges within the STRING-derived network, which were almost 20-fold more than

obtained for the same number of randomly selected IPI database identifiers (Supplementary

Figure 1). This indicated a high degree of network connectivity within the enriched sub-

proteome, which was in part due to the identification of many known kinase interactors including

several cyclins, SH2-domain containing proteins and regulatory kinase subunits (Supplementary

Figure 1, Supplementary Table 4). Moreover, we identified prominent modules of proteins

involved in translation, RNA processing and proteasomal protein degradation. Detection of

these rather abundant proteins might result from unspecific binding or sedimentation in the

inhibitor affinity purification step instead of specific interactions with coupled inhibitors or bound

inhibitor targets. However, we found the corresponding gene ontology (GO) biological process

categories highly overrepresented in the proteins detected upon kinase inhibitor enrichment

compared to those identified in a parallel analysis of K562 total cell extracts (data not shown),

thus pointing to preferred detection of these protein machineries as a by-product of our chemical

proteomics strategy.

ge 15 of 38




17/39

16

Identification of Imatinib-interacting Proteins. Due to the SILAC strategy combined with

parallel inhibitor affinity purifications we could identify proteins that exhibited decreased resin

binding upon prior imatinib treatment of K562 cells. To obtain reliable data, we filtered all

SILAC-based quantifications for proteins that were recorded in both biological replicate

experiments. Moreover, we considered only those proteins as target candidates which were

reproducibly quantified with SILAC ratios below 0.5 for affinity resin-retained proteins from 10

M imatinib versuscontrol incubated cells. (Supplementary Table 1). Evidently, proteins with

such properties comprise direct cellular imatinib targets as well as their interaction partners, as

monitored for Bcr-Abl and its associated signal transducers Grb2 and SHIP2. Grb2 is known to

bind to Bcr-Abl in a phosphotyrosine-dependent manner via its SH2 domain, whereas SHIP2

was reported to bind to the SH3 domain of Abl.7, 11 In accordance with the reported high imatinib

affinity of Bcr-Abl, its binding to the multi-inhibitor resin was already fully suppressed upon

exposure of K562 cells to 1 M imatinib (Figure 3, Supplementary Figure 2A).5 We detected

similar resin binding properties for discoidin domain receptor 1 (DDR1), a receptor tyrosine

kinase recently identified as a high affinity target by Bantscheff and colleagues.26 We further

monitored imatinib-dependent competition for quinone reductase 2 (NQO2) and the tyrosine

kinase Syk (Figure 4A). These two enzymes have been previously characterized as additional

imatinib targets.26, 39, 40 While the known high affinity for NQO2 was reflected by its nearly

complete competition at both imatinib concentrations of 1 M and 10 M, Syk binding was

prevented in a dose-dependent manner, with less than 40% still retained at the higher imatinib

concentration as indicated by an average binding ratio of 0.36 (Figure 3, Supplementary Figure

2A).Our results regarding Syk were consistent with earlier biochemical data, which identified

Syk as a low-affinity target with a reported Ki value of 5 M for imatinib.39 Furthermore, we

identified two phosphatidylinositol-4-kinase type-2 isoforms and (PIP4K2A and PIP4K2C)as

potential new imatinib targets (Figure 3, Supplementary Figure 2B). Although it cannot be

Page 16




18/39

17

formally excluded that these lipid kinases interacted indirectly with immobilized inhibitors and

imatinib, direct binding appears more likely due to their structural and functional similarities to

protein kinases. Imatinib-dependent displacement of these lipid kinases was similar as observed

for Syk, and the moderate effect of imatinib argues against their substantial cellular inhibition at

therapeutically relevant drug concentrations (Figure 3). However, as even minor structural

differences can significantly alter drug affinities, our data might warrant further testing of

phosphatidylinositol-4-kinases against related drugs such as nilotinib, NNO-406 or development

compounds based on the phenylaminopyridine scaffold of imatinib.7 Additionally, we identified

the flavine adenine dinucleotide (FAD) synthetase encoded by the FLAD1 gene as a new

potential target of imatinib. FAD synthetase is a key metabolic enzyme which catalyzes the

formation of FAD by adenylation of flavin mononucleotide (Figure 3, Supplementary Figure

2B).41 FAD represents a redox co-factor of many flavoproteins and is therefore essential for

many biological processes, suggesting that pharmacological inactivation of FAD synthetase

might cause toxicity. Notably, FAD is present as a prosthetic group in the described imatinib

target NQO2 and functions in the electron transfers catalyzed by this oxidoreductase. Thus, our

identification of FAD synthetase might point to common structural features that determine

imatinib binding to some FAD-utilizing or -containing enzymes. Alternatively, imatinib might

selectively interact with the ATP site of FAD synthetase. Binding ratios of FAD synthetase were

0.55 and 0.21 for 1 and 10 M imatinib-treated versus control incubated cells, respectively,

indicating that almost 80% of the enzyme was not retained by the affinity beads at the higher

imatinib dose. Thus, imatinib interfered with resin binding of FAD synthetase to a lesser extent

than observed for Bcr-Abl and NQO2, but had a more pronounced effect on this enzyme than

on Syk and PI4 kinases (Figure 3).

Identification of Imatinib-regulated Downstream Kinases. Our enrichment strategy enabled

the sensitive detection and quantification of protein kinases-derived phosphopeptides. To

ge 17 of 38




19/39

18

identify biologically reproducible effects induced by imatinib, we filtered our data for

phosphorylation sites that could be quantified and confidently assigned to specific residues in

both replicate experiments (Supplementary Table 1). As shown in Figure 4A, most quantified

phosphorylation sites were not affected by imatinib and were found in ratios close to one in both

experiments. Our goal was to obtain proof-of-concept that the identification of potentially drug-

regulated sites is feasible by our approach. We considered imatinib-induced changes as

relevant for further inspection in case phosphorylation sites were either consistently down- or

up-regulated by more than two-fold in both experiments, or exhibited an average regulation of at

least two-fold with both ratios differing by a factor of less than two. However, these data have to

be seen as preliminary in a sense that additional biological replicates would be needed to

enable the statistical evaluation of individual site ratios. Biologically reproducible down-

regulation according to the aforementioned criteria was observed for 70 distinct phosphorylation

sites upon cellular incubation with 10 M imatinib (Supplementary Table 3). Notably, regulation

on tyrosine residues was far more prominent than their prevalence among all phosphosites

quantified in kinase-enriched K562 cell fractions. We detected as many as 15 distinct tyrosine

phosphorylated residues and a similar number of Ser/Thr sites mapping to the Bcr-Abl

oncoprotein. These sites were found at very low ratios upon cellular imatinib treatment which

reflects the combined effect of cellular dephosphorylation and near complete prevention of Bcr-

Abl protein binding due to imatinib binding. However, these very low SILAC ratios obviate

reliable quantification of phosphorylation versus protein changes, and we therefore did not

further consider Bcr-Abl phosphorylation sites in our quantitative analysis. In case of all other

regulated phosphoproteins for which protein ratios were measured normalization of

phosphorylation changes was possible due to either less dramatic (as observed for the direct

target Syk) or no imatinib effect on the amount of resin-bound protein. Notably, most imatinib-

Page 18




20/39

19

regulated phosphorylations have not been reported in earlier studies (Supplementary Table 3),

including all down-regulated sites listed in Table 1 and discussed in the following part.

Syk phosphorylation site ratios were on average threefold more strongly reduced upon 10 M

imatinib compared to Syk protein binding, our results indicated cellular dephosphorylation at

residues such Tyr-323, Tyr-348 and Tyr-352. Previous reports revealed that Syk

phosphorylation on these sites creates binding sites for signaling proteins such as

phospholipase C, the guanine nucleotide exchange factor Vav, phosphatidylinositol 3-kinase

and c-Cbl42-45. Interestingly, while Tyr-348 and Tyr-352 (or the corresponding residues in mouse

Syk) were shown to positively contribute to cellular Syk function, Tyr-317 was characterized as

a negative regulatory site in Ag and Fc receptor signaling45-47. By extension, our results point to

possible functional modulation of Syk-mediated signaling at the higher imatinib dose in CML

cells.

The major goal of our phosphoproteomics analysis was the identification of protein kinases that

transduce signals emanating from direct imatinib interactors, as a strategy to identify potential

alternative targets in case of imatinib resistance formation due to Bcr-Abl mutations oroverexpression. In contrast to direct cellular imatinib targets, imatinib treatment would not affect

inhibitor resin binding of such downstream signaling kinases, but instead selectively repress

site-specific phosphorylations in our experimental approach. To identify such imatinib-induced

effects, we focused on phosphorylation sites that could be quantified and confidently assigned

to specific residues in both replicate experiments. Notably, we recorded effects on a number of

phosphorylation sites with reported regulatory functions. According to our analysis, imatinibtreatment exerted a dose-dependent effect on the phosphorylation of the cytoplasmic tyrosine

kinase BTK at Tyr-551, with a threefold down-regulation measured for the 10 M imatinib

concentration (Table 1). Quantification of non-phosphorylated peptides from BTK revealed that

ge 19 of 38




21/39

20

comparable protein amounts were retained by the inhibitor resin from imatinib-treated cells. We

observed similar regulation patterns for Tyr-714 of the cytoplasmic tyrosine kinase Fer and Tyr-

774 of the receptor tyrosine kinase EphB4 (Fig. 4B, Table 1). Our evidence of selective

dephosphorylation in the absence of protein changes points to these three tyrosine kinases as

potential downstream signaling elements of direct imatinib targets. Notably, these imatinib-

repressed phosphorylations occurred in the activation loop regions of the BTK, Fer and EphB4,

at a conserved position that stabilizes the active kinase conformation in a phosphorylation-

dependent manner.23, 48 Thus, our data further suggest cellular inhibition of BTK, Fer and EphB4

kinase activities upon imatinib, identifying them as candidate signal transducers of Bcr-Abl-

mediated and imatinib-sensitive leukemia cell transformation. It is noteworthy that in case of

BTK earlier data argue against an essential function in Bcr-Abl signaling, based on evidence

that BTK inactivation showed no inhibitory effect in Bcr-Abl-transformed murine cells.49

However, despite this data, cell-type specific requirements for BTK are conceivable, for example

in case of reduced signaling capacity of Bcr-Abl due to endogenous expression at considerably

lower levels compared to ectopic overexpression in murine model cell lines.

In our experiments, imatinib treatment of K562 cells markedly decreased the tyrosine

phosphorylation of focal adhesion kinase (FAK) at Tyr-883 according to the assigned IPI

database identifier (Table 1), which corresponds to Tyr-861 in the commonly used UniProt

knowledgebase entry FAK1_HUMAN. Phosphorylation of FAK at Tyr-861 is up-regulated in

Ras-transformed cells and required for Ras-mediated transformation.50 As constitutive Ras

activation represents a hallmark of Bcr-Abl transformation, our data point to FAK Tyr-861

phosphorylation as a previously unknown switch point in CML cell signaling. We further

detected dose-dependent dephosphorylation of protein kinase C (PKC) at Tyr-313 and Tyr-

334 upon imatinib treatment (Figure 4B, Table 1). These tyrosine residues reside in the hinge

region between the regulatory and catalytic domains of PKC and have been implicated in the

Page 20




22/39

21

regulation of apoptosis in glioma cells.51, 52 Phosphorylation at Tyr-313 plays a role in diverse

signaling responses including keratinocyte differentiation and thromboxane A2 generation in

platelets.53, 54 Thus, our identification of these regulation events in imatinib-treated CML cells

may reflect a role of PKCin the propagation of Bcr-Abl-induced signals and warrant further

examination of this kinase in leukemia cell transformation. We also detected imatinib-sensitive

tyrosine phosphorylation events in the N-terminal region of the Src family kinase Yes and the C-

terminal part of the cytoplasmic tyrosine kinase ACK. As these modifications have not been

functionally characterized yet, their possible roles in Bcr-Abl signaling cannot be inferred from

published data. In addition to imatinib effects on tyrosine phosphorylation levels we detected

inhibitor-repressed serine and threonine phosphorylations on protein kinases such as mitogen-

activated protein kinase kinase kinase 3 (MAP3K3), 90 kDa ribosomal protein S6 kinase

(RSK1), casein kinase 2 (CK22) and PCTAIRE1, with the latter exhibiting as much as 75%

reduction of Ser-71 phosphorylation even at the low inhibitor dose of 1 M imatinib (Table 1). To

explore potential relationships among all proteins that were either prevented from resin binding

or harbored down-regulated phosphorylation sites upon imatinib treatment, we mapped them on

known protein interactions by STRING analysis (Figure 5). Notably, imatinib-regulated

phosphoproteins such as BTK, Fer, FAK and others were extensively connected to direct

imatinib targets like Bcr-Abl and Syk, further emphasizing their putative roles as downstream

signal transducers.

Surprisingly, imatinib treatment of cells not only caused site-specific reduction of but also

triggered a subset of phosphorylations in the kinase inhibitor-enriched subproteome

(Supplementary Table 3). These were exclusively found on serine and threonine residues, thus

contrasting the high prevalence of tyrosine amongst the imatinib-repressed phosphorylations.

Almost half of all proteins with imatinib-induced phosphorylation sites have reported cell cycle

functions. For example, we detected increased phosphorylation on the protein kinases polo-like

ge 21 of 38




23/39

22

kinase 1 (Plk1), TTK, Wee1 and Myt1, which have well established functions in the entry and

progression through mitosis. Plk1, which is highly expressed in many leukemia cell lines, has

recently been described as a promising target for therapeutic intervention in hematological

malignancies.55 Notably, our analysis revealed Plk1 phosphorylation occurring at Thr-210 in the

activation loop, thus indicating enzymatic activation of the kinase upon short term imatinib

treatment. Although the statistical significance of this regulation needs to be verified and the

underlying molecular mechanisms remain to be elucidated, Plk1 activation may be part of a

cellular response to counteract the immediate cellular consequences of Bcr-Abl kinase

suppression. Thus, our results might warrant investigations how or whether therapeutic Plk1

inhibition synergizes with cellular Bcr-Abl inactivation regarding the anti-proliferative and

apoptotic effects on CML cells.

Page 22




24/39

23

Conclusions and Outlook

Targeted intervention strategies with kinase inhibitors have already made an enormous impact

on the treatment of several human cancers. The role of such therapies is likely to increase in the

years to come, considering the large number of kinase-selective drugs currently in pre-clinical

and clinical development.4 Proteomics approaches can contribute valuable information to such

efforts, including drug selectivity assessments in relevant biological systems and the

identification of alternative molecular targets for pharmacological intervention. Target flexibility in

cancer treatment is desirable, given that the selective pressure imposed on cancer cells

frequently leads to drug resistance due to desensitizing mutations in the targeted, disease-

causing oncogenes.7, 8 This has been extensively documented for the transforming Bcr-Abl

tyrosine kinase in imatinib-treated chronic myeloid leukemia (CML) patients, but represents a

pervading theme as evident from, for example, the occurrence of drug-resistant epidermal

growth factor receptor (EGFR) kinase mutants in non-small cell lung cancer therapy with the

EGFR inhibitors gefitinib and erlotinib.56-58 One potential strategy to overcome resistance could

involve inhibition of protein kinases, which are essential downstream mediators of oncogenic

signaling emanating from kinases such as Bcr-Abl. We have implemented a chemical

phosphoproteomics strategy to identify such transducers upon imatinib exposure, by targeted

analysis of a pharmacologically tractable sub-proteome isolated by affinity purification with non-

selective kinase inhibitors. These proof-of-concept experiments provide preliminary evidence for

so far unknown kinases in imatinib-regulated K562 cell signaling, which represent candidates for

further validation and functional studies. Our proteomics strategy is generic and can be applied

to other kinase inhibitors. For example, comprehensive analysis of essential and druggable

mediators of EGFR signaling in non-small cell lung cancer cells might define therapeutic back-

up strategies to overcome the frequent EGFR resistance upon prolonged gefitinib or erlotinib

treatment. Moreover, concomitant inhibition of both primary oncogenic kinases and their

ge 23 of 38




25/39

24

essential signal transducers might effectively counteract resistance formation, as individual

target mutations would not suffice to evade from such poly-pharmacological regimens.

In addition to the phosphoproteomic identification of signaling factors situated downstream of

direct imatinib targets, our integrated approach recapitulated known imatinib targets from K562

cells, as these were competed from the generic kinase inhibitor resin in lysates from imatinib

treated cells.26 Notably, this part of our analysis was not only confirmatory but also identified

previously unknown imatinib-interacting proteins such as the key metabolic enzyme FAD

synthetase. Our identification of such a key metabolic enzyme as off-target raises the issue of

potential dose-limiting toxicity resulting from its likely pharmacological inhibition. We think further

in vitroand cellular studies are warranted to verify enzymatic inhibition of FAD synthetase by

imatinib and by related compounds in clinical development. Taken together, our sensitive

proteomics approach that integrates kinase inhibitor selectivity analysis with phosphoproteome

quantification in the kinase-enriched sub-proteome should have considerable utility for discovery

and development efforts aiming for improved targeted intervention strategies in human

malignancies.

Page 24




26/39

25

Acknowledgment.We thank Axel Ullrich for his generous support of our work. We thank

Matthias Mann and Jrgen Cox for early access to the MaxQuant software. We further thank

Jesper Olsen and Kirti Sharma for help and advice. This work was supported by a grant from

the Novartis-Stiftung fr therapeutische Forschung.

Supporting Information Available: Supplementary Table 1, list of all protein groups

identified in this study. Supplementary Table 2, peptide evidence data for all identified peptides.

Supplementary Table 3, all identified class I phosphorylation sites. Supplementary Table 4,

STRING-derived interactions among all kinase inhibitor resin-bound proteins that were

quantified with protein ratios and/or class I phosphorylation site ratios in both biological replicate

experiments. Supplementary Table 5, STRING-derived interactions among all proteins with

either imatinib-reduced kinase inhibitor resin binding or were imatinib-inhibited phosphorylation

sites. Supplementary Figure 1, interaction network constituted by inhibitor resin-bound proteins.

Supplementary Figure 2, representative MS spectra of K562 cell proteins exhibiting reduced

inhibitor resin binding upon cellular imatinib treatment.

ge 25 of 38




27/39

26

References

1. Blume-Jensen, P.; Hunter, T., Oncogenic kinase signalling. Nature2001, 411, (6835), 355-65.

2. Krause, D. S.; Van Etten, R. A., Tyrosine kinases as targets for cancer therapy. N Engl J Med

2005, 353, (2), 172-87.

3. Gschwind, A.; Fischer, O. M.; Ullrich, A., The discovery of receptor tyrosine kinases: targets for

cancer therapy. Nat Rev Cancer2004, 4, (5), 361-70.

4. Zhang, J.; Yang, P. L.; Gray, N. S., Targeting cancer with small molecule kinase inhibitors. Nat

Rev Cancer2009, 9, (1), 28-39.

5. Capdeville, R.; Buchdunger, E.; Zimmermann, J.; Matter, A., Glivec (STI571, imatinib), a

rationally developed, targeted anticancer drug. Nat Rev Drug Discov2002, 1, (7), 493-502.

6. Faderl, S.; Talpaz, M.; Estrov, Z.; O'Brien, S.; Kurzrock, R.; Kantarjian, H. M., The biology ofchronic myeloid leukemia. N Engl J Med1999, 341, (3), 164-72.

7. Weisberg, E.; Manley, P. W.; Cowan-Jacob, S. W.; Hochhaus, A.; Griffin, J. D., Second

generation inhibitors of BCR-ABL for the treatment of imatinib-resistant chronic myeloid leukaemia. Nat

Rev Cancer2007, 7, (5), 345-56.

8. Daub, H.; Specht, K.; Ullrich, A., Strategies to overcome resistance to targeted protein kinase

inhibitors. Nat Rev Drug Discov2004, 3, (12), 1001-10.

9. Quintas-Cardama, A.; Kantarjian, H.; Cortes, J., Flying under the radar: the new wave of BCR-

ABL inhibitors. Nat Rev Drug Discov2007, 6, (10), 834-48.

10. Schindler, T.; Bornmann, W.; Pellicena, P.; Miller, W. T.; Clarkson, B.; Kuriyan, J., Structural

mechanism for STI-571 inhibition of abelson tyrosine kinase. Science2000, 289, (5486), 1938-42.

11. Brehme, M.; Hantschel, O.; Colinge, J.; Kaupe, I.; Planyavsky, M.; Kocher, T.; Mechtler, K.;

Bennett, K. L.; Superti-Furga, G., Charting the molecular network of the drug target Bcr-Abl. Proc Natl

Acad Sci U S A 2009, 106, (18), 7414-9.

12. Preisinger, C.; Kolch, W., The Bcr-Abl kinase regulates the actin cytoskeleton via a GADS/Slp-

76/Nck1 adaptor protein pathway. Cell Signal2010, 22, (5), 848-56.

13. Olsen, J. V.; Blagoev, B.; Gnad, F.; Macek, B.; Kumar, C.; Mortensen, P.; Mann, M., Global, in

vivo, and site-specific phosphorylation dynamics in signaling networks. Cell2006, 127, (3), 635-48.

Page 26




28/39

27

14. Schreiber, T. B.; Mausbacher, N.; Breitkopf, S. B.; Grundner-Culemann, K.; Daub, H.,

Quantitative phosphoproteomics--an emerging key technology in signal-transduction research.

Proteomics2008, 8, (21), 4416-32.

15. Lemeer, S.; Heck, A. J., The phosphoproteomics data explosion. Curr Opin Chem Biol2009, 13,

(4), 414-20.

16. Dephoure, N.; Zhou, C.; Villen, J.; Beausoleil, S. A.; Bakalarski, C. E.; Elledge, S. J.; Gygi, S. P.,

A quantitative atlas of mitotic phosphorylation. Proc Natl Acad Sci U S A 2008, 105, (31), 10762-7.

17. Choudhary, C.; Olsen, J. V.; Brandts, C.; Cox, J.; Reddy, P. N.; Bohmer, F. D.; Gerke, V.;

Schmidt-Arras, D. E.; Berdel, W. E.; Muller-Tidow, C.; Mann, M.; Serve, H., Mislocalized activation of

oncogenic RTKs switches downstream signaling outcomes. Mol Cell2009, 36, (2), 326-39.

18. Grimsrud, P. A.; Swaney, D. L.; Wenger, C. D.; Beauchene, N. A.; Coon, J. J.,

Phosphoproteomics for the masses. ACS Chemical Biology2010, 5, (1), 105-19.

19. Zhao, Y.; Jensen, O. N., Modification-specific proteomics: strategies for characterization of post-

translational modifications using enrichment techniques. Proteomics2009, 9, (20), 4632-41.

20. Cox, J.; Mann, M., MaxQuant enables high peptide identification rates, individualized p.p.b.-range

mass accuracies and proteome-wide protein quantification. Nat Biotechnol2008, 26, (12), 1367-72.

21. Goss, V. L.; Lee, K. A.; Moritz, A.; Nardone, J.; Spek, E. J.; MacNeill, J.; Rush, J.; Comb, M. J.;

Polakiewicz, R. D., A common phosphotyrosine signature for the Bcr-Abl kinase. Blood2006, 107, (12),

4888-97.

22. Pan, C.; Olsen, J. V.; Daub, H.; Mann, M., Global effects of kinase inhibitors on signaling

networks revealed by quantitative phosphoproteomics. Mol Cell Proteomics2009, 8, (12), 2796-808.

23. Nolen, B.; Taylor, S.; Ghosh, G., Regulation of protein kinases; controlling activity through

activation segment conformation. Mol Cell2004, 15, (5), 661-75.

24. Daub, H.; Olsen, J. V.; Bairlein, M.; Gnad, F.; Oppermann, F. S.; Korner, R.; Greff, Z.; Keri, G.;

Stemmann, O.; Mann, M., Kinase-selective enrichment enables quantitative phosphoproteomics of the

kinome across the cell cycle. Mol Cell2008, 31, (3), 438-48.

25. Wissing, J.; Jansch, L.; Nimtz, M.; Dieterich, G.; Hornberger, R.; Keri, G.; Wehland, J.; Daub, H.,

Proteomics analysis of protein kinases by target class-selective prefractionation and tandem mass

spectrometry. Mol Cell Proteomics2007, 6, (3), 537-47.

ge 27 of 38




29/39

28

26. Bantscheff, M.; Eberhard, D.; Abraham, Y.; Bastuck, S.; Boesche, M.; Hobson, S.; Mathieson, T.;

Perrin, J.; Raida, M.; Rau, C.; Reader, V.; Sweetman, G.; Bauer, A.; Bouwmeester, T.; Hopf, C.; Kruse,

U.; Neubauer, G.; Ramsden, N.; Rick, J.; Kuster, B.; Drewes, G., Quantitative chemical proteomics

reveals mechanisms of action of clinical ABL kinase inhibitors. Nat Biotechnol2007, 25, (9), 1035-44.

27. Godl, K.; Gruss, O. J.; Eickhoff, J.; Wissing, J.; Blencke, S.; Weber, M.; Degen, H.; Brehmer, D.;

Orfi, L.; Horvath, Z.; Keri, G.; Muller, S.; Cotten, M.; Ullrich, A.; Daub, H., Proteomic characterization of

the angiogenesis inhibitor SU6668 reveals multiple impacts on cellular kinase signaling. Cancer Res

2005, 65, (15), 6919-26.

28. Wessel, D.; Flgge, U. I., A method for the quantitative recovery of protein in dilute solution in the

presence of detergents and lipids. Analytical Biochemistry1984, 138, (1), 141-143.

29. Shevchenko, A.; Tomas, H.; Havlis, J.; Olsen, J. V.; Mann, M., In-gel digestion for mass

spectrometric characterization of proteins and proteomes. Nat Protoc2006, 1, (6), 2856-60.

30. Rappsilber, J.; Mann, M.; Ishihama, Y., Protocol for micro-purification, enrichment, pre-

fractionation and storage of peptides for proteomics using StageTips. Nat Protoc2007, 2, (8), 1896-906.

31. Larsen, M. R.; Thingholm, T. E.; Jensen, O. N.; Roepstorff, P.; Jorgensen, T. J., Highly selective

enrichment of phosphorylated peptides from peptide mixtures using titanium dioxide microcolumns. Mol

Cell Proteomics2005, 4, (7), 873-86.

32. Pinkse, M. W.; Uitto, P. M.; Hilhorst, M. J.; Ooms, B.; Heck, A. J., Selective isolation at the

femtomole level of phosphopeptides from proteolytic digests using 2D-NanoLC-ESI-MS/MS and titanium

oxide precolumns. Anal Chem2004, 76, (14), 3935-43.

33. Schroeder, M. J.; Shabanowitz, J.; Schwartz, J. C.; Hunt, D. F.; Coon, J. J., A neutral loss

activation method for improved phosphopeptide sequence analysis by quadrupole ion trap mass

spectrometry. Anal Chem2004, 76, (13), 3590-8.

34. Olsen, J. V.; de Godoy, L. M. F.; Li, G.; Macek, B.; Mortensen, P.; Pesch, R.; Makarov, A.; Lange,

O.; Horning, S.; Mann, M., Parts per Million Mass Accuracy on an Orbitrap Mass Spectrometer via Lock

Mass Injection into a C-trap. Mol Cell Proteomics2005, 4, (12), 2010-2021.

35. Jensen, L. J.; Kuhn, M.; Stark, M.; Chaffron, S.; Creevey, C.; Muller, J.; Doerks, T.; Julien, P.;Roth, A.; Simonovic, M.; Bork, P.; von Mering, C., STRING 8--a global view on proteins and their

functional interactions in 630 organisms. Nucl. Acids Res. 2009, 37, (suppl_1), D412-416.

36. Cline, M. S.; Smoot, M.; Cerami, E.; Kuchinsky, A.; Landys, N.; Workman, C.; Christmas, R.;

Avila-Campilo, I.; Creech, M.; Gross, B.; Hanspers, K.; Isserlin, R.; Kelley, R.; Killcoyne, S.; Lotia, S.;

Page 28




30/39

29

Maere, S.; Morris, J.; Ono, K.; Pavlovic, V.; Pico, A. R.; Vailaya, A.; Wang, P. L.; Adler, A.; Conklin, B. R.;

Hood, L.; Kuiper, M.; Sander, C.; Schmulevich, I.; Schwikowski, B.; Warner, G. J.; Ideker, T.; Bader, G.

D., Integration of biological networks and gene expression data using Cytoscape. Nat Protoc 2007, 2,

(10), 2366-82.

37. Godl, K.; Wissing, J.; Kurtenbach, A.; Habenberger, P.; Blencke, S.; Gutbrod, H.; Salassidis, K.;

Stein-Gerlach, M.; Missio, A.; Cotten, M.; Daub, H., An efficient proteomics method to identify the cellular

targets of protein kinase inhibitors. Proc Natl Acad Sci U S A 2003, 100, (26), 15434-9.

38. Sharma, K.; Kumar, C.; Keri, G.; Breitkopf, S. B.; Oppermann, F. S.; Daub, H., Quantitative

analysis of kinase-proximal signaling in lipopolysaccharide-induced innate immune response. J Proteome

Res2010, 9, (5), 2539-49.

39. Atwell, S.; Adams, J. M.; Badger, J.; Buchanan, M. D.; Feil, I. K.; Froning, K. J.; Gao, X.; Hendle,

J.; Keegan, K.; Leon, B. C.; Muller-Dieckmann, H. J.; Nienaber, V. L.; Noland, B. W.; Post, K.;

Rajashankar, K. R.; Ramos, A.; Russell, M.; Burley, S. K.; Buchanan, S. G., A novel mode of Gleevec

binding is revealed by the structure of spleen tyrosine kinase. J Biol Chem2004, 279, (53), 55827-32.

40. Rix, U.; Hantschel, O.; Durnberger, G.; Remsing Rix, L. L.; Planyavsky, M.; Fernbach, N. V.;

Kaupe, I.; Bennett, K. L.; Valent, P.; Colinge, J.; Kocher, T.; Superti-Furga, G., Chemical proteomic

profiles of the BCR-ABL inhibitors imatinib, nilotinib, and dasatinib reveal novel kinase and nonkinase

targets. Blood2007, 110, (12), 4055-63.

41. Huerta, C.; Borek, D.; Machius, M.; Grishin, N. V.; Zhang, H., Structure and mechanism of a

eukaryotic FMN adenylyltransferase. J Mol Biol2009, 389, (2), 388-400.

42. Law, C. L.; Chandran, K. A.; Sidorenko, S. P.; Clark, E. A., Phospholipase C-gamma1 interacts

with conserved phosphotyrosyl residues in the linker region of Syk and is a substrate for Syk. Mol Cell

Biol1996, 16, (4), 1305-15.

43. Schymeinsky, J.; Sindrilaru, A.; Frommhold, D.; Sperandio, M.; Gerstl, R.; Then, C.; Mocsai, A.;

Scharffetter-Kochanek, K.; Walzog, B., The Vav binding site of the non-receptor tyrosine kinase Syk at

Tyr 348 is critical for beta2 integrin (CD11/CD18)-mediated neutrophil migration. Blood2006, 108, (12),

3919-27.

44. Schymeinsky, J.; Then, C.; Sindrilaru, A.; Gerstl, R.; Jakus, Z.; Tybulewicz, V. L.; Scharffetter-

Kochanek, K.; Walzog, B., Syk-mediated translocation of PI3Kdelta to the leading edge controls

lamellipodium formation and migration of leukocytes. PLoS One2007, 2, (11), e1132.

ge 29 of 38




31/39

30

45. Yankee, T. M.; Keshvara, L. M.; Sawasdikosol, S.; Harrison, M. L.; Geahlen, R. L., Inhibition of

signaling through the B cell antigen receptor by the protooncogene product, c-Cbl, requires Syk tyrosine

317 and the c-Cbl phosphotyrosine-binding domain. J Immunol1999, 163, (11), 5827-35.

46. Simon, M.; Vanes, L.; Geahlen, R. L.; Tybulewicz, V. L., Distinct roles for the linker region

tyrosines of Syk in FcepsilonRI signaling in primary mast cells. J Biol Chem2005, 280, (6), 4510-7.

47. Keshvara, L. M.; Isaacson, C. C.; Yankee, T. M.; Sarac, R.; Harrison, M. L.; Geahlen, R. L., Syk-

and Lyn-dependent phosphorylation of Syk on multiple tyrosines following B cell activation includes a site

that negatively regulates signaling. J Immunol1998, 161, (10), 5276-83.

48. Oppermann, F. S.; Gnad, F.; Olsen, J. V.; Hornberger, R.; Greff, Z.; Keri, G.; Mann, M.; Daub, H.,

Large-scale proteomics analysis of the human kinome. Mol Cell Proteomics2009, 8, (7), 1751-64.

49. MacPartlin, M.; Smith, A. M.; Druker, B. J.; Honigberg, L. A.; Deininger, M. W., Bruton's tyrosine

kinase is not essential for Bcr-Abl-mediated transformation of lymphoid or myeloid cells. Leukemia2008,22, (7), 1354-60.

50. Lim, Y.; Han, I.; Jeon, J.; Park, H.; Bahk, Y. Y.; Oh, E. S., Phosphorylation of focal adhesion

kinase at tyrosine 861 is crucial for Ras transformation of fibroblasts. J Biol Chem2004, 279, (28), 29060-

5.

51. Lu, W.; Finnis, S.; Xiang, C.; Lee, H. K.; Markowitz, Y.; Okhrimenko, H.; Brodie, C., Tyrosine 311

is phosphorylated by c-Abl and promotes the apoptotic effect of PKCdelta in glioma cells. Biochem

Biophys Res Commun2007, 352, (2), 431-6.

52. Lu, W.; Lee, H. K.; Xiang, C.; Finniss, S.; Brodie, C., The phosphorylation of tyrosine 332 is

necessary for the caspase 3-dependent cleavage of PKCdelta and the regulation of cell apoptosis. Cell

Signal2007, 19, (10), 2165-73.

53. Murugappan, S.; Shankar, H.; Bhamidipati, S.; Dorsam, R. T.; Jin, J.; Kunapuli, S. P., Molecular

mechanism and functional implications of thrombin-mediated tyrosine phosphorylation of PKCdelta in

platelets. Blood2005, 106, (2), 550-7.

54. Balasubramanian, S.; Zhu, L.; Eckert, R. L., Apigenin inhibition of involucrin gene expression is

associated with a specific reduction in phosphorylation of protein kinase Cdelta Tyr311. J Biol Chem2006, 281, (47), 36162-72.

55. Ikezoe, T.; Yang, J.; Nishioka, C.; Takezaki, Y.; Tasaka, T.; Togitani, K.; Koeffler, H. P.;

Yokoyama, A., A novel treatment strategy targeting polo-like kinase 1 in hematological malignancies.

Leukemia2009, 23, (9), 1564-76.

Page 30




32/39

31

56. Kobayashi, S.; Boggon, T. J.; Dayaram, T.; Janne, P. A.; Kocher, O.; Meyerson, M.; Johnson, B.

E.; Eck, M. J.; Tenen, D. G.; Halmos, B., EGFR mutation and resistance of non-small-cell lung cancer to

gefitinib. N Engl J Med2005, 352, (8), 786-92.

57. Pao, W.; Miller, V. A.; Politi, K. A.; Riely, G. J.; Somwar, R.; Zakowski, M. F.; Kris, M. G.; Varmus,

H., Acquired resistance of lung adenocarcinomas to gefitinib or erlotinib is associated with a second

mutation in the EGFR kinase domain. PLoS Medicine2005, 2, (3), e73.

58. Blencke, S.; Ullrich, A.; Daub, H., Mutation of threonine 766 in the epidermal growth factor

receptor reveals a hotspot for resistance formation against selective tyrosine kinase inhibitors. J Biol

Chem2003, 278, (17), 15435-40.

ge 31 of 38




33/39

32

Figure Legends

Figure 1. Schematic illustration of the experimental strategy. Three populations of K562 CML

cells were SILAC-encoded with normal or isotopically labeled arginine and lysine and either 1

M imatinib, 10 M imatinib or DMSO before lysis. Two biological replicate experiments were

performed with different SILAC schemes. In both experiments, each of the three lysates was

subjected to separate affinity purification with a mixture of five kinase inhibitor resins. Resin-

bound proteins were eluted and pooled. Subsequently, fractions of the pooled kinase-enriched

material were separated by gel electrophoresis prior to or by SCX chromatography after

digestion with trypsin. Phosphopeptides were enriched with titanium dioxide beads and total

peptide samples were prepared by desalting with C18-StageTips from all peptide fractions. All

resulting samples were analyzed by LC-MS/MS on an LTQ-Orbitrap.

Figure 2. Overview of results from SILAC experiments. (A) Comparison of the two independent

SILAC experiments regarding the quantified proteins and quantified phosphorylation sites with

confident site localization (class I sites with p 0.75). Numbers are separately shown for all

proteins and for protein kinases. (B) Numbers of all proteins and protein kinases for which

protein ratios and class I phosphorylation site ratios were quantified. The overlapping regions

indicate the protein and protein kinase numbers for which both protein and phosphorylation site

ratios were obtained within this study. (C) Numbers and distribution of serine, threonine and

tyrosine phosphorylation for all quantified class I phosphorylation sites are shown for each of the

two biological replicate experiments and the overlap between the two experiments.

Figure 3. Quantified K562 proteins exhibiting dose-dependent suppression of kinase inhibitor

resin binding upon cellular imatinib treatment. Average values are shown for K562 cell proteins

Page 32




34/39

33

quantified in both replicate experiments whose interaction with the immobilized kinase inhibitors

was reduced by at least 50% upon 10 M imatinib treatment of K562 cells.

Figure 4. Quantitative phosphoproteomics of imatinib effects on kinase inhibitor beads-captured

proteins. (A) Scatter plot comparison of log2 transformed class I phosphorylation site ratios upon

10 M cellular imatinib treatment. Sites that were consistently down-regulated (up-regulated) in

both biological replicate experiments are highlighted in red (green). Moreover, phosphorylation

ratios for Tyr-714 of FER and for Tyr-313 of PKC are indicated. (B) Examples of imatinib-

regulated downstream kinases identified by quantitative MS. SILAC spectra are shown forimatinib-regulated, pTyr-containing phosphopeptides (left panels) and unchanged, non-

phosphorylated peptides (right panels) derived from the protein kinases Fer and PKC in

experiment 1. Values for measured and pooling error-corrected (normalized) SILAC ratios are

shown.

Figure 5. Interactome of imatinib-regulated proteins. All proteins that either exhibited reduced

kinase inhibitor resin binding or which harbored imatinib-repressed phosphorylation sites were

used for STRING analysis. The resulting network was visualized with Cytoscape.

ge 33 of 38




35/39

DMSO 1 M imatinib 10 M imatinibExp. 1

1 M imatinib 10 M imatinib DMSOExp. 2

0 0Arg /Lys 6 4Arg /Lys 10 8Arg /Lys

K562 K562 K562

0 0Arg /Lys 6 4Arg /Lys 10 8Arg /LysK562 K562 K562

Cell lysis

NH

OH

O

NH

O

SU6668

N

N N

N

HO

Cl

COOH

Purvalanol B

N

NH2

HN

N

O O

BisX

N ONH

OH2N

VI16832

O

N

N

HN Cl

F

O

H2N

AX14596

Elution

Pooling

LC-MS/MS analysis on LTQ-Orbitrap

Trypticdigestion

Trypticdigestion

TiO2TiO2

Titanspherephosphopeptideenrichment

C18desalting

StageTip

TiO2TiO2

C18 C18

Figure 1

Page 34




36/39

137

33 13

898574

370

487 683

105

504

201 163

47

30

136

Proteins quantifiedwith pSTY ratios

Proteins quantifiedwith protein ratios901 356374

Experiment 2

Experiment 1

All proteins Protein kinases

Quantifiedprotein ratios

QuantifiedpSTY ratios

A

B

C

All proteins Protein kinases

Figure 2

81.25% 13.93% 4.82%

79.10% 15.14% 5.76%

81.07% 13.25% 5.68%

1472Exp. 1 1196 205 71

1268Exp. 2 1003 192 73

898Both 728 119 51

Class I total pSer pThr pTyr

ge 35 of 38




37/39

Figure 3

Page 36




38/39

7 04.0 705 .0 706.0 707.0 708.0 709.0

m/z0

10

20

30

40

50

60

70

80

90

100

704.30

705.81

704.80706.31

707.80708.30

706.81705.30

708.81707.30 709.31

703.79

0LysDMSO control

4Lys1 M imatinib

8Lys10 M imatinib

FER: QEDGGV SSSGLKpY

704 705 706 707 708 709 710 711

m/z0

10

20

30

40

50

60

70

80

90

100707.02 709.35

709.68706.69

704.01

707.36

710.01

704.34707.69

710.35709.01

708.02704.67 710.68708.69

705.01

703.34703.67

0ArgDMSO control

6Arg1 M imatinib

10Arg10 M imatinib

FER: ESHGKPGEYVLSVYSDGQR

1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030

m/z0

10

20

30

40

50

60

70

80

90

100

1019.95

1023.971024.47

1027.96

1020.45 1024.981028.46

1028.961025.48

1026.441020.96 1029.47

1018.95 1019.45

0LysDMSO control

4Lys1 M imatinib

8Lys10 M imatinib

6 79 .0 6 80 .0 6 81 .0 6 82 .0 6 83 .0 6 84 .0 6 85 .0m/z0

10

20

30

40

50

60

70

80

90

100

683.39

678.89

681.90 683.89

679.39684.39

682.40

679.89682.89

684.89680.39

678.39 681.40

0ArgDMSO control

6Arg1 M imatinib

10Arg10 M imatinib

Relativeabundance

Relativeabundance

Relativeabundance

Relative

abundance

Measured Normalized1 M imatinib / control 0.38 0.52

10 M imatinib / control 0.18 0.25

Measured Normalized1 M imatinib / control 0.77 1.21

10 M imatinib / control 0.78 1.17

Measured Normalized1 M imatinib / control 0.43 0.6210 M imatinib / control 0.21 0.36

Measured Normalized1 M imatinib / control 0.85 1.1310 M imatinib / control 0.83 1.06

A

B

Down-regulated

Up-regulatedOther

Log (10 M imatinib / control) - exp. 12

Lo

g

(10M

imati

nib

/co

ntro

l)-

exp

.2

2

pSTY sites

Figure 4

FER-Tyr-714

ge 37 of 38




39/39

Regulated pSTY ratio

Regulated protein ratio

Protein kinase

Non protein kinase

BCR-ABL SHIP2

Figure 5

Page 38Journal of Proteome Research

journal of proteome research 2010 breitkopf

Documents