Phosphoproteomics Study on the Activated PKCδ-Induced Cell Death

Article pubs.acs.org/jpr

Phosphoproteomics Study on the Activated PKCδ-Induced Cell Death Li Xia,† Tong-Dan Wang,† Shao-Ming Shen,† Meng Zhao,† Han Sun,§ Ying He,§ Lu Xie,§ Zhao-Xia Wu,† San-Feng Han,† Li-Shun Wang,†,* and Guo-Qiang Chen†,‡,* †

The Department of Pathophysiology and Shanghai Universities E-Institute for Chemical Biology, Key Laboratory of Cell Differentiation and Apoptosis of National Ministry of Education, Shanghai Jiao Tong University School of Medicine (SJTU-SM), Shanghai, P.R. China, 200025 ‡ Institute of Health Sciences, Shanghai Institutes for Biological Sciences & SJTU-SM, Shanghai, P.R. China, 200025 § Shanghai Center for Bioinformatics Technology, Shanghai, P.R. China, 200235 S Supporting Information *

ABSTRACT: The proteolytic activation of protein kinase Cδ (PKCδ) generates a catalytic fragment called PKCδ-CF, which induces cell death. However, the mechanisms underlying PKCδ-CF-mediated cell death are largely unknown. On the basis of an engineering leukemic cell line with inducible expression of PKCδ-CF, here we employ SILAC-based quantitative phosphoproteomics to systematically and dynamically investigate the overall phosphorylation events during cell death triggered by PKCδ-CF expression. Totally, 3000 phosphorylation sites were analyzed. Considering the fact that early responses to PKCδ-CF expression initiate cell death, we sought to identify pathways possibly related directly with PKCδ by further analyzing the data set of phosphorylation events that occur in the initiation stage of cell death. Interacting analysis of this data set indicates that PKCδ-CF triggers complicated networks to initiate cell death, and motif analysis and biochemistry verification reveal that several kinases in the downstream of PKCδ conduct these networks. By analysis of the specific sequence motif of kinase-substrate, we also find 59 candidate substrates of PKCδ from the up-regulated phosphopeptides, of which 12 were randomly selected for in vitro kinase assay and 9 were consequently verified as substrates of PKCδ. To our greatest understanding, this study provides the most systematic analysis of phosphorylation events initiated by the cleaved activated PKCδ, which would vastly extend the profound understanding of PKCδ-directed signal pathways in cell death. The MS data have been deposited to the ProteomeXchange with identifier PXD000225. KEYWORDS: Protein kinase Cδ (PKCδ), phosphoproteomics, cell death, kinase substrates

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INTRODUCTION

During cell death, PKCδ-CF functions as a kinase to phosphorylate substrates involving in various signaling pathways.12−17 For examples, upon direct phosphorylation by PKCδ, p52ShcA stimulates the extracellular signal-regulated kinase (ERK)-mediated apoptosis in H2O2-treated cells.18 The Ser46 and Ser15 of p53, a tumor suppressor and important regulator of the genotoxic checkpoint to regulate apoptosis, could be phosphorylated by PKCδ in response to DNA damage in MCF-7 and 293T cells19 or nitric oxide-induced apoptosis in dopaminergic cells.20 p73, a homologous gene of p53, also responds to DNA damage via the direct phosphorylation within the DNA binding domain by the PKCδ-CF.21 The phosphorylation of p73β prolongs its half-life and induces apoptosis through its potent transcriptional activity. Also, phosphorylation of Ser355 of VRK1, a nuclear serine/threonine kinase, by PKCδ negatively regulates its expression and activity and leads to cell cycle arrest and apoptosis induction in a p53-dependent manner.22

Protein kinase Cδ (PKCδ), a member of the novel PKC family, is a serine/threonine kinase ubiquitously expressed in eukaryote.1 It is well-known that its activation by the proteolytic cleavage by caspase 3 is involved in the death of various cell types upon exposure to multiple stimuli,2 such as tumor necrosis factor alpha (TNFα), Fas ligand, as well as various DNA damage agents.3−5 However, our previous work showed that the proteolytic cleavage of PKCδ was rapidly induced by NSC606985, a camptothecin analog, while the PKCδ inhibitor rottlerin could completely block the NSC606985-induced mitochondrial transmembrane potential (ΔΨm) loss, caspase-3 activation, and cell apoptosis. Meanwhile, inhibition of caspase-3 by z-DEVD-fluoromethyl ketone (Z-DEVD-fmk) only partially attenuates PKCδ activation and apoptosis,6 suggesting that the proteolytic cleavage of PKCδ might precede the caspase-3 activation. Actually, the increasing lines of evidence showed that the overexpression of the PKCδ-CF is sufficient to induce apoptosis,4,7 and overexpression of either PKCδ kinase inactive mutant8,9 or PKCδ mutated in cleavage sites protects cells from apoptosis.10,11 © 2013 American Chemical Society

Received: January 30, 2013 Published: July 23, 2013 4280

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recombinant plasmid construct, pTRE2hyg-PKCδ-CF, was confirmed by BamH I/Not I digestion and DNA sequencing. To generate U937PKCδ-CF stable transformants, 1 × 107 U937T cells were washed in RPMI 1640 medium and resuspended in 0.2 mL of Isceve’s modified Dulbecco’s medium without FBS. Twenty micrograms of pTRE2hyg PKCδ-CF plasmid in 20 μL of ddH2O was transferred to an electroporation cuvette with a 0.4 cm gap (Bio-Rad, Hercules, CA). Electroporation was performed using a Gene-Pulser II (Bio-Rad) at 170 V and 960 μF. The samples were then transferred to complete RPMI1640 medium. Twenty-four hours later, 1 μg/mL tetracycline, 0.5 μg/mL puromycin, and 500 μg/mL hygromycin B (Clontech) were added. Cells were then incubated at 37 °C in 5% CO2. Positive polyclonal populations were identified based on Western blot after tetracycline removal and were maintained in RPMI-1640 medium supplemented with 10% FBS, 1 μg/mL tetracycline, 0.5 μg/mL puromycin, and 500 μg/mL hygromycin B.

Notably, PKCδ is localized in almost all the subcellular organelles, including mitochondria,23,24 nucleus,8 cell membrane,25 Golgi apparatus,26 endoplasmic reticulum,27,28 and lysosome.29 The p38 mitogen-activated protein kinase (MAPK)/ ERK/JNK signaling pathway could be ignited by the activated PKCδ in different cellular compartments.30 The cytoplasmic PKCδ induces apoptosis through the activation of p38-MAPK and the inhibition of Akt and XIAP (X-linked inhibitor of apoptosis protein), while nuclear PKCδ induces apoptosis through the activation of JNK. In accordance, the downstream targets of PKCδ are closely related to apoptosis distributed in different subcellular organelles. For instance, PLS3,31,32 Mcl-1,33 Bax,34 and Drp135,36 are located in mitochondria, and DNA-PK,37 lamin B,38 Rad 9,39 TOP2α,40 p53,19 p73,21 and hnRNP K41 are nuclear proteins. Albeit an increasing number of PKCδ substrates have been unveiled, the exact mechanisms by which PKCδ regulates apoptosis still remain elusive. A systematic analysis of its substrates in response to cell death would shed lights on the detailed understanding of PKCδ-induced apoptosis. We have previously performed quantitative proteomic analysis with 2D-DIGE to identify proteins that are modulated upon PKCδ activation in NSC606985 treatment in U937 cells.6 Proteomic analysis has also been applied to detect alterations of mitochondrial function in PKCδ null mice.42,43 However, few phosphoproteins with accurate phosphosites have been identified as substrates of PKCδ in these studies. To systematically investigate the overall phosphoproteins involved in signaling pathways triggered by PKCδ, here we performed quantitative analysis of nuclear and cytoplasmic phosphoproteomics in the leukemic U937 cells bearing inducible expression of the PKCδ-CF. As a result, dozens of new substrates of PKCδ are identified and a systematic phosphorylation network initiated by PKCδ is constructed, which would extend our understanding of the PKCδ-directed signaling pathway.

Cell Treatment and Fractionation

U937, the human acute myeloid leukemia (AML) cell line, was grown in RPMI-1640 medium (Sigma-Aldrich, St. Louis, MO) supplemented with 10% heat-inactivated fetal bovine serum (FBS, Gibco BRL, Gaithersburg, ML) in 5% CO2/95% air humidified atmosphere at 37 °C. U937T cells, which were kindly provided by Dr. Tenen D.G. at the Harvard Medical School (Boston, MA),44 were stably transfected with a pUHD-tTA under the control of a tetracycline-inducible promoter. U937T cells were cultured in RPMI-1640 medium supplemented with 1 μg/mL tetracycline (Tet, Sigma-Aldrich) and 0.5 μg/mL puromycin (Sigma-Aldrich) with 10% FBS. 293T cells were cultured in high-glucose (400 mg/dL) Dulbecco’s modified Eagle’s medium containing 10% fetal bovine serum and were maintained at 37 °C in an environment with 5% CO2.

U937-PKCδ-CF cells were labeled with either Lys-12C614N2/ Arg-12C614N4 (named Light, Sigma) or Lys-13C615N2/Arg-U− 13 15 C6 N4 (named Heavy, Cambridge Isotope Laboratories Inc., Andover, MA), followed by incubating in the Tet-free medium for 0−5 days. Cells were harvested by centrifugation for 5 min at 1000g and rinsed twice in ice-cold PBS. The light or heavylabeled cells in the Tet-containing medium (designated as day 0), which were used as a common reference point in the two experimental sets, were mixed at the radio of 1:1 with the reciprocally labeled cells grown in the Tet-free medium for the different days. After being washed in 0.25 M sucrose-solution [10 mM Tris-HCl, 250 mM sucrose plus EDTA-free complete protease inhibitor cocktail (Roche), and phosphatase inhibitor cocktail (Roche)], the pooled cell suspensions were resuspended in hypotonic buffer (10 mM Tris-HCl, pH 7.9, 1.5 mM MgCl2, 10 mM KCl, 1 mM DTT plus EDTA-free complete protease inhibitor cocktail and phosphatase inhibitor cocktail) on ice for 10 min. The cell membranes were disrupted using a Dounce glass homogenizer with 20 time’s gentle strokes. Nuclear pellets were separated from cytoplasm by centrifugation for 10 min at 1000g. The supernatants (cytoplasmic extract) were collected. Nuclear pellets were washed twice with hypotonic buffer and resuspended in the nuclear extraction buffer (50 mM Tris-HCl in 0.25 M sucrose, pH 7.9, 1 mM MgCl2, 1 mM DTT, 0.1% NP40, 250 units/mL Benzonase (Sigma) plus EDTA-free complete protease inhibitor cocktail and phosphatase inhibitor cocktail) for 3 h at 4 °C. The nuclear extracts were centrifuged for 10 min at 12000g. These nuclear or cytoplasmic extracts were mixed with four volumes of ice-cold acetone to precipitate proteins at −20 °C for 2 h, followed by centrifugation for 5 min at 9000g. Pellets were collected until the acetone completely evaporated in room temperature.

Establishment of U937PKCδ-CF Stable Transformant

Apoptosis Assay

The PKCδ-CF coding sequence was amplified by PCR from pEGFP-PKCδ-CF (kindly provided by Dr. Reyland ME in School of Dentistry, University of Colorado Health Sciences Center, Denver, CO) using the primers containing BamH I and Not I (Takara Shuzo Co., Ltd., Kyoto, Japan) restriction sites. The PCR product was subcloned into the expression vector pTRE2hyg (BD clonth, Palo Alto, CA), which expresses a gene of interest (Gene X) in Clontech’s Tet-On and Tet-Off Gene Expression Systems and Tet-On and Tet-Off cell lines. The

For suspension cells, apoptotic cells in the populations were measured with a FACScan flow cytometer (Becton-Dickinson) by the AnnexinV Fluos Apoptosis detection kit (Roche Molecular Biochemicals, Mannheim, Germany). Cells were stained with Annexin-V-FITC for exposure of phosphatidylserine on the cell surface as an indicator of apoptosis, following the manufacturer’s instruction (BD Biosciences). Data acquisition and analysis were performed using a BD Biosciences FASCalibur flow cytometer with CellQuest software. Positively stained by

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EXPERIMENTAL PROCEDURES

Cell Culture

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In-Solution Digestion

annexin-V-FITC only (early apoptosis) and propidiumiodide (late apoptosis) were quantitated, and both subpopulations were considered as overall apoptotic cells.

Cytoplasmic and nuclear proteins were dissolved by denaturation buffer (6 M urea, 10 mM Tris-HCl) and centrifuged for 30 min at 40000g at 4 °C. Protein concentration was measured by the RC DC assay (Bio-Rad, Hercules, CA). Mixed protein lysates were reduced for 2 h at room temperature by addition of 100 mM to a final concentration of 1 mM dithiothreitol (DTT) and then alkylated for 1 h at room temperature in the dark by addition of 550 mM to a final concentration of 5.5 mM iodoacetamide (IAA). The protein mixtures were diluted with 10 mM Tris-HCl, pH 8.0 to achieve a final urea concentration below 1.5 M and digested with sequencing grade modified trypsin (Promega, Madison, WI) at a protein-to-enzyme ratio of 100:1 at 37 °C overnight. Trypsin activity was quenched by acidification using trifluoroacetic acid (TFA) to a final concentration of 1%. Tryptic peptides were desalted on a preequilibrated Sep-Pak Vac tc18 cartridge (Waters), and the columns were washed with 0.1% TFA, 2% ACN. Bound peptides were gradually eluted by the 20%, 30%, 40%, 50%, and 70% ACN in 0.1% TFA. The elution fractions were combined and lyophilized.

Plasmids and Constructs

Full-length human C/EBPα cDNA was amplified by PCR from pCMV-SPORT-C/EBPα plasmid (kindly provided by Dr. Gombart AFin Cedars-Sinai Medical Center, Los Angeles, CA) and subcloned into pCMV-flag expression vector to form the pCMV-flag-C/EBPα plasmid. The pEGFP-PKCδ-CF plasmid was kindly provided by Dr. Reyland ME at the School of Dentistry, University of Colorado Health Sciences Center. The pCMV-Tag2B EDD (37188) was purchased from ADDGENE. Transfection and Immunoprecipitation

After 293T cells were cotransfected with pEGFP-PKCδ-CF or pEGFP-N1 and pCMV-flag-C/EBPα or pCMV-Tag2B EDD using the Lipofectamine 2000 transfection reagent (invitrogen) for 36 h, cells were lysed and sonicated in the buffer (50 mM Tris HCl pH 7.4, 150 mM NaCl, 1% Np-40, 1 mM EDTA, plus protease inhibitors and phosphatase inhibitors) and cell extracts were incubated with mouse antiflag M2-agarose affinity gel (Sigma, St Louis, MI) overnight at 4 °C. The immunoprecipitates were washed three times with the lysis buffer and then were eluted by 2 × Laemmli buffer (125 mM Tris-HCl pH 6.8, 4% SDS, 20% glycerol, 100 mM DTT, 0.01% bromophenol blue) and assessed by immunoblotting with the indicated antibodies.

Phosphopeptides Enrichment

The digested peptides (each containing about 1 mg) were fractionated by strong cation exchange (SCX) chromatography.45 Dried peptide pellets were dissolved in 100 μL of SCX buffer A (5 mM KH2PO4 (pH 2.65), 30% ACN) and then separated on a 5 × 50 mm column packed with polystyrene/divinyl benzene (Mono S 5/50 GL, GE Healthcare), using a Paradigm MDLC pump (Michrom BioResources HPLC) operating at 1 mL/min and a K-2501UV detector (KNAUER) at 214 nm with a 35 min separation gradient from 0% to 21% buffer B (5 mM KH2PO4 (pH 2.65), 30% CAN, 350 mM KCl). Twenty five separate fractions were collected and pooled up to 10 fractions depending on UV intensity. Each SCX fraction was dried in a SpeedVac evaporator (Thermo fisher) and desalted using 100 mg tC18 Sep-Pak cartridges (Waters). Eluted peptides were lyophilized and dissolved in 300 μL of TiO2 wash/ equilibration buffer (65% ACN, 2% TFA, 0.14 M glutamic acid). Peptides that were added to 1 mg of pre-equilibrated TiO2 (5 μm, GL Sciences, Tokyo, Japan) were incubated for 30 min with end-over-end rotation. Nonphosphorylated peptides were removed by four washing steps: once with equilibration buffer, twice with 65% ACN and 0.5% TFA, and finally once with 65% ACN and 0.1% TFA. Phosphopeptides were collected by two elution steps using the solvents: once with 50% ACN and 0.3 M NH4OH, and once with 50% ACN and 0.5 M NH4OH. Both elutes were pooled. The pH of solution was rapidly adjusted to 2.7 using 10% TFA, and then the solution was desalted with MicroTrap C8 (Michrom, U.S.).

Western Blot

The protein lysates dissolved by urea solution (6 M urea, 10 mM Tris-HCl) were mixed with an equal volume of 2 × Laemmli buffer, boiled for 3 min at 100 °C, and then resolved by SDS-PAGE on a 10% gel using a mini gel apparatus (BioRad). Subsequently, the proteins were electrophoretically transferred to an NC membrane (Bio-Rad). The membranes were blocked with 2% BSA solution in TBS (pH 7.6) for 1 h at room temperature and then incubated in primary antibody dissolved in block solution at 4 °C overnight. The protein was probed by antibodies against PKCδ (Santa Cruz Biotech, Santa Cruz, CA), poly(ADP [adenosine diphosphate]ribose) polymerase (PARP, Santa Cruz Biotech), cleaved caspase-3 (Cell Signaling, Beverly, MA), Lamin B (Santa Cruz Biotech), and β-actin (Oncogene, San Diego, CA). After washing, the blots were incubated with horseradish peroxidase (HRP)-conjugated secondary antibody (Dako Cytomation, Denmark) corresponding to the primary antibody in blocking buffer for 1 h at room temperature, and proteins were detected using a luminol detection reagent (Santa Cruz). Preparative SDS-PAGE and In-Gel Digestion

The cytoplasmic and nuclear protein lysates dissolved by urea solution (6 M urea, 10 mM Tris-HCl) were respectively mixed with an equal volume of 2 × Laemmli buffer and boiled for 3 min at 100 °C. 100 μg of denatured proteins were separated by a 10% SDS-PAGE. Subsequently, 17 gel slices were excised from each sample lane, which were subjected to in-gel digestion respectively. Gel slices were destained with 50% ACN in 50 mM ammonium bicarbonate. The dried gel was sequentially reduced with 10 mM DTT and alkylated with 55 mM iodoacetamide. The proteins were digested overnight at 37 °C with sequencing grade modified trypsin (Promega, Madison, WI) at a protein-to-enzyme ratio of 50:1. Peptides were extracted from gel slices with 60% ACN, 1% TFA and then desalted with MicroTrap C8 (Michrom, US).

LC-MS/MS Analysis

The eluted peptides were lyophilized using a SpeedVac (ThermoSavant, USA) and resuspended in 20 μL of 0.1% formic acid/2% acetonitrile. All mass spectrometric experiments were performed on a LTQ orbitrap “XL” mass spectrometer (Thermo Fisher Scientific, San Jose, CA) connected to a Paradigm MDLC nanoflow LC system (Michrom BioResources, USA) via an ADVANCE Spray Source LC-MS interface (MICHROM BioResources, USA). The peptide mixture was loaded onto a 15 cm with 0.1 mm inner diameter column packed with Magic C18AQ 3-μm reversed phase resin (Michrom BioResources, USA), and separated with 120 min linear gradient from 100% solvent A (0.1% formic acid/2% acetonitrile/98% water) to 4282

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35% solvent B (0.1% formic acid/100% acetonitrile) at a flow rate of 500 nL/min. The spray voltage was set to 1.5 KV, and the temperature of the ion transfer capillary was 160 °C. The mass spectrometer were operated in positive ion mode and employed in the data-dependent mode to automatically switch between MS and MS/MS using the Tune and Xcalibur 2.5.5 software package. One full MS scan from 350 to 1800 m/z was acquired at high resolution R = 100,000 (defined at m/z = 400), followed by fragmentation of the ten most abundant multiply charged ions (singly charged ions and ions with unassigned charge states were excluded). In CID mode, multistage activation (MSA) with excitation at pseudo-mass-losses of 97.97, 48.99, and 32.66 Da from the precursor was performed on enriched phosphopeptide sample with two technical replicates. Ions selected for MS/MS were limited on a dynamic exclusion list for 30 s.

Gene Ontology and Protein Interaction Network Analysis

The annotation tool DAVID 6.749 (http://david.abcc.ncifcrf. gov/home.jsp) was used for functional annotation and GO enrichment analysis of phosphoproteins. The P value threshold was kept below 0.01. The networks of interacted phosphoproteins were constructed on the basis of the STRING 9.0 database 50 (http://string-db.org/) and KEGG database (http://www.genome.jp/kegg/). Subsequently, interaction data were processed with Cytoscape 2.8.251 (http://www. cytoscape.org/) to construct functional networks. CentiScape 1.0,52 a Cytoscape plug-in software, was applied to the PPI network for calculating topological parameters. Phosphorylation Motif Analysis and Substrate Prediction

Motif-X 1.2 10.05.0653 (http://motif-x.med.harvard.edu/motif-x. html) was used to enrich overrepresented motif form phosphopeptides. Consensus motifs were identified with the following parameters: prealigned sequence with 13 amino acids centered around a serine or threonine residue, occurrence at 15, P values below 10−6, and the complete human IPI protein database as background data. However, if the number of phosphopeptides was below 100, the parameters were little modified to occurrence at 5 and P values below 10−4. In order to predict substrates of a specific kinase, GPS 2.154 (groupbased prediction system) searched the phosphopeptides as the same prealigned sequence as Motif-X did. Due to the family hierarchy and the strong conservation between the PKC families, we predicted the substrates of PKCδ by four hierarchies, including “AGC/PKC”, “AGC/PKC/Delta”, “AGC/PKC/Delta/PKCd”, and “AGC/PKC/Delta/PKCt”, with a high stringency of 2% FPR. The phosphopeptide achieved a score that exceeded the threshold of any abovementioned hierarchy and was then screened as a candidate substrate of PKCδ.

Database Search and Data Analyses

All MS/MS ion spectra were analyzed using Sequest version v.27, rev. 1146 (Thermo Fisher Scientific, San Jose, CA), which were incorporated into the Sorcerer engine version 4.0.4 build (Sage-N Research). Sequest were set up to search the ipi.HUMAN.v3.65 database (86379 entries) (ftp.ebi.ac.uk/pub/ databases/IPI/current) with its target-decoy database as a reversed complement, assuming the semienzyme tryptic digestion allowed for three missed tryptic cleavages with full mass from 600 to 4600. The precursor ion tolerance was set to ±10 ppm, and MS2 ions tolerance was set to 1 Da. Search parameters for non-phosphopeptides were allowed for a static modification of carbamidomethyl cysteine (C, +57.021465) and dynamic modifications of oxidized methionine (M, +15.99492), SILAClabeled lysine (K, 6C2N, +8.014199), and SILAC-labeled arginine (R, 6C4N, +10.008269) with up to four total dynamic modifications and up to three of any particular dynamic modification. One more search parameter for phosphopeptides was allowed for dynamic modifications of phosphorylation serine (S), threonine (T), and tyrosine (Y) (+79.966331) with up to six total dynamic modifications and up to four of any particular dynamic modification. The “ASCORE algorithm” was selected to assess the possibility of phosphorylation sites.47 DATSelect 2.0.39 was used to filter and group the data files derived from SORCERER-SEQUEST. The filter thresholds were set to XCorr 2+, 3+, 4+: >2.5, >3.0, >3.5, and DeltaCN > 0.08. For protein identification, two peptides were required with estimated false-discovery rates (FDR) < 1%. For phosphopeptide identification, only unique peptide was required with FDR 0.125 if the FDR for phosphopeptide assignments was >2%. Furthermore, additional filtering was used to remove those indubitably false phosphopeptides, including sequences that contained both heavy and light isotopic variants of arginine and lysine. Finally, the FDR fall within 1%. Two data files, including a raw data converted by RAWXreact 1.9.9.1 and its corresponding annotated file filtered by DATSelect, were processed by Census 1.72,48 to quantify the area ratio for phosphopeptides and peptides in fractions (cytoplasm and nucleus) of cells grown in Tet-free medium for the different days. The ratios for the intensities of phosphorylated peptides or peptides of heavy- or light-labeled cells in Tet-free medium for 0−5 days against those in the reciprocally (light- or heavy-) labeled cells at day 0 were calculated. The values of ≥1.5 and ≤0.67 were defined as be up-regulated and down-regulated, respectively.

In Vitro Kinase Assays

In vitro kinase assays, including purified PKCδ assay and cell lysate assay, were performed to confirm the kinase−substrate relationship, verify the phosphoproteome study, and specify the sites phosphorylated by PKCδ. Non-phosphopeptides of candidate substrates were synthesized by ChinaPeptides, and the purity was >90%. For in vitro kinase assay with purified PKCδ, each synthetic peptide (1 mM) as a substrate was mixed with the active PKCdelta (0.584 μg/μL) (Millipore) to a final concentration of 100 μM in the 10 μL reaction mixture containing 5 mM MOPS, pH 7.2, 2.5 mM β-glycerophosphate, 1 mM EGTA, 0.4 mM EDTA, 5 mM MgCl2·6H2O, 0.05 mM DTT, 1 mM ATP (Millipore), and 1 × PKC Lipid Activator (Millipore). For in vitro kinase assay with cell lysate, 3 × 106 of U937-PKCδ-CF cells treated by withdrawal of tetracycline for day 0 and day 3 were washed with ice-cold PBS and lysed with 50 μL of ice-cold lysis buffer (10 mM K2HPO4, pH 7.0, 0.1% Brij 35, 0.1% deoxycholic acid Na, 0.5% NP-40, 1 mM EDTA, 5 mM EGTA, 50 mM β-glycerophosphate, 10 mM MgCl2.6H2O, 1 mM sodium orthovanadate, 2 mM DTT, 1 tablet/10 mL protease inhibitor cocktail (Roche), and 1 tablet/10 mL phosphatase inhibitor cocktail (Roche). Homogenates were centrifuged at 10,000g for 15 min at 4 °C, and the supernatant was used as lysate. Protein concentration was quantified by modified BCA. Cell lysates was aliquoted and quickly frozen by liquid nitrogen. Subsequently, 4 μg of cell lysate was mixed to a final volume of 10 μL containing 25 mM Tris pH 7.5, 7.5 mM MgCl2, 0.2 mM EGTA, 7.5 mM β-glycerophosphate, 0.1 mM sodium orthovanadate, 0.1 mM DTT, 5 mM ATP, and 100 μM 4283

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each synthetic peptide. Kinase reactions were incubated at 30 °C for 60 min and were stopped with 100 μL of 1% trifluoroacetic acid (TFA). The reaction buffer was desalted with a solid phase extraction cartridge (Sep-Pak tC18 50 mg, Waters). Phosphopeptides were enriched by MicroTrap Titanium Dioxide Phosphopeptide Trap (1 × 8 mm, Michrom) as the manual described. In brief, the reaction products dissolved in the equilibration buffer (2% ACN, 5% TFA) were loaded into the pre-equilibrated TiO2 MicroTrap, followed by 10 CV wash with 80% ACN and 5% TFA, and eluted by 5 CV with 2% ACN and 1% NH4OH. The conversion rate of phosphorylation could be calculated from the spectrum detected by MALDITOF (4700 Proteomics Analyzer, Applied Biosystems), and the formula is E = [I(p)/(I(p) + I(NP))] × 100% (E: the conversion rate of phosphorylation; I(p) and I(NP): the intensities of phosphopeptide and the corresponding non-phosphopeptide in the same spectrum of MALDI-TOF). Then, LTQ-Orbitrap was used to identify the specific phosphosite, and pLabel 2.455 was used to assign the appropriate “y-ions” or “b-ions” to the fragment ions.

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Figure 1. Experimental strategy and workflow of phosphoproteomics analysis in PKCδ-CF induced cell death. U937-PKCδ-CF cells were encoded with heavy arginine and lysine isotopes and cultured in tetracycline-free medium for 2, 3, 4, and 5 days. Cells from each day were mixed respectively with cells from day 0 encoded with light Arg/ Lys isotopes (experiment 1), or heavy Arg/Lys-labeled cells at day 0 were mixed with the light Arg/Lys-labled cells in tetracycline-free medium for 2, 3, 4, and 5 days (experiment 2). Those equally mixed cells were separated into cytoplasm and nuclear fractions, and the proteins were enzymatically digested. The peptide mixtures were separated by strong-cation exchange chromatography (SCX), and the phosphopeptides were enriched by titanium dioxide (TiO2) beads, followed by LC-MS. The MS raw data were processed by the SorcererSequest for protein identification and then by Census for phosphopeptide quantitation. Finally, bioinformatics were applied for data mining, and an in vitro kinase assay was performed to verify the predicted substrates of PKCδ.

RESULTS

Phosphoproteome Modulation in Response to the Ectopic Expression of PKCδ-CF

To systematically identify the phosphorylation events during the cell death induced by PKCδ, the quantitative phosphoproteomics strategy was applied to investigate leukemic U937 cells carrying inducible expression of the PKCδ-CF. For this purpose, we first assessed the induction of PKCδ-CF expression and cell death after tetracycline withdrawal in U937-PKCδ-CF cells. Consistent with our previous report,9 PKCδ-CF accumulated in the cytoplasmic and nuclear fractions, respectively, on day 3 and day 4 after tetracycline withdrawal, followed by the appearance of cleaved fragments of caspase-3 and PARP1 (Supporting Information (SI) Figure S1A). Given the significant increase of cell death on day 4 post tetracycline withdrawal (SI Figure S1B), day 2 and day 3 were considered as the initiation stage of apoptosis, and the last two days as the execution stage. Then, these cells were efficiently labeled by the heavy and light stable Arg/Lys isotope (SI Figure S2) and cytoplasmic and nuclear proteins extracted from these cells were applied to the quantitative phosphoproteomics analysis according to the flow chart shown in Figure 1.56−58 As a result, 3000 unique phosphosites on 2531 phosphopeptides corresponding to 1544 nonredundant phosphoproteins were identified (Figure 2A and SI Tables S1, S2, S9, and S10). Among these phosphoproteins, 1173 proteins containing 2070 phosphosites in the cytoplasm and 698 proteins containing 1544 phosphosites in the nucleus were detected. The proportions of serine, threonine, and tyrosine phosphorylation were in accordance with the previous report,59 in which serine phosphorylation predominately accounted for 80% of all phosphosites (Figure 2B). Meanwhile, monophosphosites hold overwhelming superiority against multiphosphosites and made up about 85% of all phosphosites (Figure 2C). All identified unique phosphopeptides and unique phosphosites were shown in SI Tables S1 and S2, respectively. Furthermore, the quantitative phosphoproteome data set was analyzed with the Census exploited by the John R. Yates’s lab48 (SI Tables S3, S9, and S10), and the fold threshold against cells at day 0 was set at |log2(H/L)| = 0.58 (here H and L indicate heavy and light isotopes, respectively) to evaluate the

significantly regulated phosphopeptides. As shown in Figure 2D, regulated phosphopeptides in both cytoplasm and nucleus had similar trends at the same time point. At day 0, the ratio of phosphopeptides was about 3% out of the given range, while at day 2 and day 3 post tetracycline withdrawal, the number changed to 7% and 29−45%, respectively. Finally, the number dramatically reached to 80% at days 4 and 5. While phosphopeptides of late stage were predominantly down-regulated, the numbers of the down-regulated and up-regulated phosphopeptides of the early stage were to a similar degree (Figure 2A). However, 21% phosphopeptides were up-regulated and only 8% were down-regulated in the cytoplasm at day 3, and on the contrary, 42% were down-regulated and only 3% were upregulated in the nucleus at the same time (Figure 2D). The level of protein phosphorylation was regulated not only by kinases and phosphatases but also by other biological processes, such as protein translocation, protein degradation, protein splicing, and other post-translational modifications (PTMs) on the same site. In order to define the correlation between phosphorylation level and the corresponding protein level, we also acquired the protein ratio histogram of each time point (SI Figure S3 and SI Tables S11 and S12). Unexpectedly, a similar ratio distribution was obtained between phosphopeptides and peptides, especially at the late stage of cell death. Furthermore, we normalized every phosphopeptide ratio to its corresponding protein ratio (SI Table S13). However, only about 50% phosphopeptides could be normalized due to the absence of the corresponding protein completely or at a certain time point 4284

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Figure 2. Phosphoproteome profiling modulated by PKCδ. (A) The pie chart summarized the total number of phosphoproteins, phosphopeptides, and phosphosites respectively identified in cytoplasm or in nucleus. The dysregulated phosphopeptides were classified into early (day 2 and day 3) or late (day 4 and day 5) responsive stages. (B) According to the identified phosphosites, the distribution of the individual residue was presented: S, serine; T, threonine; Y, tyrosine. (C) According to the identified phosphopeptides, the distribution of phospho-groups per peptide was presented: mono, single phosphosite per peptide; multi, no less than two phosphosites per peptide. (D) Distribution of log2 ratios of all detected phosphopeptides in experiment 1. The values between the dotted lines showed the percentage of the unregulated phosphopeptides at the indicated time, and the values out of the dotted line showed the ratio of peptides that were significantly increased (≥1.5-fold) or decreased (≤0.67-fold): Cyto, cytoplasm; Nu, nucleus.

the roles of these differential phosphorylations in this study, gene ontology (GO) enrichment analysis using DAVID tools49 was applied to the early responsive phosphoproteome consisting of up-regulated and down-regulated phosphopeptides in both the cytoplasm and nucleus (Figure 3, SI Table S6). Based on the analysis of the cellular component (CC), as expected, all regulated phosphoproteins detected in the nucleus were annotated as nuclear proteins, and most in the cytoplasm were annotated as cytoplasmic proteins, indicating the proper separation of cytoplasm and nucleus. In consistence with the prediction, several hundred regulated phosphoproteins identified in this research were involved in diverse functions, such as RNA processing, RNA/mRNA splicing, protein biosynthesis, the ubiquitin-dependent protein catalytic process, the cell cycle process, and others. To further seek the relationship between the gene functions and the dynamic phosphoproteome modulations,61 the early regulated phosphopeptides with similar dynamics were first clustered by a Short Time-series Expression Miner (STEM),62 and five significantly dynamic patterns were enriched (SI Figure S4). Then, each of them was analyzed by GO enrichment (SI Table S7). The results showed that some deregulated phosphopeptides with similar dynamics executed the same function in response to the apoptosis. For example, the clusters of up-regulated phosphopeptides in cytoplasm at both day 2 (SI Figure S4A) and day 3(SI Figure S4B) enriched protein kinase C binding proteins, including MARCKS and FLNA, and cell cycle related proteins, including CUL4A, CDK11A, RB1, STMN1, PSMD2, CLASP2, and SART1. Both the clusters of deregulated phosphopeptides in nucleus (SI Figures S4D and S4E) enriched methylation related proteins,

(SI Table S13) and about 25% regulated phosphopeptides could be still detected after normalization (SI Table S14). Then, we compared the phosphopeptide ratio and normalized ratio to evaluate the deviation between them. As shown in SI Figure S20, there was no significant difference at days 0, 2, and 3. However, the two differed greatly at days 4 and 5, as the normalized ratios were obviously higher than the phosphopeptide ratios. This was reasonable, since the phosphopeptides and the corresponding proteins were down-regulated in parallel at the late stage of apoptosis. The results suggested that the changes of most phosphopeptides were independent of the change of protein at the early stage of apoptosis, but the changes of most phosphopeptides were accompanied with the degradation of protein at the late stage of apoptosis. Taken together, at the late stage of apoptosis, the levels of peptides and phosphopeptides declined in parallel (SI Figure S3B), indicating that at this stage down-regulation of phosphopeptides was mainly caused by protein degradations.60 Our results showed that the trends of the phosphopeptides modulation were consistent with the processing of cell death and the phosphoproteome modulations that happened before the third day were more closely related to PKCδ. Based on this reason, the following data mining on network construction and substrate prediction was focused on the phosphoproteome data set of the early stage which is listed in SI Table S4 and protein data in SI Table S5. Network of the Early Responsive Phosphoproteins to PKCδ

Complicated biological processes and multiple signaling pathways were involved in cell death. To get an overview of 4285

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Figure 3. Heat map of the early responsive phosphoproteins analyzed by gene ontology (GO). GO enrichment analysis of cellular components, molecular functions, and the biological process was applied to four groups derived from the early responsive phosphoproteome to PKCδ-induced apoptosis using DAVID 6.7 (Cyto_Up, upregulated phosphoproteins in cytoplasm; Cyto_Down, downregulated phosphoproteins in cytoplasm; Nu_Up, upregulated phosphoproteins in nucleus; Nu_Down, downregulated phosphoproteins in nucleus). Only GO terms were exhibited if shared by at least three proteins in one cluster and p-value 20, color code ranging from 20 to 55.

were searched against the STRING database to evaluate the interaction between them with high confidence 0.7. The output data from STRING combined with those 26 proteins and TOP2A directly related to PKCδ were inputted into the Cytoscap,63 a tool to create the network on the local PC. After manually integrating interactions and deleting the isolated proteins, 223 nodes and 461 edges were screened out. As shown in SI Figure S6, PKCδ (purple, diamond) was designated as the central point that directly interacted with (red lines) 27 proteins as the first-order signaling, through which the signaling was passed down. Moreover, to highlight the most important biological procedures hiding in the elaborate networks, node degree, to a certain extent, provided an effective way to dig into the network. The scatter plot of node degree was charted by the CentiScape,52 a plug-in of Cytoscape (SI Figure S7). The critical point was set at 10% for the most highly distributed nodes; that was to say, the hub node except PKCδ must have no less than 10 edges. According to this principle, there were 21 proteins, including 6 up-regulated, 11 downregulated, and 4 undulated, as hub proteins in the PKCδinduced network (SI Table S8). It was notable that three hub proteins, SRSF11, CREB1, and DNMT1, were directly related to PKCδ according to STRING analysis and there were 15 down-regulated phosphoproteins among all 21 hub phosphoproteins. All 21 hub proteins with PKCδ were uploaded to the online server of STRING and generated a new core network that connected all of them (Figure 4B). Intriguingly, the PPI analysis of the core network demonstrated that RNA processing was a significant procedure under proteolytic activation of PKCδ (SI Figure S8A), which was in exact consistence with

including EZH2, GATAD2A, DNMT1, MLL2, and DMAP1, and the ubiquitination related proteins, including SNRNP200, DNMT1, NUSAP1, HNRNPC, RBM25, RBBP6, RNF20, MYCBP2, and CTR9. The results inferred that PKCδ could bind and phosphorylate its downstream targets to regulate varying signaling pathways, such as protein methylation, protein degradation, and so on, and finally influence the biological processes, such as the cell cycle. Furthermore, to screen how many early responsive phosphoproteins in this research had been reported to directly interact with PKCs, all 487 modulated phosphoproteins at the early stage were searched against the STRING database with the threshold set at 0.15.50 Consequently, 26 phosphoproteins, including 21 up-regulated and 5 down-regulated, were regarded as the direct interacting proteins of PKCδ (Table 1). TOP2A (topoisomerase II alpha), a known direct substrate of PKCδ, found in our phosphoproteome data, was not linked to PKCδ in the STRING database. To figure out the relationship between these proteins, the PPI network of the 26 proteins together with PKCδ was created by STRING (Figure 4A). Besides their interaction with PKCδ, Figure 4A showed that those proteins were also associated among themselves. With the GO-enrichment analysis integrated into STRING, additionally, 10 proteins, including IRS2, DNAPK (HYRC), CREB1, CFL1, BCLAF1, HMGB1, NOTCH2, TOP2A, PLA2G4, and NOS3, that accounted for 37% of the 26 interacting proteins (p = 8.44 × 10−8), were involved in the regulation of apoptosis (SI Figure S5). To systematically investigate the interactions among regulated phosphoproteome, all the modulated phosphoproteins 4286

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4287

PLCB3

MAP3K2

HYRC

MAPT

TOP2B

CREB1

CFB1 JUNB BCLAF1

DNMT1

APLP1 FLNA

HMCB1 PNN NOTCH2

FIP1L1 STMN1

FAP TOP2A

PLA2G4A

NOS3

LMNB2

CEBPE

SRSF11

up

up

up

up

up

up

up up up

up

up up

up up up

up up

up up

down

down

down

down

down

uniport

Q05519

Q15744

Q03252

P29474

P47712

Q12884 P11388

Q6UN15 P16949

P09429 Q9H307 Q04721

P51693 P21333

P26358

P23528 P17275 Q9NYF8

P16220

Q02880

P10636

P78527

Q9Y2U5

Q01970

Q9Y4H2 Q07157

P29966

0.232

0.414

0.619

0.82

0.849

0.167 0

0.179 0.179

0.198 0.191 0.187

0.198 0.198

0.243

0.324 0.3 0.279

0.366

0.443

0.604

0.655

0.8

0.802

0.817 0.813

0.936

string

N

N

N

N

C

C CN

C C

C C N

N CN

C

C N C

N

C

C

CN

C

CN

C C

CN

Com

phosphosite(s)

reported

S206

S108

S403

S483

S383 S1106/S1247/ S1347/S1377 S436

S259/S492 S16/S38

S35 S66 S1778

S578 S2152/S2526

S3 T255/S259 S177/S578/ S658 S714

S156

S1466

S721

S3205

S153

S1105

reported phospho site(s)

Nterminal(1−446)

S133

S313

S152/S156/S163 (rat brain)

known

known

known

unknown

known

unknown known

known known

T495;S1177(Dephospho)

S16/S25

unknown T614 known/ unknown known known known

known

known known known

known

known

known

known

known

known

S25/S46/S101/ known S150 5391/S1174 known T1513/T516 unknown

reported kinase

events

PKCδ

PKC

PKC

PKCδ

PKCδ

TOP2B-mediated differentiation block

kinase-substrate

kinase-substrate

up regulation

Br. J. Haematol. 2010, 148(6), 868− 878

Biochemists 1997, 36(15), 4643−4649 J. Biol. Chem. 2003, 278(26), 23561− 23569 Lab. Invest. 2009, 89(8), 948−959

Blood 2003, 101(10), 3885−3892 Mol. Cell. Biol. 2007, 27(24), 8480− 8491 μBC Biol. 2011, 9(1): 31

J. Biol. Chem. 2004, 279 (29), 30123−30132

in vivo

in vivo

Mol. Cell. Biol. 2006, 26(9), 3414− 3431 Biochem. Biophys. Res. Commun. 2004, 321(3), 657−664 J. Biol. Chem. 2001, 276 (21), 17625−17628 J. Biol. Chem. 1991, 266 (30), 20018−20023 Leukemia 2010, 24(4), 729−739

2D gels J Biochem Biophys-Methods 1997, 36 (1), 51−61

IP

in vitro in vitro

in vitro

CHIP

in vivo

in vitro

kinase-sub strate.apoptosis in vitro

bistratene A (a specific activator of PKCδ)

NOTCH2-dependent CD23 expression

PKCδ

PKCδ

interaction

kinase-substrate kinase-substrate

kinase-substrate

JUNB expression level interaction, apoptosis

TOP2B-mediated differentiation block kinase-substrate

kinase-substrate

Mol. Cell. Biol. 1998, 18 (11), 6719− 6728 J. Biol. Chem. 1992, 267 (22), 15721−15728 Leukemia 2010, 24 (4), 729−739

ref Arch. Biochem. Biophys. 1998, 359 (2), 151−159 Cancer Res. 2009, 69(4), 1350−1357 Neurosci. Lett. 2010, 468 (3), 254− 258 Biochem. Biophys. Res. Commun. 1997, 240 (2), 304−308 J. Clin. Invest. 1995, 96 (1), 438−446

exp in vivo

PKC-dependent activation of Raf-1 and MAPKs kinase-substrate, apoptosis in vitro

in the PLC/Ca2+ system

in IRS2 signal pathway during aglycemic hypoxia

kinase-substrate

PKC

PKCα, PKCβ, PKCβII, PKSδ, PKCγ, PKCθ, PKCε, PKCμ PKC PKCα

PKCδ PKCδ

PKCδ, p90RSK

PKCδ

PKC

PKCδ

PKC

PKC

PKCδ PKCβ, PKCδ

PKC

up, up-regulated phosphoproteins; down, down-regulated phosphoprotetns; protein, official gene symbol; uniport, the accession number for Swiss-Port; string, the PPI score for PKCδ from STRING; C, cytosol; N, nucleus; CN, the protein identified both in cytosol and in nucleus; phosphosite(s), the phosphosite(s) identified in this study; reported, the known or unknown phosphosite(s); reported phosphosites, the reported phosphosites relevant to PKCδ; reported kinase, the reported kinase relevant to the PKC family; events, the protein involved in the PKCδ signaling pathway.

a

IRS2 TJP1

up up

protein

MARCKS

up

status

Table 1. Early Responsive Phosphoproteins Relevant to PKCδ Extracted from STRINGa

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Figure 4. Network analysis of the early responsive phosphoproteome. (A) The network for phosphoproteins directly interacting with PKCδ. All early regulated phosphoproteins were searched against the STRING (V9.0) database with a 0.15 cutoff score, and 26 known PKCδ-interacting proteins were screened out. All 26 proteins were then submitted to STRING to create the direct PPI network for PKCδ. (B) The core PPI network for the early response to PKCδ. Hub proteins were defined as the node at the top 10% of the degree distribution, which had at least 10 edges. All 21 hub proteins were then submitted to STRING with a 0.15 cutoff score to create the core network in the process of PKCδ-induced apoptosis. Color and node size are arbitrary.

GO analysis as shown in Figure 3. In conclusion, three networks, including the network for proteins directly interacting with PKCδ, the PPI network, and the core PPI network for the

early response to PKCδ-CF, were constructed with our phosphoproteome data of apoptosis induced by PKCδ. These networks suggested that many important phosphoproteins 4288

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Figure 5. Phosphorylation consensus sequences and the predicted kinases. Consensus sequences identified with Motif-X for up-regulated (A) and down-regulated (B) early responsive phosphopeptides, and for the candidate substrates of protein kinase C predicted by GPS (C). Matches, the occurrences of phosphopeptides within a cluster; Fold, the fold increase for overrepresented sequence motifs as compared to the whole proteome. (D) Predicted kinases during the early response to PKCδ-CF. According to NetworkKIN, a PKCδ-driven kinase network, composed of all five kinases, including PKCδ (PRKCD), P38MAPK (MAPK1), PKA (PRKACA), CDK5, and CK2 (CSNK2A2), was created by STRING with a 0.15 threshold. (E) U937-PKCδ-CF cells were incubated in medium with or without tetracycline for 3 days and were treated for an additional 24 h in the presence or absence of 10 μM SP600125, a specific JNK inhibitor. The indicated proteins were determined. Here, Pi-JNK indicated the phosphorylated JNK. Δcaspase3 and ΔPARP1 indicated activated fragments of caspase-3 and cleaved fragments of PARP, respectively.

x-x-x-S-D-x-E and x-x-D-S-x-x-x, were identified. Compared to the 17 phosphopeptides and 19-fold enrichment in the upregulated phosphoproteome, x-x-x-S-D-x-E had been unbiasedly enriched in the down-regulated phosphoproteome with 22 phosphopeptides and 31-fold enrichment. In the meantime, x-xD-S-x-x-x was only identified in the down-regulated phosphoproteome. While the above two groups of motif were detected in both up- and down-regulated phosphosites, R-x-x-S-x-x-x, substrate recognition of basic serine/threonine kinase was detected only in the up-regulated phosphoproteome with 33 phosphopeptides and 3-fold enrichment. Besides the fact that arginine occupied the −3 site of phosphosites in this motif, arginine and lysine almost dominated the −2 and −4 sites, and a hydrophobic amino acid, such as phenylalanine or isoleucine, dominated the +1 site. Furthermore, the kinase motifs of the regulated normalized phosphorylation sites were similar with the enriched motif of the regulated non-normalized phosphorylation sites (SI Figure S21). These results inferred that alkaline serine/threonine kinases, such as PKCδ, took chief charge of substrate phosphorylation, and acidic serine/threonine kinases were partially involved in the early response to PKCδ-CF.

directly or indirectly regulated by PKCδ composed complex signaling pathways of apoptosis initiation. PKCδ Downstream Kinases

Although phosphoproteome cannot directly recognize the relationship between kinase and substrate, the characterization of the linear sequence motif from modulated phosphopeptides could be used to anticipate which kinase may be involved in the signal transduction.64 By the same token, to speculate the kinases that participated in the signaling cascades following PKCδ-CF expression, the consensus motifs from the early responsive phosphosites to PKCδ-CF were first enriched by Motif-X.53 As shown in Figure 5A/B, there were seven kinds of consensus motifs within three groups. Among them, the substrate recognition motifs of proline-oriented kinase, including x-x-x-S-P-x-x, x-x-x-T-P-x-x, x-x-x-S-P-x-K, and x-x-x-S-P-x-P (x represents any amino acid), accounted for more than 70%, of which the former two were predominant. It was known that these two predominant motifs were the leading contributors to phosphorylation events, and thus 104 and 126, respectively, enriched in up- and down-regulated phosphosites corresponded to only 4- to 7-fold enrichment. In addition, substrate recognition motifs of acidic serine/threonine kinase, including 4289

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4290

Cyto Cyto Cyto Cyto Cyto Cyto Cyto Nucl Cyto Cyto Cyto Nucl Nucl Cyto Cyto Cyto Cyto Cyto Nucl Nucl Cyto

Cyto Nucl Cyto Nucl Cyto Cyto Cyto Cyto Cyto Cyto Cyto Nucl Cyto Nucl Cyto

Nucl

IPI

IPI00908880 IPI00954819 IPI00935649 IPI00619921 IPI00910983 IPI00872650 IPI00644576 IPI00644576 IPI00012491 IPI00168609 IPI00217466 IPI00217466 IPI00943093 IPI00911008 IPI00208659 IPI00015568 IPI00966657 IPI00719650 IPI00472161 IPI00956371 IPIOO 181283 IPIOO 181283 IPI00910253 IPI00000015 IPIOO170934 IPI00815713 IPI00797331 IPI00329547 IPI00013174 IPI00798375 IPI00552213 IPI00965841 IPI00555865 IPI00413173 IPI00719650 IPI00026271 IPI00216613

Com

protein

SART1 SRSF4 STK11IP TCOF1 WIPF1 ZC3H13 C0AA DDX5 DLG1 IGLV1−40 LRWDl MKI67 MMP23B RPS14 SFPQ

PLCB3

APBB1IP CDK17 CUL4A DIDO1 EIF4B FAM129A FLNA FLNA GUCY2C HBS1L HIST1H1D HIST1H1D HMHA1 IRF2BP2 ITPR1 KBTBD11 MATR3 MMP23B NFRKB NOP56 PLCB3

0.802

0.802

0.198 0.198

STRING

RVSEVEEEKEPVPQPLPSDDTR GESENAGTNQETR RASISEPSDTDPEPR KLSGDQPAAR NLSLSSSTPPLPSPGR LRSPSNDSAHR LSESQLSFR LLQLVEDRGSGE EQMMNSSISSGSGSLR GNSNRPSGVPDRFSGSK RPDDVPLSLSPSKR AQSLVISPPAPSPR LSFPRNLLSPR IEDVTPIPSDSTR FATHAAALSVR

HNSISEAK

RSSDTSGSPATPLK RASLSEIGFGK KGSFSALVGR RNSVERPAEPVAGAATPSLVEQQK AASIFGGAKPVDTAAR RASAILPGVLGSETLSHEYFQESEEEKQPEVPSSLAK RAPSVANVGSHCDLSLK RAPSVANVGSHCDLSLK STNCVVDSRMVVKITD LSSTDSLESLLSK KASGPPVSELLTK KASGPPVSELETK AGSPSPQPSGELPR NSNSPPSPSSMNQR RDSVLAASR AGSRPQSPSGDADAR RDSFDDRGPSLNPV LSFPRNLLSPR KGSLAALYDLAVLK KFSKEEPVSSGPEEAVGK HNSISEAK

sequence

identified peptide

Table 2. Candidate Substrates of Protein Kinase C Predicted by GPSa

3 3 3 3 3 3 7 10 14 3 12 12 9 11 9

3

3 3 3 3 3 3 4 4 1 3 3 3 3 2 3 3 3 2 3 3 3

site

P

S S S S S S S S S S S S S S S

S

S S S S S S S S S S S S S S S S S S S S S

134.06 38.04 30.92 0 29.72 1.96 40.6 0 22.33 4.82 0.74 42.82 0 10.97 70.46

19.98

25.97 15.6 31.34 27.68 82.28 18.44 63.08 72.67 33.64 10.1 73.89 82.4 7.35 6.3 41.48 16.29 81.23 0 69.06 70.29 28.49

ASCORE

RGRRVSEVEEEK REGRGESENAGIN VRVRRASISEPSD SRKRKLSGDQPAA LPQRNLSLSSSTP TSGRLRSPSNDSA LSESQLSFRRSPT LVEDRGSGRSRGR ISSGSGSLRTSQK LLIYGNSNRPSGV PLSLSPSKRACAS ISPPAPSPRKTPV FPRNLLSPRETRR TPIPSDSTRRKGG THAAALSVRNLSP

DRKRHNSISEAKM

PPVRRSSDTSGSP RRSRRASLSEIGF EAPRKGSFSALVG ADVRRNSVERPAE QSTRAASIFGGAK SPARRASAILPGV RRRRAPSVANVGS RRRRAPSVANVGS VHGRIKSTNCVVD PSVRLSSTDSLES AGKRKASGPPVSE AGKRKASGPPVSE KKNRASGSPSPQRS STTRRNSNSPPSP NAARRDSVLAASR AGERAGSRPQSPS RHFRRDSFDDRGP LTYRILSFPRNLL NAGRKGSLAALYD KKKRKFSKEEPVS DRKRHNSISEAKM

sequence

13 13 13 13 13 13 13 13 13 13 13 13 13 13 13

13

13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13

length

aligned seq

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

7

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

site

P

S S S S S S S S S S S S S S S

S

S S S S S S S S S S S S S S S S S S S S S

motif

...R..S...... ...R..S...... ...R..S...... ...R..S...... ...R..S...... ...R..S...... ......S.R.... ......S.R.... ......S.R.... ......S.R.... ......S.R.... ......S.R.... ......S.R.... ......S.R.... ......S.R....

...R..S......

...R..S...... ...R..S...... ...R..S...... ...R..S...... ...R..S...... ...R..S...... ...R..S...... ...R..S...... ...R..S...... ...R..S...... ...R..S...... ...R..S...... ...R..S...... ...R..S...... ...R..S...... ...R..S...... ...R..S...... ...R..S...... ...R..S...... ...R..S...... ...R..S......

3.516 3.511 2.31

2.804

4.729 2.397 4.467 3.462 2.436 2.339 4.494 4.177 3.538

3.961

2.278 2.278 2.581 3.593 5.16 2.671 4.576 2.504 2.567 3.971 3.961

2.973 5.823 2.385 5.182 2.755 2.717 5.121 5.121

AGC/ PKC >2.201

4.913

3.935

4. 174

4.391

4.043

3.696

3.652

4.13 4.522 4.435 4.957

AGC/ PKC/ Delta/ PKCd >3.478

4.484

3.968

4

4.419

4.29 4.645 4.516 4.323

AGC/ PKC/ Delta >3.806

4.375

4.625 4.5 4.625 5.125

4.25

AGC/ PKC/ Delta/ PKCt >4.125

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4291

Cyto Cyto Nucl Cyto Cyto Nucl Cyto Cyto Cyto Nucl Nucl Nucl Cyto Cyto Cyto Nucl Nucl Cyto Cyto Nucl Cyto Nucl Nucl Cyto Nucl Nucl Nucl Cyto

protein

SPTLC3 TC0F1 ZNF620 ARAP1 CBX3 CBX3 CDK11A HUWE1 MAP3K2 SMARCC1 SRRM1 ARHGEF15 DCTN4 EIF3A HIRIP3 HIRIP3 MECP2 METAP2 RABEP2 SCML2 SEC22B SEC22B SUV39H1 UBR5 WHSC1 XRCC1 ZBTB7A ZC3H18

0.8

STRING TIKDVLEVYGTGVASTRH AALAPAKESPR ETESFRLMVGGLPGNVSQHLDFGSSL SVAAFTADPLSLLR SLSDSESDDSK RKSLSDSESDDSK RDSLEEGELR SHHAASTTTAPTPAAR RLSIIGPTSR SQKEEDEQEDLTK HRPSPPATPPPK LSLLSNHQGRPTHRLLQA AGASISTLAGLSLE RQTIEER GRPDLSTLTHSIVR GRPDLSTLTHSIVR KPGSVVAAAAAEAk SKGPSAAGEQEPDKESGASVDEVAR HAPSLHGSTELLPLSR SPQQTVPYVVPLSPK NLGSINTELQDVQR NLGSINTELQDVQR MDSNFGLAGLPGSPK RATLLSSR IQDPTEDAEAEDTPR AIGSTSKPQESPK GGAPDPSPGATATPGAPAQPSSPDAR LGVSVSPSR

sequence

identified peptide

15 9 4 1 1 3 3 1 3 1 4 12 4 3 11 11 4 1 4 1 4 4 13 3 13 11 21 6

site

P S S S S S S S S S S S T S S S S S S S S S S S T T S S S

10.38 0 1.5 54.7 21.37 7.76 0 83.79 45.75 101.53 43.31 0 25.3 0 31.38 15.48 0 12.69 36.4 13.6 50.82 25.25 133.44 22.69 138.84 26.51 5.29 23.23

ASCORE YGTGVASTRHEMG LAPAKESPRKGAA VSKETESFRLMVG ENEMRRSVAAFTA DGTKRKSLSDSES DGTKRKSLSDSES RDSKRDSLEEGEL PVRQRRSHHAAST EEKKELSIIGPTS LYGKRRSQKEEDE RRRHRPSPPATPP NHQGRPTHRLLQA RPRAGASISTLAG ILARRQTIEERKE LSTLTHSIVRRRY LSTLTHSIVRRRY RGRKPGSVVAAAA EEKEEESKGPSAA RPRHAPSLHGSTE RQSTKRSPQQTVP ARRNLGSINTELQ ARRNLGSINTELQ LAGLPGSPKKRVR EGRRRATLLSARQ DAEAEDTPRKRLR TSKPQESPKGKRK GAPAQPSSPDARR KLGVSVSPSRARR

sequence 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13 13

length

aligned seq

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7

site

P S S S S S S S S S S S T S T S S S S S S S S S T T S S S

motif ......S.R.... ......S.R.... ......S.R.... ....R.S...... ....R.S...... ....R.S...... ....R.S...... ....R.S...... ....R.S...... ....R.S...... ....R.S...... 2.385 3.019 3.012 3.012 3 3.678 3.533 3.157 3.705 2.484 2.211 3.765 2.349 2.349 2 77 4.266 2.426 2.906 2.465 2.465 2.308 4.993 2.746 2.375 2.254 2.845

AGC/ PKC >2.201

3.522 3.522

4.258 4.258

4.226

3.913

AGC/ PKC/ Delta/ PKCd >3.478

4.581

AGC/ PKC/ Delta >3.806

4.125

4.125 4.625

AGC/ PKC/ Delta/ PKCt >4.125

Cyto, cytosol; Nucl, nucleus; IPI, IPI accession number; protein, official gene symbol; STRING, the PPI score for PKCδ from STRING; site, the phosphosite localization; P, the phosphosite type; Ascore, an ambiguity score to assign phosphorylation site localizations and measure the confidence of assignment; aligned Seq, 13 amino acid sequence centered around the identified phosphosite; motif, the enriched motif extracted from Motif-X; AGC/PKC (AGC/PKC/Delta, AGC/PKC/Delta/PKCd, AGC/PKC/Delta/PKCd), the kinase family hierarchy established by GPS to predict the kinase−substrate relationship at high stringency (the theoretically maximal FDR indicated in each kinase family hierarchy).

a

IPI

IPI00794843 IPI00815713 IPI00166247 IPI00828080 IPI0029T5T9 IPI00297579 IPI00070809 IPI00456919 IPI00513803 IPI00234252 IPI00647720 IPI00909033 IPI00914006 IPI00029012 IPI00304875 IPI00304875 IPI00645192 IPI00033036 IPI00412500 IPI00646311 IPI00006865 IPI00006865 IPI00941101 IPI00935432 IPI00334604 IPI00911037 IPI00026317 IPI00908725

Com

Table 2. continued

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identified peptide

4292

4 7 7 7 7 7 7 7 7 7 7 7

DRKRHNSISEAKM ISSGSGSLRTSQK

TPIPSDSTRRKGG THAAALSVRNLSP PVRQRRSHHAAST

ERKKRLSIIGPTS LYGKRRSQKEEDE ASTLVHSVKKEQE KKHPDASVNFSEF ELEKRASGQAFEL

P+1

1484.89 1637.82 1455.78 1505.74 1477.76

1564.86 1717.79 1535.75 1585.70 1557.73

782.93 859.40 768.37 793.35 779.37

522.29 573.26 512.58 529.23 519.91

1371.73 1451.70 726.35 484.57 1336.73 1416.70 708.85 472.90 1502.80 1582.77 791.89 528.26

1571.81 1651.77 826.39 551.26 1307.69 1387.66 694.33 463.22

1278.73 1358.69 679.85 453.56 1590.93 1670.90 835.95 557.63

synthesized peptide phosphosite M + 1

RKGSFSALVGRT KKKRKFSKEEPVS

0.279 0.198 0.179

0.8

0.802

3.533 R.S 3.157 R.S

3.511 S.R 2.31 S.R 3.678 R.S

3.961 R..S 3.538 S.R

3.685 R..S 3.971 R..S

25.0% 1.1% 0.6% 0.0% 0.0%

Y Y Y N N

1.1 Y 0.4% N 8.3% Y

25.7% Y 1.1% Y

14.4% Y 0.7% N

7 7 7,2 7 7

7 7 7

7 7

4 7

TiO2

3 days

7.3 3.9

7.5% 93.9% 12.5 2.0% 5.4% 2.7 0.8% 2.7% 3.4 0.0% 0.4% 3D 0.1% 0.4% 3.0

4.3% 58.8% 13.6 0.0% 1.3% 3D 3.1% 59.3% 19.2

86.9% 95.7% 1.1 2.8% 33.5% 11.9

10.2% 74.6% 1.4% 5.6%

7 7 7 7 7

7 7 7

7 7

4 7

3 days/ LC 3 0 MSMS days days phophos

Maldi C ratio

6.4 7.6 10.7 5.7 4.6

10.3 13.3 13.8

2.0 3.7

18.7 2.0

Ratio

TiO2-LC

3 days/0 days

2

LC MSMS phophos

TiO2

3 days

in vitro kinase assy with the cell lysate

1.2

ratio

TiO2-LC

3 days/0 days

a Protein, official gene symbol; (S, T, Y)#, the symbol of identified phosphosite; synthesized peptide, the synthesized peptide used to kinase assay; M + 1, (M + l)/l for the synthesized peptide; P + l (+2, +3), (M + z)/z for the synthesized peptide added by phosphorylation; STRING, the PPI score for PKCδ from STRING; GPS, the kinase-substrate relationship score for PKCδ from GPS; the motif, the enriched motif extracted from Motif-X; Maldi C ratio, the phosphorated conversion rate detected by MALDI-TOF; TiO2, the phosphopeptides eluted from TiO2.

R.KGS#FSALVGR.T R. KFS#KEEPVSSGPEEAVGK.S PLCB3 R.HXS#ISEAK.M DLG1 R.E QMMNSSISSGSGS#LR.T RPS14 R.IEDVTPIPSDS#TR.R SFPQ R.FATHAAALS#VR.N HUWE1 R. S#HHAASTTTAPTPAAR.S MAP3K2 K.RLS#IIGPTSR.D SMARCC1 R.S#QKEEDEQEDLTK.D BCLAF1 R.LLASTLVHS#VK.K HMGB1 K.HPDAS#VNFSEFSK.K STMN1 K.RAS#GQAFELILSPR.S

protein

CUL4A NOP56

TiO2

in vitro kinase assy with purified PKCδ

Maldi LC C MSMS 0 P + 2 P + 3 STRING GPS motif ratio Maldi phophoS days

Table 3. Potential Substrates of PKCδ Testified by the in Vitro Kinase Assaya

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Figure 6. In vitro PKCδ kinase assay for Cul4A. The kinase−substrate relationship was confirmed by the in vitro kinase activity of purified PKCδ and the cell lysate. (A) Spectrum acquired by MALDI-TOF for detecting the phosphopeptides obtained from in vitro purified PKCδ kinase activity. The reaction product without (upper panel) and with (middle panel for the flow through (FT) and lower one for the elution) enrichment by TiO2. (B) Spectrum acquired by MALDI-TOF for detecting the phosphopeptide products obtained from cell lysate incubation. PKCδ-CF was absent in the cell lysate at day 0 (upper panel) and was highly expressed at day 3 (lower panel). The symbol * indicates phosphopeptide. (C) The two XIC (extracted ion chromatograms) of the same phosphopeptides were integrated. The phosphopeptide products derived from the incubation with cell lysate at day 0 or day 3 were respectively enriched by TiO2 and identified by LC-LTQ-Orbitrap. The peak area surrounded by the red line or by the blue line indicated the phosphopeptide intensity at day 3 or at day 0, respectively. (D) The MS2 spectrum obtained by LTQ-MS/MS for the enriched phosphopeptide was labeled by Plabel to specify the phosphosites: yellow letter, y ion; green letter, b ion; red “p”, phosphosite.

Furthermore, we tested one of the above hypotheses by using SP600125, a specific inhibitor for JNK, which was a member of the MAPK family (Figure 5E). In a U937-PKCδ‑CF cell, the phosphorylation activation of JNK could be induced on day 3 after tetracycline removal when the cleavage of caspase 3 and PARP was observed. SP600125 treatment completely antagonized JNK activation as well as the cleavage of caspase 3 and PARP, strongly suggesting that phosphorylation activation of JNK was required for early signaling transduction of PKCδinduced apoptosis.

Then, the relationship between kinase and substrate motif was comprehensively analyzed by the prevalent tools, including NetPhorest,65 NetPhosK,66 and PhophoMotifFinder.67 The proline-oriented kinase was overridingly predicted against two kinases, CDK5 and P38MAPK; the acidic serine/threonine kinase was against CK2; and the alkaline serine/threonine kinase was against PKC and PKA. However, the above kinases were not detected in our phosphoproteome analysis, possibly for their low copies in the cells. Finally, according to NetworkKIN,64 a new PKCδ-driven kinase network composed of all five kinases, PKCδ (PRKCD), P38MAPK (MAPK1), PKA (PRKACA), CDK5, and CK2 (CSNK2A2), was created by STRING at the 0.15 threshold (Figure 5D). The kinase network hinted at the possible chronological order, of which PKCδ, as an inherent ignition point, directly activated the second nodal point, such as MAPK1, and indirectly regulated the activity of the third nodal point, such as CDK5 and PKA.

New Substrates of PKCδ

To further discover the direct substrates of PKCδ, GPS (groupbased prediction system)54 was applied to analyze the motif of the early up-regulated phosphopeptides. The phosphorylation domain of substrates is evolutionarily conserved among the PKC family. The substrate motif of PKCδ has 87% similarity with that of PKCθ’s, 65% with PKCζ’s, and even 58% with 4293

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Figure 7. Immunological confirmation using phospho-specific antibodies. 293T cells were transfected with pEGFP-N1 or pEGFP-PKCδ-CF for 36 h. Cul4A was immunoprecipitated by anti-Cul4A antibody (A), and the transfected pCMV-flag-C/EBPα (B) or pCMV-Tag2B-EDD (UBR5) (C) was immunoprecipitated by antiflag M2-agarose affinity gel. The phosphoproteins were detected by Phospho-(Ser) PKC Substrate Antibody (#2261, cell signaling), and other proteins in immunoprecipitated lysate or in whole lysates were detected by indicated antibodies.

Figure 8. Signaling pathway modules directly relevant to PKCδ. The KEGG database (http://www.genome.jp/kegg/) was applied to screen the early responsive phosphoproteins, and then Cytoscape was used to build a network in which every signaling transduction contained at least two proteins with PKCδ (yellow) as the initiation of the signaling network. Protein name and line in blue, known phosphoproteins related to PKCδ by STRING; protein name and line in red, potential substrates predicted by GPS.

of the kinase family, four hierarchies, including “AGC/ PKC”, “AGC/PKC/Delta”, “AGC/PKC/Delta/PKCd”, and

PKA, who does not belong to the PKC family.68 For this reason, according to the GPS-defined hierarchical ordering 4294

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“AGC/PKC/Delta/PKCt”, were set at the highest confidence threshold (FPR ≤ 2%): 2.201, 3.806, 3.478, and 4.125, respectively. GPS screened out 59 phosphopeptides on 57 phosphoproteins, including 41 phosphoproteins in cytoplasm and 23 in the nucleus (Table 2). Among them, three proteins, including FLNA, PLCB3, and MAP3K2, were directly related to PKCδ based on the STRING database. Moreover, to confirm whether the domain of candidate substrates was of the same characteristics as the substrate of PKCδ,68 Motif-X was again utilized to enrich the overexpression motif of candidate phosphopeptides. Because of the relatively low number of peptides, two parameters of Motif-X, significance and occurrences, were adjusted to 10−4 and 5, respectively. The results showed that the candidate substrates were classified into three groups. The first group included 32 phosphopeptides with two kinds of motifs, “R-x-x-S-x-x-x” and “x-R-x-S-x-x-x”, which accounted for 54% of all candidates with 7-fold enrichment (Figure 5C). These motifs were canonical substrates of PKCδ with basic amino acids on the −2 to −6 sites and hydrophobic ones on the +1 site. The second group included 12 phosphopeptides with the motif of “x-x-x-S-x-R-x”, which accounted for 20% with 7-fold enrichment. It was a noncanonical motif of PKCδ with basic amino acids on the +2 and +3 sites instead of the −2 to −6 sites. The last group consisted of the rest of the 15 phosphopeptides without an enriched motif. The integration of the GPS prediction data, the protein− protein interaction information, as well as further verification are more reliable ways to find the candidate substrates. Hence, 12 phosphopeptides were randomly selected for further validation, including three phosphopeptides from each group and three potential interaction phosphoproteins indicated by STRING, who had neither the characteristic motif of PKCδ substrate nor the positive score by GPS (Table 3). The in vitro kinase assay using the optimal peptides of chemical synthesis69,70 was an important means for studying kinase activity or determining the substrates specificity of kinases.70 MALDI-TOF analysis is an easy and reliable way to detect the products of kinase reactions, as the covalent bonding of HPO3− added 79.96 Da. As shown in Figure 6A (top panel) and SI Figure S9A−19A (top panels), most of the phosphopeptides could be detected without TiO2 enrichment. In the meantime, the conversion rates of NOP56 and SFPQ, 2 of the 9 candidate substrates, were 0.7% and 0.4%, respectively, and the rates of the other 7 candidates were higher than 1%. It was notable that PLCB3 and MAP3K2, which were known PKCδ substrates containing different motifs, obtained a high conversation rate of 25%. In accordance, among the three peptides with neither consensus motif nor positive value predicted by GPS, only the peptide of BCLAF1 was phosphorylated with the conservation rate of 0.6%. To increase the sensitivity, the TiO2 column was applied to enrich the phosphopeptides from the reaction product. Both non-phosphopeptides in flow through (Figure 6A middle panel and SI Figures S9A−19A middle panels) and phosphopeptides in elution (Figure 6A bottom panel and SI Figures S9A−19A bottom panels) were checked by MALDITOF. In conformity to our previous results, the intensity of phosphopeptides whose conversation rates were originally higher than 1% markedly increased after TiO2 enrichment. However, for those 5 peptides with conversation rates below 1%, only the phosphopeptide of BCLAF1 could be detected with S/N 33 (SI Figure S17A, bottom panel). The results suggested that the four peptides, NOP56, SFPQ, HMGB1, and

STMN1, may not directly interact with PKCδ. To exclude nonspecificity, even though the phosphopeptide was detected at a low magnitude (peak intensity less than 104) in the subsequent analysis with LTQ-Orbitrap, the peptide would not be considered as a PKCδ substrate. To simulate the in vivo effect of PKCδ on substrate, all the 12 peptides were incubated respectively with PKCδ-CF free cell lysate from day 0 and PKCδ-CF overexpressed cell lysate from day 3 to produce different reaction products which were subsequently checked by MALDI-TOF. As illustrated in Figure 6B and SI Figure S9B−19B, only 5 phosphopeptides could be detected at day 0, while all 12 peptides generated corresponding phosphopeptides at day 3. The conversion rates of 11 peptides on day 3 were at least 3 times higher compared to the rates on day 0, except PLCB3, whose conversation rate was 86.9% and 95.7% on day 0 and day 3, respectively (Table 3). Furthermore, the phosphopeptides were enriched by TiO2 and then identified by LC-LTQ-Orbitrap. The combined XIC (extracted ion chromatograms) of the same phosphopeptides at day 0 and day 3 showed that the phosphorylation degrees (intensity area) of all 12 peptides on day 3 were at least two times higher than the degrees on day 0 (Figure 6C and SI Figures S9C−19C and Table 3). The results of in vitro kinase assay with cell lysates were in agreement with the phosphorylation trends obtained from the phosphoproteomics data set. In addition, we also verified our results with immunological analyses which provided evidence on the protein level. Two potential substrates identified by GPS, Cul4A and UBR5, along with CCAAT/enhancer binding protein alpha (C/EBPα), were subjected to immnunoprecipitation and western-blot assay (Figure 7). The results showed that these proteins were efficiently phosphorylated by PKCδ. Taken together, the above results validated the reliability of our phosphoproteomics data. To confirm the specificity of the kinase-substrate, namely to verify the phosphorylated sites identified by our phosphoproteome, the enriched phosphopeptides obtained from in vitro kinase reactions were analyzed by LTQ-Orbitrap (Figure 6D and SI Figures S9D−S19D and Table 3). The result showed that the phosphosites of every peptide derived from the kinase reaction were identical to that of our phosphoproteome data, except that the phosphopeptide of BCLAF1 produced two phosphosites, Ser2 and Ser7, in the kinase reaction (SI Figure S17D). However, only the phosphorylation of Ser7 increased dramatically when the peptide of BCLAF1 was incubated with PKCδ-expressing lysate, indicating that Ser7 was specifically related to PKCδ (SI Figure S17, Table 3). In summary, our results revealed that seven of the nine peptides predicted by GPS were potent candidate substrates of PKCδ, and a new phosphosite of BCLAF1, one of the known PKCδ interacting proteins, was activated by PKCδ. The other four proteins, NOP56, SFPQ, HMGB1, and STMN1, were indirectly induced by PKCδ. Finally, to deeply explore the PKCδ-induced signaling transduction via those candidate substrates, the KEGG database (http://www.genome.jp/kegg/) was applied to screen the early responsive phosphoproteins, and then Cytoscape was used to build a new network in which every signaling transduction contained at least two proteins except PKCδ. As shown in Figure 8, the known phosphoproteins related to PKCδ by STRING (the name of protein in blue) were integrated with the potential substrates predicted by GPS (the name of protein in red). Three proteins, FLNA, MAP3K, and PLCB3, which were screened out by both STRING and GPS, were connected 4295

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phosphorylates the C-terminal of HYRC in vitro, preventing HYRC from forming a complex with DNA, finally leading to the inhibition of DNA restoration and DNA fragmentation. On the contrary, PKCδ overexpression in HYRC deprived cells precludes the possibility of apoptosis. These results suggest that association of HYRC with PKCδ impacts apoptosis.37 Topoisomerase II alpha (TOP2A) is a direct substrate of PKCδ in response to genotoxic agents. Phosphorylation of TOP2A increases its expression and activity following DNA damage. Conversely, inhibition of PKCδ activity attenuated the activity of TOP2A induced by DNA damage. Therefore, PKCδ directs TOP2A to response to genotoxic stress induced apoptosis.40 BCL2-associated transcription factor 1 (BCLAF1) binds p53 promoter and interacts with PKCδ upon exposure to DNA damage to modulate the PKCδ-dependent p53 transcription activity as well as apoptosis.80 cAMP responsive element binding protein 1 (CREB1) is phosphorylated upon PMA treatment in mature B cells. The specific PKCδ activator or inhibitor, bistratene A or rottlerin, got hold of further evidence that CREB is specifically phosphorylated by PKCδ, not by PKA or calmodulin kinase. Phosphorylated CREB acts as an important transcription factor to regulate the transcriptional activity of survival related genes in B cells.81 DNA (cytosine-5-)methyltransferase 1 (DNMT1) tightens the DNA replication folk through its methyltransferase activity to sustain DNA methylation during S phase. Depriving the tumor cell of DNMT1 by siRNA blocks proliferation, arrests cell cycle at the S phase, and sensitizes tumor cells to TRAIL-induced cell death.82 More importantly, DNMT1 can be phosphorylated on a variety of serine/threonine sites, which is dependent upon cell context or biological condition. The in vitro kinase assay demonstrated that the N terminal (1−446) of DNMT1 is prone to phosphorylation by about eight members within all three PKC families, including PKCδ.83 The increased phosphorylation of DNMT1 by PKCδ may regulate its activity and cell fate. Taken together, these observations reported by others validated the reliability of our results. However, we also noticed that most of the phosphosites within these proteins identified by others were not found in our result. We presumed that the differences in cellular context, stimulus, and experimental technologies might be the reasons. Moreover, a core network consisting of 21 hub phosphoproteins which was extracted from the complicated signaling pathway may be straightforward to illustrate the mechanism of the diverse cellular process. Based on the GOenrichment analysis, the core network was classified into three vital biological processes: RNA/mRNA processing and splicing (SI Figure S8A), DNA-dependent regulation of transcription (SI Figure 8B), and cell cycle (SI Figure 8C). It is intriguing that cell cycle was included in the core network, although it was not directly regulated by PKCδ. RB1, one well-known tumor suppressor, supervises cell cycle to control cell proliferation.84 The interaction between DNMT1 and RB1, both up-regulated phosphoproteins in our study, has been evidenced by in vivo and in vitro assays, and was the constituent part of the stable complex of DNMT1-RB-E2F1-HDAC1. Inhibition of the RB signaling pathway caused by the methylation of EF2 response elements through the specific DNA binding of DNMT1 contributed to the aberrant growth of the tumor cell.85 On the contrary, the signaling pathway of RB/E2F1 could also regulate the transcriptional activity of DNMT1.86 Aurora kinase B (AURKB) is another highly conserved serine/threonine kinase required for cell mitosis. During the transition from metaphase to anaphase, AURKB located in the centromere and the central

by a double line. The network consists of three groups of signaling pathways directly associated with PKCδ (central point): signaling transduction in black writing, such as the MAPK signaling pathway, calcium signaling pathway, and base excision repair, involved both known and potential substrates; signaling transduction in blue writing, such as cell cycle and nonhomologous end-joining, involved known substrates; more importantly, signaling transduction in red writing, such as ubiquitin-mediated proteolysis, lysine degradation, nucleotide excision repair, and the mTOR signaling pathway, involved those potential candidate substrates. This observation inferred that there might be more important biological processes in apoptosis directly regulated by PKCδ.

■

DISCUSSION In eukaryotic cells, phosphorylation is involved in the whole process of signal transduction, including signal input, integration, and output. Recently, the high-throughput phosphoproteomics have served as an important technical platform to reveal the involvement of phosphoproteins in signaling pathways related to growth factor receptor,71−73 cell cycle regulation,74 DNA damage response,56−58 and so on. As a serine/ threonine kinase,1,75,76 overexpression or activation of PKCδ induces apoptosis,3,77 of which the underlying mechanisms remain largely unknown. In this study, we aimed to figure out the possible mechanisms by which PKCδ initiates apoptosis via systematically investigating the overall phosphoproteins involved in signaling pathways triggered by PKCδ. As shown in SI Figure S1, we found the apoptotic procedure induced by PKCδ included two stages: one was the initiation stage, which comprised the PKCδ-CF expression−induction and was almost cell death-free, while the other was the execution stage, which was characterized by the caspase-3 activation, massive protein cleavage degradation, and cell death. In the execution stage, the deregulated phosphopeptides were paralleled with the degradation of corresponding proteins, indicating that changes of phosphopeptides at this stage of apoptosis were more likely nonspecifically contributed by protein cleavage and degradation. However, in the initiation stage, the levels of most proteins remain unchanged, indicating that the changes of phosphorylation are more directly and specifically associated with PKCδ. Based on the above reasons, the data set of phosphopeptides of early responses was preferred to further analyze it in detail. To annotate and comprehend the phosphorylation networks modulated by PKCδ in apoptosis induction, the differential phosphoproteome of early apoptosis was analyzed by bioinformatics for function clustering and protein interaction. The results showed that multiple cellular signaling processes were involved in PKCδ-induced cell death. For example, some upregulated phosphoproteins in the cytoplasm were involved in ubiquitin-dependent protein catabolic processes, the cell cycle, and enzyme binding. As PKCδ was the only stimulating factor here, the direct interacting proteins of PKCδ might play important roles in the induction of cell apoptosis. The bioinformatics analysis against the STRING database revealed that 26 modulated phosphoproteins in our phosphoproteome had been reported to interact with PKCδ or PKCs and a third of them had functions closely related to apoptosis. HYRC, also known as the DNA-dependent protein kinase catalytic subunit (DNAPK), acts as a serine/threonine protein kinase to repair double-strand break78 and to interact with c-ABL79 in response to the DNA damage. Activated PKCδ interacts with and 4296

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closely related to some signaling pathways rarely reported to be directly induced by PKCδ, such as ubiquitin-mediated proteolysis, lysine degradation, nucleotide excision repair, and the mTOR signaling pathway. Consistent with our recent results, the ubiquitin−proteasome system was involved in the protein degradation in the process of apoptosis. In NSC606985treated U937 cells, both C/EBPα9 and hnRNPK41 were found to be degraded in a caspase3-independent manner at the early stage of apoptosis. Further experiments revealed that ectopic expression of PKCδ contributed to the phosphorylationdependent ubiquitination and proteasomal degradation of C/ EBPα and hnRNPK. In our study, according to the KEGG database, five deregulated phosphoproteins are involved in proteome-mediated protein degradation, and all of them are E3 ligase. More strikingly, three of these E3 ligases were predicted as candidate substrates of PKCδ by GPS and were further confirmed by in vitro kinase assay or immunological assay. One is Cul4A, a core component of E3 ubiquitin−protein ligase complexes that were responsible for ubiquitination and degradation of several target proteins involved in DNA replication and cell cycle in response to DNA damage.103 Our results strongly implied that PKCδ activated the ubiquitin−proteasome system, which is worth further studies. Despite the 75% positive prediction rate, there are possibilities that real PKCδ substrates might be ignored by the GPS. These prediction tools only recognize a linear sequence motif but will not take the spatial conformation into consideration. Here are some examples, it has been reported that Thr431 of mouse eEF-1α (RFAVRDMRQT*VAVGVIKAVDKK, *-phosphosite),104 Ser46 of p53 (AMDDLMLS*PDDIEQW),19 Ser15 of p53 (PSVEPPLS*QETFSDL),20 and Ser727 of STAT1 (DNLLPMS*PEEFDE)105 were directly phosphorylated by PKCδ, but none of these phosphopeptides has the classical motif of alkaline kinase substrate, “R-x-x-S/T-x-x-x”. GPS and ScanSite106 failed to predict their relationship with PKCδ as well. Till now, less than 5% phosphopeptides has been annotated with the corresponding kinase.107 The phenomenon that a phosphosite or motif cannot be associated with its corresponding kinase is called “orphan”.45,108 Besides elucidating the downstream signaling network conducted by PKCδ, here we especially attempted to explore the potential PKCδ substrates. The discovery of specific substrates not only provides valuable information for understanding the biological functions of kinases but also provides new clues for developing specific inhibitors and drug targets research. Due to the multifunctional features of kinases, drugs targeting the kinase itself are always accompanied by sideeffects. Therefore, targeting substrates with relatively narrow function instead of the kinase itself may result in a more specific effect.109 As we reported previously,6 NSC606985 could trigger leukemic cell apoptosis via proteolytic action of PKCδ in vivo and in vitro. However, this compound killed normal proliferating cells such as white cells and lymphocyte cells. Revealing the events downstream of PKCδ to find proteins involved in apoptosis will be our first step to solve this problem. Further differential analysis of these proteins between normal and cancer cells will endow them with the potential to be drug targets. For example, Cul4A, which was revealed by this study as a potential PKCδ substrate, overexpresses in cancer cells. Skin-specific Cul4A ablation dramatically increased resistance to UV-induced skin carcinogenesis in mice,110 leading researchers to recognize its potential as an anticancer target.111

spindle with the inner centromere protein and surviving protein.87 Recent studies demonstrate that the apoptosis occurs in the cells deprived of AURKB by selective kinase inhibitor or siRNA.88,89 As well-known, dephosphorylation by phosphatase is as important as phosphorylation by kinase in the process of signal transduction.57 The fact that 15 were down-regulated among all 21 hub phosphoproteins partially confirmed such a view. RNA binding motif protein 5 (RBM5), which was dephosphorylated in our experimental condition, is known as an apoptosis regulatory protein and a potential tumor repressor.90 Recently, RBM5 was proved to be dephosphorylated by growth factor deficiency and would not restore the high level phosphorylation until growth factor supply. The reversible modulation of phosphorylation implies that dephosphorylation of RBM5 may be involved in apoptosis.91 Those proteins have previously been reported to play important roles in apoptosis. Our phosphoproteomics study not only confirmed this but also connected them with PKCδ. Furthermore, to reveal the relationship between PKCδ and other kinases, we employed NetworkKIN to create a new downstream kinase network induced by PKCδ. As mentioned above, PKCδ can interact with several members of the family, including p38,30 ERK,13 and MEKK1/MAPK/JNK,15 to activate the downstream signal transduction. In the salivary gland exposed to PMA, activated PKCδ induced the biphasic activation of JNK and the inactivation of ERK during apoptosis induction.92 Hence, the substrate motif of proline-oriented kinase that was modulated by MAPK1 or CDK5 was identified in both up- and down-regulated phosphoproteome. CDK5, another proline-oriented serine/threonine kinase, was a member of the CDK family. Not like other members of CDK, CDK5 was mostly involved in the programmed cell death of neurons instead of controlling cell cycle.93 PKC could indirectly activate CDK5 via phosphorylating its coactivator p3594.95 PKA, a very in-depth studied alkaline serine/threonine kinase, activates or phosphorylates substrate to release the second messenger cAMP, which is associated with a variety of biological process, including apoptosis. In the last two years, increasing evidence supported the role of PKA in apoptosis.96−98 CK2 belongs to a small family of serine/threonine kinase and was an important antiapoptotic protein.99 Studies confirmed that the overexpression of CK2 was resistant to drug-induced apoptosis,100 and conversely the depletion of CK2 by chemical inhibitor, antisense CK2, or siRNA promoted the apoptotic induction.101 There was evidence to show that phosphorylated CK2 weakened the DNA binding ability of p53 by forceful inhibition of the PKC-mediated phosphorylation of the p53 C-terminus.102 With these studies, our results implied that CK2 may be inactivated during PKCδ-induced apoptosis. Notably, the motif of early responsive phosphopeptides was analyzed by the GPS (group-based prediction system). Fiftynine phosphopeptides on fifty-seven phosphoproteins, including three known substrates, were screened out, and most of them had the specific motif for PKCδ, as confirmed by the Motif-X analysis. To verify the candidates, an in vitro kinase assay was performed, and 9 of the 12 synthetic phosphopeptides were proved to be potent substrates of PKCδ. To make better use of the data set of candidate substrates, KEGG analysis was employed to generate a signaling network including known and potential substrates of PKCδ. Interestingly, the results showed that, besides the expected signaling pathways, such as the MAPK signaling pathway, the calcium signaling pathway, and the cell cycle, the potential substrates were also 4297

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CONCLUSIONS Taken together, phosphorylation as an intensely explored PTM comprehensively affects the biological functions of various proteins. Modulated phosphoproteins detected by the phosphoproteomics provide a valuable source of information not only for elucidating the physiological and pathological process but also for developing the new specific kinase inhibitor and more effective drug targets. This study provided by far the most systematic analysis of phosphorylation events initiated by PKCδ, which would vastly extend the understanding of PKCδdirected signal pathways in cell death and may provide new clues for investigating cell death-based cancer therapy.

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trometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange.org) via the PRIDE partner repository with the data set identifier PXD000225.

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(1) Nishizuka, Y. The molecular heterogeneity of protein kinase C and its implications for cellular regulation. Nature 1988, 334 (6184), 661−5. (2) Emoto, Y.; Manome, Y.; Meinhardt, G.; Kisaki, H.; Kharbanda, S.; Robertson, M.; Ghayur, T.; Wong, W. W.; Kamen, R.; Weichselbaum, R.; et al. Proteolytic activation of protein kinase C delta by an ICE-like protease in apoptotic cells. EMBO J. 1995, 14 (24), 6148−56. (3) Brodie, C.; Blumberg, P. M. Regulation of cell apoptosis by protein kinase c delta. Apoptosis 2003, 8 (1), 19−27. (4) Kanthasamy, A. G.; Kitazawa, M.; Kanthasamy, A.; Anantharam, V. Role of proteolytic activation of protein kinase Cdelta in oxidative stress-induced apoptosis. Antioxid Redox Signal 2003, 5 (5), 609−20. (5) Kikkawa, U.; Matsuzaki, H.; Yamamoto, T. Protein kinase C delta (PKC delta): activation mechanisms and functions. J. Biochem. 2002, 132 (6), 831−9. (6) Song, M. G.; Gao, S. M.; Du, K. M.; Xu, M.; Yu, Y.; Zhou, Y. H.; Wang, Q.; Chen, Z.; Zhu, Y. S.; Chen, G. Q. Nanomolar concentration of NSC606985, a camptothecin analog, induces leukemic-cell apoptosis through protein kinase Cdelta-dependent mechanisms. Blood 2005, 105 (9), 3714−21. (7) Emoto, Y.; Kisaki, H.; Manome, Y.; Kharbanda, S.; Kufe, D. Activation of protein kinase Cdelta in human myeloid leukemia cells treated with 1-beta-D-arabinofuranosylcytosine. Blood 1996, 87 (5), 1990−6. (8) DeVries, T. A.; Neville, M. C.; Reyland, M. E. Nuclear import of PKCdelta is required for apoptosis: identification of a novel nuclear import sequence. EMBO J. 2002, 21 (22), 6050−60. (9) Zhao, M.; Duan, X. F.; Zhao, X. Y.; Zhang, B.; Lu, Y.; Liu, W.; Cheng, J. K.; Chen, G. Q. Protein kinase Cdelta stimulates proteasome-dependent degradation of C/EBPalpha during apoptosis induction of leukemic cells. PLoS One 2009, 4 (8), e6552. (10) D’Costa, A. M.; Denning, M. F. A caspase-resistant mutant of PKC-delta protects keratinocytes from UV-induced apoptosis. Cell Death Differ. 2005, 12 (3), 224−32. (11) Sun, F.; Kanthasamy, A.; Song, C.; Yang, Y.; Anantharam, V.; Kanthasamy, A. G. Proteasome inhibitor-induced apoptosis is mediated by positive feedback amplification of PKCdelta proteolytic activation and mitochondrial translocation. J. Cell. Mol. Med. 2008, 12 (6A), 2467−81. (12) Murriel, C. L.; Churchill, E.; Inagaki, K.; Szweda, L. I.; MochlyRosen, D. Protein kinase Cdelta activation induces apoptosis in response to cardiac ischemia and reperfusion damage: a mechanism involving BAD and the mitochondria. J. Biol. Chem. 2004, 279 (46), 47985−91. (13) Lomonaco, S. L.; Kahana, S.; Blass, M.; Brody, Y.; Okhrimenko, H.; Xiang, C.; Finniss, S.; Blumberg, P. M.; Lee, H. K.; Brodie, C. Phosphorylation of protein kinase Cdelta on distinct tyrosine residues induces sustained activation of Erk1/2 via down-regulation of MKP-1: role in the apoptotic effect of etoposide. J. Biol. Chem. 2008, 283 (25), 17731−9. (14) Basu, A.; Tu, H. Activation of ERK during DNA damageinduced apoptosis involves protein kinase Cdelta. Biochem. Biophys. Res. Commun. 2005, 334 (4), 1068−73. (15) Yoshida, K.; Miki, Y.; Kufe, D. Activation of SAPK/JNK signaling by protein kinase Cdelta in response to DNA damage. J. Biol. Chem. 2002, 277 (50), 48372−8. (16) Efimova, T.; Broome, A. M.; Eckert, R. L. Protein kinase Cdelta regulates keratinocyte death and survival by regulating activity and subcellular localization of a p38delta-extracellular signal-regulated kinase 1/2 complex. Mol. Cell. Biol. 2004, 24 (18), 8167−83.

ASSOCIATED CONTENT

* Supporting Information S

Tables of phosphopeptides and phosphorylated sites identified in phosphoproteome events in the PKCδ-induced cell death; the time course dataset of the events in the PKCδ-induced cell death; the early or late responsive phosphopeptides and phosphoproteins to PKCδ-induced cell death; the significant GO terms extracted from DAVID for the early responsive phosphoproteins; the significant GO terms extracted from DAVID for the clustering of temporal profiles for early responsive phosphopeptides; the hub proteins in the networks of early responsive phosphoproteins to apoptosis induced by PKCδ; complete raw lists of all redundant phosphopeptides identified at the cytoplasm and nuclear fractions in phosphoproteome events responded to apoptosis induced by PKCδ; a complete raw list of all redundant peptides identified at the cytoplasm and nuclear fractions in proteome events responded to apoptosis induced by PKCδ; the time course data set of the normalized phosphoproteome events in the PKCδ-induced cell death; and the early or late responsive normalized phosphopeptides to PKCδ-induced cell death. Figures showing The U937 apoptotic cells used for phosphoproteomics analysis, the SILAC labeling efficiency, the identification and quantitation of total proteins in response to PKCδ-induced cell death, clustering of temporal profiles for early regulated phosphopeptides, GO-enrichment analysis of proteins directly interacting with PKCδ in the PPI network, the PPI network for the early responsive phosphorproteome to PKCδ, the distribution of node degree, GO-enrichment analysis of hub proteins in the process of PKCδ-induced apoptosis, in vitro PKCδ kinase assay, the distribution of partial phosphopeptide ratio and its corresponding normalized ratio, and phosphorylation consensus sequences of the regulated normalized phosphopeptides. This material is available free of charge via the Internet at http:// pubs.acs.org.

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REFERENCES

AUTHOR INFORMATION

Corresponding Author

*Tel: 86-21-63846590-776529; Fax: 86-21-64154900; E-mail: [email protected], [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work is supported in part by grants from the National Basic Research Program of China (NO2009CB918404) and the National Natural Science Foundation of China (NSFC, 30600261, 31070752, 81000214, 81071668). The mass spec4298

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(17) Lee, Y. J.; Soh, J. W.; Dean, N. M.; Cho, C. K.; Kim, T. H.; Lee, S. J.; Lee, Y. S. Protein kinase Cdelta overexpression enhances radiation sensitivity via extracellular regulated protein kinase 1/2 activation, abolishing the radiation-induced G(2)-M arrest. Cell Growth Differ 2002, 13 (5), 237−46. (18) Hu, Y.; Kang, C.; Philp, R. J.; Li, B. PKC delta phosphorylates p52ShcA at Ser29 to regulate ERK activation in response to H2O2. Cell Signal 2007, 19 (2), 410−8. (19) Yoshida, K.; Liu, H.; Miki, Y. Protein kinase C delta regulates Ser46 phosphorylation of p53 tumor suppressor in the apoptotic response to DNA damage. J. Biol. Chem. 2006, 281 (9), 5734−40. (20) Lee, S. J.; Kim, D. C.; Choi, B. H.; Ha, H.; Kim, K. T. Regulation of p53 by activated protein kinase C-delta during nitric oxide-induced dopaminergic cell death. J. Biol. Chem. 2006, 281 (4), 2215−24. (21) Ren, J.; Datta, R.; Shioya, H.; Li, Y.; Oki, E.; Biedermann, V.; Bharti, A.; Kufe, D. p73beta is regulated by protein kinase Cdelta catalytic fragment generated in the apoptotic response to DNA damage. J. Biol. Chem. 2002, 277 (37), 33758−65. (22) Park, C. H.; Choi, B. H.; Jeong, M. W.; Kim, S.; Kim, W.; Song, Y. S.; Kim, K. T. Protein kinase Cdelta regulates vaccinia-related kinase 1 in DNA damage-induced apoptosis. Mol. Biol. Cell 2011, 22 (8), 1398−408. (23) Majumder, P. K.; Pandey, P.; Sun, X.; Cheng, K.; Datta, R.; Saxena, S.; Kharbanda, S.; Kufe, D. Mitochondrial translocation of protein kinase C delta in phorbol ester-induced cytochrome c release and apoptosis. J. Biol. Chem. 2000, 275 (29), 21793−6. (24) Li, L.; Lorenzo, P. S.; Bogi, K.; Blumberg, P. M.; Yuspa, S. H. Protein kinase Cdelta targets mitochondria, alters mitochondrial membrane potential, and induces apoptosis in normal and neoplastic keratinocytes when overexpressed by an adenoviral vector. Mol. Cell. Biol. 1999, 19 (12), 8547−58. (25) von Burstin, V. A.; Xiao, L.; Kazanietz, M. G. Bryostatin 1 inhibits phorbol ester-induced apoptosis in prostate cancer cells by differentially modulating protein kinase C (PKC) delta translocation and preventing PKCdelta-mediated release of tumor necrosis factoralpha. Mol. Pharmacol. 2010, 78 (3), 325−32. (26) Kajimoto, T.; Shirai, Y.; Sakai, N.; Yamamoto, T.; Matsuzaki, H.; Kikkawa, U.; Saito, N. Ceramide-induced apoptosis by translocation, phosphorylation, and activation of protein kinase Cdelta in the Golgi complex. J. Biol. Chem. 2004, 279 (13), 12668−76. (27) Kajimoto, T.; Ohmori, S.; Shirai, Y.; Sakai, N.; Saito, N. Subtype-specific translocation of the delta subtype of protein kinase C and its activation by tyrosine phosphorylation induced by ceramide in HeLa cells. Mol. Cell. Biol. 2001, 21 (5), 1769−83. (28) Zrachia, A.; Dobroslav, M.; Blass, M.; Kazimirsky, G.; Kronfeld, I.; Blumberg, P. M.; Kobiler, D.; Lustig, S.; Brodie, C. Infection of glioma cells with Sindbis virus induces selective activation and tyrosine phosphorylation of protein kinase C delta. Implications for Sindbis virus-induced apoptosis. J. Biol. Chem. 2002, 277 (26), 23693−701. (29) Parent, N.; Scherer, M.; Liebisch, G.; Schmitz, G.; Bertrand, R. Protein kinase C-delta isoform mediates lysosome labilization in DNA damage-induced apoptosis. Int. J. Oncol. 2011, 38 (2), 313−24. (30) Gomel, R.; Xiang, C.; Finniss, S.; Lee, H. K.; Lu, W.; Okhrimenko, H.; Brodie, C. The localization of protein kinase Cdelta in different subcellular sites affects its proapoptotic and antiapoptotic functions and the activation of distinct downstream signaling pathways. Mol. Cancer Res. 2007, 5 (6), 627−39. (31) Liu, J.; Chen, J.; Dai, Q.; Lee, R. M. Phospholipid scramblase 3 is the mitochondrial target of protein kinase C delta-induced apoptosis. Cancer Res. 2003, 63 (6), 1153−6. (32) Liu, J.; Dai, Q.; Chen, J.; Durrant, D.; Freeman, A.; Liu, T.; Grossman, D.; Lee, R. M. Phospholipid scramblase 3 controls mitochondrial structure, function, and apoptotic response. Mol. Cancer Res. 2003, 1 (12), 892−902. (33) Sitailo, L. A.; Tibudan, S. S.; Denning, M. F. The protein kinase C delta catalytic fragment targets Mcl-1 for degradation to trigger apoptosis. J. Biol. Chem. 2006, 281 (40), 29703−10.

(34) Sitailo, L. A.; Tibudan, S. S.; Denning, M. F. Bax activation and induction of apoptosis in human keratinocytes by the protein kinase C delta catalytic domain. J. Invest. Dermatol. 2004, 123 (3), 434−43. (35) Frank, S.; Gaume, B.; Bergmann-Leitner, E. S.; Leitner, W. W.; Robert, E. G.; Catez, F.; Smith, C. L.; Youle, R. J. The role of dynaminrelated protein 1, a mediator of mitochondrial fission, in apoptosis. Dev. Cell 2001, 1 (4), 515−25. (36) Qi, X.; Disatnik, M. H.; Shen, N.; Sobel, R. A.; Mochly-Rosen, D. Aberrant mitochondrial fission in neurons induced by protein kinase C{delta} under oxidative stress conditions in vivo. Mol. Biol. Cell 2011, 22 (2), 256−65. (37) Bharti, A.; Kraeft, S. K.; Gounder, M.; Pandey, P.; Jin, S.; Yuan, Z. M.; Lees-Miller, S. P.; Weichselbaum, R.; Weaver, D.; Chen, L. B.; Kufe, D.; Kharbanda, S. Inactivation of DNA-dependent protein kinase by protein kinase Cdelta: implications for apoptosis. Mol. Cell. Biol. 1998, 18 (11), 6719−28. (38) Cross, T.; Griffiths, G.; Deacon, E.; Sallis, R.; Gough, M.; Watters, D.; Lord, J. M. PKC-delta is an apoptotic lamin kinase. Oncogene 2000, 19 (19), 2331−7. (39) Yoshida, K.; Wang, H. G.; Miki, Y.; Kufe, D. Protein kinase Cdelta is responsible for constitutive and DNA damage-induced phosphorylation of Rad9. EMBO J. 2003, 22 (6), 1431−41. (40) Yoshida, K.; Yamaguchi, T.; Shinagawa, H.; Taira, N.; Nakayama, K. I.; Miki, Y. Protein kinase C delta activates topoisomerase IIalpha to induce apoptotic cell death in response to DNA damage. Mol. Cell. Biol. 2006, 26 (9), 3414−31. (41) Gao, F. H.; Wu, Y. L.; Zhao, M.; Liu, C. X.; Wang, L. S.; Chen, G. Q. Protein kinase C-delta mediates down-regulation of heterogeneous nuclear ribonucleoprotein K protein: involvement in apoptosis induction. Exp. Cell Res. 2009, 315 (19), 3250−8. (42) Mayr, M.; Chung, Y. L.; Mayr, U.; McGregor, E.; Troy, H.; Baier, G.; Leitges, M.; Dunn, M. J.; Griffiths, J. R.; Xu, Q. Loss of PKCdelta alters cardiac metabolism. Am. J. Physiol. Heart Circ. Physiol. 2004, 287 (2), H937−45. (43) Mayr, M.; Metzler, B.; Chung, Y. L.; McGregor, E.; Mayr, U.; Troy, H.; Hu, Y.; Leitges, M.; Pachinger, O.; Griffiths, J. R.; Dunn, M. J.; Xu, Q. Ischemic preconditioning exaggerates cardiac damage in PKC-delta null mice. Am. J. Physiol. Heart Circ. Physiol. 2004, 287 (2), H946−56. (44) Boer, J.; Bonten-Surtel, J.; Grosveld, G. Overexpression of the nucleoporin CAN/NUP214 induces growth arrest, nucleocytoplasmic transport defects, and apoptosis. Mol. Cell. Biol. 1998, 18 (3), 1236− 47. (45) Beausoleil, S. A.; Jedrychowski, M.; Schwartz, D.; Elias, J. E.; Villen, J.; Li, J.; Cohn, M. A.; Cantley, L. C.; Gygi, S. P. Large-scale characterization of HeLa cell nuclear phosphoproteins. Proc. Natl. Acad. Sci. U. S. A. 2004, 101 (33), 12130−5. (46) Yates, J. R., 3rd; Eng, J. K.; McCormack, A. L.; Schieltz, D. Method to correlate tandem mass spectra of modified peptides to amino acid sequences in the protein database. Anal. Chem. 1995, 67 (8), 1426−36. (47) Beausoleil, S. A.; Villen, J.; Gerber, S. A.; Rush, J.; Gygi, S. P. A probability-based approach for high-throughput protein phosphorylation analysis and site localization. Nat. Biotechnol. 2006, 24 (10), 1285−92. (48) Park, S. K.; Venable, J. D.; Xu, T.; Yates, J. R., 3rd. A quantitative analysis software tool for mass spectrometry-based proteomics. Nat. Methods 2008, 5 (4), 319−22. (49) Huang da, W.; Sherman, B. T.; Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 2009, 4 (1), 44−57. (50) Szklarczyk, D.; Franceschini, A.; Kuhn, M.; Simonovic, M.; Roth, A.; Minguez, P.; Doerks, T.; Stark, M.; Muller, J.; Bork, P.; Jensen, L. J.; von Mering, C. The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 2011, 39 (Database issue), D561−8. (51) Smoot, M. E.; Ono, K.; Ruscheinski, J.; Wang, P. L.; Ideker, T. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 2011, 27 (3), 431−2. 4299

dx.doi.org/10.1021/pr400089v | J. Proteome Res. 2013, 12, 4280−4301

Journal of Proteome Research

Article

(52) Scardoni, G.; Petterlini, M.; Laudanna, C. Analyzing biological network parameters with CentiScaPe. Bioinformatics 2009, 25 (21), 2857−9. (53) Schwartz, D.; Gygi, S. P. An iterative statistical approach to the identification of protein phosphorylation motifs from large-scale data sets. Nat. Biotechnol. 2005, 23 (11), 1391−8. (54) Xue, Y.; Ren, J.; Gao, X.; Jin, C.; Wen, L.; Yao, X. GPS 2.0, a tool to predict kinase-specific phosphorylation sites in hierarchy. Mol. Cell. Proteomics 2008, 7 (9), 1598−608. (55) Wang, L. H.; Li, D. Q.; Fu, Y.; Wang, H. P.; Zhang, J. F.; Yuan, Z. F.; Sun, R. X.; Zeng, R.; He, S. M.; Gao, W. pFind 2.0: a software package for peptide and protein identification via tandem mass spectrometry. Rapid Commun. Mass Spectrom. 2007, 21 (18), 2985− 91. (56) Matsuoka, S.; Ballif, B. A.; Smogorzewska, A.; McDonald, E. R., 3rd; Hurov, K. E.; Luo, J.; Bakalarski, C. E.; Zhao, Z.; Solimini, N.; Lerenthal, Y.; Shiloh, Y.; Gygi, S. P.; Elledge, S. J. ATM and ATR substrate analysis reveals extensive protein networks responsive to DNA damage. Science 2007, 316 (5828), 1160−6. (57) Bensimon, A.; Schmidt, A.; Ziv, Y.; Elkon, R.; Wang, S. Y.; Chen, D. J.; Aebersold, R.; Shiloh, Y. ATM-dependent and -independent dynamics of the nuclear phosphoproteome after DNA damage. Sci. Signal. 2010, 3 (151), rs3. (58) Bennetzen, M. V.; Larsen, D. H.; Bunkenborg, J.; Bartek, J.; Lukas, J.; Andersen, J. S. Site-specific phosphorylation dynamics of the nuclear proteome during the DNA damage response. Mol. Cell. Proteomics 2010, 9 (6), 1314−23. (59) de Graauw, M.; Hensbergen, P.; Van de Water, B. Phosphoproteomic analysis of cellular signaling. Electrophoresis 2006, 27 (13), 2676−86. (60) Dix, M. M.; Simon, G. M.; Cravatt, B. F. Global mapping of the topography and magnitude of proteolytic events in apoptosis. Cell 2008, 134 (4), 679−91. (61) Kondo, S.; Perrimon, N. A genome-wide RNAi screen identifies core components of the G(2)-M DNA damage checkpoint. Sci. Signal. 2011, 4 (154), rs1. (62) Ernst, J.; Bar-Joseph, Z. STEM: a tool for the analysis of short time series gene expression data. BMC Bioinf. 2006, 7, 191. (63) Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N. S.; Wang, J. T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13 (11), 2498−504. (64) Linding, R.; Jensen, L. J.; Ostheimer, G. J.; van Vugt, M. A.; Jorgensen, C.; Miron, I. M.; Diella, F.; Colwill, K.; Taylor, L.; Elder, K.; Metalnikov, P.; Nguyen, V.; Pasculescu, A.; Jin, J.; Park, J. G.; Samson, L. D.; Woodgett, J. R.; Russell, R. B.; Bork, P.; Yaffe, M. B.; Pawson, T. Systematic discovery of in vivo phosphorylation networks. Cell 2007, 129 (7), 1415−26. (65) Miller, M. L.; Jensen, L. J.; Diella, F.; Jorgensen, C.; Tinti, M.; Li, L.; Hsiung, M.; Parker, S. A.; Bordeaux, J.; Sicheritz-Ponten, T.; Olhovsky, M.; Pasculescu, A.; Alexander, J.; Knapp, S.; Blom, N.; Bork, P.; Li, S.; Cesareni, G.; Pawson, T.; Turk, B. E.; Yaffe, M. B.; Brunak, S.; Linding, R. Linear motif atlas for phosphorylation-dependent signaling. Sci. Signal. 2008, 1 (35), ra2. (66) Blom, N.; Sicheritz-Ponten, T.; Gupta, R.; Gammeltoft, S.; Brunak, S. Prediction of post-translational glycosylation and phosphorylation of proteins from the amino acid sequence. Proteomics 2004, 4 (6), 1633−49. (67) Amanchy, R.; Periaswamy, B.; Mathivanan, S.; Reddy, R.; Tattikota, S. G.; Pandey, A. A curated compendium of phosphorylation motifs. Nat. Biotechnol. 2007, 25 (3), 285−6. (68) Fujii, K.; Zhu, G.; Liu, Y.; Hallam, J.; Chen, L.; Herrero, J.; Shaw, S. Kinase peptide specificity: improved determination and relevance to protein phosphorylation. Proc. Natl. Acad. Sci. U. S. A. 2004, 101 (38), 13744−9. (69) Yasuda, I.; Kishimoto, A.; Tanaka, S.; Tominaga, M.; Sakurai, A.; Nishizuka, Y. A synthetic peptide substrate for selective assay of protein kinase C. Biochem. Biophys. Res. Commun. 1990, 166 (3), 1220−7.

(70) Kemp, B. E.; Pearson, R. B. Design and use of peptide substrates for protein kinases. Methods Enzymol. 1991, 200, 121−34. (71) 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. Cell 2006, 127 (3), 635−48. (72) Yu, Y.; Yoon, S. O.; Poulogiannis, G.; Yang, Q.; Ma, X. M.; Villen, J.; Kubica, N.; Hoffman, G. R.; Cantley, L. C.; Gygi, S. P.; Blenis, J. Phosphoproteomic analysis identifies Grb10 as an mTORC1 substrate that negatively regulates insulin signaling. Science 2011, 332 (6035), 1322−6. (73) Hsu, P. P.; Kang, S. A.; Rameseder, J.; Zhang, Y.; Ottina, K. A.; Lim, D.; Peterson, T. R.; Choi, Y.; Gray, N. S.; Yaffe, M. B.; Marto, J. A.; Sabatini, D. M. The mTOR-regulated phosphoproteome reveals a mechanism of mTORC1-mediated inhibition of growth factor signaling. Science 2011, 332 (6035), 1317−22. (74) Olsen, J. V.; Vermeulen, M.; Santamaria, A.; Kumar, C.; Miller, M. L.; Jensen, L. J.; Gnad, F.; Cox, J.; Jensen, T. S.; Nigg, E. A.; Brunak, S.; Mann, M. Quantitative phosphoproteomics reveals widespread full phosphorylation site occupancy during mitosis. Sci. Signal. 2010, 3 (104), ra3. (75) Nishizuka, Y. The role of protein kinase C in cell surface signal transduction and tumour promotion. Nature 1984, 308 (5961), 693− 8. (76) Nishizuka, Y. Intracellular signaling by hydrolysis of phospholipids and activation of protein kinase C. Science 1992, 258 (5082), 607−14. (77) Yoshida, K. PKCdelta signaling: mechanisms of DNA damage response and apoptosis. Cell. Signal. 2007, 19 (5), 892−901. (78) Smith, G. C.; Jackson, S. P. The DNA-dependent protein kinase. Genes Dev. 1999, 13 (8), 916−34. (79) Kharbanda, S.; Pandey, P.; Jin, S.; Inoue, S.; Bharti, A.; Yuan, Z. M.; Weichselbaum, R.; Weaver, D.; Kufe, D. Functional interaction between DNA-PK and c-Abl in response to DNA damage. Nature 1997, 386 (6626), 732−5. (80) Liu, H.; Lu, Z. G.; Miki, Y.; Yoshida, K. Protein kinase C delta induces transcription of the TP53 tumor suppressor gene by controlling death-promoting factor Btf in the apoptotic response to DNA damage. Mol. Cell. Biol. 2007, 27 (24), 8480−91. (81) Blois, J. T.; Mataraza, J. M.; Mecklenbrauker, I.; Tarakhovsky, A.; Chiles, T. C. B cell receptor-induced cAMP-response elementbinding protein activation in B lymphocytes requires novel protein kinase Cdelta. J. Biol. Chem. 2004, 279 (29), 30123−32. (82) Kurita, S.; Higuchi, H.; Saito, Y.; Nakamoto, N.; Takaishi, H.; Tada, S.; Saito, H.; Gores, G. J.; Hibi, T. DNMT1 and DNMT3b silencing sensitizes human hepatoma cells to TRAIL-mediated apoptosis via up-regulation of TRAIL-R2/DR5 and caspase-8. Cancer Sci. 2010, 101 (6), 1431−9. (83) Lavoie, G.; Esteve, P. O.; Laulan, N. B.; Pradhan, S.; St-Pierre, Y. PKC isoforms interact with and phosphorylate DNMT1. BMC Biol. 2011, 9 (1), 31. (84) Goodrich, D. W.; Wang, N. P.; Qian, Y. W.; Lee, E. Y.; Lee, W. H. The retinoblastoma gene product regulates progression through the G1 phase of the cell cycle. Cell 1991, 67 (2), 293−302. (85) Robertson, K. D.; Ait-Si-Ali, S.; Yokochi, T.; Wade, P. A.; Jones, P. L.; Wolffe, A. P. DNMT1 forms a complex with Rb, E2F1 and HDAC1 and represses transcription from E2F-responsive promoters. Nat. Genet. 2000, 25 (3), 338−42. (86) McCabe, M. T.; Davis, J. N.; Day, M. L. Regulation of DNA methyltransferase 1 by the pRb/E2F1 pathway. Cancer Res. 2005, 65 (9), 3624−32. (87) Andrews, P. D.; Knatko, E.; Moore, W. J.; Swedlow, J. R. Mitotic mechanics: the auroras come into view. Curr. Opin. Cell. Biol. 2003, 15 (6), 672−83. (88) den Hollander, J.; Rimpi, S.; Doherty, J. R.; Rudelius, M.; Buck, A.; Hoellein, A.; Kremer, M.; Graf, N.; Scheerer, M.; Hall, M. A.; Goga, A.; von Bubnoff, N.; Duyster, J.; Peschel, C.; Cleveland, J. L.; Nilsson, J. A.; Keller, U. Aurora kinases A and B are up-regulated by 4300

dx.doi.org/10.1021/pr400089v | J. Proteome Res. 2013, 12, 4280−4301

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Myc and are essential for maintenance of the malignant state. Blood 2010, 116 (9), 1498−505. (89) Addepalli, M. K.; Ray, K. B.; Kumar, B.; Ramnath, R. L.; Chile, S.; Rao, H. RNAi-mediated knockdown of AURKB and EGFR shows enhanced therapeutic efficacy in prostate tumor regression. Gene Ther. 2010, 17 (3), 352−9. (90) Mourtada-Maarabouni, M.; Williams, G. T. RBM5/LUCA-15– tumour suppression by control of apoptosis and the cell cycle? Sci. World J. 2002, 2, 1885−90. (91) Shu, Y.; Rintala-Maki, N. D.; Wall, V. E.; Wang, K.; Goard, C. A.; Langdon, C. E.; Sutherland, L. C. The apoptosis modulator and tumour suppressor protein RBM5 is a phosphoprotein. Cell. Biochem. Funct. 2007, 25 (6), 643−53. (92) Reyland, M. E.; Barzen, K. A.; Anderson, S. M.; Quissell, D. O.; Matassa, A. A. Activation of PKC is sufficient to induce an apoptotic program in salivary gland acinar cells. Cell Death Differ. 2000, 7 (12), 1200−9. (93) Weishaupt, J. H.; Neusch, C.; Bahr, M. Cyclin-dependent kinase 5 (CDK5) and neuronal cell death. Cell Tissue Res. 2003, 312 (1), 1−8. (94) Sahin, B.; Hawasli, A. H.; Greene, R. W.; Molkentin, J. D.; Bibb, J. A. Negative regulation of cyclin-dependent kinase 5 targets by protein kinase C. Eur. J. Pharmacol. 2008, 581 (3), 270−5. (95) Zhao, C. T.; Li, K.; Li, J. T.; Zheng, W.; Liang, X. J.; Geng, A. Q.; Li, N.; Yuan, X. B. PKCdelta regulates cortical radial migration by stabilizing the Cdk5 activator p35. Proc. Natl. Acad. Sci. U. S. A. 2009, 106 (50), 21353−8. (96) Zhang, L.; Yun, H.; Murray, F.; Lu, R.; Wang, L.; Hook, V.; Insel, P. A. Cytotoxic T lymphocyte antigen-2 alpha induces apoptosis of murine T-lymphoma cells and cardiac fibroblasts and is regulated by cAMP/PKA. Cell. Signal. 2011, 23 (10), 1611−6. (97) Zambon, A. C.; Wilderman, A.; Ho, A.; Insel, P. A. Increased Expression of the Pro-apoptotic Protein BIM, a Mechanism for cAMP/Protein Kinase A (PKA)-induced Apoptosis of Immature T Cells. J. Biol. Chem. 2011, 286 (38), 33260−7. (98) Ojeda, F.; Folch, H.; Guarda, M. I.; Jastorff, B.; Diehl, H. A. Induction of apoptosis in thymocytes: new evidence for an interaction of PKC and PKA pathways. Biol. Chem. Hoppe-Seyler 1995, 376 (6), 389−93. (99) Ahmad, K. A.; Wang, G.; Unger, G.; Slaton, J.; Ahmed, K. Protein kinase CK2–a key suppressor of apoptosis. Adv. Enzyme Regul. 2008, 48, 179−87. (100) Di Maira, G.; Brustolon, F.; Tosoni, K.; Belli, S.; Kramer, S. D.; Pinna, L. A.; Ruzzene, M. Comparative analysis of CK2 expression and function in tumor cell lines displaying sensitivity vs. resistance to chemical induced apoptosis. Mol. Cell. Biochem. 2008, 316 (1−2), 155−61. (101) Wang, G.; Unger, G.; Ahmad, K. A.; Slaton, J. W.; Ahmed, K. Downregulation of CK2 induces apoptosis in cancer cells–a potential approach to cancer therapy. Mol. Cell. Biochem. 2005, 274 (1−2), 77− 84. (102) Pospisilova, S.; Brazda, V.; Kucharikova, K.; Luciani, M. G.; Hupp, T. R.; Skladal, P.; Palecek, E.; Vojtesek, B. Activation of the DNA-binding ability of latent p53 protein by protein kinase C is abolished by protein kinase CK2. Biochem. J. 2004, 378 (Pt 3), 939− 47. (103) Sugasawa, K. The CUL4 enigma: culling DNA repair factors. Mol. Cell 2009, 34 (4), 403−4. (104) Kielbassa, K.; Muller, H. J.; Meyer, H. E.; Marks, F.; Gschwendt, M. Protein kinase C delta-specific phosphorylation of the elongation factor eEF-alpha and an eEF-1 alpha peptide at threonine 431. J. Biol. Chem. 1995, 270 (11), 6156−62. (105) DeVries, T. A.; Kalkofen, R. L.; Matassa, A. A.; Reyland, M. E. Protein kinase Cdelta regulates apoptosis via activation of STAT1. J. Biol. Chem. 2004, 279 (44), 45603−12. (106) Obenauer, J. C.; Cantley, L. C.; Yaffe, M. B. Scansite 2.0: Proteome-wide prediction of cell signaling interactions using short sequence motifs. Nucleic Acids Res. 2003, 31 (13), 3635−41.

(107) Gao, J.; Thelen, J. J.; Dunker, A. K.; Xu, D. Musite, a tool for global prediction of general and kinase-specific phosphorylation sites. Mol. Cell. Proteomics 2010, 9 (12), 2586−600. (108) Statsuk, A. V.; Maly, D. J.; Seeliger, M. A.; Fabian, M. A.; Biggs, W. H., 3rd; Lockhart, D. J.; Zarrinkar, P. P.; Kuriyan, J.; Shokat, K. M. Tuning a three-component reaction for trapping kinase substrate complexes. J. Am. Chem. Soc. 2008, 130 (51), 17568−74. (109) Berwick, D. C.; Tavare, J. M. Identifying protein kinase substrates: hunting for the organ-grinder’s monkeys. Trends Biochem. Sci. 2004, 29 (5), 227−32. (110) Liu, L.; Lee, S.; Zhang, J.; Peters, S. B.; Hannah, J.; Zhang, Y.; Yin, Y.; Koff, A.; Ma, L.; Zhou, P. CUL4A abrogation augments DNA damage response and protection against skin carcinogenesis. Mol. Cell 2009, 34 (4), 451−60. (111) Jia, L.; Sun, Y. SCF E3 ubiquitin ligases as anticancer targets. Curr. Cancer Drug Targets 2011, 11 (3), 347−56.

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dx.doi.org/10.1021/pr400089v | J. Proteome Res. 2013, 12, 4280−4301