Identification of Novel PAMP-Triggered ... - ACS Publications

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Identification of Novel PAMP-Triggered Phosphorylation and Dephosphorylation Events in Arabidopsis thaliana by Quantitative Phosphoproteomic Analysis Naganand Rayapuram,†,‡,# Ludovic Bonhomme,†,‡,⊥,# Jean Bigeard,§ Kahina Haddadou,†,‡,§ Cédric Przybylski,†,‡ Heribert Hirt,*,§,∥ and Delphine Pflieger*,†,‡ †

CNRS, UMR 8587, boulevard François Mitterrand, 91025 Evry, France Université Evry Val d’Essonne (UEVE), LAMBE, boulevard François Mitterrand, 91025 Evry, France § URGV Plant Genomics, INRA/CNRS/Université d’Evry Val d’Essonne, 2 rue Gaston Crémieux, 91057 Evry, France ∥ Center for Desert Agriculture, KAUST, 4700 Thuwal, Saudi Arabia ‡

S Supporting Information *

ABSTRACT: Signaling cascades rely strongly on protein kinasemediated substrate phosphorylation. Currently a major challenge in signal transduction research is to obtain high confidence substrate phosphorylation sites and assign them to specific kinases. In response to bacterial flagellin, a pathogen-associated molecular pattern (PAMP), we searched for rapidly phosphorylated proteins in Arabidopsis thaliana by combining multistage activation (MSA) and electron transfer dissociation (ETD) fragmentation modes, which generate complementary spectra and identify phosphopeptide sites with increased reliability. Of a total of 825 phosphopeptides, we identified 58 to be differentially phosphorylated. These peptides harbor kinase motifs of mitogenactivated protein kinases (MAPKs) and calcium-dependent protein kinases (CDPKs), as well as yet unknown protein kinases. Importantly, 12 of the phosphopeptides show reduced phosphorylation upon flagellin treatment. Since protein abundance levels did not change, these results indicate that flagellin induces not only various protein kinases but also protein phosphatases, even though a scenario of inhibited kinase activity may also be possible. KEYWORDS: phosphoproteomics, Arabidopsis, MAP kinase, flagellin, pathogen, electron transfer dissociation

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INTRODUCTION Plants possess pattern recognition receptors that detect conserved pathogen-associated molecular patterns (PAMPs) and initiate PAMP-triggered immunity (PTI).1 Successful pathogens deliver effectors to the plant apoplast as well as to various intracellular compartments; these effectors suppress PTI and thereby facilitate host invasion. As a consequence, plants evolved intracellular receptors with nucleotide binding (NB)- leucine-rich repeat (LRR) domains that sense effectors and mediate effector-triggered immunity (ETI).1 The bacterial PAMP flg22, a conserved 22-amino-acid peptide derived from Pseudomonas syringae flagellin, has provided a very powerful tool to decipher PAMP-induced signaling pathways and revealed the complexity of mitogenactivated protein kinase (MAPK) cascades. In A. thaliana, flg22 recognition is mediated by the LRR receptor kinase FLS2 to induce an array of defense responses, including the generation of reactive oxygen species (ROS), callose deposition, ethylene production, and reprogramming of host cell genes.2−4 Flg22 recognition also leads to the activation of two MAPK signaling pathways. One of these MAPK cascades is defined by the © 2014 American Chemical Society

MAPKKs MKK4 and MKK5, which act redundantly to activate the MAPKs MPK3 and MPK6.5 The second flg22-activated cascade is defined by the MAPKKK MEKK1, which activates the MAPKKs MKK1 and MKK2, which act redundantly on the MAPK MPK4.6 Recently, the activity of a fourth MAPK, MPK11, was shown to be induced by flg22, but its function in plant immunity remains to be clarified.7 Flg22 also transiently activates multiple calcium-dependent protein kinases (CDPKs) in A. thaliana and four related CDPKs, CPK4, CPK5, CPK6, and CPK11, were identified as early transcriptional regulators in PAMP signaling.8 Notably, PAMP-induced protein kinase cascades ultimately lead to the differential expression of immune response genes to adjust the metabolism and physiological status of the challenged plant. An understanding of the phosphorylation-mediated PAMP signaling requires deciphering the sequential kinase-substrate relationships from the plasma membrane-located FLS2 receptor via the protein kinase pathways down to the level of their substrates and Received: December 18, 2013 Published: March 6, 2014 2137

dx.doi.org/10.1021/pr401268v | J. Proteome Res. 2014, 13, 2137−2151

Journal of Proteome Research

Article

addition, several proteins showed flg22-reduced phosphorylation status, indicating the activation of several phosphatases by PAMP signaling. The newly identified proteins that change phosphorylation status upon flagellin treatment are involved in different biological processes, including signaling, gene expression, cytoskeleton, and metabolism, indicating that PAMP signaling targets a large variety of biological processes to coordinate defense.

among them to the transcriptional regulators. Reliably determining the phosphorylation sites in substrates is then critical to be able to assign them to specific (classes of) kinases. In addition, only with the confident knowledge of phosphosites can one afford time-consuming validation assays, such as testing the physical interaction of a kinase with its putative substrates or analyzing phosphosite-mutated versions of the putative substrates. Phosphoproteomic approaches relying on LC−MS/MS have been extensively used to study signaling pathways, leading to the identification of thousands of phosphorylated peptide sequences. Among these, a few show stimulus-dependent modification of the phosphorylation levels pointing to the corresponding proteins as relays in the given cascade.9−11 Still, identification of phosphopeptides is not trivial, because both the sequence of amino acids and the precise phosphosite(s) need to be reliably established from MS/MS spectra, which may lack discriminating fragment ions that permit assigning the phosphosite(s) to specific Ser/Thr/Tyr residues. To circumvent this issue, several groups have previously assessed the advantage of acquiring two complementary fragmentation spectra on every peptide precursor to increase the chances of phosphopeptide identification, which is particularly possible on the hybrid LTQ-Orbitrap instrument. For instance, to get sensitive identification and precise quantification information from each spectrum, the combination of classical CID in the linear ion trap and HCD in a dedicated cell was often selected for the analysis of iTRAQ-labeled peptides.12−15 CID and ETD spectra were also suggested to be consecutively acquired, with ETD generating more fragment ions that enable phosphosite localization.16 Even though the idea of acquiring two fragmentation spectra to improve phosphopeptide characterization has been iteratively suggested, until recently the development of bioinformatics tools to reconcile the information embedded in both fragmentation patterns to reveal the most probable modified sequences has been lagging behind. In particular, simply merging the CID and ETD spectra was reported to result in poorer identification rates.17 In an effort to fill this gap, we developed the program FragMixer,18 which allows handling LC−MS/MS data sets consisting of one type of spectra (e.g., multistage activation (MSA)) or spectrum pairs (any combination such as MSA/ETD or MS2/HCD). FragMixer processes the outputs of the database search engine Mascot and provides lists of phosphopeptides with definite or unclear phosphosites, depending on the values of Mascot Delta scores (MD-scores) calculated for each identified spectrum as compared to a threshold MD-score associated to a given false localization rate (FLR).19 Elsewhere, a phosphosite localization algorithm derived from Ascore20 that applies to paired MSA and ETD spectra was described to improve phosphopeptide characterization.21 In the present work, we first investigated MSA and ETD complementarity for the study of PAMP-triggered phosphorylation in A. thaliana and showed that the combination of these two modes extends the repertoire of identified phosphopeptides. Then, we compared mock-treated and flg22-treated plants by a label-free approach.22 Whereas previous phosphoproteomic studies focused on plasma membrane flg22-induced responses in A. thaliana cultured cells,9,11 this work focused on cytoplasmic proteins. We identified 58 differentially regulated phosphopeptides belonging to 50 unique proteins. These proteins are potential protein kinase substrates of MAPKs, CDPKs, SnRKs, CKs, and yet unknown classes of kinases. In

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MATERIAL AND METHODS

Plant Growth Conditions and Treatments

Arabidopsis thaliana seeds, ecotype Columbia-0 (Col-0), were surface sterilized and stratified for several days at 4 °C. Seedlings were first grown for 5 days in liquid 1/2 MS medium (M6899, Sigma-Aldrich) containing 0.5 g/L MES (M8250, Sigma-Aldrich) pH 5.7 and 1% (w/v) sucrose (S5016, SigmaAldrich) in a culture chamber at 24 °C with a 16 h photoperiod. Then, seedlings were transferred to Erlenmeyer flasks and grown under shaking in the same medium, in the dark, at 25 °C for about 1 month. We decided to grow plants in the dark to avoid high RuBisCo (ribulose-1,5-bisphosphate carboxylase/ oxygenase) content in the plant tissues as RuBisCo is the most abundant protein in photosynthetic organisms.23 RuBisCo is located in the chloroplasts, and these organelles often break when plant cells have been frozen and ground in liquid nitrogen. Moreover, RuBisCo is phosphorylated in vivo at several sites24,25 and can thus hinder in-depth analysis of plant proteomes and phosphoproteomes.26 The culture in liquid medium allowed applying the PAMP flg22 easily and on a large plant surface. Plants were finally mock- or flg22-treated, harvested, blot dried with paper, flash-frozen in liquid nitrogen, and stored at −80 °C. More precisely for the treatments, plants were first left on the bench for 3 h before water (mock) or 1 μM flg22 (final concentration) was applied for 15 min to the plants. Plants were also kept in the dark during the resting period and the treatment, by maintaining the flasks wrapped in an aluminum foil except when promptly adding water or the flg22-containing solution. For the comparative analysis of mock- and flg22-treated samples, about 7 g of plants was used for each condition, and three biologically independent pairs of samples were prepared and analyzed. Plant Protein Extractions

Frozen plants were ground in a mortar with liquid nitrogen and were then resuspended in a solution containing 0.4 M sucrose, 10 mM Tris-HCl pH 8.0, 10 mM MgCl2, 5 mM βmercaptoethanol (Sigma-Aldrich chemicals), protease inhibitors (Complete cocktail, Roche), and phosphatase inhibitors (1 mM NaF, 0.5 mM Na3VO4, 15 mM β-glycerophosphate, 15 mM 4-nitrophenyl phosphate, Sigma-Aldrich chemicals). After filtration on Miracloth, the samples were centrifuged for 20 min at 3,400g at 4 °C, and the supernatants, corresponding mainly to the cytoplasmic fraction, were collected. Proteins were finally precipitated with TCA/acetone and stored at −80 °C. Alternatively, whole protein extracts were also prepared from ground material using a direct TCA/acetone extraction method based on the protocol described in Méchin et al.27 Whole protein extracts and cytoplasmic extracts were used to evaluate the benefits of the MSA/ETD paired spectrum approach; only cytoplasmic extracts were analyzed for the comparative analysis of mock- and flg22-treated samples. 2138



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Immunoblotting

Phosphopeptide samples were completely dried using a speedvac concentrator and kept at −80 °C until LC−MS/MS analysis.

About 80−100 mg of frozen plant powder was resuspended in 200 μL of a buffer containing 50 mM Tris-HCl pH 7.5, 150 mM NaCl, 0.1% NP40, 5 mM EGTA, 0.1 mM DTT (SigmaAldrich chemicals), protease inhibitors (Complete cocktail, Roche, and 1 mM PMSF, Sigma-Aldrich), and phosphatase inhibitors (1 mM NaF, 0.5 mM Na3VO4, 15 mM βglycerophosphate, 15 mM 4-nitrophenyl phosphate, SigmaAldrich chemicals). The suspension was centrifuged at 20,000g for 15 min at 4 °C, and the supernatant (150 μL) was collected. Protein quantification was carried out by Bradford method (B6916, Sigma-Aldrich), and the normalized protein amounts of all samples were denatured with SDS-sample buffer by boiling them at 95 °C for 10 min. Protein samples were resolved by SDS-PAGE at a constant amperage of 15 mA and transferred onto methanol-activated PVDF membranes (GE Healthcare) for 1 h at a constant voltage of 100 V. Blots were blocked with 5% BSA (A9647, Sigma-Aldrich) in 1x TBST for 1 h and then probed with Phospho-p44/42 MAPK (Erk1/2) (Thr202/Tyr204) (D13.14.4E) XP rabbit monoclonal antibody (#4370, Cell Signaling) at a dilution of 1:1,500 in 5% BSA in 1x TBST overnight at 4 °C. The membranes were washed three times with 1x TBST. Goat anti-rabbit antibodies (at a dilution of 1:15,000 in 5% BSA in 1x TBST) conjugated to horseradish peroxidase were used as secondary antibodies. The membranes were washed again three times with 1x TBST, and the antigen− antibody interaction was detected with enhanced chemiluminescence reagent (ECL Prime, GE Healthcare) using a GeneGnome imaging system (Syngene). Coomassie blue staining of blots was then carried out for protein visualization.

LC−MS/MS Analyses

All buffers and solutions were prepared using ultrapure water (Milli-Q, Millipore). NanoLC−MS/MS analyses were performed on a Dual Gradient Ultimate 3000 chromatographic system (Dionex). Phosphopeptide samples resuspended in buffer A (water/ACN/formic acid, 95/5/0.1, v/v/v) were loaded onto a C18 precolumn (Acclaim PepMap C18, 5 mm length × 300 μm i.d., 5 μm particle size, 100 Å porosity, Dionex). After desalting for 5 min with buffer A, peptide separation was carried out on a C18 capillary column (Acclaim PepMap C18, 15 cm length × 75 μm i.d., 3 μm particle size, 100 Å porosity, Dionex) with a gradient starting at 100% solvent A, ramping to 50% solvent B (water/ACN/formic acid, 20/80/0.1, v/v/v) over 60 min, then to 100% solvent B over 3 min (held 10 min), and finally decreasing to 100% solvent A in 3 min. The column was finally re-equilibrated with 100% solvent A for 30 min. The LC eluent was sprayed into the MS instrument with a glass emitter tip (Pico-tip, FS360-50-15-CE20-C10.5, New Objective, Woburn, MA, USA). The LTQOrbitrap XL ETD mass spectrometer (Thermo-Fisher Scientific) was operated in positive ionization mode. The lock mass option using as reference the ion at m/z 445.120025 was activated to ensure more accurate mass measurements in FTMS mode. The minimum MS signal for triggering MS/MS was set to 500. In all scan modes one microscan was acquired. The Orbitrap cell recorded signals between m/z 400 and 1400 in profile mode with a resolution set to 30,000 in MS mode. During MS/MS scans, fragmentation and detection occurred in the linear ion trap analyzer in centroid mode. The automatic gain control (AGC) allowed accumulating up to 106 ions for FTMS scans and 104 ions for ITMSn scans; for ETD reagent (fluoranthene), the AGC was set to 2 × 105. Maximum injection time was set to 500 ms for FTMS scans, 100 ms for ITMSn scans and 50 ms for the ETD reagent. When performing ETD analysis, a charge-state-dependent reaction time was used according to the formula CS-depRT = 100 × 2/z ms, with z being the precursor charge state. Finally, supplemental activation was set to 20% in the Tune Page to increase ETD fragmentation efficiency.28 To compare the MSA and ETD fragmentation modes in parallel for phosphopeptide sample characterization, five independent samples were analyzed in quadruplicate. At first, two exploratory injections targeted either ionic species of charge states 2 or 3 (later designated as CS23 species) or of charge states ≥4 (later designated as CSsup4 species). Then, to obtain further data on the samples, two additional injections targeting CS23 or CSsup4 species were designed that excluded the m/z ratios previously identified in the exploratory runs. More precisely, when selecting CS23 species, the neutral loss masses 49, 32.67, 98, and 65.34 were specified for MSA fragmentation; when selecting CSsup4 species, the neutral loss masses were 24.5, 19.6, 49, and 39.2. These two injections allowed considering the relevant neutral losses expected from 2+3+ or 4+5+ species that bear one or two phosphorylation moieties. Few identifications could indeed be expected from higher-charge-state species, even from ETD spectra,29 and of sequences harboring more than two phosphorylations. To study the phosphorylation events in A. thaliana plants upon flg22 treatment, we injected the flg22-treated (F) and

Protein Digestion and C18-IMAC Protocol

HPLC grade acetonitrile (ACN) and normapur grade formic acid and acetic acid (AA) were purchased from VWR. Typically 300−500 μg of protein material was used per experiment. Samples were resuspended in 100 μL of buffer 8 M urea in 100 mM triethylammonium bicarbonate (TEAB) pH 8.5 (reference T7408 from Sigma-Aldrich). Proteins were then reduced by addition of 10 mM tris(2-carboxyethyl)phosphine (TCEP, reference C-4706 from Sigma-Aldrich) and incubation at 37 °C for 1 h and then alkylated by addition of 20 mM methyl methanethiosulfonate (MMTS, from Sigma-Aldrich) and incubation at ambient temperature for 15 min. After dilution of the samples to 1 M urea with 100 mM TEAB, proteolysis was performed by adding trypsin (sequencing-grade modified porcine trypsin (EC 3.4.21.4 from Promega), used for the study of the response to flg22) or Lys-C (V1071, from Promega) at an enzyme/substrate ratio of 1/50 (w/w) and overnight incubation at 37 °C. Proteolytic peptide samples were acidified with AA to reach a pH of about 3 and cleaned on C18 spin columns (reference 74-4101, Harvard Apparatus). They were eluted from the C18 phase with 100 μL of 60% ACN/3% AA to which 100 μL of water was added, to reach a composition of 30% ACN/1.5% AA that is compatible with phosphopeptide enrichment on the IMAC resin. Each sample was then incubated with 10 μL of packed IMAC beads (reference P9740, Sigma-Aldrich) for 2 h on a wheel rotating at 15 rpm. After removal of the supernatant, IMAC beads were washed three times with 200 μL of 30% ACN/1.5% AA with 2 min incubation each time and quickly once with 200 μL of water. Peptides retained on the resin were finally eluted with 50 μL of 400 mM NH4OH. The pH of the IMAC eluates was immediately lowered to about 6−7 by addition of 10% AA. 2139



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mock-treated (M) samples iteratively as follows: (1) a pair of MSA/ETD analyses targeting CS23 or CSsup4 species, (2) a pair of such analyses excluding the m/z ratios of phosphopeptides identified in the previous analyses, (3) two MSA/ETD analyses targeting CS23 ions with incremented exclusion lists; CSsup4 ions were not targeted anymore, given the fact that their selection already led to few identifications in the run programmed at step (2). In summary, a total of 6 LC−MS/MS runs were acquired for each F or M peptide sample. To estimate the relative protein abundance in paired F and M samples, the IMAC flow-through samples (1/200 from each sample) were analyzed by LC−MS/MS using the same acquisition conditions as for phosphopeptide samples, except that 6 MS2 spectra were acquired in the linear ion trap after each FTMS preview scan and only CS23 species were fragmented.

phosphopeptides, the relevance of this relationship was confirmed to be good, yet it appeared that the actual FLR was a bit underestimated33) . The LC−MS/MS runs acquired on IMAC flow-throughs were processed using the X!tandem pipeline (http://pappso. inra.fr/bioinfo/xtandempipeline/) to get peptide and protein identifications. The same database search parameters were specified as when using Mascot to interpret data obtained on phosphopeptide samples, except that we allowed variable oxidation of methionine residues, protein N-terminal acetylation, and cyclization of the N-terminal glutamine residues of tryptic peptides (loss of NH3). Phosphorylated sequences identified with confident phosphosites (FLR < 0.05 according to ref 19) were submitted to motif-x (http://motif-x.med.harvard.edu/motif-x.html) to uncover over-represented phosphorylation patterns. Parameters set for the width, occurrences, significance, and background were 13, 20, 10−6, and IPI Arabidopsis proteome, respectively.

Database Searches and Phosphoproteomics Data Analysis

RAW data files acquired on the LTQ-Orbitrap XL ETD instrument were processed using the software Proteome Discoverer 1.3 (Thermo Scientific) as interface. Two different workflows were created to handle MSA or ETD data separately. For ETD spectra interpretation, the workflow included a nonfragment filter node that removed a 4-Da window around the selected precursor ion, a 2-Da window around chargereduced precursors, and a 2-Da window around known neutral losses up to 120 Da in mass listed in ref 30. Database searches were performed with the Mascot server v2.2.07 while specifying the following parameters: database TAIR10 (release 2010/12/ 14, 35386 sequences); enzymatic specificity: tryptic with two allowed missed cleavages; fixed modification of cysteine residues (Methylthio(C)); possible phosphorylation of S, T and Y residues; 5 ppm tolerance on precursor masses and 0.6 Da tolerance on fragment ions. Fragment types taken into account were those specified in the configuration ‘ESI-trap’ in the MSA workflow and ‘ETD-trap’ in the ETD workflow. Because former studies using ETD fragmentation made contradictory observations and variable recommendations in terms of fragment types to take into account for database searches,17,31,32 we verified that the data generated on our LTQ-Orbitrap XL ETD instrument led to a maximum of identifications at a given FDR when considering the fragment types specified in the original “ETD-trap” definition of Mascot (Supplementary Figure S1). Finally, the possible oxidation of methionine residues was ignored, since we verified that its addition did not allow increasing the number of unique phosphopeptides identified at a fixed FDR (ref 18 and data not shown). The data was then processed using FragMixer,18 an inhouse developed computational tool that collates the information provided by the database search engine Mascot, from two different spectra with respect to amino acid sequence and location of phosphosite using two simple filtering rules relying on the peptide scores and Mascot Delta scores (MDscores). It can be flexibly configured independently of Mascot search parameters. It is publically available and can be downloaded from http://proteomics.fr/FragMixer. FragMixer was used to reach an estimated FDR of 1% for all analyses and a false localization rate (FLR) of phosphosites below 5% (we specified MD-score thresholds associated to a FLR below 5% according to ref 19; it may be worth mentioning that the relationship between MD-scores and FLR was estimated in this publication using a few hundreds of synthetic phosphopeptides; in another study that used many thousands of synthetic

Label-Free Relative Quantification of Phosphopeptides and Statistical Assessment

To determine the relative abundance of phosphopeptides between mock-treated and flg22-treated plants, we converted the .RAW data files into .mzXML files (using the ReAdW 4.3.1 tool with the option of transforming FTMS spectra acquired in profile mode into centroid data). The obtained files were submitted to the program MassChroQ,34 together with the information of phosphopeptide identifications retrieved from FragMixer results (.txt files containing in successive columns the scan numbers, M+H masses, naked peptide sequences, protein accession numbers, modified sequences) for quantitation. The detailed parameters used for MassChroQ are provided in Supplementary File “MassChroQ_parameters”; briefly, the method Obiwarp was used to align the LC−MS runs from all three biological replicates, and the option of postmatching was activated to integrate the area of a maximum number of chromatographic peaks. Raw quantification data obtained from MassChroQ were normalized to take into account possible global variations between LC−MS runs. The median of all measured chromatographic peak areas in each LC−MS run was computed and used to calculate a correction factor as compared to a chosen reference sample. Changes in phosphopeptide amount were determined using the R program v2.15.2 (R Development Core Team, 2010). Statistical analyses were performed on log2transformed normalized data. Only phosphopeptides that had been quantified in all 3 replicates per condition were kept for statistical analysis. Significance of flg22-induced changes compared to mock-treated samples was assessed using a linear model of analysis of variance while considering the treatment, the batch effect, and the injection date as main effects. The R package QVALUE was used to calculate q-values from the resulting p-values computed for the treatment effect. A q-value lower than 0.05 was defined in order to control the positive false-discovery rate (FDR) at a 5% level, corresponding to a pvalue lower than 0.0077. To estimate relative protein abundance between paired F and M samples, we normalized the XIC areas measured on individual peptides as described for phosphopeptides. An F/ M ratio was calculated as the average of the F/M protein ratios determined on the three biological replicates. 2140



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Figure 1. Evaluation of the fractions of identified phosphopeptides for which the dual spectrum MSA/ETD strategy was worth to get their full characterization. Results shown are for (a) CS23 species and (b) CSsup4 species.

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RESULTS

peptide identifications than the corresponding ETD spectra; in those cases, MSA strengthened the identification of the amino acid sequence, whereas the information of phosphorylation position was determined from only the ETD spectrum. In conclusion, for around 25% of all the peptides identified from CS23 ions (14.4% + 11.2%), it was worth acquiring MSA and ETD spectra to obtain both amino acid sequence and phosphosite information. A similar reasoning was followed for CSsup4 species (Figure 1) and also allowed concluding that close to a quarter of peptide ions benefited from the dual spectrum strategy to yield both reliable amino acid sequence and phosphosite positioning. The peptide properties, such as length and hydrophilic nature, that render identification more successful using ETD or classical CID have previously been described,29,35,37 and we will not further discuss it here. Besides, regarding the ability to identify kinase substrates corresponding to certain phosphorylation motifs, several pioneering phosphoproteomic studies using the ETD technology described in particular that distinct basic and/or (S/T)*P-centered motifs could be newly extracted from the analysis of complex phosphopeptide samples from human cells16,38 and from the plant Medicago truncatula.36 In the latter study, the authors compared their own ETD data to published data on A. thaliana and human cells obtained using CID, which may have been largely MS2 and not specifically MSA. Then, given that we were particularly interested in identifying substrates of proline-directed kinases, among which are MAPKs, we evaluated the respective contribution of MSA and ETD spectra for extracting kinase motifs from the obtained data sets. From the above MSA/ETD analyses on 5 samples, the phosphopeptides that were fully characterized (reliable amino acid sequence and confident phosphosite localization at a FLR < 5%) by MSA spectra or by ETD spectra or from the sequences established by FragMixer from paired spectra were submitted to motif-x. The extracted motifs are shown in Supplementary Figure S3. Interestingly, the motifs S*P and T*P are the S- and T-centered motifs matching the largest populations of phosphorylated sequences. In addition, the numbers of unique phosphopeptides corresponding to these two motifs increased by about 40% when considering the FragMixer sequences as compared to the peptides identified from one fragmentation mode only. A similar observation could be made for the motifs S-D-x-E, S-x-x-D, and R-x-x-S. Within

Relevance of the MSA/ETD Spectrum Pair Strategy

We chose to decipher the signaling cascades triggered in A. thaliana upon stimulation with the peptide flg22 by acquiring pairs of MSA and ETD spectra on each precursor ion. The global gain brought by this strategy in identified phosphorylation sites at a given FLR was already described in ref 21. The authors developed the C-score, an algorithm based on the Ascore,20 that allowed merging the information contained in paired MSA and ETD spectra. Because the acquisition of spectrum pairs significantly lowered the duty cycle of the instrument, we analyzed the same sample iteratively, by selecting species of different charge states: 2+ and 3+ species (named CS23) or 4+ and higher-charge-state species (named CSsup4); this is readily programmed in the acquisition method. To explore further the samples, we designed additional injections targeting CS23 or CSsup4 species that excluded the m/z ratios previously identified in the exploratory runs. We then briefly verified the worthiness of the paired spectra method within our analytical scheme targeting CS23 and CSsup4 species. Based on the analysis of five phosphopeptide mixtures enriched by IMAC from tryptic or Lys-C digests of A. thaliana protein extracts, we observed that MSA globally provided higher-score peptide identifications than the set of corresponding ETD spectra, and the inverse trend was observed for CSsup4 species, as previously reported29,35 (Supplementary Figure S2). Yet, we determined from these samples that for 14.4% ± 7.4% of spectrum pairs acquired on CS23 ions, ETD spectra provided peptide identification, whereas the corresponding MSA spectra did not (Figure 1). In addition, in the previous population of spectrum pairs, 85.4% ± 4.0% of ETD spectra provided precise phosphosite localization (MD-score >1 ensuring a FLR < 5% according to ref 19). In excellent agreement with other reports,36 this value reflects its high discriminative power with regard to positioning of the modification sites. In addition, for 11.2% ± 3.8% of spectrum pairs acquired on CS23 species, both ETD and MSA spectra led to confident amino acid sequence determination, but only the ETD spectrum allowed precise phosphosite localization (MD-score(MSA) < 4 and MD-score(ETD) > 1). Within this subpopulation of spectrum pairs, in about half of the cases the MSA spectra were associated with higher-score 2141



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the general (S/T)*P centered motif, we noticed that the P-x(S/T)*-P was dominant in the three compared phosphopeptide lists. This phosphorylated stretch corresponds to the highstringency motif attributable to a number of MAP kinase substrates,39,40 yet this motif can also include certain substrates of GSK3 and cyclin-dependent kinases.41 Interestingly, similar numbers of P-x-S*-P-containing peptides were identified from MSA or ETD spectra (22 or 23), and their number increased by 50% (to 33 phosphopeptides) in FragMixer sequences. Besides, the numbers of PxT*P-containing peptides were 6, 15, and 16 in MSA, ETD, or FragMixer sequences, respectively. Overall, the above analyses confirmed the complementary nature of MSA and ETD fragmentation modes to identify substrates of proline-directed, basophilic (R-x-x-S), and acidophilic (S-D-x-E and S-x-x-D) kinases and thus supported the idea of acquiring both fragmentation spectra on each phosphopeptide to increase the chances of attributing it to a specific class of kinases.

by our phosphopeptide list, 571 were present in the database PhosPhAt (released on the 04/29/2013). GO analysis indicated a major representation of cytoplasmic and nuclear proteins. Besides, the keywords ‘response to stress’ and ‘response to biotic and abiotic stimulus’ were the precise attributes accounting for the biggest fractions of identified phosphoproteins (Supplementary Figure S5). The lists of precisely identified phosphopeptides (reliable amino acid sequence and precise phosphosites) from all of the LC−MS/MS analyses acquired on either mock- or flg22-treated samples were submitted to the program motif-x. The S- or Tcentered phosphorylation motifs enriched in the two data sets are represented in Figure 2. The minimal MAPK target motif S*P accounted for 107 and 150 unique sequences in the M and F data sets, respectively. The equivalent T-centered motif T*P exhibited populations of 31 and 45 different sequences in these two data sets. Interestingly, the higher-stringency MAPK

Identification of Phosphorylation and Dephosphorylation Events Triggered by the PAMP flg22

We applied the spectrum pair method to study the phosphorylation-mediated response triggered in A. thaliana plants upon challenge with the 22-amino-acid-long PAMP flg22 peptide that has been proven to activate several MAPK and CDPK cascades. To pinpoint proteins whose phosphorylation levels are modified with respect to this PAMP, we treated plants for 15 min with a solution of flg22 (F) or with water as a mock treatment (M) in parallel. Three biologically independent sets of plants were thus prepared and analyzed several times by CS23 and CSsup4 acquisition methods, using iteratively updated exclusion lists to reach a total of 6 LC−MS/MS runs per F or M sample (see Material and Methods). The data generated on the three biological F/M replicates were aligned and quantified using the program MassChroQ as described in the Material and Methods section. Activation of MAP kinases 3, 4, and 6 by application of flg2239 was confirmed by Western blot analysis (Supplementary Figure S4a): a signal was detected at molecular masses corresponding to MPK3, MPK4, and MPK6 upon stimulation of the plants with flg22, whereas only a faint signal was visible for MPK6 in the corresponding mock-treated plants. Activation of MPKs was maximal between 10 and 30 min of stimulation, which led us to select the time point of 15 min, corresponding to the peak of MAPK activation observed in plants grown in darkness or in the light.40 Within LC−MS/MS runs, tryptic peptides overlapping with the activation loops of MPK4 and MPK6 were identified in the three biological replicates, with F/ M abundance ratios being always above 2.6 (Supplementary Figure S4b). Interestingly, we also detected these sequences as being singly phosphorylated on threonine, VTSESDFM(t)*EYVVTR, and TKSETDFM(t)*EYVVTR (Supplementary Figure S4c), indicating that both kinases follow the same ordered phosphorylation or dephosphorylation pattern. From the analyses of the three biological preparations, we identified a total of 825 unique phosphopeptides (when choosing not to distinguish phosphopeptides that only differed by phosphosite positioning), corresponding to an overall of 509 different proteins. The phosphopeptides identified in each LC− MS/MS analysis of M and F samples are provided as Supplementary Tables “All_FragMixer outputs_Msamples_analyses” and “All_FragMixer outputs_Fsamples_analyses”. Among the 756 different amino acid sequences accounted for

Figure 2. Phosphorylation motifs extracted by motif-x when pooling the phosphopeptide lists identified from the iterative LC−MS/MS analyses of the protein samples from mock (left) or flg22-treated (right) plants. The LC−MS/MS runs consisted of MSA/ETD spectrum pairs. Motifs were determined from FragMixer sequences exhibiting clearly identified phosphosites. The figures indicate the populations of phosphopeptides matching the different motifs. 2142



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Table 1. List of the 58 Phosphopeptides Exhibiting an Abundance Variation upon flg22 Treatmenta

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Table 1. continued

a Classified according to their kinase motif. The sequences in white background were determined to be significantly regulated by flg22; the phosphopeptides in grey background were additionally manually selected given their high F/M ratios in all three biological replicates. For phosphopeptides in white background, we calculated the F/M ratio as the average of normalized areas in all analyses of F samples divided by the average of normalized areas in all analyses of M samples. For phosphopeptides in grey background, we provide the F/M ratios calculated for each of the three independent biological preparations. p-values: these values refer to the ANOVA tests that were computed to estimate the flg22 treatment effect; a value below 0.0077 indicates a significant effect at a positive false-discovery rate below 5%. When a phosphosite was shown to be of increased abundance in this study and considered to be a putative MPK3/6 target in Hoehenwarter et al.,42 we indicated it next to the peptide sequence by [42]. In a few instances, different peptide sequences (and thus different phosphosites) from the same protein were identified in the present study and in ref 42; we then marked the protein with [42].

substrates of several classes of kinases, some of which are known to be activated by the PAMP flg22: proline-directed kinases, among which are MAPKs; basophilic CDPKs and SnRKs (motif R-x-x-S*); acidophilic protein kinases (motif S*D/E-D/E-D/E); and kinases recognizing the motifs S-x-S* or G-S*.

recognition motif, P-x-S*-P, was extracted from 31 and 38 unique sequences belonging to M and F data sets, respectively. The twin motif, PxT*P, did not appear in the motif-x results because of the selected thresholds, yet we counted that 11 versus 18 peptides exhibited such a stretch of residues within the more relaxed T*P motif. Globally, the extraction of these motifs illustrated the potential of our procedure to identify 2144



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SnRK/CDPK- and CK-related phosphopeptides displayed the smallest variations. The observed variations in phosphopeptide abundance upon treatment with the peptide flg22 are most likely attributable to a change in phosphorylation stroichiometry, but it cannot be excluded at this point that some phosphopeptide level changes might be due to differences in protein abundance. We therefore decided to compare the abundance between M and F samples of the peptides not retained on the IMAC resin. Statistical assessment of the quantification results (see Supplementary Tables “Quantification Information on Nonmodified Peptides”) indicated that only 7 out of 472 proteins identified and quantified with at least 2 peptides showed a significant change in abundance (p < 0.05). Of note, 7 of the phosphoproteins present in Table 1 were also identified in the IMAC flowthrough samples but did not change in abundance (quantification based on 2−19 different peptide sequences). Their F/M ratios are provided in Table 2. These data support our conclusion that the changes in phosphorylation levels for these proteins are not due to changes in protein abundance.

To determine the sequences that become significantly more or less phosphorylated upon challenge with flg22, we extracted abundance values of the identified phosphopeptides using the program MassChroQ and these RAW quantification data were normalized and statistically evaluated (see Material and Methods and Supplementary Tables “Quantification Information on Phosphopeptides”). For a chromatographic peak to be regarded as a reliable signal, a stringent filter was applied, that is, the peak must either be detected or absent in all three biological replicates. A total of 476 phosphopeptides passed this filter. As a whole, a set of 4 phosphopeptides were exclusively detected in flg22- but not in mock-treated samples (see Table 1), while 44 other sequences displayed highly significant varying abundances in response to flg22-induced stress (p-value < 0.0077 and FDR < 5%). Yet the doubly phosphorylated MAPK activation loops were absent from the previous list: this is due to the fact that, depending on the M sample considered, no signal at all or a faint signal could be quantified by MassChroQ. This led us to scrutinize the data looking for all the phosphopeptides that exhibited a quantified signal in all three F samples, irrespective of the status in the paired M samples, or vice versa. More precisely, MAPK activation loops yielded F/M ratios systematically above 2.67 for each of the three biological preparations; we then additionally extracted from the data set the 10 phosphopeptides for which the three calculated F/M ratios were above that value (highlighted in gray in Table 1). No phosphopeptide corresponded to the inverse situation where F/M ratios were systematically below 1/2.67. Finally, a total of 58 different flg22-sensitive phosphopeptides corresponding to 50 proteins were grouped in Table 1 and classified according to their kinase motif: 40 phosphorylated sequences containing the stretch (S/T)*P and corresponding to 33 distinct proteins might be MAPK targets. Whereas 7 phosphoproteins might be CDPK/SnRK substrates (motif R-xx-S*), 4 others might be targets of casein kinases (motif S(x)(2 or 3)−S*), and the remaining 4 phosphorylated sequences are modified by unknown kinases (Figure 3). As a whole, F/M

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DISCUSSION We performed a phosphoproteomics study while systematically acquiring MSA/ETD spectrum pairs to increase the chances of phosphopeptide identification, in terms of amino acid sequence and precise phosphosites. The complementary nature of these fragmentation modes and the benefits of acquiring both on each peptide precursor have been previously reported.17,21,35 We nonetheless verified that, using our scheme of analyses targeting CS23 or CSsup4 ions, the two spectra (i) provided differing information that led to better phosphopeptide characterization in about 25% of cases and (ii) identified complementary lists of phosphopeptides corresponding to proline-directed, basophilic (R-x-x-S) and acidophilic (S-D-x-E and S-x-x-D) kinases. To compensate for the decreased number of fragmented precursors compared to a single spectrum method, we injected each sample iteratively, while targeting CS23 or CSsup4 ionic species and establishing exclusion lists of formerly identified m/z ratios. Performing these repeated injections of the samples had the additional advantage of allowing the estimation of peptide relative abundance from multiple measurements of chromatographic peak areas, which rendered quantification more reliable. We report phosphorylation events induced by a 15-min treatment with the PAMP flg22 in A. thaliana, in which MPK3, 4, 6, and 11 are notable key players. Two former reports studied by phosphoproteomics the responses of A. thaliana to PAMPs, such as flg22 or xylanase.9,11 However, both of these studies worked on suspension cultured cells and focused on the early signaling events occurring at the plasma membrane. We looked for a possible overlap between the phosphoproteins showing PAMP-sensitive phosphorylation levels in our study and in the two former reports, but no single protein appeared to be in common. Besides, Hoehenwarter et al.42 recently published a phosphoproteomics study on A. thaliana plants expressing NtMEK2DD (expression induced for 6 h under the control of a DEX-inducible promoter); this constitutively active tobacco MAPKK phosphorylates and thus activates AtMPK3 and AtMPK6. In both studies, a certain number of MAPK targets were expected to be identified, exhibiting (S/T)*Pcontaining sequences whose abundance would increase compared to the respective controls. In Hoehenwarter et al.,42 a total of 35 proteins phosphorylated on (S/T)*P were

Figure 3. Histogram showing the distribution of the 55 phosphopeptides exhibiting an abundance variation upon flg22 treatment with respect to their kinase motif and F/M ratio (the three phosphopeptides overlapping with the MAPK activation loops were left aside).

ratios ranged from 0.39 to more than 4.5. Most of these flg22induced changes depicted fine regulations of phosphopeptide abundance with nearly 64% of the F/M ratios that did not exceed a 2-times difference between flg22- and mock-treated plants. Interestingly, the largest abundance changes were detected for phosphopeptides displaying MAPK motifs while 2145



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Table 2. Proteins of Table 1 Quantified from Non-modified Peptides Identified by LC−MS/MS Analysis of the IMAC FlowThroughsa AGI no.

protein description

phosphopeptides in Table 1

F/M (phos)

N (pep seq)

F/M (protein)

2

0.95

12 19

1.09 1.00

8

0.98

8

1.01

AT4G01370

MPK4

TKSETDFM(T)*E(Y)*VVTR

AT3G16420 AT4G37870

PBP1, JAL30 | PYK10-binding protein 1 PCK1, PEPCK | phosphoenolpyruvate carboxykinase 1

FVYDK(S)*PEEVTGEEHGK TDGST(T)*PAYAHGQHHSIFSPATGAVSDSSLK

∞, ∞, 2.67 1.67 1.45

AT3G57410

VLN3, ATVLN3 | Villin 3

AT4G20260

ATPCAP1, PCAP1 | plasma-membrane associated cation-binding Protein 1 Adenine nucleotide alpha hydrolases-like superfamily protein Aluminum induced protein with YGL and LRDR motifs

(S)*APT(T)*PINQNAAAAFAAVSEEER AEALAALTSAFNSSPS(S)*K(S)*PPR AEALAALTSAFNSSP(S)*(S)*KSPPRR or AEALAALTSAFNS(S)*P(S)*SKSPPRR AEALAALTSAFNSSPSSK(S)*PPR KEEATPAPAVVE(T)*PVKEPETTTTAPVAEPPKP

0.51 ∞ ∞; 4.02; 6.42 0.39 1.29

KSPTVVTVQPS(S)*PRFPI(S)*TPTAGAQR

∞

2

1.07

TVANSPEALQ(S)*PHSSESAFALK

2.16

2

0.98

AT1G11360 AT5G43830

a The phosphopeptides shown to be flagellin-sensitive are indicated here again, with their corresponding F/M ratios (column headed “F/M (phos)”). N (pep seq): number of different peptide sequences allowing establishment of the relative protein abundance F/M (protein). The F/M ratio of a protein was calculated in each biological replicate as the sum of normalized XIC areas measured on its proteolytic peptides in the F sample divided by the sum of the normalized XIC areas measured in the corresponding M sample; finally, the F/M ratio shown here is the average of the F/M protein ratios calculated for the three biological replicates.

Information on Phosphopeptides”). As suggested by Hoehenwarter et al.,42 we also identified the transcription regulator NOT2/NOT3/NOT5, splicing factor PWI domain-containing protein, PEARLI 4 family protein, and two chaperones of the DnaJ-domain superfamily proteins as putative MAPK substrates. The identified differentially phosphorylated (S/T)*Pcontaining phosphopeptides very probably correspond to direct targets of MAP kinases. Besides, we identified seven putative SnRK/CDPK substrates (Table 1) in agreement with the fact that CDPKs are also activated by flg22.8 CDPKs belong to the basophilic group of protein kinases46 and have substrate specificities where one or several basic amino acids are found around the S/T phosphosite (simple motif 1: R/K-X-X-S/T or simple motif 2: S-X-R/K). In addition, we found a number of motifs that fit neither the MAPK nor the CDPK specificities and hence have to be considered to be targeted by other classes of protein kinases. Among 58 phosphopeptides that exhibited variable phosphorylation states after treatment with flg22, some have already been shown to participate in biotic stress responses, whereas such a link has not yet been established for others. The major known functional categories of the corresponding differentially phosphorylated proteins were the following classes: gene regulation, signal transduction, cytoskeleton, and metabolism (Figure 4).

selected as putative MPK3/6 substrates; among these, 17 were also identified in our analyses, with the same phosphosites. Given that different growth conditions were used (i.e., darkversus light-grown plants), different protein cell fractions were analyzed (cytoplasmic extracts in the present study versus total cell extract), different affinity media were utilized for phosphoprotein/peptide enrichment (IMAC versus tandem metal oxide affinity), and variable fragmentation modes were implemented, the overlapping results of these two studies reinforce the conclusions of each work. Next, we looked at the phosphorylated sequences exhibiting a dramatic response (ratio >3) in at least two of the three biological replicates analyzed in.42 Among 11 phosphopeptides meeting this criterion, 7 were also identified in our data set, out of which 4 were quantified in at least two biological preparations and showed an increase in phosphorylation levels. These four sequences were TYVADVSEYLGSN(s)*PRDPYLER from the ras group-related LRR9 (not detected in any mock-treated plant), MVLFPK(s)*PSPVNK from a ubiquitin interaction-motif containing protein (F/M ratios were ∞; ∞ and 7.13), NIMGVESNVQPLT(s)*PLSK in the transcription regulator NOT2/NOT3/ NOT5 (F/M ratios were ∞; 1.91 and ∞), as shown in Table 1, and LLPLFPVT(s)*PR in the VQ motif-containing protein VQ4 (AT1G28280) (not detected in one F/M plant pair; the F/M ratios estimated from two biological replicates were ∞ and 1.60). Another difference between the two approaches was that we harvested plant material at maximal MAPK activation (15 min upon flg22 treatment), whereas Hoehenwarter et al. used transgenic plants in which the expression of active NtMEK2DD was induced by applying DEX for 6 h, so MPK3/6 were activated for a few hours before a snapshot of the phosphorylated proteins was acquired. The above four commonly detected phosphoproteins are then most likely targets of MPK3/6 and involved in early as well as late responses to biotic stress. From our data, known MAPK substrates such as ATMKP1,43 SCL30,44 and MAP65,45 in the MAPK signaling cascade were identified (Table 1 and Supplementary Tables “Quantification

Gene Expression

The flg22-induced phosphorylation of the transcription regulators NOT2/NOT3/NOT5 and VQ4 and the fact that they were identified as probable targets of MPK3/642 was already discussed above. VQ4 belongs to the same family as the VQ-domain protein MKS1, which is a well characterized substrate of MPK4 and interacts with the transcription factors WRKY25 and WRKY33.47 VQ4 is transcriptionally induced by bacterial infection and was also shown to interact with WRKY25 and WRKY33,48 suggesting that VQ4 and MKS1 could be key factors for regulating the transcriptional activity of these WRKY proteins. The transcriptional corepressor TOPLESS (TPL) was first shown to be involved in embryogenesis, 2146



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proteins showed a relation to post-translational modification by ubiquitin, such as the ubiquitin-interaction motif containing protein AT1G43690 or the Damaged DNA Binding1-CULLIN4 ASSOCIATED FACTOR (DCAF). A. thaliana harbors 85 DCAF proteins. DCAF1, like the best studied human DCAF1, contains two WDxR motifs within its WD40 domain essential for its interaction with Damaged DNA Binding1 (DDB1). TDNA insertion mutants homozygous for DCAF1 have an embryo lethal phenotype, and other co-suppression lines show several developmental defects, suggesting various roles for DCAF1 and the CUL4-DDB1-DCAF1 E3 ubiquitin ligase complex in multiple biological processes.55 Other proteins included the cation-binding protein AtPCAP1 and the Arabidopsis Nuclear Shuttle Protein (NSP)-interacting GTPase NIG, which is important for nucleo-cytoplasmic shuttling. AtPCAP1 is a hydrophilic cation binding protein that lacks a transmembrane domain but anchors to the plasma membrane due to the myristoylation of a glycine residue. In a knockout mutant, the accumulation and movement of potyviruses was shown to be perturbed.56 NIG was shown to be a specific cofactor of NSP involved in nucleocytoplasmic trafficking of viral DNA. On coexpressing NSP and NIG transiently in tobacco leaf epidermis, NIG altered the localization of NSP and was excluded from the nucleus and detected more in the cytoplasm. Additionally, the overexpression of NIG in transgenic plants resulted in enhanced susceptibility to geminivirus infection.57

Figure 4. Differentially phosphorylated proteins upon stimulation by flg22 can be categorized into the following classes: metabolism, signal transduction, gene regulation, and cytoskeleton. The proteins on a solid background show enhanced phosphorylation (red, MAPK motifs; dark blue, SnRK/CDPK simple motif 1; purple, CDPK simple motif 2; brown, CK motif; green, others), and all of the proteins on their respective dotted background showed reduced phosphorylation upon flg22 treatment, suggesting that they become dephosphorylated by protein phosphatases. BSL2 has a MAPK motif as well as an SnRK/ CDPK motif and so has been represented in red and blue. VLN3 is represented by three peptides among which two show enhanced phosphorylation and one shows reduced phosphorylation and hence has been represented in red and pale red. PCK1 is represented by two peptides with one peptide showing enhanced phosphorylation and the other showing reduced phosphorylation and has been represented in red and pale red. Twenty-nine peptides listed in Table 1 have been represented in the figure. Of the remaining peptides, the functions for 12 are unknown and 5 have functions that cannot be categorized into the above-mentioned groups.

Cytoskeleton

Apart from the important MAP65 protein that is directly regulated by MAPKs,45 TPX2 is another microtubuleassociated protein that plays a role in organizing the spindle apparatus.58 However, our work revealed that actin-associated proteins such as villin 3 (VLN3) also appear to be targeted by flg22-induced kinases. Villin proteins are involved in the formation of thick actin filament bundles, and a VLN2/3 double mutant has distorted leaves, stems, roots and siliques due to the abnormal cell lengths.59 Another study showed reduction in the stem size of inflorescences and fewer vascular bundles while attributing this to the role of VLN2/3 in bundling actin filaments.60 VLN3 was also found to be differentially phosphorylated in another large-scale proteomics study, wherein changes in water status were studied in Zea mays.61

repressing the expression of root-promoting genes in the apical region so that it develops into a proper shoot.49 Later TPL was also implicated in the repression of auxin and jasmonate responses, suggesting it to be a general corepressor in different signaling pathways in plants.50,51 As previously revealed, proteins associated with RNA modification and splicing, such as SCL30,44 are prominent targets of PAMP signaling. Our data revealed several other splicing factors, such as APUM5, which belongs to the family of RNA-binding proteins (PUF family) that are highly conserved across different kingdoms and have been shown to function as post-transcriptional and translational repressors. Recently several groups have shown the involvement of APUM5 and other members of the family in the defense against viral infections.52,53 We also found flg22enhanced phosphorylation of a PWI-domain containing protein splicing factor. The co-suppression of a human splicing factor PWI-domain containing protein ortholog in Caenorhabditis elegans was shown to result in aberrant growth or developmental defects,54 suggesting this class of proteins to be important regulators of gene expression.

Metabolism

A significantly large number of metabolic enzymes (8 in total) showed flg22-induced changes in their phosphorylation levels. Among these are found the key regulators of the primary carbon metabolism cytosolic invertase (CINV), sucrosephosphate synthase (SPS), fructose-2,6,-bisphosphatase (FBPase), PEP (phosphoenolpyruvate) carboxykinase (PEPCK), and PEP carboxylase (PEPC). These results suggest that PAMP signaling also results in the rapid reorganization of the metabolism. Interestingly, the amounts of some of the identified phosphorylation sites in the enzymes such as CINV1, SPS, PEPCK, and PEPC decreased in abundance in response to flg22. Earlier studies of PAMP-induced phosphorylation events in Arabidopsis cultured cells also reported the existence of such decreased phosphorylation levels.9,11 In the case of the enzymes CINV1, SPS, PEPCK, and PEPC, since we determined by analysis of IMAC flow-throughs that the levels of all identified proteins (including PEPCK) but for very few exceptions did not change, we concluded that the most likely explanation is

Signal Transduction

The largest fraction of flg22-induced phosphorylation targets (15 out of 58) were functionally associated with signal transduction (Figure 4), including the protein kinases PHOT1 (Phototropin1) and AT5G14720 or the protein phosphatases MKP1 and BSL2. Some proteins were clearly related to calcium signaling, such as the calcium-dependent lipid binding domain (CaLB) proteins or the calciumdependent protein kinase CPK5, whereas a number of these 2147



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that these sites become dephosphorylated by a flg22-induced activation of one or several phosphatases. However, phosphorylation is the result of both protein kinase and protein phosphatase activities. Therefore, a more complex scenario cannot be ruled out where the decreased activities of the responsible kinases together with a constant phosphatase activity might be responsible for the observed decreases in phosphorylation levels. It should be noted that a similar argument can also hold true for finding enhanced phosphorylation levels. Here, a decreased phosphatase activity in the presence of a constant kinase activity would result in enhanced steady state levels of a phosphorylation site. Unfortunately, without being able to measure the enzyme activities of the respective protein kinases and phosphatases, no clear mechanism can be derived. The flg22-induced changes observed in the phosphorylation status of the above-described proteins could affect their stability, localization, activity, substrate specificity, complex formation, or other properties that are important for raising an appropriate pathogen response. Very little is currently known on this level of fine regulation and further work is needed to understand the implications in the biological responses. The full complexity of flg22-induced signal transduction is also shown by the fact that we obtained in some cases phosphopeptides that corresponded to the same protein but on different sites and according to the motif analysis also should be targeted by different protein kinases. An example for this kind of regulation is BSL2: this protein was detected via two phosphopeptides the abundance of which increased upon flg22 stimulation, one being a possible target of MAPKs and the other of SnRKs/CDPKs. Interestingly, BSL2 is a phosphatase, whose activity might thus be modulated by these two variable phosphorylations and might in turn be responsible for the decrease/increase in phosphorylation of phosphoproteins. Another phosphatase likely involved in the regulation of the biotic stress signaling studied here is MKP1, which we detected by two phosphorylated sequences, namely, VHAFPL(s295)*PTSLLR and FSSLSLLPSQT(s558)*PK (see Supplementary Tables “Quantification Information on Phosphopeptides”). MKP1 is able to dephosphorylate MAPKs and has also been shown to be a substrate of MPK6. More precisely, Ser558 was proven to be phosphorylated by MPK6 in vitro,62 whereas Ser295 was previously identified as phosphorylated in other mass-spectrometry-based phosphoproteomics studies.43 Most research in signal transduction has focused so far on the de novo or enhanced phosphorylation status of peptides that are catalyzed by protein kinases. Little attention has been paid to the role of protein phosphatases. This might be due to the consideration that many fewer protein phosphatase genes are generally found in genome analyses of eukaryotes. However, many phosphatases assemble with several regulatory subunits into multisubunit complexes and thereby can considerably increase the number of entities carrying different target specificities.63 In the context of our work, it is interesting to note that, among the 58 differentially phosphorylated peptides that we identified with high confidence, 12 show a reduction in the abundance of their phosphorylation status upon flg22 treatment, suggesting they are substrates of phosphatases. An explanation for finding reduced or enhanced phosphorylation levels in some proteins could be that these proteins undergo degradation or de novo synthesis or become relocalized to certain subcellular compartments, such as membranes, upon flagellin signaling, and that these proteins will therefore be

excluded from the analyzed material. However, by analyzing 472 proteins, we only found evidence for 7 proteins to be changing their abundance levels. In this set of proteins, seven proteins corresponded to phosphoproteins that change their degree of phosphorylation but did not change protein abundance (Table 2). We therefore conclude that flg22 signaling rapidly induces different families of protein kinases and phosphatases and suggest that to understand the full complexity of PAMP signaling, future research should devote more attention to the function of these flg22-induced protein phosphatases.

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CONCLUSIONS

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ASSOCIATED CONTENT

In summary, we combined the highly complementary fragmentation modes MSA and ETD to study phosphorylation events induced in A. thaliana by 15-min treatment with the PAMP flg22. We could identify 58 (S/T)*-containing sequences, the abundance of which increased or decreased significantly upon flg22 treatment. The majority of the phosphosites correspond to MAPK motifs, but we also identified a number of putative CDPK, SnRK, and CK substrates. To definitely attribute substrates to these kinases requires substantial more evidence, but for MAPKs and CDPKs, one can envision carrying out the herein described analytical strategy on large-scale phosphopeptide samples obtained from flg22-treated mpk and cdpk mutant plants. Measurements of the relative phosphopeptide abundance between WT and mutant plants should help to assign a given substrate to a specific MAPK or CDPK. Since MAPKs and CDPKs are involved in the regulation of a variety of biological processes, a combination of reverse genetics and molecular biology will pave the way for efficient kinase-substrate assignment.

S Supporting Information *

MassChropQ_parameters.txt. All_FragMixer_outputs_Fsamples_analyses. All_FragMixer_outputs_Msamples_analyses. Supplementary and figures. We also provide ZIP files containing all the annotated MS/MS spectra leading to phosphopeptide identification (at an FDR of 1% estimated by FragMixer), with names such as: F_02_23_B_ETD (for Flgtreated sample, biological replicate “02”, ions of charge states 2 + 3+ selected for MS/MS, B = Blind analysis, ETD fragmentation mode); or M_03_S4_E2_MSA (for Mocktreated sample, biological replicate “03”, ions of charge states ≥4 selected for MS/MS, analysis performed with a 2nd Exclusion list combining the m/z ratios of peptides identified in the two former analyses of the same sample; MSA fragmentation mode). This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Authors

*Phone: +33 1 60 87 45 08. Fax: +33 1 60 87 45 10. E-mail: [email protected]. *Phone: +33 1 69 47 76 54. Fax: +33 1 69 47 76 55. E-mail: delphine.pfl[email protected]. 2148



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networks using mass spectrometry. Mass Spectrom. Rev. 2011, 30 (2), 268−97. (11) Nuhse, T. S.; Bottrill, A. R.; Jones, A. M.; Peck, S. C. Quantitative phosphoproteomic analysis of plasma membrane proteins reveals regulatory mechanisms of plant innate immune responses. Plant J. 2007, 51 (5), 931−40. (12) Kocher, T.; Pichler, P.; Schutzbier, M.; Stingl, C.; Kaul, A.; Teucher, N.; Hasenfuss, G.; Penninger, J. M.; Mechtler, K. High precision quantitative proteomics using iTRAQ on an LTQ Orbitrap: a new mass spectrometric method combining the benefits of all. J. Proteome Res. 2009, 8 (10), 4743−52. (13) Pflieger, D.; Junger, M. A.; Muller, M.; Rinner, O.; Lee, H.; Gehrig, P. M.; Gstaiger, M.; Aebersold, R. Quantitative proteomic analysis of protein complexes: concurrent identification of interactors and their state of phosphorylation. Mol. Cell. Proteomics 2008, 7 (2), 326−46. (14) Przybylski, C.; Junger, M. A.; Aubertin, J.; Radvanyi, F.; Aebersold, R.; Pflieger, D. Quantitative analysis of protein complex constituents and their phosphorylation states on a LTQ-Orbitrap instrument. J. Proteome Res. 2010, 9 (10), 5118−32. (15) Wu, J.; Warren, P.; Shakey, Q.; Sousa, E.; Hill, A.; Ryan, T. E.; He, T. Integrating titania enrichment, iTRAQ labeling, and Orbitrap CID-HCD for global identification and quantitative analysis of phosphopeptides. Proteomics 2010, 10 (11), 2224−34. (16) Molina, H.; Horn, D. M.; Tang, N.; Mathivanan, S.; Pandey, A. Global proteomic profiling of phosphopeptides using electron transfer dissociation tandem mass spectrometry. Proc. Natl. Acad. Sci. U.S.A. 2007, 104 (7), 2199−204. (17) Kim, M. S.; Zhong, J.; Kandasamy, K.; Delanghe, B.; Pandey, A. Systematic evaluation of alternating CID and ETD fragmentation for phosphorylated peptides. Proteomics 2011, 11, 1−5. (18) Vandenbogaert, M.; Hourdel, V.; Jardin-Mathe, O.; Bigeard, J.; Bonhomme, L.; Legros, V.; Hirt, H.; Schwikowski, B.; Pflieger, D. Automated phosphopeptide identification using multiple MS/MS fragmentation modes. J. Proteome Res. 2012, 11 (12), 5695−703. (19) Savitski, M. M.; Lemeer, S.; Boesche, M.; Lang, M.; Mathieson, T.; Bantscheff, M.; Kuster, B. Confident phosphorylation site localization using the Mascot Delta Score. Mol. Cell. Proteomics 2011, 10 (2), M110 003830. (20) 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. (21) Hansen, T. A.; Sylvester, M.; Jensen, O. N.; Kjeldsen, F. Automated and high confidence protein phosphorylation site localization using complementary collision-activated dissociation and electron transfer dissociation tandem mass spectrometry. Anal. Chem. 2012, 84 (22), 9694−9. (22) Pflieger, D.; Chabane, S.; Gaillard, O.; Bernard, B. A.; Ducoroy, P.; Rossier, J.; Vinh, J. Comparative proteomic analysis of extracellular matrix proteins secreted by two types of skin fibroblasts. Proteomics 2006, 6 (21), 5868−79. (23) Spreitzer, R. J.; Salvucci, M. E. Rubisco: structure, regulatory interactions, and possibilities for a better enzyme. Annu Rev Plant Biol 2002, 53, 449−75. (24) Reiland, S.; Messerli, G.; Baerenfaller, K.; Gerrits, B.; Endler, A.; Grossmann, J.; Gruissem, W.; Baginsky, S. Large-scale Arabidopsis phosphoproteome profiling reveals novel chloroplast kinase substrates and phosphorylation networks. Plant Physiol. 2009, 150 (2), 889−903. (25) Reiland, S.; Finazzi, G.; Endler, A.; Willig, A.; Baerenfaller, K.; Grossmann, J.; Gerrits, B.; Rutishauser, D.; Gruissem, W.; Rochaix, J. D.; Baginsky, S. Comparative phosphoproteome profiling reveals a function of the STN8 kinase in fine-tuning of cyclic electron flow (CEF). Proc. Natl. Acad. Sci. U.S.A. 2011, 108 (31), 12955−60. (26) Kim, Y. J.; Lee, H. M.; Wang, Y.; Wu, J.; Kim, S. G.; Kang, K. Y.; Park, K. H.; Kim, Y. C.; Choi, I. S.; Agrawal, G. K.; Rakwal, R.; Kim, S. T. Depletion of abundant plant RuBisCO protein using the protamine sulfate precipitation method. Proteomics 2013, 13 (14), 2176−9.

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UMR 1095 Génétique, Diversité et Ecophysiologie des Céréales, 5 Chemin de Beaulieu, 63039 Clermont-Ferrand cedex 2, France.

Author Contributions #

These authors contributed equally to this work.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS D.P. thanks the Agence Nationale pour la Recherche (ANR) for the funding ANR-2010-JCJC-1608. This work was supported by the CNRS, Genopole-France, Institut National de la Recherche Agronomique, Université d’Evry Val d’Essonne and Région Ile-de-France. We are also indebted to Benoit Valot, Edlira Nano, Olivier Langella, and Michel Zivy for help with using MassChroQ.

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ABBREVIATIONS CDPK, calcium-dependent protein kinase; CK, casein kinase; ETD, electron transfer dissociation; FDR, false discovery rate; FLR, false localization rate (of phosphosites); Flg22, flagellin 22 (22-amino-acid-long conserved sequence); MAPK, mitogenactivated protein kinase; MAPKK, MAPK kinase; MAPKKK, MAPKK kinase; MSA, multistage activation; PAMP, pathogenassociated molecular pattern; SnRK, sucrose nonfermenting related kinase

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REFERENCES

(1) Jones, J. D.; Dangl, J. L. The plant immune system. Nature 2006, 444 (7117), 323−9. (2) Gomez-Gomez, L.; Felix, G.; Boller, T. A single locus determines sensitivity to bacterial flagellin in Arabidopsis thaliana. Plant J. 1999, 18 (3), 277−84. (3) Zipfel, C.; Robatzek, S.; Navarro, L.; Oakeley, E. J.; Jones, J. D.; Felix, G.; Boller, T. Bacterial disease resistance in Arabidopsis through flagellin perception. Nature 2004, 428 (6984), 764−7. (4) Ranf, S.; Eschen-Lippold, L.; Pecher, P.; Lee, J.; Scheel, D. Interplay between calcium signalling and early signalling elements during defence responses to microbe- or damage-associated molecular patterns. Plant J. 2011, 68 (1), 100−13. (5) Asai, T.; Tena, G.; Plotnikova, J.; Willmann, M. R.; Chiu, W. L.; Gomez-Gomez, L.; Boller, T.; Ausubel, F. M.; Sheen, J. MAP kinase signalling cascade in Arabidopsis innate immunity. Nature 2002, 415 (6875), 977−83. (6) Gao, M.; Liu, J.; Bi, D.; Zhang, Z.; Cheng, F.; Chen, S.; Zhang, Y. MEKK1, MKK1/MKK2 and MPK4 function together in a mitogenactivated protein kinase cascade to regulate innate immunity in plants. Cell Res. 2008, 18 (12), 1190−8. (7) Bethke, G.; Pecher, P.; Eschen-Lippold, L.; Tsuda, K.; Katagiri, F.; Glazebrook, J.; Scheel, D.; Lee, J. Activation of the Arabidopsis thaliana mitogen-activated protein kinase MPK11 by the flagellinderived elicitor peptide, flg22. Mol. Plant-Microbe Interact. 2012, 25 (4), 471−80. (8) Boudsocq, M.; Willmann, M. R.; McCormack, M.; Lee, H.; Shan, L.; He, P.; Bush, J.; Cheng, S. H.; Sheen, J. Differential innate immune signalling via Ca(2+) sensor protein kinases. Nature 2010, 464 (7287), 418−22. (9) Benschop, J. J.; Mohammed, S.; O’Flaherty, M.; Heck, A. J.; Slijper, M.; Menke, F. L. Quantitative phosphoproteomics of early elicitor signaling in Arabidopsis. Mol. Cell. Proteomics 2007, 6 (7), 1198−214. (10) Pflieger, D.; Gonnet, F.; de la Fuente van Bentem, S.; Hirt, H.; de la Fuente, A. Linking the proteins–elucidation of proteome-scale 2149



Article

(27) Mechin, V.; Damerval, C.; Zivy, M. Total protein extraction with TCA-acetone. Methods Mol. Biol. 2007, 355, 1−8. (28) Swaney, D. L.; McAlister, G. C.; Wirtala, M.; Schwartz, J. C.; Syka, J. E.; Coon, J. J. Supplemental activation method for highefficiency electron-transfer dissociation of doubly protonated peptide precursors. Anal. Chem. 2007, 79 (2), 477−85. (29) Swaney, D. L.; McAlister, G. C.; Coon, J. J. Decision tree-driven tandem mass spectrometry for shotgun proteomics. Nat. Methods 2008, 5 (11), 959−64. (30) Cooper, H. J.; Hakansson, K.; Marshall, A. G.; Hudgins, R. R.; Haselmann, K. F.; Kjeldsen, F.; Budnik, B. A.; Polfer, N. C.; Zubarev, R. A. Letter: the diagnostic value of amino acid side-chain losses in electron capture dissociation of polypeptides. Comment on: ″Can the (M-X) region in electron capture dissociation provide reliable information on amino acid composition of polypeptides?″ Eur. J. Mass Spectrom. 8, 461−469. Eur. J. Mass Spectrom. 2003, 9 (3), 221−2 2002. (31) Leinenbach, A.; Hartmer, R.; Lubeck, M.; Kneissl, B.; Elnakady, Y. A.; Baessmann, C.; Muller, R.; Huber, C. G. Proteome analysis of Sorangium cellulosum employing 2D-HPLC−MS/MS and improved database searching strategies for CID and ETD fragment spectra. J. Proteome Res. 2009, 8 (9), 4350−4361. (32) Chalkley, R. J.; Medzihradszky, K. F.; Lynn, A. J.; Baker, P. R.; Burlingame, A. L. Statistical analysis of peptide electron transfer dissociation fragmentation mass spectrometry. Anal. Chem. 2010, 82 (2), 579−84. (33) Marx, H.; Lemeer, S.; Schliep, J. E.; Matheron, L.; Mohammed, S.; Cox, J.; Mann, M.; Heck, A. J.; Kuster, B. A large synthetic peptide and phosphopeptide reference library for mass spectrometry-based proteomics. Nat. Biotechnol. 2013, 31 (6), 557−64. (34) Valot, B.; Langella, O.; Nano, E.; Zivy, M. MassChroQ: a versatile tool for mass spectrometry quantification. Proteomics 2011, 11 (17), 3572−7. (35) Domon, B.; Bodenmiller, B.; Carapito, C.; Hao, Z.; Huehmer, A.; Aebersold, R. Electron transfer dissociation in conjunction with collision activation to investigate the Drosophila melanogaster phosphoproteome. J. Proteome Res. 2009, 8 (6), 2633−9. (36) Grimsrud, P. A.; den Os, D.; Wenger, C. D.; Swaney, D. L.; Schwartz, D.; Sussman, M. R.; Ane, J. M.; Coon, J. J. Large-scale phosphoprotein analysis in Medicago truncatula roots provides insight into in vivo kinase activity in legumes. Plant Physiol. 2010, 152 (1), 19−28. (37) Good, D. M.; Wirtala, M.; McAlister, G. C.; Coon, J. J. Performance characteristics of electron transfer dissociation mass spectrometry. Mol. Cell. Proteomics 2007, 6 (11), 1942−51. (38) Swaney, D. L.; Wenger, C. D.; Thomson, J. A.; Coon, J. J. Human embryonic stem cell phosphoproteome revealed by electron transfer dissociation tandem mass spectrometry. Proc. Natl. Acad. Sci. U.S.A. 2009, 106 (4), 995−1000. (39) Pearson, G.; Robinson, F.; Beers Gibson, T.; Xu, B. E.; Karandikar, M.; Berman, K.; Cobb, M. H. Mitogen-activated protein (MAP) kinase pathways: regulation and physiological functions. Endocr. Rev. 2001, 22 (2), 153−83. (40) Berriri, S.; Garcia, A. V.; Dit Frey, N. F.; Rozhon, W.; Pateyron, S.; Leonhardt, N.; Montillet, J. L.; Leung, J.; Hirt, H.; Colcombet, J. Constitutively active mitogen-activated protein kinase versions reveal functions of Arabidopsis MPK4 in pathogen defense signaling. Plant Cell 2012, 24 (10), 4281−93. (41) Li, M.; Stefansson, B.; Wang, W.; Schaefer, E. M.; Brautigan, D. L. Phosphorylation of the Pro-X-Thr-Pro site in phosphatase inhibitor2 by cyclin-dependent protein kinase during M-phase of the cell cycle. Cell. Signalling 2006, 18 (8), 1318−26. (42) Hoehenwarter, W.; Thomas, M.; Nukarinen, E.; Egelhofer, V.; Rohrig, H.; Weckwerth, W.; Conrath, U.; Beckers, G. J. Identification of novel in vivo MAP kinase substrates in Arabidopsis thaliana through use of tandem metal oxide affinity chromatography. Mol. Cell. Proteomics 2012, 12 (2), 369−80.

(43) Gonzalez Besteiro, M. A.; Ulm, R. Phosphorylation and stabilization of Arabidopsis MAP kinase phosphatase 1 in response to UV-B stress. J. Biol. Chem. 2013, 288 (1), 480−6. (44) de la Fuente van Bentem, S.; Anrather, D.; Dohnal, I.; Roitinger, E.; Csaszar, E.; Joore, J.; Buijnink, J.; Carreri, A.; Forzani, C.; Lorkovic, Z. J.; Barta, A.; Lecourieux, D.; Verhounig, A.; Jonak, C.; Hirt, H. Sitespecific phosphorylation profiling of Arabidopsis proteins by mass spectrometry and peptide chip analysis. J. Proteome Res. 2008, 7 (6), 2458−70. (45) Sasabe, M.; Kosetsu, K.; Hidaka, M.; Murase, A.; Machida, Y. Arabidopsis thaliana MAP65−1 and MAP65−2 function redundantly with MAP65−3/PLEIADE in cytokinesis downstream of MPK4. Plant Signaling Behav. 2011, 6 (5), 743−7. (46) Harper, J. F.; Harmon, A. Plants, symbiosis and parasites: a calcium signalling connection. Nat. Rev. Mol. Cell. Biol. 2005, 6 (7), 555−66. (47) Andreasson, E.; Jenkins, T.; Brodersen, P.; Thorgrimsen, S.; Petersen, N. H.; Zhu, S.; Qiu, J. L.; Micheelsen, P.; Rocher, A.; Petersen, M.; Newman, M. A.; Bjorn Nielsen, H.; Hirt, H.; Somssich, I.; Mattsson, O.; Mundy, J. The MAP kinase substrate MKS1 is a regulator of plant defense responses. EMBO J. 2005, 24 (14), 2579− 89. (48) Cheng, Y.; Zhou, Y.; Yang, Y.; Chi, Y. J.; Zhou, J.; Chen, J. Y.; Wang, F.; Fan, B.; Shi, K.; Zhou, Y. H.; Yu, J. Q.; Chen, Z. Structural and functional analysis of VQ motif-containing proteins in Arabidopsis as interacting proteins of WRKY transcription factors. Plant Physiol. 2012, 159 (2), 810−25. (49) Long, J. A.; Ohno, C.; Smith, Z. R.; Meyerowitz, E. M. TOPLESS regulates apical embryonic fate in Arabidopsis. Science 2006, 312 (5779), 1520−3. (50) Szemenyei, H.; Hannon, M.; Long, J. A. TOPLESS mediates auxin-dependent transcriptional repression during Arabidopsis embryogenesis. Science 2008, 319 (5868), 1384−6. (51) Pauwels, L.; Barbero, G. F.; Geerinck, J.; Tilleman, S.; Grunewald, W.; Perez, A. C.; Chico, J. M.; Bossche, R. V.; Sewell, J.; Gil, E.; Garcia-Casado, G.; Witters, E.; Inze, D.; Long, J. A.; De Jaeger, G.; Solano, R.; Goossens, A. NINJA connects the co-repressor TOPLESS to jasmonate signalling. Nature 2010, 464 (7289), 788−91. (52) Huh, S. U.; Kim, M. J.; Paek, K. H. Arabidopsis Pumilio protein APUM5 suppresses Cucumber mosaic virus infection via direct binding of viral RNAs. Proc. Natl. Acad. Sci. U.S.A. 2013, 110 (2), 779− 84. (53) Un Huh, S.; Paek, K. H., Role of Arabidopsis Pumilio RNA binding protein 5 in virus infection. Plant Signaling Behav. 2013, 8, (5). (54) Fortes, P.; Longman, D.; McCracken, S.; Ip, J. Y.; Poot, R.; Mattaj, I. W.; Caceres, J. F.; Blencowe, B. J. Identification and characterization of RED120: a conserved PWI domain protein with links to splicing and 3′-end formation. FEBS Lett. 2007, 581 (16), 3087−97. (55) Zhang, Y.; Feng, S.; Chen, F.; Chen, H.; Wang, J.; McCall, C.; Xiong, Y.; Deng, X. W. Arabidopsis DDB1-CUL4 ASSOCIATED FACTOR1 forms a nuclear E3 ubiquitin ligase with DDB1 and CUL4 that is involved in multiple plant developmental processes. Plant Cell 2008, 20 (6), 1437−55. (56) Zhou, X.; Zhang, H.; Li, G.; Shaw, B.; Xu, J. R. The Cyclaseassociated protein Cap1 is important for proper regulation of infection-related morphogenesis in Magnaporthe oryzae. PLoS Pathog. 2012, 8 (9), e1002911. (57) Carvalho, C. M.; Fontenelle, M. R.; Florentino, L. H.; Santos, A. A.; Zerbini, F. M.; Fontes, E. P. A novel nucleocytoplasmic traffic GTPase identified as a functional target of the bipartite geminivirus nuclear shuttle protein. Plant J. 2008, 55 (5), 869−80. (58) Vos, J. W.; Pieuchot, L.; Evrard, J. L.; Janski, N.; Bergdoll, M.; de Ronde, D.; Perez, L. H.; Sardon, T.; Vernos, I.; Schmit, A. C. The plant TPX2 protein regulates prospindle assembly before nuclear envelope breakdown. Plant Cell 2008, 20 (10), 2783−97. (59) van der Honing, H. S.; Kieft, H.; Emons, A. M.; Ketelaar, T. Arabidopsis VILLIN2 and VILLIN3 are required for the generation of 2150



Article

thick actin filament bundles and for directional organ growth. Plant Physiol. 2012, 158 (3), 1426−38. (60) Bao, C.; Wang, J.; Zhang, R.; Zhang, B.; Zhang, H.; Zhou, Y.; Huang, S. Arabidopsis VILLIN2 and VILLIN3 act redundantly in sclerenchyma development via bundling of actin filaments. Plant J. 2012, 71 (6), 962−75. (61) Bonhomme, L.; Valot, B.; Tardieu, F.; Zivy, M. Phosphoproteome dynamics upon changes in plant water status reveal early events associated with rapid growth adjustment in maize leaves. Mol. Cell. Proteomics 2012, 11 (10), 957−72. (62) Park, H. C.; Song, E. H.; Nguyen, X. C.; Lee, K.; Kim, K. E.; Kim, H. S.; Lee, S. M.; Kim, S. H.; Bae, D. W.; Yun, D. J.; Chung, W. S. Arabidopsis MAP kinase phosphatase 1 is phosphorylated and activated by its substrate AtMPK6. Plant Cell Rep. 2011, 30 (8), 1523−31. (63) Uhrig, R. G.; Labandera, A. M.; Moorhead, G. B. Arabidopsis PPP family of serine/threonine protein phosphatases: many targets but few engines. Trends Plant Sci. 2013, 18 (9), 505−513.

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