Plant Phosphoproteomics: Methods and Protocols [1 ed.] 1493926470, 978-1-4939-2647-3, 978-1-4939-2648-0

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Methods in Molecular Biology 1306

Waltraud X. Schulze Editor

Plant Phosphoproteomics Methods and Protocols

METHODS

IN

MOLECULAR BIOLOGY

Series Editor John M. Walker School of Life and Medical Sciences University of Hertfordshire Hatfield, Hertfordshire, AL10 9AB, UK

For further volumes: http://www.springer.com/series/7651

Plant Phosphoproteomics Methods and Protocols Edited by

Waltraud X. Schulze Department of Plant Systems Biology, Universität Hohenheim, Stuttgart, Germany

Editor Waltraud X. Schulze Department of Plant Systems Biology Universität Hohenheim Stuttgart, Germany

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-4939-2647-3 ISBN 978-1-4939-2648-0 (eBook) DOI 10.1007/978-1-4939-2648-0 Library of Congress Control Number: 2015937355 Springer New York Heidelberg Dordrecht London © Springer Science+Business Media New York 2015 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper Humana Press is a brand of Springer Springer Science+Business Media LLC New York is part of Springer Science+Business Media (www.springer.com)

Preface Phosphorylation is one of the most important posttranslational modifications involved in many regulatory processes within living cells. Over the recent decades, proteomic methods were refined to study the significance and dynamics of protein phosphorylation in various biological contexts. However, working with plant tissue imposes particular challenges to the biologist which are attributed to the rigid cell wall making protein extraction more difficult, the skewed protein abundance with Rubisco as a highly abundant protein, and a large central vacuole leading to low protein yield and increased degradative enzyme activity. The issue of Methods in Molecular Biology on “Plant Phosphoproteomics” addresses recent developments in phosphoproteomic techniques with a particular focus on the plant system and is particularly designed as a protocol reference book compiled by leading experts in the field. Stuttgart, Germany

Waltraud X. Schulze

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Contents Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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1 The Plant Kinome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Monika Zulawski and Waltraud X. Schulze 2 Phosphatases in Plants. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Alois Schweighofer and Irute Meskiene 3 Phosphoproteomics in Cereals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pingfang Yang 4 Screening of Kinase Substrates Using Kinase Knockout Mutants . . . . . . . . . . . Taishi Umezawa 5 Phosphopeptide Profiling of Receptor Kinase Mutants . . . . . . . . . . . . . . . . . . Xu Na Wu and Waltraud X. Schulze 6 Combining Metabolic 15N Labeling with Improved Tandem MOAC for Enhanced Probing of the Phosphoproteome . . . . . . . . . . . . . . . . . Martin Thomas, Nicola Huck, Wolfgang Hoehenwarter, Uwe Conrath, and Gerold J.M. Beckers 7 Kinase Activity and Specificity Assay Using Synthetic Peptides . . . . . . . . . . . . . Xu Na Wu and Waltraud X. Schulze 8 Absolute Quantitation of Protein Posttranslational Modification Isoform . . . . Zhu Yang and Ning Li 9 Phosphorylation Stoichiometry Determination in Plant Photosynthetic Membranes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Björn Ingelsson, Rikard Fristedt, and Maria V. Turkina 10 Phosphopeptide Immuno-Affinity Enrichment to Enhance Detection of Tyrosine Phosphorylation in Plants . . . . . . . . . . . . . . . . . . . . . . . Sharon C. Mithoe and Frank L.H. Menke 11 The Peptide Microarray ChloroPhos1.0: A Screening Tool for the Identification of Arabidopsis thaliana Chloroplast Protein Kinase Substrates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anna Schönberg and Sacha Baginsky 12 Plant Protein Kinase Substrates Identification Using Protein Microarrays . . . . Shisong Ma and Savithramma P. Dinesh-Kumar 13 Targeted Analysis of Protein Phosphorylation by 2D Electrophoresis. . . . . . . . Kristin Mayer, Sally Albrecht, and Andreas Schaller 14 Computational Phosphorylation Network Reconstruction: Methods and Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Guangyou Duan and Dirk Walther

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15 Computational Identification of Protein Kinases and Kinase-Specific Substrates in Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Han Cheng, Yongbo Wang, Zexian Liu, and Yu Xue 16 Databases for Plant Phosphoproteomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Waltraud X. Schulze, Qiuming Yao, and Dong Xu 17 Phosphorylation Site Prediction in Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qiuming Yao, Waltraud X. Schulze, and Dong Xu Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Contributors SALLY ALBRECHT • Institute of Plant Physiology and Biotechnology, University of Hohenheim, Stuttgart, Germany SACHA BAGINSKY • Department of Plant Biochemistry, Institute of Biochemistry and Biotechnology, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany GEROLD J.M. BECKERS • Plant Biochemistry and Molecular Biology Group, Department of Plant Physiology, RWTH Aachen University, Aachen, Germany HAN CHENG • Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China UWE CONRATH • Plant Biochemistry and Molecular Biology Group, Department of Plant Physiology, RWTH Aachen University, Aachen, Germany SAVITHRAMMA P. DINESH-KUMAR • Department of Plant Biology and the Genome Center, College of Biological Sciences, University of California Davis, Davis, CA, USA GUANGYOU DUAN • Max Planck Institute for Molecular Plant Physiology, Potsdam-Golm, Germany RIKARD FRISTEDT • Biophysics of Photosynthesis Physics and Astronomy Faculty of Sciences, VU University Amsterdam, Amsterdam, The Netherlands WOLFGANG HOEHENWARTER • Leibniz Institute of Plant Biochemistry, Halle (Saale), Germany BJÖRN INGELSSON • Division of Cell Biology, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden NING LI • Division of Life Science, The Hong Kong University of Science and Technology, Hong Kong, SAR, China ZEXIAN LIU • Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China SHISONG MA • Department of Plant Biology and the Genome Center, College of Biological Sciences, University of California Davis, Davis, CA, USA KRISTIN MAYER • Institute of Plant Physiology and Biotechnology, University of Hohenheim, Stuttgart, Germany FRANK L.H. MENKE • Proteomics, The Sainsbury Laboratory, Norwich, UK IRUTE MESKIENE • Institute of Biotechnology, University of Vilnius, Vilnius, Lithuania; Max F. Perutz Laboratories, University and Medical University of Vienna, Vienna, Austria SHARON C. MITHOE • John Innes Centre, Norwich, UK NICOLA HUCK • Plant Biochemistry and Molecular Biology Group, Department of Plant Physiology, RWTH Aachen University, Aachen, Germany ANDREAS SCHALLER • Institute of Plant Physiology and Biotechnology, University of Hohenheim, Stuttgart, Germany ANNA SCHÖNBERG • Department of Plant Biochemistry, Institute of Biochemistry and Biotechnology, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany WALTRAUD X. SCHULZE • Department of Plant Systems Biology, Universität Hohenheim, Stuttgart, Germany

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ALOIS SCHWEIGHOFER • Institute of Biotechnology, University of Vilnius, Vilnius, Lithuania; Max F. Perutz Laboratories, University and Medical University of Vienna, Vienna, Austria MARTIN THOMAS • Plant Biochemistry and Molecular Biology Group, Department of Plant Physiology, RWTH Aachen University, Aachen, Germany MARIA V. TURKINA • Division of Cell Biology, Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden TAISHI UMEZAWA • Faculty of Agriculture, Tokyo University of Agriculture and Technology, Tokyo, Japan; PRESTO, Japan Science and Technology Agency, Saitama, Japan DIRK WALTHER • Max Planck Institute for Molecular Plant Physiology, Potsdam-Golm, Germany YONGBO WANG • Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China XU NA WU • Department of Plant Systems Biology, Universität Hohenheim, Stuttgart, Germany DONG XU • Department of Computer Science and Bond Life Sciences Center, University of Missouri, Columbia, MO, USA YU XUE • Department of Biomedical Engineering, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China PINGFANG YANG • Key Laboratory of Plant Germplasm Enhancement and Speciality Agriculture, Chinese Academy of Sciences, Wuhan, China ZHU YANG • Division of Life Science, The Hong Kong University of Science and Technology, Hong Kong SAR, China QIUMING YAO • Department of Computer Science and Bond Life Sciences Center, University of Missouri, Columbia, MO, USA MONIKA ZULAWSKI • Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany

Chapter 1 The Plant Kinome Monika Zulawski and Waltraud X. Schulze Abstract Plant kinases are one of the largest protein families in Arabidopsis. There are almost 600 membranelocated receptor kinases and almost 400 soluble kinases with distinct functions in signal transduction. In this minireview we discuss phylogeny and functional context of prominent members from major protein kinase subfamilies in plants. Key words Kinase, Phylogeny, Protein family, MAP-kinase, Receptor kinase, Calcium-dependent kinase

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Introduction Reversible phosphorylation is one of the most widespread and already well-investigated posttranslational modifications in prokaryotic and eukaryotic organisms. It is a key mechanism for the regulation of general signal transduction pathways as well as specific protein properties [1]. This reversible process of phosphorylation is catalyzed by a specific enzyme group, the kinases. The reverse process called dephosphorylation is catalyzed by phosphatases. While the human genome codes for around 500 kinases [2], the kinome of plants is generally larger, counting about 1,000 kinases in Arabidopsis thaliana [3]. Eukaryotic plant kinases group into two paraphyletic groups: the membrane-located receptor-like kinases and the soluble cytosolic kinases. Both kinase groups transfer a phosphoryl group from ATP to the hydroxyl group of specific amino acids within their target proteins, mainly serine, threonine, or tyrosine [4]. In addition, some atypical kinase families of prokaryotic origin were identified in plants: the histidine kinases with functionalities of the prokaryotic two-component phosphorelay system, the Arabidopsis response regulator (ARR) and the family of bc1 complex kinases in organelles [5, 6], as well as some atypical kinases with nonstandard protein kinase domains and already proved kinase activity [7], like the pyruvate dehydrogenase kinase (PDK) [8].

Waltraud X. Schulze (ed.), Plant Phosphoproteomics: Methods and Protocols, Methods in Molecular Biology, vol. 1306, DOI 10.1007/978-1-4939-2648-0_1, © Springer Science+Business Media New York 2015

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Kinases

2.1 Receptor-Like Kinase Families

Plant receptor-like kinases are either membrane-spanning leucinerich repeat proteins with localization in the plasma membrane (receptor protein kinases) or membrane-associated proteins with localization in the cytoplasm near the plasma membrane (receptorlike cytoplasmic kinase, RLCK). Typical plant receptor kinases have one membrane-spanning domain, a large extracellular ligand-binding domain, and contain a catalytic domain on the cytoplasmic side. They are autophosphorylating serine/threonine kinases and function in signal perception and transmission of signals coming primarily from outside of the cell [9]. Receptor-like cytoplasmic protein kinases lack an extracellular domain; they are bound to the membrane by either a myristoylation site [10] or a membrane-embedding domain and contain a catalytic domain on the cytoplasmic side. Often they are involved in transduction of signals from receptor kinases to soluble kinases, being a substantial component of plant signal transduction systems. Arabidopsis contains approximately 560 receptor-like protein kinases which divide into a large number of families based on the kinase domain sequence [3]. To date, the function of just a small number of RLKs has been worked out and the classification of RLK families according to their function is yet not possible. Most of the already described RLKs have either a visible phenotype or are closely related to those. Attempts to categorize the RLKs are based on specific structures of the extracellular domains or a systematic numbering of phylogenetic subfamilies [11, 12]. A general paradigm of receptor kinase activation suggests cascade of protein interactions and phosphorylation/dephosphorylation events, starting by ligand binding on the extracellular side of the plasma membrane and producing a specific response by activation of transcription factors at the end of the signaling cascade. Activated transcription factors then lead to altered expression of genes as reaction to the signal perception outside the cell. Such a cascade of phosphorylation events involves generally a subset of kinases (and phosphatases) of different families, beginning with ligand-binding LRR kinases, involving membrane-associated RLCKs with possible regulatory function and cytoplasmic kinases, which can enter the nucleus and activate or inactivate the appropriate transcription factors. Activation/inactivation of such signaling cascades include autophosphorylation, interaction of proteins and dimerization of receptor proteins, protein stabilization or degradation, and change in localization [13, 14]. A nice example for the cascade signaling model is the brassinosteroid signaling cascade, in which a phosphatase and kinases of two LRR kinase families (LRR_10 with BRI1 and LRR_2 with

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Fig. 1 Phylogeny of kinase families involved in brassinosteroid signaling. Blue: LRR receptor-like kinases; red: cytoplasmic kinases; violet: receptor-like cytoplasmic kinases. Rhombs mark kinases with proven function in BR signaling; asterisks mark known phenotypes according to Lloyd 2012 (green: morphologic, red: lethal, violet: conditional)

SERK1), one receptor-like cytoplasmic kinase family (RLCK_2 with BSK1), and a cytoplasmic kinase family (SLK/GSK3 with BIN2) transmit the plant hormone signal from outside the cell to the BR-related transcription factors BES1 and BZR1 [14]. It could be shown that the BR signaling reactions are catalyzed by more than one member of the given family and closely related kinases have redundant functions (Fig. 1). The most prominent family members have a morphological phenotype, and the other kinases were identified by homology. LRR kinases with a large extracellular domain play a crucial role in ligand binding and signal perception, being often specific to a single signaling pathway. However, membrane-spanning kinases

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with smaller extracellular domains modulate more than one signaling pathway. LRR_2 family members, the somatic embryogenesis-like kinases SERK, with function in BR signaling were also shown to interact with the flagellin receptor FLS2, playing a crucial role in defense against pathogens [15, 16]. That makes them a multifunctional kinase family. Other well-known receptor-like kinases are for example CLAVATA1 for meristem development [17], HAESA for regulation of abscission [18], and ERECTA for shape and size formation of organs [19]. More recently, members of the LRR-RLK family were found to directly modulate the activity of membrane transporters, particularly the plasma membrane ATPase and aquaporins. For example, the plasma membrane ATPase was shown to be activated through direct phosphorylation by BRI1 [20]. In contrast stimulation of LRR-RLK FERONIA leads to inactivation of the ATPase by increased phosphorylation of an inhibitory site [21]. Another LRR-RLK, SIRK1, was shown to directly phosphorylate and activate aquaporins [22]. In contrast, receptor-like cytoplasmic kinases are known to play regulative functions in different signaling pathways. BSKs for example belong to the receptor-like cytoplasmic kinase family 2 (RLCK_2) and are localized and associated with BRLs on the cytoplasmic side of the membrane. Nine of twelve BSKs were shown to act as isoforms in BR signaling [23]. However, it is possible that BSKs are also involved in other signaling pathways; there are indications for BSKs being also involved in sugar signaling [24]. 2.2 Soluble Cytosolic Kinase Families

Soluble kinases can act as part of a signaling cascade and can be activated by RLKs or internal stimuli. Second messengers like oscillating calcium signals, cyclic nucleotides, as well as metabolites, hormones, or sensing results of biotic and abiotic factors can also activate soluble kinases. Target proteins of soluble kinases are often directly the metabolic enzymes or cell cycle proteins, signaling components of hormone metabolism, ribosomal proteins, transcription factors, and other kinases. The phosphorylation event leads to a direct change in characteristics of a specific target protein such as alterations in subcellular location, activity, or structure. In principle, the characteristics of individual kinases of one family and of whole kinase families can differ in function and properties. Kinases of one family can regulate either many different pathways and act in crosstalk mediation or be part of just one specific pathway; one kinase can be involved in the regulation of one specific pathway or regulate many different pathways; and one pathway is generally regulated by many kinases of one or more kinase families (Fig. 2). Well-known eukaryotic cytosolic kinase families are the MAPK signaling cascade; the calcium-activated kinase families CDPK and CIPK (SnRK3); the cell function-regulating kinase

Fig. 2 Phylogeny and already identified pathway involvement of cytosolic kinase families specified as number of kinases of given kinase family involved in particular pathway. Numbers of family members are given in parentheses

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families CDK, CKL, and NIMA/NEK; the regulators of circadian rhythm CKII and WNK; as well as AGC kinases, SnRK1 and SnRK2. Further, the Arabidopsis kinome comprises a subset of kinases without family assignment and atypical kinases of prokaryotic origin. Soluble kinases are mainly localized in cytoplasma or nucleus [25] and myristoylation can attach soluble kinases to the plasma membrane [10]. About 80 kinases were identified to be a part of the Arabidopsis myristoylome, including the receptor-like cytoplasmic kinase families RLCK_2 und RLCK_7. Myristoylation of cytosolic kinases was shown for most of the calcium-dependent kinases (CDPK), all CDPK-related kinases (CDPK-RK), and all CDK-like kinases (CDKL). Additionally, two AGC kinases, one CIPK, and two soluble kinases without family assignment were identified as myristoylated [3, 10]. 2.3 Cascades in Signaling: MAP Kinases

The canonical MAPK signaling pathway consists of three linked protein kinase families for transfer of phosphorylation signals, building a cascade in which kinases are activated when phosphorylated by the upstream kinase in a hierarchical manner. Signal perception occurs by phosphorylation-dependent activation of the MAP kinase kinase kinase (MAP3K) family. The activated MAP3K transfers the signal via phosphorylation of the subsequent MAP kinase kinase (MAP2K, MKK). MAP2Ks activate MAP kinases (MAPK; MPK) by phosphorylation of threonine and tyrosine residues in the T-x-Y motifs and MAPKs transfer the signal to downstream effector proteins in cytosol and nucleus [26]. Combinations of a substantial number of such cascades were already described and are well reviewed by Colcombet and Hirt [27]. The pathways regulated by MAPK cascades are mainly hormone signaling, biotic and abiotic stress, development, and cell cycle regulation. The cascade-building MAP kinases do not group together in the phylogeny of Arabidopsis kinases, but split into four independent families. The Arabidopsis kinome contains two unrelated MAP3K families: firstly, the 48 Raf-like MAP3K, which contains the wellknown kinases CTR1, ERD1, and VIK1 and the three STY kinases, and secondly the 37 Ste-like MAP3Ks with the annotation as MEKK [3]. While MEKKs follow the common MAPK cascade by phosphorylating MAP2 kinases as well as transcription factors [28–30], Raf-like MAP3Ks do not. Ste-like MAP3Ks obviously provide certain flexibility in shortcutting a signaling pathway by phosphorylation either MAP2Ks or directly downstream effector proteins like transcription factors. As known so far, target proteins of Raf-like MAP3Ks are not MAP2Ks: CTR1 interacts with the ethylene receptor ETR1 and phosphorylates the transcription factor EIN3, regulating the ethylene response [31–33]. VIK1 phosphorylates the glucose transporter TMT1 in the vacuole membrane and plays a role in regulation of

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Fig. 3 Phylogeny, annotation, studied pathways (black text), and identified target proteins (blue text) of (a) MAP2 kinase and (b) MAP kinase family. Kinases with phenotype are framed in blue

sugar metabolism on cellular level [34]. The STY kinases STY8, STY17, and STY46 regulate the biosynthesis of isoprenoids by phosphorylating a geranyl(geranyl) diphosphate synthase [35]. As long as no Raf-like MAP3Ks were identified to be part of the canonical MAPK cascade, the annotation of this subfamily as MAP3 kinases needs to be revised. The family of MAP2 kinases is related to the Ste-like MAP3 kinases and catalyzes the second step of the MAPK signaling cascade: after activation by Ste-like MAP3 kinases, MAP2 kinases pass the signal to MAPKs by phosphorylation of the T-x-Y motif. The Arabidopsis genome encodes for 10 MAP2Ks and 20 MAPKs (Fig. 3) [3]. Similarly to the Ste-MAP3Ks, also MAP2Ks can either phosphorylate MAPKs or directly phosphorylate effector proteins such as transcription factors. Well-investigated MAPKs are MPK3, MPK4, and MPK6, which show a visible phenotype [36] and play a role in all general pathways like hormone regulation, development, biotic and abiotic stress [37], as well as specialized pathways like cell cycle and cell proliferation [38, 39]. A substantial number of diverse target proteins was identified for those three MAPKs, consisting of hormone regulators, transcription factors, ribosomal proteins, kinases, and proteins in cellular regulation. A list of soluble kinase targets is available in the database PhosPhAt [40]. 2.4 CalciumActivated Kinases: CDPK and CIPK

Calcium is an important second messenger for regulation of signal transduction processes in eukaryotes. It regulates responses to a broad spectrum of endogen and exogen signals, those playing a role in developmental and adaptive processes [41]. The signal is

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encoded by the concentration of calcium ions, the amplitude and frequency, the oscillation and duration of the signal, as well as the presence of calcium-binding proteins in the cytoplasm [42]. Plants possess two unrelated kinase families for registration and transduction of calcium signals: the calcium-dependent protein kinases (CDPKs), which directly bind free calcium ions, and the CBLinteracting kinases (CIPK/SnRK3), which interact with and are activated by calcium-binding proteins. There is no evidence for conventional calmodulin-binding kinases (CaMK), known from animal systems, in Arabidopsis. 2.4.1 CalciumDependent Protein Kinases

CPDKs are characterized by four so-called EF-hands, motives with the ability to bind free cytosolic calcium ions [43]. Arabidopsis consists of 34 genes coding for CDPKs (Fig. 4a), and eight further kinases with degenerated EF-hand motives in direct phylogenetic relationship, named CDPK-related kinases (CDPK-RK, CRK, Fig. 4b). CDPKs autophosphorylate after calcium binding and this modification leads to kinase activation. Most CDPKs were shown to play a role in regulation of osmotic stress mediated by potassium channels. Many CDPKs were identified as ABA-dependent modulators of abiotic stress. CPK21 and CPK23 phosphorylate the ion channels SLAC1 and SLAH3 and regulate the ABA-dependent activity of guard cells during osmotic stress [44–46]. CPK3 phosphorylates 38 target proteins related to hormone metabolism, drought, and osmotic stress, among them different transcription factors and transport proteins. Additionally to regulation of osmotic stress, CPK3 is also involved in the modulation of biotic stress after elicitor perception [47] and by phosphorylation of a fructose-2,6-bisphosphatase [48] and the potassium channel TPK1 [49] in glycolysis and potassium transport. The involvement of CDPKs in plant defense after biotic stress, mainly based on reactive oxygen species, was also evaluated for a subfamily of CDPKs, namely CPK4, CPK5, CPK6, CPK11, and CPK26. Two other kinases, CPK17 and CPK34, are involved in reproductive processes by regulation of tip growth in pollen tubes [50]; CPK17 further phosphorylates the nitrite reductase NIA1 on serine 535, a modification necessary for binding of 14-3-3 proteins and inactivation of a crucial enzyme in nitrogen metabolism [51]. A subfamily of CDPKs with high phylogenetic relationship to the CDPK-RK subfamily regulates developmental processes instead of biotic and abiotic stress. CPK16 plays a role in regulation of the gravitropic response and modulates the activity of Ca2+-ATPase ACA8 [52]. The CPK28 functions in plant steam elongation and vascular development, those regulating the secondary growth of Arabidopsis [53].

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Fig. 4 Phylogeny, annotation, studied pathways (black text), and identified target proteins (blue text) of (a) calcium-dependent protein kinases (CDPK), (b) CDPK-related kinases (CDPK-RK), and (c) AGC kinases. Kinases with phenotype are framed in blue

Obviously, most CDPKs catalyze the phosphorylation of similar pathways and the spectrum of biologic processes regulated by CDPKs is widespread. The entire function of each calciumdependent kinase has still to be evaluated. 2.4.2 CDPK-Related Kinases (CRK/CDPK-RK)

CDPK-related kinases form a phylogenetic subfamily of eight calcium-dependent protein kinases with degenerated EF-hands and obviously lost calcium binding activity. The analysis of two tobacco isoforms provided evidence for a potential calcium regulation by interaction with calmodulin (CaM) [54]. To date, this family is still poorly researched. In Arabidopsis, these kinases regulate miscellaneous pathways with every kinase being part of a particular signaling context. CRK1 phosphorylates a heat-shock factor and functions in the heat-shock transduction pathway [55].

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CRK3 phosphorylates the glutamine synthetase GLN1;1 in vitro and is discussed to be a senescence-regulating kinase [56]. CRK5 phosphorylates PIN2, an auxin transport protein, being an additional kinase involved in auxin signal transduction and gravitropic response [57]. The miscellaneous pathway contribution of each CDPK-RK suggests a specialized function for each kinase of this family, making these kinases good candidates also for regulation of metabolic pathways. 2.4.3 CBL-Interacting Kinases (CIPK/ SnRK3)

The second family of plant calcium-regulated kinases constitutes of 27 CBL-interacting kinases (CIPKs) in Arabidopsis (Fig. 5a) [3]. CIPKs are a subfamily of Snf1-related kinases, also called SnRK3. While SnRK1 are ubiquitous kinases in all organisms, CIPKs are plant specific. They are implicated in calcium signaling via interaction with calcineurin B-like calcium-binding proteins (CBLs), a family of ten sensor proteins with EF-hand motives for calcium binding in Arabidopsis [54]. So far, CIPKs are involved in the regulation of miscellaneous pathways, they are activated by calcium, and the interaction with CBLs is not kinase-CBL specific, but often redundant. Further, it was shown that CIPKs phosphorylate their interacting CBLs [58, 59]. Nine of 27 CIPKs function

Fig. 5 Phylogeny, annotation, studied pathways (black text), and identified target proteins (blue text) of Snf1related kinase families. Kinases with phenotype are framed in blue

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in ABA-dependent regulation of osmotic stress [60–63]; other pathways with implication of CIPKs in signaling are ROS-mediated plant defense after biotic stress, development of seedlings, regulation of potassium metabolism and transport, as well as cell cycle regulation [64–67]. Known CIPK target proteins comprise different transcription factors, the potassium transporter AKT1 and the V-ATPase AHA2 [66, 68, 69]. 2.5 Kinases in Hormone Signaling: AGC and SLK/GSK 2.5.1 GlycogenSynthetase Kinases 3/ Shaggy-Like Kinases (GSK3/ SLK)

2.5.2 cATP, cGTP, and PhospholipidDependent Kinases (AGC)

The glycogen-synthetase kinases 3/shaggy-like kinases (GSK3/SLK), named after closely related kinases in mammals and Drosophila, are hormone-regulated kinases with substantial function in organ growth and development [70]. The Arabidopsis genome encodes for ten SLKs (Fig. 1c), and most of them were identified as brassinosteroid signaling components with redundant functions [3]. SLKs are activated by receptor-like kinases and phosphorylate receptor-like cytoplasmic kinases and MAP2 kinases as well as transcription factors. Most prominent SLKs are the BRI-insensitive kinases BIN2, BIL1, and BIL2. They phosphorylate the receptorlike cytoplasmic kinases BSK and the BR-induced transcription factors, mainly BZR1 and BES1 [23, 71]. SK11 and SK31 phosphorylate the MAP2 kinase MKK4 and function in the regulation of biotic and abiotic stress as well as the morphogenesis of stomata [72, 73]. AGC kinases, named after the cAMP-dependent protein kinase A, cGTP-dependent protein kinase G, and phospholipid-dependent protein kinase C, are eukaryotic kinases which act mainly as effectors of second messengers and regulate a broad spectrum of signaling pathways in yeast and animal organisms [74]. The Arabidopsis kinome consists of 39 AGC kinases with partially identified function and mainly plasma membrane-localized targets (Fig. 4c) [3]. The kinase PDK1, the most prominent plant kinase of this family, binds a subset of signaling lipids and activates other AGC kinases, thus regulating a range of different biological pathways. S6K kinases phosphorylate the ribosomal protein RPS6 and regulate in addition to protein translation also auxin and ABA signaling pathways, hence playing a role in abiotic stress and cell proliferation [75, 76]. The phototropins PHOT1 and PHOT2 act as blue light receptors in Arabidopsis. PHOT1 autophosphorylates after perception of a blue light signal and phosphorylates an ABC transporter as well as PKS4, a protein with unknown function. PHOT2 amplifies the signal by phosphorylation of PHOT1 after blue light perception. Both proteins function in the regulation of auxin transport and hypocotyl growth [77, 78]. The AGC1 kinases AGC1.1, AGC1.2, PK5, and PK7 phosphorylate a range of PIN proteins, thereby regulating the auxin signal transduction [79, 80]. Auxin signaling and gravitropism are further regulated by PINOID, WAG1, and WAG2 via phosphorylation of diverse PIN proteins

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[81, 82]. OXI1 and OXI2 were identified as lipid-regulated kinases with function in biotic and abiotic stress. They target a subset of diverse proteins including three PTI1-RLCKs [83–85]. The function of the other AGC kinases and kinase subfamilies is to date unknown, but a relation to auxin transport, gravitropism, and early organ development of shoot and root is obvious. 2.6 Kinases for Regulation of Cell Functions: CDK, CKL, NEK, and AURORA 2.6.1 Cyclin-Dependent Kinases

Cyclin-dependent kinases are key proteins for regulation of eukaryotic cell cycle. A multiplicity of cyclins and cyclin-dependent kinases was already identified as proteins which regulate the cell cycle by interaction with each other and a subset of other regulatory proteins. A genome-wide analysis identified 14 CDKs, 30 cyclins, and 16 other proteins with cell regulatory function in Arabidopsis [86]. Phylogenetically, CDKs form a paraphyletic group with small related families (Fig. 6a) [3]. Most prominent CDK is CDKA;1, a kinase with significant phenotype after gene knockout and function in morphogenesis of all organs, cell cycle regulation, mitosis and meiosis, cytoskeletal dynamics, cell differentiation, and translation of proteins [87–89].

Fig. 6 Phylogeny, annotation, studied pathways (black text), and identified target proteins (blue text) of (a) cyclin-dependent kinases (CDK), (b) casein-like kinases (CKL), (c) casein II kinases (CKII), (d) with-no-lysine kinases (WNK), (e) NIMA-like kinases (NEK). Kinases with phenotype are framed in blue

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CDKA;1 interacts with 24 cyclin proteins [90] and phosphorylates to date 12 proteins of heterogenic function, among them cell cycle regulators like CYCD6;1 and KRP1, the cell organization protein MAP65-1, and different transcription factors [91–95]. The heterogenic composition of target proteins and the involvement in different pathways establish CDKA;1 as a multifunctional kinase and a suitable marker protein for cell cycle and developmental processes. To date, the function of the other CDKs seems to be more specialized: the four CDKBs as well as the CDKF in Arabidopsis are involved in cell division, development, and morphogenesis. CDKF is further a CDK-activation kinase and phosphorylates all three CDKD kinases. CDKCs function in the regulation of rosette growth, biotic stress and circadian rhythm, as well as transcription and splicing [96–98]. CDKF and CDKD target NRPB1, a subunit of RNA polymerase II [99]. Phylogenetically related to the CDKs are the CDK-like kinases (CDKL). The function of this family is so far mainly unknown; solely CDC2C was described as IBS in context with biotic stress and salicylic acid signaling [100, 101]. It is possible that the function of these kinases is not cell cycle but hormone-related stress signaling. 2.6.2 Casein-Like Kinases

A second kinase family with cell regulatory function is the casein-like kinase family (CKL) of 13 kinases in Arabidopsis (Fig. 6b) [3]. These kinases are potential regulators of cortical microtubule origination and the cell-to-cell communication via plasmodesmata. CKL6 is a plasmodesma-associated kinase which contains a microtubulebinding domain. This kinase phosphorylates the C-terminal residue of tobacco mosaic virus movement protein (TMV-MP), leading to changes in microtubule organization [102, 103].

2.6.3 Never in Mitosis: Related Kinases (NIMALike Kinases/NEK)

The family of NIMA/NEK kinases was firstly discovered in mammals and fungi as kinases with regulatory function in cytokinesis [104]. Seven NIMA-like kinases were identified in Arabidopsis (Fig. 6e) [3, 105] with function in cell division, ethylene biosynthesis and signaling, as well as development and morphogenesis of many organs [106, 107]. The most prominent NIMA-like kinase is NEK6, a kinase with strong visible phenotype which phosphorylates the tubulins TUB4 and TUB6. The other NEKs were also shown to regulate the directional growth of epidermal cells by association with microtubules [108–110].

2.6.4 AURORA Kinases

Aurora kinases form a small family of three kinases in Arabidopsis [3]. All three AURORA kinases regulate cell division processes via phosphorylation of histone 3 during mitosis, thus influencing developmental processes of different organs [111–114].

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2.7 Kinases for Regulation of Circadian Rhythm: WNK and CKII 2.7.1 With-No-Lysine Kinases

With-no-lysine kinases were firstly discovered in mammals as kinases which lack the invariant catalytic lysine that is crucial for binding of ATP and kinase activity [115]. The Arabidopsis genome encodes for 11 kinases assigned as WNKs (Fig. 6d) [ 3] and catalytic activity was proven for two of them: WNK1 phosphorylates APRR3, a component of the clock-associated APRR1/TOC1 quintet, and other five WNKs were found to play roles in mechanisms that generate circadian rhythms in Arabidopsis, especially flower induction [116, 117]. WNK8 further targets the V-ATPase subunit VHA-C. A disruption of WNK8 increases the catalytic activities of catalase and peroxidase, enhancing the plant tolerance to osmotic stress [118, 119].

2.7.2 Casein Kinase II

The family of casein kinase II is a high pleiotropic kinase family, which consists of four catalytic and four regulatory subunits in Arabidopsis (Fig. 6c) [3]. Lethal after knockout mutation, CKII regulates the expression of a huge number of genes involved in a variety of pathways [120]. CKII maintains cell cycle and elongation processes, protein synthesis, circadian rhythm, phototropism and photomorphogenesis, gravitropic response, and different developmental processes. The CKII kinases in Arabidopsis target a subset of elongation factors and may play a role in elF2 stability, thus regulating the initiation of translation. Further, catalytic CKII subunits phosphorylate their regulatory CKB subunits [121]. The regulation of clock-associated genes establishes the CKII kinases as important regulators of circadian rhythm [122, 123]. CKII phosphorylates the clock-associated transcription factors CCA1 and LHY, being necessary for normal functioning of the central oscillator [124, 125]. Phosphorylation of a number of light-regulated transcription factors and regulation of PIN proteins on gene and protein level link CKII further to gravitropism and photomorphogenesis [126–128].

2.8 Cross Talk with Metabolic Processes: Snf1Related Kinases

Snf1-related kinases were identified by homology to sucrosenonfermenting protein kinase Snf1 in yeast. In plant, this kinase family groups phylogenetically in three distinct families: three SnRK1 in Arabidopsis with high similarity to the yeast SNF1 and function in regulation of plant metabolism and the plant-specific SnRK2 and SnRK3 (Fig. 5). The 10 SnRK2 harbor the well-known open stomata 1 (OST1) for ABA-dependent regulation of stomata movement while the 27 SnRK3 kinases in Arabidopsis are calcium dependent and regulated by interaction with calcineurin B-like proteins [3].

2.8.1 Snf1-Related Protein Kinases 1

The Arabidopsis genome encodes three SnRK1 kinases; two of them are expressed, while SnRK1.3 has no evidence on gene and protein expression level. Expressed SnRK1 kinases, also called ANIK10 and AKIN11, regulate a subset of different pathways, including

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growth and development, phosphate and carbon metabolism, biotic and abiotic stress, and sucrose signaling, those linking metabolic and stress signaling in plants [77, 129–135]. The catalytic activity of SnRK1 is determined by cross-phosphorylation of SnRK1 and their activating kinases SnAK [136, 137]. So far, SnRK1.1 phosphorylates the nitrate reductase NIA2 as well as some transcription factors. Further, both expressed SnRK1 kinases target trehalose-5-phosphate synthetase 5, a metabolic enzyme necessary for cross talk between catabolic and anabolic processes [138–140]. 2.8.2 Snf1-Related Protein Kinases 2

SnRK2 kinases are another kinase family which plays a key role in the regulation of plant response to abiotic stress [141]. All ten Arabidopsis SnRK2, including the well-known open stomata 1 (OST1), function in ABA-dependent osmotic stress regulation. In this context, SnRK2 are regulators of abiotic stress-related pathways. Lipid and cadmium signaling as well as root morphogenesis during salt stress are regulated by the phylogenetically related SnRK2.4 and SnRK2.10 [141, 142]. Additionally, SnRK2 control ABA-dependent seed development and dormancy [143]. SnRK2.2, SnRK2.3, and SnRK2.6 regulate further the flowering time [144] and stomata movement [145]. SnRK2 kinases target mainly ABAinduced transcription factors like ABI5 and EEL and SnRK2.6 phosphorylates additionally the transport proteins SLAC1 and KAT1, two regulators of ABA-dependent stomata movement [146, 147]. Phosphorylation of 14-3-3 proteins establishes SnRK2.8 in the regulation of plant growth and connects this kinase family to metabolic processes [148].

2.9 Kinases with No Family Assignment

Most plant kinases are integrated and described within a phylogenetic family. Regardless, a substantial number of kinases without family assignment were identified during the analysis of the Arabidopsis kinome [3]. Most of these kinases were yet not named and the function is still unknown. However, some of them were already investigated and a function could be identified. STN kinases STN7 and STN8 are known regulators of photosynthesis; they phosphorylate light harvesting complexes and a huge number of other photosynthesis-associated proteins [149–151]. GCN2 phosphorylates an elongation factor and regulates the biosynthesis of amino acids and proteins [152–154]. AFC2 is involved in protein synthesis by phosphorylating splicing factors [155]. RUNKEL is another microtubule-associated kinase for regulation of cytokinesis [156, 157]. WEE1 targets different cyclin-dependent kinases, those being an additional regulatory component besides cyclins in cell cycle regulation [158, 159]. The four IRE1 homologues in Arabidopsis are ER located, affect plant response to ER stress, and play a role in biotic and abiotic stress response [160, 161].

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Atypical Kinase Families of Prokaryotic Origin Beyond the kinome comprising serine threonine and tyrosine kinases of eukaryotic origin, plants consist of a number of atypical kinases of prokaryotic origin: firstly, kinases that function based on phospho-relay according to the bacterial two-component signaling system. Important members of this family are the receptors for cytokinin and ethylene [6]. Second group of plant atypical kinases are the 17 bc1 complex kinases (ABC1K) in Arabidopsis localized in mitochondria and plastids [7].

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component of the clock-associated APRR1/ TOC1 quintet is phosphorylated by a novel protein kinase belonging to the WNK family, the gene for which is also transcribed rhythmically in Arabidopsis thaliana. Plant Cell Physiol 43:675–683 Wang Y, Liu K, Liao H, Zhuang C, Ma H, Yan X (2008) The plant WNK gene family and regulation of flowering time in Arabidopsis. Plant Biol (Stuttg) 10(5):548–562 Hong-Hermesdorf A, Brux A, Gruber A, Gruber G, Schumacher K (2006) A WNK kinase binds and phosphorylates V-ATPase subunit C. FEBS Lett 580:932–939 Zhang B, Liu K, Zheng Y, Wang Y, Wang J, Liao H (2013) Disruption of AtWNK8 enhances tolerance of Arabidopsis to salt and osmotic stresses via modulating proline content and activities of catalase and peroxidase. Int J Mol Sci 14:7032–7047 Moreno-Romero J, Armengot L, MarquesBueno MM, Cadavid-Ordonez M, Martinez MC (2011) About the role of CK2 in plant signal transduction. Molecular and cellular biochemistry. Mol Cell Biochem 356:233–240 Dennis MD, Browning KS (2009) Differential phosphorylation of plant translation initiation factors by Arabidopsis thaliana CK2 holoenzymes. J Biol Chem 284:20602–20614 Lu SX, Liu H, Knowles SM, Li J, Ma L, Tobin EM, Lin C (2011) A role for protein kinase casein kinase2 alpha-subunits in the Arabidopsis circadian clock. Plant Physiol 157:1537–1545 Mulekar JJ, Huq E (2012) Does CK2 affect flowering time by modulating the autonomous pathway in Arabidopsis? Plant Signal Behav 7:292–294 Daniel X, Sugano S, Tobin EM (2004) CK2 phosphorylation of CCA1 is necessary for its circadian oscillator function in Arabidopsis. Proc Natl Acad Sci U S A 101:3292–3297 Sugano S, Andronis C, Green RM, Wang ZY, Tobin EM (1998) Protein kinase CK2 interacts with and phosphorylates the Arabidopsis circadian clock-associated 1 protein. Proc Natl Acad Sci U S A 95:11020–11025 Bu Q, Zhu L, Dennis MD, Yu L, Lu SX, Person MD, Tobin EM, Browning KS, Huq E (2011) Phosphorylation by CK2 enhances the rapid light-induced degradation of phytochrome interacting factor 1 in Arabidopsis. J Biol Chem 286:12066–12074 Hardtke CS, Gohda K, Osterlund MT, Oyama T, Okada K, Deng XW (2000) HY5 stability and activity in Arabidopsis is regulated by phosphorylation in its COP1 binding domain. EMBO J 19:4997–5006

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128. Marques-Bueno MM, Moreno-Romero J, Abas L, De Michele R, Martinez MC (2011) A dominant negative mutant of protein kinase CK2 exhibits altered auxin responses in Arabidopsis. Plant J 67:169–180 129. Fragoso S, Espíndola L, Páez-Valencia J, Gamboa A, Camacho Y, Martínez-Barajas E, Coello P (2009) SnRK1 isoforms AKIN10 and AKIN11 are differentially regulated in Arabidopsis plants under phosphate starvation. Plant Physiol 149(4):1906–1916 130. Halford NG, Hey SJ (2009) Snf1-related protein kinases (SnRKs) act within an intricate network that links metabolic and stress signalling in plants. Biochem J 419(2):247–259 131. Jossier M, Bouly JP, Meimoun P, Arjmand A, Lessard P, Hawley S, Grahame Hardie D, Thomas M (2009) SnRK1 (SNF1-related kinase 1) has a central role in sugar and ABA signalling in Arabidopsis thaliana. Plant J 59:316–328 132. Li XF, Li YJ, An YH, Xiong LJ, Shao XH, Wang Y, Sun Y (2009) AKINbeta1 is involved in the regulation of nitrogen metabolism and sugar signaling in Arabidopsis. J Integr Plant Biol 51(5):513–520 133. Tsai AY, Gazzarrini S (2012) AKIN10 and FUSCA3 interact to control lateral organ development and phase transitions in Arabidopsis. Plant J 69:809–821 134. Tsai AY, Gazzarrini S (2012) Overlapping and distinct roles of AKIN10 and FUSCA3 in ABA and sugar signaling during seed germination. Plant Signal Behav 7:1238–1242 135. Zhang Y, Primavesi LF, Jhurreea D, Andralojc PJ, Mitchell RA, Powers SJ, Schluepmann H, Delatte T, Wingler A, Paul MJ (2009) Inhibition of SNF1-related protein kinase1 activity and regulation of metabolic pathways by trehalose-6-phosphate. Plant Physiol 149:1860–1871 136. Hey S, Mayerhofer H, Halford NG, Dickinson JR (2007) DNA sequences from Arabidopsis, which encode protein kinases and function as upstream regulators of Snf1 in yeast. J Biol Chem 282:10472–10479 137. Shen W, Reyes MI, Hanley-Bowdoin L (2009) Arabidopsis protein kinases GRIK1 and GRIK2 specifically activate SnRK1 by phosphorylating its activation loop. Plant Physiol 150:996–1005 138. Delatte TL, Sedijani P, Kondou Y, Matsui M, de Jong GJ, Somsen GW, Wiese-Klinkenberg A, Primavesi LF, Paul MJ, Schluepmann H (2011) Growth arrest by trehalose-6-phosphate: an astonishing case of primary metabolite control over growth by way of the SnRK1 signaling pathway. Plant Physiol 157:160–174

139. Harthill JE, Meek SE, Morrice N, Peggie MW, Borch J, Wong BH, Mackintosh C (2006) Phosphorylation and 14-3-3 binding of Arabidopsis trehalose-phosphate synthase 5 in response to 2-deoxyglucose. Plant J 47:211–223 140. Glinski M, Weckwerth W (2005) Differential multisite phosphorylation of the trehalose-6phosphate synthase gene family in Arabidopsis thaliana: a mass spectrometry-based process for multiparallel peptide library phosphorylation analysis. Mol Cell Proteomics 4(10):1614–1625 141. Kulik A, Anielska-Mazur A, Bucholc M, Koen E, Szymanska K, Zmienko A, Krzywinska E, Wawer I, McLoughlin F, Ruszkowski D, Figlerowicz M, Testerink C, Slodowska A, Wendehenne D, Dobrowolska G (2012) SNF1-Related Protein Kinases Type 2 are involved in plant responses to cadmium stress. Plant Physiol 160:868–883 142. McLoughlin F, Galvan-Ampudia CS, Julkowska MM, Caarls L, van der Does D, Lauriere C, Munnik T, Haring MA, Testerink C (2012) The Snf1-related protein kinases SnRK2.4 and SnRK2.10 are involved in maintenance of root system architecture during salt stress. Plant J 72(3):436–449 143. Nakashima K, Fujita Y, Kanamori N, Katagiri T, Umezawa T, Kidokoro S, Maruyama K, Yoshida T, Ishiyama K, Kobayashi M (2009) Three Arabidopsis SnRK2 protein kinases, SRK2D/SnRK2.2, SRK2E/SnRK2.6/OST1 and SRK2I/SnRK2.3, involved in ABA signaling are essential for the control of seed development and dormancy. Plant Cell Physiol 50:1345–1363 144. Wang Y, Li L, Ye T, Lu Y, Chen X, Wu Y (2013) The inhibitory effect of ABA on floral transition is mediated by ABI5 in Arabidopsis. J Exp Bot 64:675–684 145. Acharya BR, Jeon BW, Zhang W, Assmann SM (2013) Open Stomata 1 (OST1) is limiting in abscisic acid responses of Arabidopsis guard cells. New Phytol 200(4):1049–1063 146. Fujii H, Verslues PE, Zhu JK (2011) Arabidopsis decuple mutant reveals the importance of SnRK2 kinases in osmotic stress responses in vivo. Proc Natl Acad Sci U S A 108(4):1717–1722 147. Sirichandra C, Davanture M, Turk BE, Zivy M, Valot B, Leung J, Merlot S (2010) The Arabidopsis ABA-activated kinase OST1 phosphorylates the bZIP transcription factor ABF3 and creates a 14-3-3 binding site involved in its turnover. PLoS One 5(11): e13935

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Chapter 2 Phosphatases in Plants Alois Schweighofer and Irute Meskiene Abstract Reversible protein phosphorylation is an essential posttranslational modification mechanism executed by opposing actions of protein phosphatases and protein kinases. About 1,000 predicted kinases in Arabidopsis thaliana kinome predominate the number of protein phosphatases, of which there are only ~150 members in Arabidopsis. Protein phosphatases were often referred to as “housekeeping” enzymes, which act to keep eukaryotic systems in balance by counteracting the activity of protein kinases. However, recent investigations reveal the crucial and specific regulatory functions of phosphatases in cell signaling. Phosphatases operate in a coordinated manner with the protein kinases, to execute their important function in determining the cellular response to a physiological stimulus. Closer examination has established high specificity of phosphatases in substrate recognition and important roles in plant signaling pathways, such as pathogen defense and stress regulation, light and hormonal signaling, cell cycle and differentiation, metabolism, and plant growth. In this minireview we provide a compact overview about Arabidopsis protein phosphatase families, as well as members of phosphoglucan and lipid phosphatases, and highlight the recent discoveries in phosphatase research. Key words Arabidopsis, Phosphatase, PPP, PP2C, PTP, DSP, MAPK phosphatase, PP1, PP2A, PPKL, HAD, PTEN, Lipid phosphatase, Dephosphorylation, Phosphoglucan phosphatase

1

Introduction Protein phosphorylation occurs predominantly on Ser, Thr, and Tyr residues, although six other amino acids can also be phosphorylated (cysteine, arginine, lysine, aspartate, glutamate, and histidine) [1, 2]. Accordingly, protein phosphatases are classified based on their targeted phospho-amino acids, primary protein sequence, and catalytic mechanism into four groups: phosphoprotein phosphatases (PPPs), metal-dependent (magnesium or manganese) protein phosphatases (PPM), protein Tyr phosphatases (PTPs), and aspartatebased phosphatases represented by FCP (TFIIF-associating component of RNA polymerase II CTD phosphatase)/SCP (small CTD phosphatase) and haloacid dehalogenase (HAD) phosphatases [3].

Waltraud X. Schulze (ed.), Plant Phosphoproteomics: Methods and Protocols, Methods in Molecular Biology, vol. 1306, DOI 10.1007/978-1-4939-2648-0_2, © Springer Science+Business Media New York 2015

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Protein Tyrosine Phosphatases

Protein Ser/Thr Phosphatases

Asp-based

P P Substrate-S/T Substrate-S/T

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DxDxT

LMW

PTP

DSP

PPP

PP2C/PPM

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1

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80

Fe::Zn

Mn/Mg

Cys-SH

P Substrate-Y

Substrate-Y

P P Substrate-S/T

Substrate-S/T

Fig. 1 Classification of protein phosphatases (according to [4]) with indicated numbers of predicted Arabidopsis genes (according to [3, 25]). The family of protein tyrosine phosphatases (PTPs) use an active Cys for catalysis and is divided into dual-specificity phosphatases (DSPs; including PTEN), classical PTP, and low-molecular-weight PTP (LMW) phosphatases. The Ser⁄Thr phosphatases are shown in three families, with indicated required metal ions for PPP and PP2C/ PPM families and the conserved DxDxT motif for Asp-based phosphatases

PPP, PPM, and aspartate-based phosphatases represent the majority of phospho-Ser/Thr dephosphorylating enzymes, whereas the Tyr phosphatase family members (described as classical PTPs) target either phospho-Tyr or both phospho Ser/Thr and phospho-Tyr (described as dual-specificity phosphatases; DSPs). During the dephosphorylation reactions PTPs and DSPs use an active cysteine residue, while FCP/SCP and HAD phosphatases use an aspartate-based mechanism for catalysis. PPP and PPM enzymes require metal ions for their activity [4] (Fig. 1). In this minireview we highlight the features and functions of Arabidopsis phosphatase members of PTP, PPP (including the novel PPP members protein phosphatases containing kelch-like repeat domains, PPKLs, and Shewanella-like protein (SLP) phosphatases), PPM/PP2C, and Asp-based PPs as well as lipid phosphatases and phosphoglucan phosphatases, enzymes that dephosphorylate nonproteinous substrates.

2

Plant Protein Phosphatase Families

2.1 Protein Tyrosin Phosphatases: DSPs and PTP

The Arabidopsis thaliana PTP superfamily currently contains 22 identified DSPs, one classical PTP, and one low-molecular-weight (LMW) PTP phosphatase [3]. The common hallmark of this superfamily is the usage of an active cysteine within the catalytic signature C(X)5R for the enzymatic reaction. DSPs include five

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members of MAPK phosphatases (MKPs) harboring the active site motif VHC(X2)GXSRS(X5)AYLM, which is similar to mammalian MKP active site [5, 6]. These MKPs have been described to interact with and dephosphorylate MAPKs [7–11]. 2.2 MAP Kinase Phosphatase 1

MKP1 interacts with the MAPKs MPK6, MPK3, and MPK4 as shown in two hybrid (Y2H) assays in yeast [12] and bimolecular fluorescence complementation (BiFC) in transiently transformed mustard hypocotyl cells. MKP1 deactivates MPK6 in suspension cell protoplasts, where the interaction between MKP1 and MPK6 occurs mainly in the cytoplasm [7]. Plants lacking MKP1 in the Arabidopsis Wassilewskija (Ws) accession (mkp1, (Ws)) are hypersensitive to UV-C and methyl methanesulfonate treatment and show increased resistance to elevated salinity [13]. Introgression of the mutant allele into the Columbia (Col) accession (mkp1(Col)) revealed pleiotropic phenotypes (e.g., dwarfism, early senescence, reduced fertility, enhanced salicylic acid (SA) amounts, pathogen-related gene expressions) [7], which were additionally enhanced by introduction of a ptp1 null mutation of the protein tyrosine phosphatase PTP1 (see below) pointing to functional redundancy between MKP1 and PTP1. The phenotypes of mkp1(Col) and mkp1(Col)/ptp1 revealed to be dependent on MKP1 substrates, functional MPK3 and MPK6 proteins, as crossing with mpk3 or mpk6 null mutant plants (partially) restored the aberrant growth phenotypes as well as suppressed the SA accumulation in mkp1(Col) and mkp1(Col)/ptp1 plants [7]. MKP1 controls MPK6-dependent pathogen-associated molecular pattern (PAMP) responses (e.g., MAPK activation, ROS production, seedling growth inhibition) [14]. mkp1(Col) plants demonstrated MPK6- but not MPK3-dependent enhanced resistance against Pseudomonas syringae pv tomato DC3000 (Pto DC3000). mkp1(Col) and mkp1(Col)/ptp1 growth phenotypes as well as elevated PR1 and PR5 expression levels are dependent on SNC1 (suppressor of npr1-1, constitutive 1 (SNC1)) [15], an R-gene, which triggers plant innate immunity. These phenotypes were reduced by introducing a loss-of-function allele of snc1. SCN1 is absent in the Ws accession and investigations of mkp1(Ws) show the SCN1independent enhanced resistance against Pto DC3000 requires MPK6 but not MPK3 [7]. MKP1 also contributes by regulation of the UV-B stress response pathway to achieve UV-B tolerance [16, 17]. In plants MKP1 is phosphorylated and accumulates after UV-B treatment [17], possibly, but not exclusively by MPK6, which can phosphorylate and activate MKP1 in vitro [18]. The UV-B hypersensitive phenotype of mkp1(Col) is caused most likely by MPK3 and MPK6 activation as mkp1/mpk3 and mkp1/mpk6 double-mutant plants are significantly less sensitive to UV-B stress [16].

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2.3 MAP Kinase Phosphatase 2

MKP2 dephosphorylates protein substrates MPK3 and MPK6 in vitro [8] and interacts with the same MAPKs in cytoplasm and nucleus in vivo as revealed using BiFC [10]. A functional interaction between transiently coexpressed MKP2 and MPK6 was observed during MPK6-induced hypersensitive response (HR) in N. benthamiana after fungal elicitor treatment. MKP2 was able to reduce the MPK6-induced hypersensitive response (HR) [10]. MKP2 has been described as a positive regulator of oxidative stress tolerance [8, 10], since MKP2-RNAi and mkp2 mutant plants are hypersensitive to ozone and methyl-viologen treatment [8, 10]. Interestingly, mkp2 mutant plants displayed delayed wilting symptoms in response to pathogen Ralstonia solanacearum, but an enhanced susceptibility to Botrytis cinerea indicating differential functions in pathogen-induced responses.

2.4

DsPTP1 is closely related to MKP2 [5] and its activity is modulated by calmodulin (CaM) binding via two Ca2+-dependent CaMbinding domains in the phosphatase [19]. DsPTP1 dephosphorylates MPK4 but not MPK3 or MPK6 in vitro [8, 20], where its catalytic activity depends on the conserved cysteine [20]. The functions of DsPTP1 in vivo still wait to be identified.

DsPTP1

2.5 Indole-3-Butyric Acid Response 5

The Arabidopsis DSP IBR5 was isolated as null mutant ibr5 in a screen to inhibitory effects of a natural auxin, indole-3-butyric acid (IBA) [21]. Although ibr5 plants exhibit phenotypes similar to other auxin response mutants, like increased leaf serration, long root and short hypocotyl in light, aberrant vascular patterning, and a reduced accumulation of an auxin-inducible reporter, overexpression of IBR5 does not significantly alter auxin sensitivity. ibr5 enhances auxin-response defects of tir1 auxin receptor mutant, since double mutants ibr5/tir1 show enhanced auxin resistance compared to each single mutant [22]. ibr5 phenotypes are partially suppressed by mutations in genes encoding ABC transporters pleiotropic drug resistance 8 (PDR8) and PDR9, which are involved in auxin efflux [23]. IBR5 interacts with MPK12 out of 20 MAPKs tested in Y2H, and is able to interact and dephosphorylate MPK12 in vitro and in protoplasts [9]. IBR5 interacts with the C-terminal domain of MPK12 whose catalytic activity is not required for the interaction tested in yeast. ibr5 auxin-insensitive phenotype can be partially suppressed by hypersensitivity to auxin phenotype of MPK12-RNAi knock down indicating that reduction of MPK12 function can partially restore normal auxin signaling of ibr5 [9].

2.6 PHS1: Dual Function as MAPK Phosphatase and Autoregulated Tubulin Kinase

PHS1 (propyzamide hypersensitive 1) was identified as semidominant phs1-1 allele in a screen for mutants more sensitive to the microtubule-destabilizing drug propyzamide [24]. phs1-1 mutant harbors an amino acid substitution (R64C) in a putative MAP kinase interaction motif (KIM), which is also present in many

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mammalian MKPs and plant MAPK phosphatases of PP2C type (see below) [6, 25]. PHS1 interacts with Arabidopsis MAPKs MPK12 and MPK18 in yeast assays and with MPK18 also in plant cells, where interaction by BiFC was observed in the cytoplasm [11]. PHS1 is able to dephosphorylate MPK18 in vitro and both genes share similar expression patterns in planta. phs1-1 plants are affected in cortical microtubule functions; however null mutants show wild-type-like phenotypes [24, 26]. Genetic evidence supports contribution of MPK18 and PHS1 to microtubule-related functions, since mpk18 plants are more resistant to microtubuledisrupting drugs and partially rescue the phs1-1 sensitivity phenotype in mkp18/phs1-1 double mutants [11]. A reduction of PHS1 in the mutant line psh1-3 by T-DNA insertion in the PHS1 promoter region [27] resulted in plants hypersensitive to ABA, with ABA-induced inhibition of germination and light-triggered stomata opening. Thus, PHS1 might function as a negative regulator of ABA signaling as psh1-3 plants show enhanced upregulation of ABA-induced and downregulation of ABA-repressed genes [27]. Interestingly, PHS1 phosphatase contains a domain with a homology to the slime mold actin-fragmin kinase [5]. Recently, it was demonstrated that PHS1 shows a Mn2+-dependent atypical kinase activity, which enables α-tubulin phosphorylation, thereby generating a polymerization-incompetent tubulin isoform [28]. Inducible expression of a phosphatase-dead version of PHS1, but not of the other four DSP MKPs, led to seedling dwarfism and radial expansion of root epidermal elongation as well as differentiation of cells with destabilized cortical microtubules (MTs) [28]. It was also found that transient expression of a truncated PHS1 version lacking the putative KIM domain and the phosphatase catalytic part (PHS1ΔP) was necessary and sufficient for the MT-destabilizing activity, as the retained kinase domain was responsible for α-tubulin phosphorylation in vivo. Based on these findings a model was proposed [28] where in nonstressed plant cells the phosphatase activity of PHS1 suppresses its intrinsic tubulin kinase activity, but during stress MPK18 and possibly other MAPKs may activate the kinase domain of PHS1 for α-tubulin phosphorylation [29], which prevents polymerization of cortical microtubules [28]. These MAPKs might be normally inactivated by the phosphatase activity of PHS1, which interacts with and dephosphorylates MPK18 [11]. This illustrates that PHS1-dependent tubulin phosphorylation by the PHS1 intrinsic kinase activity is responsible for the stress-induced microtubule depolymerization, which is suppressed by the PHS1 phosphatase activity under normal conditions [28]. 2.7 Other DSPs: Phosphoglucan Phosphatases

The Arabidopsis genome encodes three DSP phosphoglucan phosphatases related to mammalian laforin, namely starch excess 4 (SEX4), like SEX4 1 (LSF1) and LSF2. SEX4 and LSF2 have been

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shown to dephosphorylate glucans counteracting the phosphorylation by dikinases to enable starch breakdown by providing the access for amylases [30]. Besides the DSP domain these phosphatases exhibit a chloroplast transit peptide [31–34]. SEX4 dephosphorylates amylopectin glucosyl units at C3 and C6 positions [35, 36], whereas LSF2 is specific for C3 [37]. SEX4 requires the canonical cysteine for catalytic activity and Arabidopsis sex4 mutant plants demonstrate stunt phenotype and accumulate more starch at the end of the night [38]. lsf2 mutation enhances the sex4 phenotype in sex4/lsf2 double-mutant plants, indicating overlapping functions of LSF2 and SEX4 in starch dephosphorylation [37]. Plants devoid of LSF1 accumulate more starch than wild-type plants, and lsf1 also enhances the sex4 phenotype in sex4/lsf1 double-mutant plants [31]. LSF1 phosphatase activity could not be determined so far, proposing that it may serve as a putative scaffold in protein-protein interactions [30]. 2.8 Other DSPs: Lipid Phosphatases (AtPTEN)

PTEN (phosphatase and tensin homolog) phosphatase possesses dual function containing both protein and phosphoinositide phosphatase activity. PTEN has been described as tumor suppressor in mammals mainly due to its role in negative regulation of the PI3K (phosphatidylinositol 3-kinase)/Akt signaling pathway [39, 40]. The Arabidopsis genome encodes three PTEN genes, AtPTEN1 and AtPTEN2a and 2b. AtPTEN1 demonstrates tyrosine phosphatase activity and dephosphorylates phosphatidylinositol (3, 4, 5)trisphosphate (PIP3) [41] in vitro. AtPTEN1 is expressed exclusively in pollen and is essential for pollen development, as RNAi silencing of AtPTEN1 causes pollen cell death and sterility [41]. AtPTEN2a and AtPTEN2b are differentially expressed during plant development and in response to NaCl and mannitol treatment [42]. AtPTEN2a dephosphorylates preferentially phosphatidylinositol 3-phosphate (PI3P), one of the major phosphoinositides in plants, and binds to phosphatidic acid, an important second messenger with functions in stress and hormonal signaling. It remains still to be resolved how and if phosphatidic acids modulate AtPTEN2a activity in vivo and with which signaling pathways AtPEN2a and b are associated [42].

2.9

Myotubularins (MTMs) are DSP phosphatases that dephosphorylate lipids in vivo; however dephosphorylation of proteins has not been identified yet. MTMs target phosphatidylinositol 3-phosphate (PI3P) and phosphatidylinositol (3,5)-bisphosphate (PI(3,5) P2) as substrates, whereas reaction with the latter leads to generation of PI5P. Arabidopsis contains two genes, myotubularin 1 and 2 (AtMTM1 and AtMTM2) and both are active lipid phosphatases [43]. MTM1 has physiological role in dehydration stress as demonstrated by significant resistance to water withdrawal in mtm1 mutant plants and MTM1 requirement for the increase of PI5P

Myotubularins

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normally observed in response to dehydration. Concomitantly, upon dehydration stress MTM1::GUS gene expression increases in hydathodes [43]. 2.10 Classical Protein Tyrosine Phosphatase: AtPTP1

3

Only one member of a classical PTP, AtPTP1, is found in Arabidopsis genome. Recombinant AtPTP1 dephosphorylates MPK4 in vitro [44, 45]. The activity of AtPTP1 is possibly controlled by ROS produced during stress conditions since the conserved catalytic cysteine must be in reduced form for its tyrosine phosphatase activity [45]. Alongside with this outline AtPTP1 expression is stress responsive [46, 47]. Although no aberrant phenotype was detected in ptp1 single null mutant plants, AtPTP1 functions in cell signaling are supported by the enhanced constitutive defense response phenotype of Arabidopsis ptp1/mkp1(Col) double mutant compared to mkp1(Col) [7].

Phosphoprotein Phosphatases The Ser/Thr phosphatases of the plant phosphoprotein phosphatase (PPP) family have been reviewed recently [48, 49]; thus here we indicate the important features of these protein phosphatases in a compact way to provide an overview about the current research development. The Arabidopsis genome encodes 26 PPP catalytic subunits [3, 48, 49] which are grouped based on similarities in sequence and structure into PP1, PP2A, PP4, PP5, PP6, PP7, kelch-like repeat domains (PPKLs), and Shewanella-like protein (SLP) phosphatases. PP2B/PP3 calcineurin-like phosphatases are absent in plants, whereas PPKLs and SLPs are lacking in mammals. PPPs’ activities are inhibited with different sensitivities by okadaic acid, calyculin A, microcystin, and nodularin, whereas the Ser/Thr PP2C/PPM phosphatases are structurally unrelated to PPPs and are not inhibited by known phosphatase inhibitors. PP2Cs will be described separately (see below).

3.1

PP1

Arabidopsis contains nine PP1 genes, TOPP1-8 (type one protein phosphatase 1-8) and PP1iso8 [48, 49]. PP1 phosphatases obtained substrate specificity through interaction with regulatory subunits, where mainly the conserved binding site “RVXF” mediates interactions. Several plant PP1-interacting proteins have been identified (RSS1, SDS22, I3, PRSL, GEM, NIPP1) [50]. For some of the PP1 interactors the physiological roles have been characterized in Arabidopsis. I3 has an essential function in embryo development as demonstrated by embryo lethality of inh3, a homozygous mutant for I3 [51]. PRSL1 (PP1 regulatory subunit2-like protein1) was isolated based on sequence similarity to Vicia faba PRS2, which

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interacted with PP1c in Y2H [52]. Arabidopsis prsl1 mutants exhibit normal phototropic activities, but are affected in blue lightdependent stomatal opening, H+ pumping, and H+-ATPase phosphorylation. The prsl1 phenotype could not be complemented by PRSL1 containing a mutation in the RVxF motif, suggesting that PRSL1 functions by binding PP1 via this motif [52]. 3.2

PP2A, PP4, PP6

The PP2A holoenzyme consists of a heterotrimer, which involves a catalytic subunit (C) interacting with a regulatory/scaffolding subunit (A) and a regulatory subunit (B) [4, 53]. The B subunit is suggested to determine subcellular localization and substrate specificity. As the catalytic subunit sequences of PP2A (PP2A-C, 5 members in Arabidopsis), PP4 (PP4-C; 2 members in Arabidopsis), and PP6 (PP6-C; 2 members) are closely related, PP4 and PP6 are considered to be PP2A-like phosphatases [49, 54, 55]. Arabidopsis encodes 5 catalytic C, 3 scaffolding A, and 17 regulatory B (grouped into B, B′, and B″) PP2A subunits providing the theoretical possibility for 255 different heterotrimer combinations [56, 57]. The A subunit PP2A-A1/RCN1 (roots curl in naphthylphthalamic acid 1), originally identified in a mutant screen for altered response to an auxin efflux inhibitor naphthylphthalamic acid (NPA) [58], regulates auxin hormone signaling [59, 60]. Mutant analysis of the PP2A A scaffolding subunits revealed that single and double mutants of pp2a-a2 (alias pdf1) and pp2a-a3 (alias pdf2) appear phenotypically normal, whereas double-mutant combinations pp2a-a1/pp2a-a2 and pp2a-a1/pp2a-a3 show severe phenotypes including perturbed root growth/gravity with affected auxin transport [59, 60]. PP2A-A and PINOID KINASE (PID) act antagonistically in root and embryo development. Loss of PP2A-A (pp2a-a) led to a polar shift of the PIN-formed (PIN) auxin efflux carriers PIN1, PIN2, and PIN4 and it was shown that PIN1 protein is PID-phosphorylated in vivo. Phosphorylation of PIN hydrophilic loops is positively regulated by PID and negatively by PP2A activity [59]. Plant mutants of PP2A catalytic C subunits (C1–C5) indicate functions in ABA signaling (C2; [61]), brassinosteroid signaling (C5; [62]), and plant defense responses [63]; however related functions have also been described for PP2A-A and B subunits [62, 64, 65], including metabolism regulation [66, 67]. Furthermore, PP2A negatively regulates accumulation and activity of ACS isozymes in ethylene production [68], as pp2a-a1 (rcn1) mutant plants produce higher amounts of ethylene [69]. PP2A also specifically dephosphorylates the light sensor PHOT2 (phototropin 2) for regulation of stomata opening and phototropism [70]. PP2A co-purifies with histone acetyltransferase ELP3 and histone deacetylase HDA14, which also targets α-tubulin [71], suggesting that PP2A may control microtubule function.

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PP2A catalytic subunits PP2A-C3 and PP2A-C4 are essential for embryo patterning and have a central role in auxin distribution by affecting the polarity of auxin efflux carrier PIN1 [56]. PP2A catalytic subunits bind to regulatory subunit TAP46 (type 2A phosphatase-associated protein of 46 kDa), which is a substrate of target of rapamycin (TOR) kinase. TAP46-RNAi silencing revealed TAP46 function in autophagy and plant growth in planta [72]. PP2A has also been implicated in brassinosteroid (BR) signaling with dual roles [73]: (1) members of PP2A complexes (B′ subunits) were identified to interact, dephosphorylate, and thereby activate BZR transcription factors, which are responsible for BR-induced gene expressions [62], and (2) by dephosphorylation of BRI1 (brassinosteroid-insensitive 1), the major BR receptor [74]. BRI1 accumulates in pp2a-a1/rcn1 mutants and methylation of PP2A-C subunit by SBI1, a bri1 suppressor, controls PP2A membrane-associated subcellular localization. A model is proposed where BR-induced SBI1 activity triggers several events that lead to dephosphorylation and subsequent degradation of activated BRI1, thereby reducing BR signaling strength [74]. Currently, no clear functions for plant PP4 have been identified [48, 49, 75]. Closely related to PP4 are the PP6 phosphatases, which are encoded in Arabidopsis by AtFyPP1 and AtFyPP3. They have been shown to modulate control of flowering time [76], regulate ABA signaling by dephosphorylating the ABI5 transcription factor [77], and interact with a subset of PIN proteins, thus regulating their phosphorylation and targeting in vivo [78]. Both PP6-Cs are required for polar auxin transport, PIN polar localization, and act antagonistically to PID. AtFyPP forms a heterotrimer holoenzyme together with PP2A-A and SAL1 (a B subunit of PP6), suggesting that PP2A-A can participate in assembly of both PP2A and PP6 holoenzymes. Thus, it cannot be excluded that PP6 may be the primary phosphatase, which regulates processes in auxin transport-dependent plant development [78]. 3.3

PP5

PP5 proteins contain a conserved N-terminal tetratricopeptide (TRP) protein domain (implicated in autoinhibition and proteinprotein interactions) and a C-terminal catalytic domain [49]. The single Arabidopsis gene PP5 is expressed in two splicing variants resulting in proteins localized differently in plant cells. The 62-kDa isoform localizes to the endoplasmic reticulum and the 55-kDa isoform, called PAPP5 (phytochrome-associated protein phosphatase 5), to both cytosol and nucleus [79, 80]. Plant PP5 functions have been assigned to thermotolerance [81, 82], disease resistance [83, 84], tetrapyrrole-mediated signaling in plastids [85], and light detection. PAPP5 shows spectral form-dependent interaction with phytochrome phyA and phyB and regulates positively phyA- and phyB-mediated photoresponses, as shown by analysis of pp5 mutants and PP5-overexpressing lines [80].

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PP7

PP7 phosphatase is unique to plants and encoded by a single gene in Arabidopsis [86]. Unlike other PPP members PP7 contains a C-terminal nuclear localization signal [87] and insertions within the catalytic domain. Ca2+-dependent calmodulin (CaM) binding in vitro inhibits PP7 catalytic activity [88]. In plants PP7 function has been implicated in thermotolerance [89] and light sensing through regulation of phytochrome and cryptochrome [90–92]. Together with HRB1, a nuclear ZZ-type zinc finger protein, PP7 acts downstream of blue light perception in stomatal aperture control [92].

3.5 Protein Phosphatases with Kelch-Like Domains

Protein phosphatases with Kelch-like domains (PPKL) contain tandem N-terminal kelch repeats that form β-propeller structures for protein-protein interactions [93]. Phylogenetic analysis demonstrated that the family of PPKL is encoded in the genomes of land plants, green algae, and alveolates, but not in other eukaryotic lineages [55, 93, 94]. PPKL have been described as positive regulators of brassinosteroid (BR) signaling in plants. BSU1 (brassinosteroidinsensitive 1 suppressor) was identified by activation tagging as dominant suppressor for bri1 [95] as one of the four homologous Arabidopsis PPKL genes. Interestingly, BSU1 is able to dephosphorylate both p-Thr and p-Tyr residues [96]. BSU1 inactivates BIN2 (a glycogen synthase kinase-3-like kinase) by dephosphorylating a conserved p-Tyr residue [95, 96], which consequently leads to accumulation of transcription factors BZR1 and BES1 (BZR2) [95]. BSU1 is activated by phosphorylation performed by the CDG1 (constitutive differential growth 1) cytosolic receptor-like kinase, which mediates the signaling from receptor BRI1 to BSU1 [97]. The other three related PPKL BSU1-like genes (BSL1, BSL2, and BSL3) have been implicated to perform overlapping roles in the BR signaling pathway [95–98]; however, BSL T-DNA mutants exhibit only marginal effects on BR signaling [99].

3.6 Shewanella-Like Protein Phosphatases

These phosphatases have been named based on their sequence similarity to a protein phosphatase found in the marine bacterium Shewanella. Shewanella-like phosphatases (SLP) have two members in Arabidopsis AtSLP1 and AtSLP2 [100, 101], which are insensitive to the inhibitors okadaic acid and microcystin, show differential expression pattern in plant organs, and localize in chloroplasts (AtSLP1) and cytoplasm (AtSLP2) during transient expressions [102]. The physiological functions of AtSLPs remain to be identified.

3.4

4

The PPM Family: PP2C Protein Phosphatases Since Arabidopsis PP2C phosphatases have been described in several previous reviews [103–105] and also recently [25], only the most important members and features as well as significant updates will be exemplified.

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PP2C-type phosphatases (also designated as protein phosphatase metal-ion dependent 1 (PPM1) [4]) are Mg2+/Mn2+dependent Ser/Thr phosphatases insensitive to inhibitors such as okadaic acid, microcystin, and calyculin A [105]. PP2Cs are considered to function as monomers without the need for regulatory subunits and, although structurally related, they share no sequence homology with PPPs [4]. Currently, 80 members are predicated in Arabidopsis. They are organized according to sequence similarities into 12 clusters (A–L) [25, 106]. Nine PP2Cs have been clustered in clade A [25, 104] and these PP2C phosphatases exhibit common features, like transcriptional upregulation in response to high abscisic acid (ABA). Members of this cluster are ABI1 (ABA insensitive 1) [107, 108], ABI2 (ABA insensitive 2) [109, 110], AHG1 (ABA hypersensitive germination 1) [111], PP2CA/AHG3 (ABA hypersensitive germination 3) [112], HAB1 (homology to ABI1) [113], HAB2 (homology to ABI1 2) [114], HAI1 (highly ABA-induced PP2C 1), HAI2 (highly ABA-induced PP2C 2), and HAI3 (highly ABAinduced PP2C 3) [115]. Clade A PP2Cs interact with a number of proteins involved in ABA responses, including small binding proteins from the START (steroidogenic acute regulatory protein-related lipid transfer domain) superfamily which are structurally related to pollen allergen Bet v 1 [116, 117]. These PP2C-binding proteins bind ABA and have been designated as ABA co-receptor regulatory component of ABA receptor (RCAR) [116] or ABA receptor proteins pyrabactin resistance 1 (PYR1)/PYR1-like (PYL) [117]. A trimeric complex of RCARs/PYR1(-like) proteins, ABA, and PP2Cs is formed in which the phosphatase activity is inhibited, thereby enabling autophosphorylation and transactivation of SNF1-related kinases (SnRK2s), which are acting downstream of these PP2Cs as positive regulators of ABA signaling [118–120]. SnRK2 targets include, e.g., ABA-responsive transcription factors [121–124] and ion channels required for stomatal closure [125–130]. To date all 14 RCAR/PYR/PYL members are considered to be involved in the core ABA signaling pathway [131, 132] and they interact with at least eight of the nine clade A PP2Cs to inhibit the regulation of downstream targets. Several Arabidopsis PP2C genes (termed AP2Cs) [104] with homology to Medicago sativa MP2C (a MAPK phosphatase acting on alfalfa SIMK and SAMK MAPKs) [133, 134] have been characterized as MAPK phosphatases: four clade B members harbor a kinase interaction motif (KIM) in the N-terminal part required for interaction with MAPKs [135, 136]. AP2C1 - 4 are able to dephosphorylate MPK6 in vitro that was not exemplified by A cluster PP2Cs HAB1 or ABI2 [135, 137], indicating specificity in recognition of the substrates by PP2Cs. AP2C1 and the closely related AP2C3 have been demonstrated to negatively regulate wound-, PAMP-,

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and damage-associated molecular pattern (DAMP)-activated MAPKs in planta [135, 137, 138]. Overexpression of AP2C1 affects stress-ethylene amounts, elicitor-induced gene expression, basal plant resistance, and suppresses elicitor-induced protection against Botrytis cinerea [135, 138], whereas loss of AP2C1 enhances wound-jasmonate production and herbivore resistance [135]. AP2C3 is expressed in stomata and stomata lineage cells, which differs from other AP2C expressions, and ectopic AP2C3 expression is able to induce massive initiation of stomata lineage cells and conversion of epidermal cells into stomata; however neither ap2c3 single mutant nor higher order mutants with related AP2Cs conferred a visible stomatal phenotype, indicating that other MAPK phosphatases are likely involved in this process [137]. Stomata closing is also impaired by loss of AP2C1 and AP2C3 [136]. The functions of poltergeist (POL) and poltergeist-like (PLL) PP2Cs of clade C have been assigned to plant development, where POL/PLL1 act as signaling intermediates in the CLAVATA3 (CLV3)/WUSCHEL (WUS) and CLE40/WOX5 pathways in regulation of the Arabidopsis shoot and root stem cell populations, respectively [139, 140]. The physiological functions of Arabidopsis PP2C of clade D, APDs are so far unknown. All nine members have been studied for the subcellular localization using transgenic plants ectopically expressing APD-GFP fusion proteins [141]. From clades E–G only very few PP2C members have been characterized: AtPP2C6-6 (clade E) interacts with histone acetyltransferase GCN5 in yeast and plant cells. As atpp2c6-6 mutants demonstrate enhanced histone 3 acetylation, it is possible that AtPP2C6-6 negatively regulates GCN5 activity, thereby controlling stress-responsive gene activation [142]. PIA1 (PP2C induced by AvrRpm1) and WIN2 (HopW1-1-interacting protein 2) from clade F have been implicated in plant pathogen response [143, 144]. Clade G contains AtDBP1, a DNA-binding PP2C described to mediate susceptibility to potyvirus Plum pox virus (PPV) in Arabidopsis [145]. AtDBP1 demonstrates DNA binding in vitro. 14-3-3 λ/GRF6 and MPK11 were identified by phosphoproteomics comparing dbp1 mutant with wild-type plants [146]. AtDBP1 interacts with 14-3-3 λ/GRF6 and MPK11 in transient agroinfiltration assays where it negatively regulates MPK11 activity. MPK11 mediates GRF6 phosphorylation and promotes GRF6 degradation. dbp1 [145] and grf6 plants show enhanced resistance, whereas mpk11 plants are more susceptible to PPV, suggesting the involvement of these proteins in response to PPV infection [146]. The unclustered PPH1/TAP38 (thylakoid-associatedphosphatase of 38 kDa) and PBCP (photosystem II core phosphatase) (cluster K) are antagonizing the actions of STN7 and STN8 kinases during light acclimation, respectively [147–149]. STN7 kinase is required for state transitions and mainly involved in the

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phosphorylation of the light harvesting complex (LHC) II antenna proteins; STN8 is responsible for the phosphorylation of the photosystem (PS) II core proteins. PPH1/TAP38 is required for efficient dephosphorylation of LHCII antenna proteins and state transition [147, 149] and PBCP is required for dephosphorylation of several PSII core subunits in vivo and demonstrates phosphatase activity in vitro [148].

5 The HAD Superfamily of Asp-Based Phosphatases: FCP-Like, Chronophine, and EYA Phosphatases Haloacid dehalogenase (HAD) phosphatases use aspartate-based catalysis (catalytic signature: DXDXT/V) and have only been described few years ago through CTD phosphatase-like (CPL) members with homology to FCP1 (TFIIF-associating CTD phosphatase), an RNA polymerase II CTD phosphatase in human [150]. In total 23 HAD superfamily members are predicted in Arabidopsis [3], with 19 FCP-like enzymes (divided into several groups), 3 chronophines (CINs; serine protein phosphatases involved in cofilin activation [151]), and 1 EYA member (eyes absent; first described in the context of fly eye development [152]). AtCPL1/FRY2 and AtCPL2 proteins contain an RNAPII CTD phosphatase domain (CPD) and double-stranded RNAbinding motifs (DRMs), whereas AtCPL3 and AtCPL4 have in addition to CPD a breast cancer 1 (BRCA1) C-terminal (BRCT) domain [153]. AtCPL1/FRY2 and AtCPL2 dephosphorylate Ser-5 of Arabidopsis RNA polymerase II CTD heptad repeat [154]. AtCPL1/FRY2, AtCPL2, AtCPL3, and AtCPL4 were shown to be involved in stress response and development [153–157]. AtCPL1 and possibly AtCPL2 attenuate the wound-induced expression of genes involved in jasmonic acid (JA) biosynthesis [155]. AtCPL5 contains two CPDs and regulates ABA-mediated development and drought responses [158]. Recently, AtCPL1/FRY2 was shown to be required in miRNA processing [159, 160]. Dephosphorylation of the RNA-binding protein hyponastic leaves 1 (HYL1) is required for its optimal activity in RNA processing accuracy and strand selection [160]. In cpl1 mutant lines HYL1 dephosphorylation is compromised leading to enhanced expression of miRNA targets and reduction of accurately processed miRNAs [160]. The eyes-absent proteins (EYA) constitute a family of dualfunction enzymes that are phosphatases and transcriptional coactivators [152]. EYA use an aspartate as nucleophile in a metal-dependent reaction similar to haloacid dehalogenase (HAD) family phosphatases. The Arabidopsis homolog of animal Eya, AtEYA, shows tyrosine-specific phosphatase activity in vitro [161], but its function in plants remains to be elucidated.

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Taking into account that new groups and members of plant phosphatases have been discovered and described, exciting findings in plants and progress in other eukaryotic systems [162] significantly contributed towards our understanding about the functions of protein phosphatases. Importantly, the old dogma about the proposed “housekeeping” function and “unspecific” action of these enzymes is changed as evidenced by biochemical and biological specificity of protein phosphatases. Nevertheless, many new plant (protein) phosphatases, biochemical mechanisms of their activation and regulation, and their specific roles in physiological processes remain to be characterized and incorporated into functional networks.

Acknowledgements We apologize to the readers for not covering numerous important studies and to authors, whose studies could not be mentioned in this minireview due to space limitations. This work has received funding from the Lithuanian-Swiss cooperation program to reduce economic and social disparities within the enlarged European Union under project agreement No. CH-3-ŠMM-01/10, from the Lithuanian Research Council MIP003/2014 and from the Austrian Science Fund (FWF) I255. References 1. Olsen JV, Blagoev B, Gnad F, Macek B, Kumar C, Mortensen P, Mann M (2006) Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell 127:635–648 2. Sugiyama N, Nakagami H, Mochida K, Daudi A, Tomita M, Shirasu K, Ishihama Y (2008) Large-scale phosphorylation mapping reveals the extent of tyrosine phosphorylation in Arabidopsis. Mol Syst Biol 4:193 3. Kerk D, Templeton G, Moorhead GB (2008) Evolutionary radiation pattern of novel protein phosphatases revealed by analysis of protein data from the completely sequenced genomes of humans, green algae, and higher plants. Plant Physiol 146:351–367 4. Brautigan DL (2013) Protein Ser/Thr phosphatases – the ugly ducklings of cell signalling. FEBS J 280:324–345 5. Bartels S, Gonzalez Besteiro MA, Lang D, Ulm R (2010) Emerging functions for plant MAP kinase phosphatases. Trends Plant Sci 15:322–329

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Phosphatases in Plants 134. Meskiene I, Bogre L, Glaser W, Balog J, Brandstotter M, Zwerger K, Ammerer G, Hirt H (1998) MP2C, a plant protein phosphatase 2C, functions as a negative regulator of mitogen-activated protein kinase pathways in yeast and plants. Proc Natl Acad Sci U S A 95:1938–1943 135. Schweighofer A, Kazanaviciute V, Scheikl E, Teige M, Doczi R, Hirt H, Schwanninger M, Kant M, Schuurink R, Mauch F, Buchala A, Cardinale F, Meskiene I (2007) The PP2Ctype phosphatase AP2C1, which negatively regulates MPK4 and MPK6, modulates innate immunity, jasmonic acid, and ethylene levels in Arabidopsis. Plant Cell 19:2213–2224 136. Brock AK, Willmann R, Kolb D, Grefen L, Lajunen HM, Bethke G, Lee J, Nurnberger T, Gust AA (2010) The Arabidopsis mitogenactivated protein kinase phosphatase PP2C5 affects seed germination, stomatal aperture, and abscisic acid-inducible gene expression. Plant Physiol 153:1098–1111 137. Umbrasaite J, Schweighofer A, Kazanaviciute V, Magyar Z, Ayatollahi Z, Unterwurzacher V, Choopayak C, Boniecka J, Murray JA, Bogre L, Meskiene I (2010) MAPK phosphatase AP2C3 induces ectopic proliferation of epidermal cells leading to stomata development in Arabidopsis. PLoS One 5:e15357 138. Galletti R, Ferrari S, De Lorenzo G (2011) Arabidopsis MPK3 and MPK6 Play Different Roles in Basal and Oligogalacturonide- or Flagellin-Induced Resistance against Botrytis cinerea. Plant Physiol 157:804–814 139. Song SK, Lee MM, Clark SE (2006) POL and PLL1 phosphatases are CLAVATA1 signaling intermediates required for Arabidopsis shoot and floral stem cells. Development 133:4691–4698 140. Song SK, Hofhuis H, Lee MM, Clark SE (2008) Key divisions in the early Arabidopsis embryo require POL and PLL1 phosphatases to establish the root stem cell organizer and vascular axis. Dev Cell 15:98–109 141. Tovar-Mendez A, Miernyk JA, Hoyos E, Randall DD (2014) A functional genomic analysis of Arabidopsis thaliana PP2C clade D. Protoplasma 251:265–271 142. Servet C, Benhamed M, Latrasse D, Kim W, Delarue M, Zhou DX (2008) Characterization of a phosphatase 2C protein as an interacting partner of the histone acetyltransferase GCN5 in Arabidopsis. Biochim Biophys Acta 1779:376–382 143. Lee MW, Jelenska J, Greenberg JT (2008) Arabidopsis proteins important for modulating defense responses to Pseudomonas syrin-

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Chapter 3 Phosphoproteomics in Cereals Pingfang Yang Abstract Cereals are the most important crop plant supplying staple food throughout the world. The economic importance and continued breeding of crop plants such as rice, maize, wheat, or barley require a detailed scientific understanding of adaptive and developmental processes. Protein phosphorylation is one of the most important regulatory posttranslational modifications and its analysis allows deriving functional and regulatory principles in plants. This minireview summarizes the current knowledge of phosphoproteomic studies in cereals. Key words Maize, Wheat, Rice, Barley, Cereal, Phosphoproteomics

1

Introduction Being sessile, plants are constantly exposed to various environmental stimuli. To survive despite these stimuli, plants have evolved fine mechanisms which help to maintain cellular homeostasis. Compared with the regulation through transcription and translation, protein posttranslational modifications (PTMs) occur much faster, which could help to initiate an immediate response. Because of this, studies about protein PTMs have become one major focus in scientific community. Up to date, there are more than 450 PTMs in the Uniprot database [1, 2]. Among all the PTMs, phosphorylation might be the most extensively studied one, because of its wide existence and involvement in many cellular processes. Reverse phosphorylation of proteins, which is catalyzed by kinases and phosphatases, is particularly important for cell signaling [3]. In plant biology, the studies about protein phosphorylation are mainly focused on the phosphorylation or dephosphorylation of specific proteins or protein families in particular signaling pathways [4–6] at the early stage and using rather short stimulation times. With the development of mass spectrometry and phosphopeptide enrichment technique, studies about protein phosphorylation on a large scale were widely applied to analyze different physiological

Waltraud X. Schulze (ed.), Plant Phosphoproteomics: Methods and Protocols, Methods in Molecular Biology, vol. 1306, DOI 10.1007/978-1-4939-2648-0_3, © Springer Science+Business Media New York 2015

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processes in plants [7–13]. Very recently, several studies showed the power of large-scale phosphoproteomics in mining the components of plant hormones, such as ABA and ET, signaling pathways [14–16], which attract more attention from the molecular geneticists. More and more scientists are now interested in phosphoproteomic studies. Phosphorylated proteins are a subgroup of the whole protein entity. In eukaryotic cells, protein phosphorylation usually occurs on serine (S), threonine (T), and tyrosine (Y) residues. Based on the data from Arabidopsis, the frequency of pS, pT, and pY were estimated to be 85.0 %, 10.7 %, and 4.4 %, respectively [17], which is very similar to that in human cells [18–20]. To discriminate the phosphoproteins from others, some specific fluorescent dyes, such as Pro-Q [21, 22], were developed and applied in 2-DE-based phosphoproteomics. However, this method is not sensitive enough to obtain enough also on low-abundance proteins. Since most of the phosphoproteins are of very low abundance, enrichment is necessary before mass spectrometry identification. A lot of techniques were developed, which could be divided into two different levels: the first one is conducted at protein level, and the second one is at peptide level which is conducted after the digestion of whole-cell proteins. The former one usually takes advantages of anti-phosphoprotein antibody or other affinity matrix [23]. The most popular techniques that were widely used in phosphoproteomic studies are immobilized metal affinity chromatography (IMAC) and metal oxide affinity chromatography (MOAC). Fe3+ and Ca2+ ions are usually used in IMAC [24], and TiO2 is the matrix preferred in MOAC [25]. Comparison between these two techniques showed that both of them have their advantages [25]. Sometimes, combination of IMAC and MOAC techniques could be applied to obtain more specific information [26]. Along with the advancement in techniques, a lot of phosphoproteomics studies were conducted, and an increasing number of studies are under way, which will generate large amount of data. To make this data available, a database name as PhosPhAt (http:// phosphat.mpimp-golm.mpg.de/) was launched in 2007 [27, 28], which contains not only the identified phosphopeptide information and the plant-specific phosphosite predictor function, but also the kinase target information [29]. The combination of phosphopeptide enrichment and MS techniques and bioinformatic analysis tools has driven the plant phosphoproteomic studies forward dramatically, and expanded the studies from Arabidopsis to other plants, especially crops. Among all the crops, cereals including rice, maize, and wheat are one of the most important series, and provide the staple food resources for the world population. In the last two decades, large amount of proteomic studies were conducted on these species [30–32], among which a growing number of phosphoproteomics were

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Table 1 Summary of the cereal tissues that were subjected to phosphoproteomic study

Cereal Tissue/ species organ Rice

Maize

Growth condition

Plasma 8-week-old membrane plant Suspension- Culture cultured cell Seed Germination

TiO2 enrichment, 933 phosphopeptides LTQ-Orbitrap from 413 proteins 2-DE, MALDI193 phosphoproteins TOF/TOF IMAC enrichment, 3,143 phosphopeptides LC-MS/MS

Han et al. (2014a, b)

Seed Kernel starch granule Root

Germination LC-MS/MS 16 DAP 2-DE, MALDI-TOF

Leaf

Mechanical wounding

Leaf

Literature Whiteman et al. (2010) Nakagami et al. (2010)

Pollination

Salinity

Number of identified phosphoproteins/ phosphopeptide

IMAC enrichment, 30 phosphopeptides LC-MS/MS IMAC enrichment, 6,919 phosphopeptides LTQ-Orbitrap from 3,393 proteins

Pistil

Water deficit Wheat

Identification method

776 phosphoproteins 3 phosphoproteins

2-DE, 11 phosphoproteins MALDI-TOF 2-DE (PrQ 125 phosphoproteins staining), MALDI-TOF IMAC enrichment, 1,250 phosphopeptides LC-MS/MS

Fungal TiO2 enrichment, 968 phosphopeptides inoculation LTQ-Orbitrap

Unpublished data Lu et al. (2008) Grimaud et al. (2008) Zorb et al. (2010) LewandowskaGnatowska et al. (2011) Bonhomme et al. (2012) Yang et al. (2013)

included [33]. However, compared with those in Arabidopsis, the phosphoproteomics of cereals is still far behind. In this minireview, we try to summarize the studies that have been conducted on cereals, and propose the possible experiments and challenges in future study. Details about the phosphoproteomic studies in cereals discussed below are shown in Table 1.

2

Phosphoproteomics in Rice Because of its importance in agricultural production, rice might be the most extensively studied cereals in the scientific community. Two cultivars of rice have been sequenced [34, 35], which makes it an ideal system to be studied at different levels of -omics in different varieties. More importantly, rice is a monocot, and different from the dicot Arabidopsis. Phosphoproteomics analysis will not

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only help us to obtain in-depth insights into cellular signaling and gene expression regulation in rice, but also provide very good complementary resource for the studies in Arabidopsis. 2.1 Rice Kinome and Protein Phosphorylation Site Identification

As mentioned above, protein phosphorylation is catalyzed by kinases. Exploring the kinases and identification of their substrates are the important contents in rice phosphoproteomic studies. It was reported that rice genome contains over 1,400 protein kinases [36], which is 40 % more than that in Arabidopsis [37]. In addition there are expected to be 132 protein phosphatase-coding genes [38]. Some kinases were identified as cytoplasmic proteins, and some were found as functional domains of transmembrane or membrane-associated receptors [36]. However, the majority of kinases are still with unknown functions. The kinase could be divided into seven major phylogenetic groups [38, 39], among which six are known also to exist in rice. To facilitate the studies in this community, Dardick et al. [36] constructed a rice kinase database (http://rkd.ucdavis.edu) which contains available information of kinases including expressional pattern, protein-protein interaction map, kinase motifs and conserved domains, and kinase distributions among different chromosomes. Identifying the kinases’ substrates through large-scale phosphoproteomic analysis will help to explore their functions, and hence deepen our understanding on the different signaling and regulatory pathways in rice. Nakagami et al. [12] conducted a large-scale phosphoproteomic analysis on rice suspension-cultured cells. A total of 6,919 phosphopeptides were identified from 3,393 proteins through IMAC enrichment and LTQ-Orbitrap mass spectrometer analysis (Table 1). To discover the conserved phosphorylation site among different plants, the phosphopeptides were compared with those from Arabidopsis [17] and Medicago [40]. Among the identified phosphoproteins, 5,523 phosphorylation sites and 865 proteins were suggested to be unique in rice. The ratios of phospho-Ser (pS), phospho-Thr (pT), and phospho-Tyr (pY) sites were 84.8 %, 12.3 %, and 2.9 %, respectively, which are roughly same as those in Arabidopsis and Medicago. Very recently, our group also conducted a large-scale phosphoproteome profiling in rice mature pistils (manuscript submitted to Proteomics). Another 1,369 novel phosphorylation sites were identified based on the searching against P3DB database [41, 42]. And the ratios of pS, pT, and pY were estimated to be 87, 12, and 1 % accordingly, which deviated with that in the suspension-cultured cells, and might be ascribed to the different tissues and physiological status. Moreover, based on the prediction through Motif-X [43], 41 pS motifs were extracted. If all these data were submitted to the publicly available database, it will promote the bioinformatics studies in this area dramatically.

Phosphoproteomics in Cereals

2.2 Phosphoproteomic Analysis of Rice Development and Stress Response

3 3.1

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Because of the importance of rice in world agricultural systems, a growing number of proteomic studies focusing on rice development and stress response were conducted in the last decade. A large number of proteins which are important for rice growth, development, and stress response were identified [30]. Unfortunately, most of these identified proteins are those metabolism related and other housekeeping gene encoding proteins. To obtain more indepth insights into the regulatory mechanisms underlying these physiological processes, more and more scientists have shifted their research interests to the phosphoproteome. As far as 20 years ago, some in vitro assays were applied to explore the protein phosphorylation in rice. Through this method, phosphorylation of a calreticulin protein was found to be involved in the regeneration of rice suspension cells [44, 45]. Using 2-D gel and Pro-Q staining method, Tan et al. [45] identified some chromatin-associated proteins, including H3-maize, H3.3, H2A, H2B, putative WRKY DNA-binding protein, putative retrotransposon, and transposon proteins that were regulated by phosphorylation. A phosphoproteomic analysis was also conducted on plasma membrane and vacuolar membrane in rice shoot and root [46]. Many different transporters were found to be phosphorylated, including H+ pump, ammonium transporter, sugar transporter, water transporter, and H+:Na+ antiporter. Many of these proteins share conserved phosphorylation site between rice and Arabidopsis [7, 47, 48], which may indicate conserved regulatory mechanisms for nutrient transportation. It was also found that phosphorylation of these transporters can occur tissue specifically. Very recently, our lab conducted a phosphoproteomic analysis on rice seed germination [49]. A total of 933 phosphorylated peptides corresponding to 413 proteins were detected, which is much less than in other tissues. Among them, the percentage of pY was about 5 %, a little bit higher than in other tissues. These data suggested that regulation of phosphorylation in seed might not be as extensive as in other tissues, and phosphorylation on tyrosine might be more frequent. In addition, also in rice several BR signaling pathway components were found to be regulated by the phosphorylation. Meanwhile, a 2-DE-based phosphoproteomic analysis was also conducted on the rice seed germination process [50]. A total of 193 phosphoproteins were identified, including a lot of metabolism-related proteins. These data indicated the involvement of phosphorylation in regulating the activities of different cellular enzymes.

Phosphoproteomics in Other Cereals Maize

Maize originated from Mexico [51, 52], and is one of the most extensively cultivated cereal crops. It is now also one of the most studied genetic systems [53]. In the past decades, a lot of maize proteome analyses were conducted, which have been extensively

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reviewed by Pechanova et al. [31]. Recently, several phosphoproteomics studies were performed on different tissues of maize, including seed, root, and leaf. Lu et al. [54] conducted a shotgun phosphoproteomic analysis on the embryo of germinated maize seed. In their study, 37 protein kinases and 16 phosphatases were identified. The expression for most of the kinase-encoded genes was gradually increased. On the contrary, expression of the phosphatases was inhibited during germination, and increased in post-germination processes. These data indicate the involvement of phosphorylation also during regulatory processes during maize seed germination. Seeds are not only important material in maize production, but also the final product. Starch biosynthesis and accumulation can determine the economic value of maize. In order to explore the regulatory mechanism underlying starch biosynthesis, Grimaud et al. [55] analyzed the starch granule-associated phosphoproteome. Results showed that granule-bound starch synthase GBSSI, starch-branching enzyme (BE) BEIIb, and starch phosphorylase might be regulated by phosphorylation. In a salt treatment experiment, Zorb et al. [56] found that phosphorylation of fructokinase, UDP-glucosyl transferase BX9, and 2-Cys-peroxiredoxin were enhanced, whereas an isocitrate-dehydrogenase, calmodulin, maturase, and a 40-S-ribosomal protein were dephosphorylated. Maize is also a representative of C4 plants, and always used as model system to study the differentiation of C4 photosynthetic apparatus [57]. Because of this, maize leaves are the major focus in both proteomic and phosphoproteomic studies. A comprehensive proteomic and phosphoproteomic study was conducted on maize leaf at different developmental stages [58]. Comparison between the proteome and phosphoproteome suggested that phosphorylation of proteins plays a very important role in the transition of different developmental stages. Specifically, they found that the transition from proliferative cell division to cellular differentiation is tightly regulated by the phosphorylation of cell wall metabolismrelated enzymes. Some maize-specific phosphorylation sites were also identified. In another study, researchers found that water deficit could affect the phosphorylation status of proteins involved in epigenetic control, gene expression, cell cycle-dependent processes, and phytohormone-mediated responses, while the abundance of these proteins remained unaffected [59]. Because of its large size, the maize leaf is easy to be mechanically wounded, which commonly exists during the pathogen infection and herbivore attack. In a phosphoproteomic analysis on maize leaf responding to mechanical wounding, a series of signaling-related proteins were identified to be subjected to the changes in phosphorylation status [60]. All these studies suggested that the phosphoproteomic studies will help to obtain more indepth insights into the mechanisms underlying different biological processes in maize.

Phosphoproteomics in Cereals

3.2 Wheat and Others

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Wheat is also among the most important cereal crops all over the world [61]. Although the genome of wheat is not fully sequenced due to its polyploid character, the whole-genome shotgun sequencing was conducted [62]. Even before the initiation of its genome sequencing, a large amount of proteomic analyses were carried out on this cereal crop either studying different developmental stages or analyzing responses to different abiotic or biotic stresses. Compared with the work in rice and maize, there is hardly any reported phosphoproteomic analysis on this species yet. Very recently, a combination of proteomic and phosphoproteomic analyses was conducted to explore the signal communication between wheat and its fungal pathogen Septoria tritici [63]. The authors found that regulations on the phosphorylation status of a series of proteins are basic requests for wheat to respond to fungal pathogens. These identified proteins are mainly involved in signal transduction, transport, regulation of transcription and translation, and cell component organization of including receptor-like kinase, other protein kinases, transcription factors, ATPases, sugar transporters, and nuclear proteins. Specifically, the phosphorylation of some proteins related to protein biosynthesis, proteolysis, folding, and stress response was suggested to enhance the pathogen resistance in wheat. Brachypodium distachyon is a member of the Pooideae subfamily, and provides some advantages: a small genome of 272 Mb, short life cycle, and it is easy to be grown and transformed [64, 65]. These properties make it a newly emerged model plant for wheat, barley, and other cereals and grasses. Recently, its genome has been successfully sequenced [66], which provided valuable resources for proteomic studies. In a phosphoproteomic study on this plant in response to salt stress, a total of 2,839 phosphorylation sites corresponding to 1,509 phosphoproteins were identified [67]. Among them, 496 sites from 468 proteins, including many defensive and signaling proteins such as 14-3-3 protein, ABF2, TRAB1, and SAPK8, experience significant changes on their phosphorylation status in response to the stress. These data provide not only new insight into the plant salt response mechanism, but also complementary information for those studies in other cereals and dicots.

Conclusion Remarks Cereals are the major food resources for the people all over the world, and have been domesticated for a long period of time. Based on the selection criteria, they must have their advantageous features in either growth and development or coping with different environmental stresses, which is also the aim of crop breeding. To better understand the molecular mechanism underlying these

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advantageous, it is critical to explore the signal transduction pathways related to these processes, which is always mediated by a cascade of protein phosphorylation and dephosphorylation. Phosphoproteomics has been shown to be powerful in identification of signaling components. With the completion of genome sequencing in rice and maize, wheat genome sequencing is also under way. This will facilitate the phosphoproteomic studies in these species, because the phosphopeptide identification and phosphosite determination both heavily depend on the genome information. As described above, rice genome, the smallest one among all crop cereals, contains more kinase- and phosphatase-encoded genes than Arabidopsis, which indicate more complicated regulation on protein phosphorylation in cereals. Unfortunately, comparing with that in Arabidopsis, phosphoproteomics in cereals is still at its infancy stage. Furthermore, most of the phosphoproteomic analyses are focusing on specific developmental or stress response processes; the basic information about the kinome, kinase/phosphatase substrate network, and phosphor-motifs are still absent in the cereals. In the coming years, most of the work might still focus on colleting basic information for the phosphoproteomics study. On the other hand, combination analyses of mutants with specific phenotypes and phosphoproteome will also be of major interest in the community.

Acknowledgements The author is grateful to those colleagues whose nice work has been cited here, and sorry to those colleagues whose studies are not cited because of space limit. The National Natural Science Foundation of China (NSFC) provided funding support to my work (31271805). References 1. Consortium, T.U. (2012) Reorganizing the protein space at the Universal Protein Resource (UniProt). Nucleic Acids Res 40:D71–D75 2. Consortium, T.U. (2013) Controlled vocabulary of posttranslational modifications (PTM) ptmlist.txt. Available at: http://www.uniprot. org/docs/ptmlist. Release: June 2013 3. De la Fuente van Bentem S, Hirt H (2007) Using phosphoproteomics to reveal signalling dynamics in plants. Trends Plant Sci 12: 404–411 4. Camoni L, Iori V, Marra M, Aducci P (2000) Phosphorylation-dependent interaction

between plant plasma membraneH(+) ATPase and 14-3-3 proteins. J Biol Chem 275: 99919–99923 5. Hrabak EM, Chan CW, Gribskov M, Harper JF, Choi JH, Halford N et al (2003) The Arabidopsis CDPK-SnRK superfamily of protein kinases. Plant Physiol 132:666–680 6. Wang X, Goshe MB, Sonderblom EJ, Phinney BS, Kuchar JA, Li J et al (2005) Identification and functional analysis of in vivo phosphorylation sites of the Arabidopsis BRASSINOSTEROID-INSENSITIVE1 receptor kinase. Plant Cell 17:1685–1703

Phosphoproteomics in Cereals 7. Niittyla T, Fuglsang AT, Palmgren MG, Frommer WB, Schulze WX (2007) Temporal analysis of sucrose-induced phosphorylation changes in plasmamembrane proteins of Arabidopsis. Mol Cell Proteomics 6: 1711–1726 8. Li H, Wong WS, Zhu L, Guo HW, Ecker J, Li N (2009) Phosphoproteomic analysis of ethylene-regulated protein phosphorylation in etiolated seedlings of Arabidopsis mutantein2 using two-dimensional separations coupled with a hybrid quadrupole time-of-flight mass spectrometer. Proteomics 9:1646–1661 9. Reiland S, Messerli G, Baerenfaller K, Gerrits B, Endler A, Grossmann J et al (2009) Largescale Arabidopsis phosphoproteome profiling reveals novel chloroplast kinase substrates and phosphorylation networks. Plant Physiol 150: 889–903 10. Chen Y, Hoehenwarter W, Weckwerth W (2010) Comparative analysis of phytohormoneresponsive phosphoproteins in Arabidopsis thaliana using TiO2-phosphopeptide enrichment and mass accuracy precursor alignment. Plant J 63:1–17 11. Kline KG, Barrett-Wilt GA, Sussman MR (2010) In planta changes in protein phosphorylation induced by the plant hormone abscisic acid. Proc Natl Acad Sci U S A 107: 15986–15991 12. Nakagami H, Sugiyama N, Mochida K, Daudi A, Yoshida Y, Toyoda T et al (2010) Largescale comparative phosphoproteomics identifies conserved phosphorylation sites in plants. Plant Physiol 153:1161–1174 13. Engelsberger WR, Schulze WX (2011) Nitrate and ammonium lead to distinct global dynamic phosphorylation patterns when resupplied to nitrogen-starved Arabidopsis seedlings. Plant J 69:978–995 14. Wang P, Xue L, Batelli G, Lee S, Hou YJ, Oosten MJV et al (2013) Quantitative phosphoproteomics identifies SnRK2 protein kinase substrates and reveals the effectors of abscisic acid action. Proc Natl Acad Sci U S A 110:11205–11210 15. Umezawa T, Sugiyama N, Takahashi F, Anderson JC, Ishihama Y, Peck SC, Shinozaki K (2013) Genetics and phosphoproteomics reveal a protein phosphorylation network in the abscisic acid signaling pathway in Arabidopsis thaliana. Sci Signal 6:rs8 16. Yang Z, Guo G, Zhang M, Liu CY, Hu Q, Lam H et al (2013) Stable isotope metabolic labeling-based quantitative phosphoproteomic analysis of Arabidopsis mutants reveals ethylene regulated time-dependent phosphopro-

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Chapter 4 Screening of Kinase Substrates Using Kinase Knockout Mutants Taishi Umezawa Abstract Protein kinases are widely known to be major regulators of various signaling processes, particularly in eukaryotes, including plants. To understand their role in signal transduction pathways, it is necessary to determine which proteins are phosphorylated by these enzymes. Recent studies have applied a comparative phosphoproteomic approach to identify protein kinase substrates in plants. The results demonstrated that kinase knockout mutants are useful for screening protein kinase substrates via such a comparative analysis. Here some technical points are described for the experimental design and comparative analysis using kinase knockout mutants. Key words Knockout mutant, Plant, Phosphoproteomics, Protein kinase, Signal transduction

1

Introduction Protein kinases are enzymes that phosphorylate other proteins and are widely accepted as versatile mediators of cellular signaling. In plants, the protein kinase superfamily comprises over 1,000 members, corresponding to 1–2 % of all functional genes in the plant genome and suggesting their importance to plant life [1]. To date, multiple studies have demonstrated that protein kinases are involved in many regulatory cellular processes, including developmental processes and hormonal, stress, and defense responses to name a few. Therefore, the study of protein kinases may facilitate a more thorough understanding of many biological processes in plants. To understand how a protein kinase transduces intracellular signals, it is necessary to understand its signal transduction pathway consisting of upstream and downstream regulatory systems. Although each protein kinase is involved in a wide variety of upstream pathways, their downstream pathway seems to be relatively simple because it is usually initiated by phosphorylation of their respective “substrates.” However, it is still difficult to determine specific substrates of protein kinases because a typical kinase can recognize

Waltraud X. Schulze (ed.), Plant Phosphoproteomics: Methods and Protocols, Methods in Molecular Biology, vol. 1306, DOI 10.1007/978-1-4939-2648-0_4, © Springer Science+Business Media New York 2015

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multiple substrates and also because there are technical limitations that prevent their comprehensive analysis. Previous studies have utilized multiple methods to identify protein kinase substrates one by one. If a biological linkage between protein kinases and other proteins were to be identified, it would be a good indication of a protein kinase-substrate pair. However, performing this type of study, even for a single kinase, is usually time consuming; therefore, it is impossible to use this as a comprehensive approach for all protein kinases and substrates. According to one estimate, more than 70 % of human proteins are modified by phosphorylation, suggesting that one protein kinase can phosphorylate over 200 substrates on an average [2]. Therefore, the cellular protein phosphorylation network may be much larger than what is expected, and a systems approach will be required for its understanding. To date, several methods have been proposed to survey protein kinase substrates on a large scale. For example, protein microarrays and synthetic peptide libraries are used for predicting protein kinase substrates [3, 4]. Although these methods are based on in vitro kinase reactions, in vivo protein phosphorylation can be measured by phosphoproteomics, a recently developed large-scale analysis of phosphoproteins/peptides. An example of one such technique is the shot-gun analysis in combination with an efficient method for phosphopeptide enrichment, e.g., metal oxide chromatography (MOC) or immobilized metal affinity chromatography (IMAC), and a high-performance and high-accuracy LC-MS/MS system [5, 6]. Such technical development enabled the analysis of thousands of phosphoproteins and their in vivo dynamics, thus providing useful information to predict protein phosphorylation networks involved in a variety of signaling pathways [7, 8]. Furthermore, such a shot-gun phosphoproteomics technology has recently begun to be applied to screening for protein kinase substrates. For example, several recent studies determined specific protein kinase substrates by comparative phosphoproteomic analysis between different biological samples [9–13]. In comparative experiments, experimental design is critical for obtaining optimal results. Therefore, it is imperative to find a suitable condition under which protein phosphorylation is significantly changed between samples. For example, protein kinase inhibitors are often used to inhibit protein phosphorylation catalyzed by a protein kinase in mammals or yeast. However, such inhibitors are largely unavailable in plants. Instead, we can use knockout mutants, transgenic lines, various natural variations, or cultivars in which protein kinase-mediated phosphorylation is significantly blocked or enhanced (Fig. 1). Furthermore, it is necessary to have advanced information regarding the protein kinase under study, such as its biological function, the condition(s) that activate it, and the time course of when it is activated in plant cells. Therefore, the first step in developing these studies should be to collect such information (Fig. 2).

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substrates (?) Fig. 1 A basic strategy of phosphoproteomics using a kinase knockout mutant. In this case, a stimulus induces phosphorylation of proteins A, B, and C, and a protein kinase (PKase) phosphorylates A and B. Comparative phosphoproteomic analysis is able to detect that phosphorylation of A and B are impaired or depressed in a kinase knockout mutant. Red circle shows a phosphate group

1) Biological functions of PKase

2) Biochemical characterization Time course

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Fig. 2 Experimental design of a phosphoproteomic study using a kinase knockout mutant. It is important to collect multiple information of a protein kinase in advance, for example (1) biological function or (2) biochemical characterization. (1) Biological function may be determined by loss-of-function or gain-of-function analyses (e.g., what kind of stimuli can activate a protein kinase?). (2) Biochemical characterization includes a method by which to detect protein kinase activity, when it is activated, and so on. (3) Experiments should be designed based on such information of a protein kinase

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For example, the dynamics of kinase activity should be important for identifying protein kinase substrates. Prior to phosphoproteomic analysis, we must determine when we can observe protein phosphorylation mediated by the target kinase. Since protein kinase activity is often informative to determine a time course, it should be determined when the target protein kinase is activated in vivo. This can be performed with one of several available methods to measure protein kinase activity; for example, in-gel phosphorylation or immunoprecipitated kinase assays. Once some candidate phosphopeptides are identified by comparative phosphoproteomic analysis, the next step should be a validation of their biological significance (Fig. 3). First, it is essential to confirm whether the phosphoprotein is an actual substrate of the protein kinase. Then, the following characteristics should be determined: (1) whether the phosphoprotein is involved in signal transduction pathways, (2) whether it is a positive or negative regulator of biological responses, and (3) how the phosphorylation affects protein functions. Such experiments may take a long time to complete and require multiple different techniques. However, they are necessary to fully utilize phosphoproteome data. In other words, phosphoproteome data is just a catalog of phosphoproteins unless their importance is biologically confirmed. In this chapter, we describe a protocol for phosphoproteomic analysis using kinase knockout mutants of Arabidopsis and our recent Wild type

PKase KO

Crude extracts Trypsin digestion Phosphopeptide enrichment Phosphosites

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Peptide ID

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(Motif analysis)

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Fig. 3 A scheme of comparative phosphoproteomic analysis using a kinase knockout mutant. After screening of phosphopeptides by differential analysis, functional analysis of phosphoproteins should be important to verify phosphoproteomic data

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study as a model case [12]. In our study, we used a triple-knockout mutant of closely related SnRK2 protein kinases to avoid their functional redundancy in plants [12, 14]. We employed a label-free quantitation of phosphopeptides enriched by hydroxyl acid-modified metal oxide chromatography (HAMMOC) [15]. In the triple mutant, SnRK2-mediated protein phosphorylation in response to ABA was clearly impaired, allowing us to identify substrate candidates that were differentially regulated between wild-type and mutant strains. Thus, we believe that a phosphoproteomic study using kinase knockout mutants represents a powerful tool with which to identify protein kinase substrates. Such a study will allow us to bring new insights to aid in the exploration of signaling pathways in plants.

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Materials With the exception of plant medium, prepare all solutions using ultrapure water and use reagents with the highest purity or grade. Reagents are purchased from Sigma unless otherwise noted.

2.1 Sample Preparation

1. Petri dish: 90 mm Ø × 20 mm. 2. Germination medium (GM): For 1 L of the medium, add 1 pack of MS salt mix for 1 L (392-00591, Wako), 10 g sucrose, 0.5 g MES, and 1 mL Gamborg’s Vitamin B5 solution to 1 L of deionized water. Adjust the pH to 5.8 by 1 N KOH. For agar plates, 0.8 g bactoagar was added prior to autoclaving, and then pour it into petri dishes. 3. 0.5× GM solution. 4. 100 mM ABA solution: Dissolve 26.4 mg (±)-ABA in 1 mL ethanol and store at −20 °C. 5. Extraction buffer: 50 mM HEPES pH 7.5, 5 mM EDTA, 5 mM EGTA, 1 mM Na3VO4, 25 mM NaF, 50 mM ß-glycerophosphate, 10 % glycerol, 2 mM dithiothreitol (DTT), and proteinase inhibitor cocktail (see Note 1). 6. Protein quantification reagents (e.g., Bio-Rad Protein Assay). 7. Liquid nitrogen. 8. Mortar and pestle. 9. Centrifuge with an angle rotor for 50 mL tubes. 10. 50 mL centrifuge tubes. 11. Miracloth. 12. 1.5 mL tubes.

2.2 Phosphoproteomic Analysis

1. Reduction buffer (prepare just before use): 10 mM DTT in 50 mM NH4HCO3. 2. Alkylation buffer (prepare just before use): 50 mM iodoacetamide in 50 mM NH4HCO3.

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3. Lysyl endopeptidase (LysC), MS grade. 4. Trypsin, MS grade. 5. C18 Stage-Tip (see Note 2). 6. HAMMOC Stage-Tip (see Note 2). 7. Solution A: 5 % acetonitrile (ACN) and 0.1 % trifluoroacetic acid (TFA). 8. Solution B: 80 % ACN and 0.1 % TFA. 9. Solution C: 300 mg/mL lactic acid in solution B. 10. 2 % TFA. 11. 10 % TFA. 12. 0.5 % Piperidine. 13. SpeedVac. 14. LC-MS/MS system. 15. Database engine (e.g., Mascot). 16. Quantitative proteomics software package (e.g., MaxQuant, MassNavigator). 17. Spreadsheet software (e.g., Excel). 2.3 In Vitro Phosphorylation Assay

1. Expression vector: For example pGEX-4T-3 (see Note 3). 2. Host: For example E. coli BL21(DE3) (see Note 4). 3. 2× YT medium: Dissolve 16 g Bacto Tryptone, 10 g Bacto Yeast Extract, and 5 g NaCl in 800 mL H2O. Adjust pH to 7.2 with 1 N NaOH, and then make up the volume up to 1 L with H2O. Sterilize by autoclaving. Add antibiotics after cooling. 4. 1 M IPTG: Dissolve 238 mg IPTG in 10 mL H2O. Sterilize by filtration and store at −20 °C. 5. Glutathione Sepharose 4B (GE Healthcare). 6. Phosphate-buffered saline (PBS): Dissolve 8 g NaCl, 0.2 g KCl, 1.44 g Na2HPO4, and 0.24 g KH2PO4 in 800 mL H2O. Adjust pH to 7.5 with 1 N HCl, and then make up the volume to 1 L with H2O. 7. Tris-buffered saline (TBS): Dissolve 6.05 g Tris–HCl and 8.76 g NaCl in 800 mL of H2O. Adjust pH to 7.5 with 1 N HCl, and then make up the volume to 1 L with H2O. 8. Protease inhibitor cocktail (Roche). 9. Sonicator. 10. 2× sample loading buffer. 11. SDS-PAGE system. 12. 10× reaction buffer: For 100 μL, mix 50 μL of 1 M Tris–HCl (pH 7.5), 10 μL of 1 M MgCl2, 10 μL of 1 M MnCl2, 0.5 μL of 100 mM ATP, and 29.5 μL of H2O.

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13. [γ-32P]-ATP (185 MBq/mL, Perkin Elmer). 14. Phosphorimager.

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Methods Carry out all procedures at room temperatures unless otherwise specified.

3.1 Sample Preparation

Before starting the experiments, choose a strategy for MS data quantification, e.g., label free or others. In this chapter, label-free quantification is described as an example. If a different quantification method such as stable isotope labeling is chosen, additional procedures for sample preparation may be needed (see Chapter 6). 1. Sow 25–30 sterilized seeds on each GM agar plate. After 3 days at 4 °C in the dark, put the plates in a growth chamber at 21 °C on a 16-h light/8-h dark photoperiod. 2. Use 2–3-week-old seedlings for the treatment (see Note 5). 3. Store plant samples at −80 °C until use. 4. Grind 1–2 g samples to a fine powder with a mortar and pestle in liquid nitrogen. 5. Homogenize samples in 5–10 mL ice-cold extraction buffer. 6. After filtration through three layers of Miracloth, centrifuge at 8,000 × g at 4 °C for 20 min. 7. Transfer supernatant to a new tube. 8. Measure protein concentration of the extract. 9. Store at −80 °C until use.

3.2 Phosphoproteomic Analysis

This section is adapted from [17]. 1. Prepare aliquots of the extract containing 100–500 μg protein. The amount of protein, which depends on the tissues, cell type, or species, should be determined in advance. 2. Add 1 μL reduction buffer to every 50 μg protein and incubate for 30 min at room temperature. 3. Add 1 μL alkylation buffer to every 50 μg protein and incubate for 20 min at room temperature in the dark. 4. Digest proteins by LysC (1 μg for every 50 μg protein) for over 3 h at room temperature. 5. Dilute the digest with 4 volumes of 50 mM NH4HCO3. 6. Digest proteins by trypsin (1 μg for every 50 μg protein) overnight at room temperature. 7. Acidify the tryptic digests with an equal volume of 2 % TFA.

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8. Prepare a C18 Stage-Tip (1 mL) (see Note 2). 9. (Conditioning) Load 200 μL Solution B onto the Stage-Tip, and spin down at 1,000 × g for 2 min. 10. (Conditioning) Load 200 μL Solution A onto the Stage-Tip, and spin down at 1,000 × g for 2 min. 11. (Sample loading) Load the acidified sample onto the StageTip, and spin down at 1,000 × g for 2 min. 12. (Washing) Load 200 μL solution A onto the Stage-Tip, and spin down at 1,000 × g for 2 min. 13. Place the Stage-Tip on a new tube. 14. (Elution) Load 200 μL Solution B onto the Stage-Tip, and spin down at 1,000 × g for 2 min. 15. Dry the sample with a SpeedVac. 16. Resuspend the peptides in 200 μL Solution C. 17. Prepare a HAMMOC Stage-Tip (10 μL) (see Note 2). 18. (Conditioning) Load 20 μL Solution B onto a HAMMOC Stage-Tip, and spin down at 1,500 × g for 2 min. 19. (Conditioning) Load 20 μL Solution C onto the Stage-Tip, and spin down at 1,500 × g for 2 min. 20. Place the Stage-Tip on a new tube. 21. (Sample loading) Load the sample onto the Stage-Tip, and spin down at 1,500 × g for 4 min. 22. (Washing) Load 20 μL Solution C onto the Stage-Tip, and spin down at 1,500 × g for 2 min. 23. (Washing) Load 20 μL Solution B onto the Stage-Tip, and spin down at 1,500 × g for 2 min. 24. (Elution) Load 20 μL 0.5 % piperidine, and spin down at 1,500 × g for 2 min. Repeat this step twice. 25. Add 5 μL 10 % TFA to the eluate. 26. Prepare a C18 Stage-Tip (10 μL) (see Note 2). 27. (Conditioning) Load 20 μL Solution B onto the Stage-Tip, and spin down at 1,500 × g for 2 min. 28. (Sample loading) Load the sample onto the Stage-Tip, and spin down at 1,500 × g for 2 min. 29. (Washing) Load 20 μL Solution A onto the Stage-Tip, and spin down at 1,500 × g for 2 min. 30. (Elution) Load 20 μL Solution B onto the Stage-Tip, and spin down at 1,500 × g for 2 min. 31. Dry the sample with a SpeedVac. 32. Resuspend the desalted peptides in 10 μL solution A. 33. Analyze the samples by LC-MS/MS (see Note 2).

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34. Perform a database search to predict peptide IDs and phosphorylation sites. 35. Quantify each phosphopeptide using extracted ion current chromatogram. Some data analysis software for label-free quantitation is required, for example, MaxQuant and MassNavigator. 36. Calculate fold changes of phosphopeptide levels between samples. 37. Set the first criteria for screening of phosphopeptides that are responsive to the treatment. All phosphopeptides should be tested in this process (see Note 6). 38. Set the second criteria for screening of protein kinase substrate candidates. Phosphopeptides that met the first criteria should be tested (see Note 7). 39. After the second screening, list the selected phosphopeptides as candidates of protein kinase substrates. 40. (Optional) Analyze some specific phosphorylation motifs for further classification. Motif groups can be generated using some algorithms, such as Motif-X (http://motif-x.med. harvard.edu/) [16]. 41. (Optional) Compare quantitative data of each motif group and predict the target motif(s) of your protein kinase. 3.3 In Vitro Phosphorylation Assay

1. Prepare plasmid constructs in which a cDNA of your protein kinase gene is cloned into an appropriate expression vector. 2. Prepare plasmid constructs in which a cDNA of a fragment of a substrate gene is cloned into an expression vector. You can use a short fragment or a synthetic peptide containing phosphorylation site(s) when such information is known. 3. Introduce a mutation to the phosphorylation site in the substrate gene by site-directed mutagenesis. For example, change Ser/Thr to Ala. That mutation enabled us to confirm the phosphorylation site (see Note 8). 4. After transformation of E. coli, pick three colonies for subsequent experiments. 5. Culture E. coli in 2× YT and induce protein expression by adding 0.1–1 mM IPTG according to a standard protocol (see Note 9). 6. Prepare purified recombinant proteins of protein kinase or substrate in E. coli according to a standard protocol (see Note 9). 7. Measure protein concentration. 8. Prepare reaction mix on ice by mixing 0.1–1 μg protein kinase, 1 μg substrate proteins, 1 μL of 10× reaction buffer, and 0.5 μL of [γ-32P]-ATP. Then make up the volume to 10 μL with

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H2O. For multiple reactions, you can make a premix solution without proteins. 9. Incubate reaction mix at 30 °C for 30 min (see Note 10). 10. Take 10 μL aliquots and mix with 10 μL of 2× sample loading buffer. 11. Load 20 μL of the reaction mix onto an SDS-PAGE. 12. Wash the gel twice in 100 mL H2O at room temperature for 10 min. 13. (Optional) Incubate the gel in 100 mL 5 % glycerol at room temperature for 15 min. 14. Dry the gel. 15. Detect radioisotope activity by autoradiography.

4

Notes 1. It is necessary to optimize the composition of buffer depending on the samples. You must take into consideration tissue type(s), cellular localization, plant species, etc. 2. See Nakagami et al. [17]. 3. You can use any vector for expression in E. coli. 4. You can use any host strain optimized to your expression vector. 5. In the case of ABA treatment, pull out seedlings from agar plates. Do not damage roots or leaves. Place them on a new petri dish with 10 mL 1/2 GM solution overnight. Change the solution to 10 mL 1/2 GM solution containing 50 μM ABA. Collect seedlings after 0, 15, 30, and 90 min with three biological replicates for each time point. Remove water from plants, and put them rapidly into liquid nitrogen. Store at −80 °C. 6. In our case, we selected phosphopeptides which showed >3-fold upregulation in response to ABA, in at least two of the three replicates. 7. In our case, the second criteria was 98 %) of incorporation into proteins. It is necessary to determine the level of incorporation and the labeling efficiency on the experimental data. This is generally done by fitting the measured isotope patterns to the theoretical isotope patterns of the 15N-labeled peptides for incrementally increasing levels of 15N incorporation, say 97, 97.5, 98, 98.5, and 99 %. Smaller increments can be used if a more precise estimate is desired. The best fit with the highest mean or median Pearson correlation is taken as an estimate of the labeling efficiency in the experiment. This can be done with Mascot Distiller for all identified phosphopeptides. 1. Open the previously completed project. 2. Under the “Tools” tab open the format options and proceed to the quantitation options. Click the “All Options” button, in the new window, open the “Components” tree, and continue through “Components,” “Isotopes,” “Corrections” to “Value” at the lowest level. This is the assumed percent of 15N incorporation used to calculate the theoretical peptide isotope patterns that are automatically fit to the measured isotope patterns for each peptide by Mascot Distiller, producing the correlation entry in the peptide lists, and is default set to 99.0. 3. Determine the mean or median correlation dependent on the distribution of the data and the presence of outliers for all PSMs in the table_matches results export.

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4. Change the default value from 99.0 to 97.0 %. Perform peptide quantification on the same data with the new setting. Export the results; determine the mean or median Pearson correlation for the new value. 5. Repeat step 4 incrementally increasing the assumed value of 15 N incorporation depending on the desired precision of the estimate. The value giving the highest Pearson correlation between the measured and theoretical isotope patterns can be used as an estimate for the actual incorporation of the heavy nitrogen isotope and the labeling efficiency in the experiment.

4

Notes 1. We use phenol-resistant 175 ml conical bottom polypropylene copolymer (PPCO) Nalgene® centrifuge bottles. 2. This is a critical step. Avoid transfer of Al(OH)3 chromatography medium to the Amicon filter unit as residual Al(OH)3 will impede downstream analyses (e.g., isoelectric focusing, protein digestion). 3. We use Eppendorf® Protein LoBind 1.5 ml microcentrifuge tubes as this significantly increased the yield of enriched phosphoproteins. 4. Fine dispersal of the phosphoprotein pellet is important to dissolve all residual Al(OH)3 in the acidic washing buffer.

Acknowledgments We thank present and former colleagues of our labs, especially Dennis Hopkins, Petra Majovsky, Bastian Minkenberg, Matthias Nagler, and Ella Nukarinen, who contributed to the development and optimization of the strategies described in this manuscript. References 1. Nakagami H, Sugiyama N, Ishihama Y et al (2012) Shotguns in the front line: phosphoproteomics in plants. Plant Cell Physiol 53:118–124 2. Hoehenwarter W, Thomas M, Nukarinen E et al (2013) Identification of novel in vivo MAP kinase substrates in Arabidopsis thaliana through use of tandem metal oxide affinity chromatography. Mol Cell Proteomics 12:369–380 3. Molina H, Horn DM, Tang N et al (2007) Global proteomic profiling of phosphopeptides using electron transfer dissociation tandem mass spectrometry. Proc Nat Acad Sci U S A 104:2199–2204

4. Cox J, Matic I, Hilger M et al (2009) A practical guide to the MaxQuant computational platform for SILAC-based quantitative proteomics. Nat Protoc 4:698–705 5. Steen H, Jebanathirajah JA, Springer M et al (2005) Stable isotope-free relative and absolute quantitation of protein phosphorylation stoichiometry by MS. Proc Nat Acad Sci U S A 102:3948–3953 6. Arsova B, Kierszniowska S, Schulze WX (2012) The use of heavy nitrogen in quantitative proteomics experiments in plants. Trends Plant Sci 17:102–112

Chapter 7 Kinase Activity and Specificity Assay Using Synthetic Peptides Xu Na Wu and Waltraud X. Schulze Abstract Phosphorylation of substrate proteins by protein kinases can lead to activation or inactivation of signaling pathways or metabolic processes. Precise understanding of activity and specificity of protein kinases are important questions in characterization of kinase functions. Here, we describe a procedure to study kinase activity and specificity using kinase-GFP complexes purified from plant material and synthetic peptides as substrates. Magnetic GFP beads allow purifying receptor-like kinase-GFP complexes from microsomal fractions. Kinase-GFP complexes are then incubated with ATP and the synthetic peptides for kinase reaction. Phosphorylation of substrate peptides is then identified and quantified by mass spectrometry. Key words Microsome, Magnetic GFP beads, Synthetic peptides, Mass spectrometry, Kinase substrates

1

Introduction Protein kinases are key regulators of cell function that are involved in many cellular signaling pathways and metabolic processes by modulation of phosphorylation of the respective target proteins. Kinases can add a phosphate group from ATP to serine, threonine, or tyrosine of substrate proteins according to the following chemical equation: ATP + substrate protein + protein kinase → phosphorylated substrate protein + ADP + protein kinase. Particularly also in plants phosphorylation of unusual amino acids such as histidine, aspartate [1], or arginine [2] can also be observed. However, observation of these rather instable phosphorylation sites requires special means in detection which are not part of the described protocol. The importance of these protein phosphorylation reactions for various cellular processes is underlined by a very high fraction of protein kinases encoded in plant genomes [3]. Upon phosphorylation of target proteins, many biological processes such as metabolic pathways, kinase cascade activations, and membrane transport or gene transcription can be modulated. Thereby, each kinase has more or less specific preferences for the

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substrate proteins to be phosphorylated. Consequently, the study of protein kinase activities and their substrate specificities is very important for unraveling protein kinase functions. Particularly in plants, although quite a number of protein kinases have been studied regarding their kinase activities and specificities, the functions of many plant kinases remain unclear [4]. The most common method to be used for measuring protein kinase activity is to expose purified protein kinase to its target protein or peptide substrates and radiolabeled [γ-32P]-ATP [5], and phosphorylation of radiolabeled substrate proteins or peptides was analyzed by SDS-PAGE [6–8]. Alternatively, phosphorylation of specific residues in the substrate proteins can be analyzed by phospho-specific antibodies. In large-scale approaches, incorporation of radioactive ATP to immobilized protein or peptide arrays revealed new insights into kinase-substrate relationships [9] and kinase specificity [10]. However, due to increasing cost of its disposal for [γ-32P]-ATP and limitations of antibodies particularly for plant proteins, many of other nonradiometric fluorescence methods were created to measure the activity of protein kinases, such as homogeneous time-resolved fluorescence (HTRF) [11, 12] and fluorescence polarization (FP) [11, 13]. Both methods need unlabeled or labeled antibodies and the distance between donor and acceptor fluorophores (for HTRF) or the length of peptide substrate (for FP) needs to be considered [11]. More recently in the past two decades, as quantitative mass spectrometry-based proteomic methods became more popular and accessible, detection of phosphorylation by mass spectrometry became feasible. Thus, purified kinases can easily be exposed to the substrate protein or substrate peptide mixtures and phosphorylation can be detected by mass spectrometry. Such mass-spectrometry-based kinase activity assays were applied to pyruvate dehydrogenase kinase by using a mixture of synthetic peptides as substrates [14]. In general, peptide-based kinase assays using immobilized peptides are frequently used [15–17]. In contrast to these peptide filter or peptide slide-based methods, the protocol described here uses synthetic peptide-based kinase assays in solution combined with mass spectrometry for detection and quantitation of phosphorylation. We have already applied this method to measure kinase specificity of a receptor-like kinase SIRK1 [15]. Here, we describe such a peptide-based kinase assay starting from preparation of microsomal proteins using a transgenic line with overexpression of a receptor-like kinase-GFP fusion and purification of protein-GFP complexes over GFP magnetic beads to the conduction of synthetic peptide-based kinase assay in solution and data analysis by mass spectrometry.

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Materials

2.1 Buffers and Solutions

1. Isolation buffer (homogenization buffer): 330 mM Sucrose, 100 mM KCl, 1 mM EDTA, 50 mM Tris-MES, pH 7.5. Weigh out 113 g sucrose, 7.5 g KCl, 0.37 g EDTA, and 6.05 g Tris base. Add 900 mL water, mix, and adjust pH till 7.5 with 1 M MES (see Note 1). Fill water until 1 L. Store at 4 °C for shorttime use or −20 °C for long-time use. Freshly add DTT to 5 mM final concentration and general protease inhibitor cocktail (either Protease inhibitor cocktail from Sigma or Complete EDTA free from Roche) according to the manufacturer’s recommendation and phosphatase inhibitors: 10 μM leupeptin, 1 μM pepstatin, 50 mM NaF, 1 mM Na3O4, and 1 mM benzamidine. 2. Resuspension buffer (membrane buffer): 330 mM Sucrose, 25 mM Tris-MES, pH 7.5. Weigh out 113 g sucrose and 3.03 g Tris base. Add 900 mL water, mix, and adjust pH till 7.5 with 1 M MES. Fill water until 1 L. Store at 4 °C for short time, and alternatively freeze at −20 °C for long-time use. Freshly add DTT to a final concentration of 0.5 mM. 3. Wash buffer I: 10 mM Tris/Cl, pH 7.5, 150 mM NaCl, 0.01 % NP-40, and 0.5 mM EDTA. Weigh out 1.21 g Tris base, 8.77 g NaCl, 0.1 mL NP-40, and 0.186 g EDTA. Add 900 mL water, mix, and adjust pH till 7.5 with 1 N HCl. Fill water until 1 L. Store at 4 °C. 4. Wash buffer II: 10 mM Tris/Cl, pH 7.5, 300 mM NaCl, and 0.5 mM EDTA. Weigh out 1.21 g Tris base, 17.54 g NaCl, and 0.186 g EDTA. Add 900 mL water, mix, and adjust pH till 7.5 with 1 N HCl. Fill water until 1 L. Store at 4 °C. 5. ATP solution: Stock solution 100 mM. Weigh out 0.055 g ATP-Na2, add 1 mL water, and mix well. 6. Myelin basic protein (Sigma): 1 mg/mL. Weigh out 1 mg myelin basic protein powder, add 1 mL water, and mix well. 7. Synthetic peptides (target peptides): Target peptides can be obtained commercially from companies (e.g., JPT, Thermo Scientific, Sigma Aldrich). The target peptides were selected based on experimentally identified phosphopeptides in the PhosPhAt (phosphat.uni-hohenheim.de) database. We used a mixture of 43 different peptides covering 23 proteins (see Note 2). 8. Reaction buffer of kinase activity: 20 mM HEPES pH 7, 10 mM MgCl2, 2 mM DTT, 0.1 mg/mL bovine serum albumin (BSA). Weigh out 5.21 g HEPE sodium, 2.033 g MgCl2, and 1 g BSA. Add 900 mL water, mix, and adjust pH till 7.0 with HCl. Fill water until 1 L. Store at 4 °C. Freshly add 2 mM DTT.

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9. Solution A for C8 and C18 stage-tip: 0.5 % acetic acid. Add 500 μL acetic acid into 99.5 mL water, and mix well. 10. Solution B for C8 and C18 stage-tip: 80 % acetonitrile, 0.5 % acetic acid. Add 500 μL acetic acid and 80 mL acetonitrile into 19.5 mL water, and mix well. 2.2 Other Materials and Kits

1. C8 and C18 stage-tip (see Note 3): 200 μL tips and two discs containing C8 or C18 materials (Life Technologies). 2. Bio-Rad DC™ Protein Assay kit for protein concentration determination: Kits contain reagent A and reagent B. 3. Chromotek GFP-Trap_M kit (Chromotek) for immunoprecipitation of GFP fusion proteins: GFP-Trap_M contains a small GFP-binding protein covalently coupled to the surface of magnetic beads. 4. Magnetic tube rack for magnetically separating GFP beads.

3

Methods This method focuses on describing the activity of membrane-based protein kinases purified directly from plant material (see Note 4). Thus, we begin with a method to isolate microsomal fraction from plant tissue and purify membrane protein kinase with magnetic beads from such microsomal preparations. For other soluble kinases, the protocol can be entered also at Subheadings 3.2 or 3.3.

3.1 Microsome Preparation

1. Grind tissue under liquid nitrogen. Weight approximately 2 g of ground plant tissue into a 15 mL falcon tube. 2. Add 10 mL of cold isolation buffer (as described in Subheading 2.1 above) and vortex vigorously. Keep sample mixing on an over-end rotor for 15 min at 4 °C. 3. Centrifuge at 7,500 × g for 15 min in a table top centrifuge (Sigma), and collect the supernatant into a new tube. 4. Centrifuge supernatant at 48,000 × g for 70 min in a table top centrifuge (Sigma), and discard supernatant. 5. Resuspend pellet in 50–100 μL membrane buffer (see Note 5). 6. Measure protein concentration using Lowry-based method (Bio-Rad DC™ Protein Assay kit) as follows: In a microliter plate mix 1 μL sample + 4 μL H2O + 25 μL reagent A + 200 μL reagent B as described by the manufacturer. Incubate the reaction mixture at room temperature in darkness for 15 min and then measure the absorbance values at 750 nm.

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1. Microsomal proteins (300 μg) (as described in Subheading 3.1 above) were incubated with 30 μL of anti-GFP magnetic beads for 2 h on the over-end rotor at 4 °C. 2. After incubation, the beads were collected on a magnetic stand and washed two times in 350 and 500 μL wash buffer I (see Note 6). 3. The beads were washed in wash buffer II (see Note 6). 4. The beads (include GFP-tagged protein) were resuspended in 50 μL reaction buffer.

3.3 Kinase Activity Assay with Synthetic Peptides

1. Add 1 μL 100 mM ATP and 30 μg of a mixture of putative target peptides into 50 μL reaction buffer (including GFPtagged protein from 3.2 to 4) and incubate mixture at room temperature for 1 h. 2. After incubation, the reaction mixture needs to be purified over C8 stage-tip to trap undigested GFP-tagged protein and to collect the respective target peptides: C8 stage-tip is washed one time in 50 μL solution B and centrifuged. And C8 stagetip was washed two times in 100 μL solution A and centrifuged. The reaction mixture is then loaded into C8 stage-tip and centrifuged. The flow-through needs to be collected as it contains the peptide mixture from the reaction (see Note 7). 3. Collected peptide mixture then needs to be desalted over C18 prior to mass spectrometric analysis: C18 stage-tip [18] is washed once in 50 μL solution B and centrifuged (see Note 8). And C18 stage-tip is washed two times in 100 μL solution A and centrifuged at up to 5,000 × g. Collected peptide mixtures from above are acidified to 0.2 % TFA (add approx. 1/10 volume of 2 % TFA to reach pH 2.0). Acidified peptide mixture is loaded into C18 stage-tip and centrifuged. C18 stage-tip (including peptide mixture) is washed two times in 100 μL solution A and centrifuged. Peptide mixture is eluted from C18 with two times 20 μL solution B and centrifuged. Eluates are collected in a fresh tube and subsequently dried in speed-vacuum.

3.4 Mass Spectrometric Analysis of (Phospho)-Peptides

The phosphorylation of target peptides can be identified and quantified by LC-MS/MS analysis on a high-resolution mass spectrometer (see Note 9): 1. Synthetic peptide mixture was analyzed on a 1-h linear gradient via LC-MS/MS using nanoflow Easy-nLC (Thermo Scientific) as an HPLC system and a Quadrupole-Orbitrap hybrid mass spectrometer (Q-Exactive, Thermo Scientific) as a mass analyzer.

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2. Peptides are from a 15 cm long, 75 μm analytical column using a linear gradient running from 4 to 64 % acetonitrile in 60 min and sprayed directly into the Q-Exactive mass spectrometer. Proteins were identified by MS/MS by information-dependent acquisition of fragmentation spectra of multiple-charged peptides. 3. Phosphopeptide identification and intensity quantitation can be carried out by MaxQuant [19] or similar software. The TAIR database was used for spectral matching. Thereby, carbamidomethylation of cysteine is set as a fixed modification; oxidation of methionine as well as phosphorylation of serine, threonine, and tyrosine is set as variable modifications. Mass tolerance for the database search was set to 20 ppm on full scans and 0.5 Da for fragment ions. For label-free quantitation, retention time matching between runs is suggested. Peptide false discovery rate (FDR), protein FDR, and site FDR are recommended to be set to 0.01. 3.5

4

Data Analysis

In brief, the normalized ion intensities of phosphorylated and unphosphorylated peptide species are compared between different kinase treatments or different external conditions under which the kinase was expressed and isolated. Thereby, the intensity ratio of phosphorylated to non-phosphorylated peptide forms gives insights into phosphorylation efficiency and target preferences.

Notes 1. Heating MES solution in micro-oven for short time helps to dissolve the MES easily. 2. Each peptide is supplied in the assay at a concentration of 1 pmol. 3. We use the Empore discs of C18 or C8 material (Life Technologies) and use a blunt dermatological needle to punch out small discs that can be stuffed into a 200 μL pipette tip. Details on stage-tip manufacturing are described elsewhere [18]. In general, desalting over C18 can in principle also be done using alternative products such as ZIP-TIPs or C18 cartridges. 4. Plants expressing kinase-GFP fusions can be stable transformed lines of Arabidopsis or other plants or alternatively from transiently expressing systems. Nicotiana benthamiana is a suitable system for transient protein expression, but must be optimized for each construct individually. 5. Dilute microsome protein very slowly to avoid many bubbles, and keep the protein on the ice.

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6. Make sure that the magnetic beads have settled to completion at the lower back of the tube towards the magnetic stand before removing any liquid from the tubes. 7. This step is necessary to remove the undigested kinase from the peptide mixture. Peptides will not bind to C8 material while intact protein will stick to the matrix. The (phosphorylated) peptides therefore are found in the flow-through. 8. Desalting can alternatively also be done using commercial ziptips. If the self-made stage-tip system is used, centrifugation should preferentially not be done at full speed. 9. We used an Orbitrap-type mass spectrometer for analysis, but the method generally also works on other high-resolution high-mass-accuracy instruments.

Acknowledgement Thanks to Dr. Heidi Pertl-Obermeyer for optimization of the microsome preparation protocol. References 1. Grefen C, Harter K (2004) Plant twocomponent systems: principles, functions, complexity and cross talk. Planta 219(5):733–742 2. Trentini DB, Fuhrmann J, Mechtler K, Clausen T (2014) Chasing phosphoarginine proteins: development of a selective enrichment method using a phosphatase trap. Mol Cell Proteomics mcp.O113.035790 3. Zulawski M, Schulze G, Braginets R, Hartmann S, Schulze WX (2014) The Arabidopsis Kinome: phylogeny and evolutionary insights into functional diversification. BMC Genomics 15(1):548 4. Zulawski M, Braginets R, Schulze WX (2013) PhosPhAt goes kinases – searchable protein kinase target information in the plant phosphorylation site database PhosPhAt. Nucleic Acids Res 41(D1):D1176–D1184 5. Peck SC (2005) Update on proteomics in Arabidopsis. Where do we go from here? Plant Physiol 138:591–599 6. Zhang S, Jin CD, Roux SJ (1993) Casein kinase II-type protein kinase from pea cytoplasm and its inactivation by alkaline phosphatase in vitro. Plant Physiol 103(3):955–962, doi: 103/3/955 [pii] 7. Zhang S, Klessig DF (1997) Salicylic acid activates a 48-kD MAP kinase in tobacco. Plant Cell 9(5):809–824

8. Gao M, Liu J, Bi D, Zhang Z, Cheng F, Chen S, Zhang Y (2008) MEKK1, MKK1/MKK2 and MPK4 function together in a mitogen-activated protein kinase cascade to regulate innate immunity in plants. Cell Res 18(12):1190–1198 9. Popescu SC, Popescu GV, Bachan S, Zhang Z, Gerstein M, Snyder M, Dinesh-Kumar SP (2009) MAPK target networks in Arabidopsis thaliana revealed using functional protein microarrays. Genes Dev 23:80–92 10. de la Fuente van Bentem S, Anrather D, Dohnal I, Roitinger E, Csaszar E, Joore J, Buijnink J, Carreri A, Forzani C, Lorkovic ZJ, Barta A, Lecourieux D, Verhounig A, Jonak C, Hirt H (2008) Site-specific phosphorylation profiling of Arabidopsis proteins by mass spectrometry and peptide chip analysis. J Proteome Res 7(6):2458–2470 11. Clouse SD, Sasse JM (1998) Brassinosteroids: essential regulators of plant growth and development. Annu Rev Plant Phys 49:427–451. doi:10.1146/annurev.arplant.49.1.427 12. Mathis G (1995) Probing molecular interactions with homogeneous techniques based on rare earth cryptates and fluorescence energy transfer. Clin Chem 41(9):1391–1397 13. Wu JJ (2002) Comparison of SPA, FRET, and FP for kinase assays. Meth Mol Biol 190:65–85

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14. Huang Y, Houston NL, Tovar-Mendez A, Stevenson SE, Miernyk JA, Randall DD, Thelen JJ (2010) A quantitative mass spectrometry-based approach for identifying protein kinase-clients and quantifying kinase activity. Anal Biochem 402(1):69–76 15. Wu XN, Sanchez-Rodriguez C, PertlObermeyer H, Obermeyer G, Schulze WX (2013) Sucrose-induced receptor kinase SIRK1 regulates plasma membrane aquaporins in Arabidopsis. Mol Cell Proteomics 12(10): 2856–2873 16. She J, Han Z, Kim TW, Wang J, Cheng W, Chang J, Shi S, Yang M, Wang ZY, Chai J (2011) Structural insight into brassinosteroid perception by BRI1. Nature 474(7352):472–476

17. Placzek EA, Plebanek MP, Lipchik AM, Kidd SR, Parker LL (2010) A peptide biosensor for detecting intracellular Abl kinase activity using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Anal Biochem 397(1):73–78 18. Rappsilber J, Ishihama Y, Mann M (2003) Stop and go extraction tips for matrix-assisted laser desorption/ionization, nanoelectrospray, and LC/MS sample pretreatment in proteomics. Anal Chem 75(3):663–670 19. Cox J, Mann M (2008) MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteomewide protein quantification. Nat Biotechnol 26(12):1367–1372

Chapter 8 Absolute Quantitation of Protein Posttranslational Modification Isoform Zhu Yang and Ning Li Abstract Mass spectrometry has been widely applied in characterization and quantification of proteins from complex biological samples. Because the numbers of absolute amounts of proteins are needed in construction of mathematical models for molecular systems of various biological phenotypes and phenomena, a number of quantitative proteomic methods have been adopted to measure absolute quantities of proteins using mass spectrometry. The liquid chromatography-tandem mass spectrometry (LC-MS/MS) coupled with internal peptide standards, i.e., the stable isotope-coded peptide dilution series, which was originated from the field of analytical chemistry, becomes a widely applied method in absolute quantitative proteomics research. This approach provides more and more absolute protein quantitation results of high confidence. As quantitative study of posttranslational modification (PTM) that modulates the biological activity of proteins is crucial for biological science and each isoform may contribute a unique biological function, degradation, and/or subcellular location, the absolute quantitation of protein PTM isoforms has become more relevant to its biological significance. In order to obtain the absolute cellular amount of a PTM isoform of a protein accurately, impacts of protein fractionation, protein enrichment, and proteolytic digestion yield should be taken into consideration and those effects before differentially stable isotope-coded PTM peptide standards are spiked into sample peptides have to be corrected. Assisted with stable isotope-labeled peptide standards, the absolute quantitation of isoforms of posttranslationally modified protein (AQUIP) method takes all these factors into account and determines the absolute amount of a protein PTM isoform from the absolute amount of the protein of interest and the PTM occupancy at the site of the protein. The absolute amount of the protein of interest is inferred by quantifying both the absolute amounts of a few PTM-site-independent peptides in the total cellular protein and their peptide yields. The PTM occupancy determination is achieved by measuring the absolute amounts of both PTM and non-PTM peptides from the highly purified protein sample expressed in transgenic organisms or directly isolated from an organism using affinity purification. The absolute amount of each PTM isoform in the total cellular protein extract is finally calculated from these two variables. Following this approach, the ion intensities given by mass spectrometers are used to calculated the peptide amounts, from which the amounts of protein isoforms are then deduced. In this chapter, we describe the principles underlying the experimental design and procedures used in AQUIP method. This quantitation method basically employs stable isotope-labeled peptide standards and affinity purification from a tagged recombinant protein of interest. Other quantitation strategies and purification techniques related to this method are also discussed. Key words AQUIP, Posttranslational modification (PTM), Absolute quantitation, Liquid chromatography-tandem mass spectrometry (LC-MS/MS), Stable isotope labeling, Internal standard (IS), Quantitative proteomics

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Introduction Proteins are a unique group of macromolecules that regulate biological activities, and participate in almost all aspects of molecular and cellular processes throughout kingdoms of organisms. Hundreds and thousands of proteins are expressed in a synchronized fashion in various cell types and at different stages of growth and development. Together with other biomolecules, proteins form complex molecular systems and perform differential biological functions. Quantitation of absolute quantities of proteins is of pivotal importance in biological science research and especially, it provides important and accurate inputs to molecular systems biology modeling because quantitative modeling becomes possible only when quantitative data are available [1–4]. However, the existence of various isoforms of posttranslationally modified proteins further complicates the absolute quantitation of proteins. After the translation of a protein, it is usually posttranslationally modified by attaching functional groups (e.g., glycosylation and phosphorylation) or other proteins/peptides (e.g., SUMOylation and ubiquitination) to several amino acid residues of the protein, by altering the chemical nature of an amino acid (e.g., citrullination and carbamylation), by making structural changes (e.g., formation of disulfide bridges and proteolytic cleavage), and so on. Up to date, 466 different types of posttranslational modifications (PTMs), all of which occur physiologically, have been recorded in the UniProt database (http://www.uniprot.org/docs/ptmlist). Generally speaking, these protein isoforms of distinct PTMs are able to change the properties of proteins. Each of them displays its specificities on protein function, turnover, subcellular localization, and/ or interactions with other protein(s) [5, 6]. Thus, quantitative measurement of each individual protein PTM isoform in addition to the total amount of corresponding protein is of high biological significance. For example, the methylation level of the chemotaxis protein receptors serves as the memory of the chemotaxis stimuli in E. coli [7, 8] and phosphorylation level of protein KaiC reflects circadian rhythm in cyanobacteria [9, 10]. The measurement of absolute quantities of the PTM isoforms is critical to mathematical modeling of the cellular processes. With the advent of both the strategies of proteomic approach and the techniques of mass spectrometry (MS), MS-based quantitative proteomics has emerged as an efficient and accurate tool in collecting quantitative data [2, 11–18]. One example of such MS-based absolute quantitation of proteins is the inductively coupled plasma mass spectrometry (ICPMS)-based approach, in which the targeted proteins contain a detectable hetero-element that has at least two stable isotopes, such as sulfur, are quantified [19, 20]. In addition, bioinformatics approaches have been taken to achieve large-scale absolute

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quantitation by combining the large-scale label-free LC-MS data with that absolute quantities obtained from other approaches [21– 24]. Regardless of these, the primary strategy that is frequently applied in absolutely quantitative proteomics is still based on one or several internal standards (IS) spiked into the biological samples before MS analyses. The use of IS, which are counterparts of target peptides/proteins that are indispensable during MS analysis, is actually dependent on the well-established concepts borrowed from analytical chemistry. The ion intensities of the endogenous proteolytic peptides from the proteins of interest are compared to those of the corresponding IS of known amounts to calculate the absolute amounts of the peptides, which are finally converted to absolute amounts of the targeted proteins [2]. IS can be protein or peptide analogues from other species [25], peptide or protein counterparts differently labeled by chemical methods [26–28], or the most wildly used, stabled isotope-labeled peptides or proteins (reviewed by [14, 18]). The most commonly used absolute quantification (AQUA) strategy spikes the synthetic stable isotope-labeled peptides prepared based on the sequences of target peptides and their covalent modifications into the protease-digested peptide samples to quantify the certain peptide isoforms on specific modification states [29–31]. Furthermore, QconCAT strategy spikes an artificial concatemer of standard peptides (Q peptides) into the protein samples before the proteolysis step to offer IS to different native and endogenous peptides simultaneously [32–35]. Alternatively, the protein standard absolute quantification (PSAQ) strategy employs a full-length and stable isotope-labeled protein as IS and spikes it into samples at very early stages of sample preparation in order to take into account the actual efficiency of the proteolysis step [36, 37]. However, it is quite difficult to obtain the protein standards that are specific to a particular PTM isoform of a protein. Moreover, though the limits of LC-MS-based proteomics have been pushed to 10−16 mol of protein [38] or 50 copies of protein/cell without enrichment [39], strategies of pre-fractionation and/or multistep purification are still applied to improving the sensitivity to the moderate and/or low-abundance proteins by MS-based quantification approaches, especially when the protein concentrations vary through a wide range. No peptide standard, neither the chemically synthetic peptide nor proteolytic peptide from a concatemer protein, is compatible if either when a multiple or partial isolation of proteins of interest is needed or in cases where protease-mediated peptide digestion is incomplete [37]. A comprehensive method, the absolute quantitation of isoforms of posttranslationally modified proteins (AQUIP), was therefore developed in Arabidopsis to quantify the absolute abundance of site-specific phosphorylated isoform of ethylene response factor 110 (ERF110) recently [40]. Assisted with a few peptide IS,

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AQUIP determined the absolute amount of the 62nd serinephosphorylated isoform of the ERF110 protein through quantifying both the total absolute amount of the ERF110 protein and the phosphorylation occupancy on the 62nd residue of protein. The advances in the AQUIP method are that all effects of protein fractionation, protein purification, protease digestion, peptide recovery, and peptide ionization were taken into account. Although the chemically synthesized peptide standards, just as that introduced in AQUA strategy, were used in the original study in Arabidopsis [40], all IS-based strategies compatible with absolute quantitation of PTM peptides of interest can be adopted into the method. Moreover, the 14N-coded peptide standards were employed to absolutely quantify the PTM isoform from 15N-labeled plants in that work because completely metabolic labeling using 15N-enriched inorganic nitrate and/or ammonia is convenient and economical to autotrophic organisms like plants [41]. Different combinations of peptide IS and samples, such as 15N-coded peptide IS against naturally 14N-abundant samples or peptide IS of natural isotopic abundances against samples labeled by SILAC strategy [42], are also suitable to the AQUIP method.

2

Materials

2.1 Protein Samples and Internal Standards

1. Total cellular protein extract that contains the targeted protein (both the PTM and the non-PTM isoforms are included usually) is used. Protein extract from a transgenic organism, which expresses a fusion gene encoding the targeted protein recombined with a polypeptide tag compatible with tandem affinity purification (TAP) methods, such as a His8-Biotin carboxyl carrier protein (TAIR accession number AT5G16390)-His8 (HBH) tag [40], is preferred (see Note 1). 2. A few internal standards, including n (n ³ 2 ) peptide standards synthesized according to n PTM site-independent regions of the target protein (see Note 2), the PTM peptide containing the PTM site of interest, and its non-PTM cognate, are prepared. The internal standards should be distinguished from the endogenous proteolytic peptides in MS analysis, such as naturally 14N-abundant peptide standards used to quantify the completely 15N-labeled samples (see Note 3).

2.2 LC-MS/MS Analysis

1. Tandem mass spectrometers capable to identify PTM peptides are all suitable for this approach. 2. MS/MS search engines, such as SEQUEST (http://fields. scripps.edu/sequest/) [43], Mascot (http://www.smatrixscience.com) [44], PEAKS (http://www.bioinfor.com) [45], X!Tandem (http://www.thegpm.org/tandem/) [46], or Comet (http://comet-ms.sourceforge.net) [47], are employed to identify peptides from MS/MS spectra.

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3. MS quantification software, which can be facility-specific software like Analyst (AB Sciex, DC, USA), Masslynx (Waters, Milford, MA, USA), and Xcalibur (Thermo Fisher, Waltham, MA, USA); third-party software such as TPP (http://tools. proteomecenter.org/wiki/index.php?title=Software:TPP) [48], TOPP (http://open-ms.sourceforge.net) [49], and MaxQuant (http://www.maxquant.org) [50]; or any homemade quantification software, are used to quantify the ion count of each peptide molecule.

3

Methods For a particular protein, its total molar amount Ptot is the sum of the molar amounts of all possible isoforms of the protein, Pi (i Î {all isoforms}) , Ptot =

å

Pi .

(1)

i Î{all isoforms}

Our discussion focuses on the simplest yet important case, a protein that can be posttranslationally modified at one particular amino acid residue. There exist exact two isoforms for such a protein and Eq. 1 can be expressed as Ptot = PPTM + PnPTM ,

(2)

where PPTM and PnPTM represent the molar amount of the sitespecific PTM and non-PTM isoform of the protein, respectively. Generally speaking, LC-MS/MS-based absolute quantification methods can only measure the molar amounts of the PTM and non-PTM isoforms of the peptide surrounding the PTM site digested from the targeted protein (denoted as pPTM and pnPTM, respectively), which satisfy equations pPTM = kPTM × PPTM

(3)

pnPTM = knPTM × PnPTM ,

(4)

and

where kPTM and knPTM are the peptide yield of the PTM and nonPTM peptide isoform, respectively. Thus the accuracy of PPTM (or PnPTM) inferred from pPTM (or pnPTM) is largely dependent on kPTM (or knPTM), which is both protein/peptide specific and digestion procedure specific and difficult to measure directly. Alternative way employed by AQUIP approach (see Fig. 1) measures the total molar amount of the targeted protein Ptot and PTM occupancy Rocc at the site of protein of interest that is defined as Rocc º PPTM / Ptot

(5)

total cellular proteins

highly purified targeted protein multi-step purification

fractionation and enzyme digestion

fractionation and enzyme digestion peptide standards

peptide standards

mix

LC-MS/MS

LC-MS/MS

LC-MS/MS

m/z

=

Peptide amount

Peptide amount

Ion intensity

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Fig. 1 The overall workflow of absolute quantitation of PTM isoforms using AQUIP method. The total cellular protein (left top corner) is extracted from transgenic organisms expressing a fusion gene encoding a recombinant protein of interest (red lines) with the TAP-compatible HBH-tag (green lines). One part of protein extract is first subjected to TAP and the highly purified protein sample containing the targeted recombination protein with or without the certain PTM (purple triangles) is collected (right top corner). After pre-fractionation (if necessary) and enzyme digestion (e.g., in-gel, on-bead, on-column), the proteolytic peptides from both the total cellular protein extract and the highly purified protein sample are collected. First, PTM site-independent peptide IS (blue lines) of a series of amounts are spiked into l ( l = 3 in the figure) aliquots of peptide samples digested from total cellular protein to absolutely quantify the endogenous PTM site-independent peptides based on MS analysis (left middle panel). Then, certain molar amounts of PTM site-independent peptide IS are spiked into the peptide samples digested from the highly purified protein samples of known amounts to measure peptide yield of each PTM site-independent peptide (center middle panel). Last, both the PTM and the non-PTM peptide IS (blue lines with and without purple triangle, respectively) of known amounts are spiked into the aliquots of peptide sample digested from the highly purified protein sample together to obtain absolute quantities of both PTM and nonPTM peptides from the highly purified protein sample (right middle panel). Taken together, the absolute amounts ( p j ( j = 1, , n ) ) and peptidepu yields (k j ( j = 1, pu, n ) ) of each PTM site-independent peptide as well as absolute quantities of both PTM (pPTM ) and non-PTM (pnPTM ) peptide in the highly purified protein sample are used to calculate the absolute amount of the PTM isoform. Proteins/peptides other than the targeted protein/peptides are presented in gray. Peptides, signals and data points from the biological samples and IS are illustrated in red and blue, respectively. Peptides of same sequence are shown in same shape

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to calculate molar amount of the PTM isoform of the targeted protein using the equation PPTM = Ptot × Rocc .

(6)

Variables Ptot and Rocc are determined using AQUIP approach from total cellular protein extract and highly purified targeted protein, respectively (see Fig. 1). 3.1 Quantitation of the Total Molar Amount of the Protein of Interest (Ptot) in Total Cellular Protein Extract

For any PTM site-independent peptide derived from the targeted protein, its molar amount p is given by (7)

p = k × Ptot ,

where k is the peptide yield of the peptide. Eq. 7 holds for the molar amounts of n PTM site-independent peptides pj (j=1,…,n). Thus experimentally, n (n ³ 2 ) PTM site-independent peptides are usually measured (see Note 2) to determine the total molar amount of the protein using the equation Ptot =

1 n åp j / k j , n j =1

(8)

where pj and kj are the molar amount and the peptide yield of the j - th ( j = 1, ¼, n ) PTM site-independent peptide, respectively. In order to calculate absolute amount of the targeted protein according to Eq. 8, pj and kj ( j = 1, ¼, n ) should be determined from experiments. 3.1.1 Absolute Quantitation of PTM Site-Independent Peptides Derived from the Targeted Protein

The molar amount of the j -th ( j = 1, ¼, n ) PTM site-independent peptide, p j ( j = 1, ¼, n ) , can be inferred from its corresponding ion intensity of the whole isotopic envelope I j ( j = 1, ¼, n ) , which satisfies the equation pj = aj I j + bj ,

(j

= 1, ¼, n ) ,

(9)

where aj and bj ( j = 1, ¼, n ) are conversion parameters that depend only on the ionization of the peptide analyzed and detection of the mass spectrometer used. Thus these parameters keep constant for each peptide tested in same equipment and Eq. 9 holds for the molar amount pjstd and the corresponding ion intensity Ijstd of the peptide IS synthesized according to the j -th ( j = 1, ¼, n ) PTM site-independent region of the targeted protein, or say p std = a j I jstd + b j , j

(j

= 1, ¼, n ) .

(10)

Hence in order to obtain molar amount of each PTM siteindependent peptide p j ( j = 1, ¼, n ) , the following steps are performed (Fig. 1):

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1. Resuspend total cellular protein sample in a denaturing protein resuspension buffer (see Note 1). 2. Subject total cellular protein to pre-fractionation such as SDSpolyacrylamide gel electrophoresis (SDS-PAGE) (see Note 1) if necessary. 3. Perform enzyme (i.e., trypsin) digestion (in-gel, in-solution, on-bead, on-column, and so on) and extract the proteolytic peptides (see Note 1). 4. Divide the peptide extract into l (l ³ 2 ) parts in an equal amount (see Notes 4 and 5). 5. Mix all n PTM site-independent peptide IS of known amounts into the l aliquots of peptide samples. A series of l different amounts for each of the n peptide IS is used (see Note 6). 6. Desalt the mixtures and analyze each of them in LC-MS/MS. 7. Identify the targeted peptides (both the endogenous peptides and peptide IS) from MS/MS spectra using MS/MS search engines. 8. Quantify the ion count of whole isotopic envelope corresponding to each PTM site-independent peptide I j , j = 1, ¼, n std and its stable isotope-labeled standard I j , j = 1, ¼, n from MS1 data obtained from the MS analyses of the l mixtures (i.e., l × n pairs of whole isotopic envelopes are quantified).

(

(

)

)

9. For each of the n PTM site-independent peptides, a standard curve is built by fitting the parameters aj and bj ( j = 1, ¼, n ) in Eq. 10 using l pairs of molar amounts pjstd and their corresponding ion counts I jstd ( j = 1, ¼, n ) . 10. Calculate the molar amounts of the j ‐ th PTM site-independent peptide p j ( j = 1, ¼, n ) from the ion count of its corresponding whole isotopic envelope I j ( j = 1, ¼, n ) using Eq. 9. 3.1.2 Calculation of Peptide Yield of Each PTM Site-Independent Peptide Using Highly Purified Protein Samples

As shown in Eq. 7, in order to obtain the peptide yield kj of the j -th ( j = 1, ¼, n ) PTM site-independent peptide, one has to know the molar amounts of both the targeted protein (Ptot) and the j -th ( j = 1, ¼, n ) PTM site-independent peptide (pj). Since Ptot in the total protein extract is the unknown variable we are trying to determine, we cannot calculate peptide yields from it. Thus the highly purified protein samples are used instead to obtain kj. Given the purity of a purified protein sample (i.e., the mass percentage of the targeted protein in the purified protein sample) is rpu (see Note 7), the molar amount of the targeted protein in a purified protein pu sample, Ptot , is given by pu = m pu × rpu / M , Ptot

(11)

where mpu is the mass of the purified protein sample and M is the molar mass (mass/mol) of the targeted protein.

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We then assume that the peptide yield depends on the peptide itself and the protein digestion and peptide extraction procedure. Thus the peptide yield of a peptide keeps constant in the digestion process of the total cellular protein extract and in that of the highly purified protein samples. Hence we have

(

) (

)

pu k j = k jpu = p jpu / Ptot = p jpu × M / m pu × rpu , ( j = 1, ¼, n ) , (12)

where kjpu and pjpu are the peptide yield and the molar amount of the j -th ( j = 1, ¼, n ) PTM site-independent peptide obtained from the highly purified protein sample, respectively. Thus the peptide yield kj of the j -th ( j = 1, ¼, n ) PTM site-independent peptide is measured as follows (Fig. 1): 1. Purify the targeted protein from total cellular protein. For example, TAP of HBH-tagged protein using Ni2+-NTA beads and immobilized streptavidin beads is performed (see Note 1). 2. Measure protein concentration in the highly purified protein sample using protein assays. 3. Collect q (q ³ 2 ) parts of the highly purified protein sample with q different known mass (i.e., q parts of q known mass mpu) (see Note 8). 4. Subject each part of the highly purified protein sample to same pre-fractionation as the total cellular protein (see Note 9). 5. Digest each of the q parts of the highly purified protein sample and extract the proteolytic peptides. 6. Mix all n PTM site-independent peptide IS into the q parts of peptide samples. The molar amount of each peptide IS mixed in each part is equal to that of the targeted protein in that part pu (i.e., equal to Ptot in each of the q parts). 7. Obtain pjpu of the j -th ( j = 1, ¼, n ) PTM site-independent peptide in all q parts of peptide sample form the highly purified targeted protein following steps 6–10 of Subheading 3.1.1. 8. Calculate peptide yield kj of the j -th ( j = 1, ¼, n ) PTM siteindependent peptide using Eq. 12. Taken together, the total molar amount of the targeted protein in the total cellular protein is calculated from Eq. 8 (Fig. 1). 3.2 Measurement of PTM Occupancy Rocc of a Site-Specific PTM in the Highly Enriched Recombination Protein

According to Eq. 6, another variable Rocc is needed to determine the absolute molar amount of the PTM isoform of the targeted protein PPTM. In order to get the PTM occupancy Rocc of a specific PTM at a certain site, the highly purified targeted protein is used because of the high abundance of the targeted molecules, which is convenient for the peptide detection and quantification especially when one of the two isoforms is too little to be detected from crude total protein extract. Supposing that either PTM or non-PTM

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proteins can be purified unbiasedly through the multistep purification process employed, we have pu pu pu Rocc = Rocc = PPTM / Ptot ,

pu

(13)

pu

where R occ and PPTM are the PTM occupancy and molar amount of the PTM protein in the highly purified protein sample, respectively. Eqs. 2–4 hold also for the purified protein, pu pu pu Ptot = PPTM + PnPTM ,

(14)

pu pu pu pPTM = kPTM × PPTM ,

(15)

pu pu pu pnPTM = knPTM × PnPTM ,

(16)

pu

pu pu , and pnPTM are molar amount of the non-PTM where PnPTM , pPTM protein, PTM peptide, and non-PTM peptide in the highly puripu pu fied protein sample, respectively, while kPTM and knPTM are the peptide yield of the PTM peptide and non-PTM peptide from the highly purified protein sample, respectively. Hence we have pu pu pu = PPTM Rocc = Rocc / Ptot pu pu pu = PPTM + PnPTM / ( PPTM )

(17)

pu pu pu pu pu = ( pPTM / kPpuTM ) / ( pPTM / kPTM + pnPTM / knPTM ).

We further assume that peptide yield is independent to the PTM and pu pu kPTM = knPTM .

(18)

pu pu pu Rocc = pPTM / ( pPTM + pnPTM ).

(19)

Then we have

pu pu Here pPTM and pnPTM can also be determined from MS analysis using PTM and non-PTM peptide IS. Similar to Eqs. 9 and 10, the molar pu pu amounts (pPTM and pnPTM ) and the corresponding total ion intensipu pu ties (IPTM and InPTM) of the whole isotopic envelopes of the PTM and non-PTM peptide isoforms satisfy the equations pu pu pPTM = a PTM I PTM + bPTM

(20)

pu pu pnPTM = a nPTM I nPTM + bnPTM ,

(21)

and

respectively, whereas the equations std std pPTM = a PTM I PTM + bPTM

(22)

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and std std pnPTM = a nPTM I nPTM + bnPTM

(23)

std std and pnPTM ) and the ion intensity hold for the molar amount (pPTM std std (IPTM and InPTM) of the corresponding whole isotopic envelopes of the PTM and non-PTM peptide IS, respectively. Therefore in order to obtain the PTM occupancy Rocc of the PTM of interest, the following steps are performed (Fig. 1):

1. Digest the highly purified protein sample and extract the proteolytic peptides. 2. Follow steps 4–10 of Subheading 3.1.1 to determine pPTMpu and pnPTMpu in the highly purified protein sample using the PTM and non-PTM peptide IS, respectively. 3. Calculate the PTM occupancy Rocc from Eq. 19. Finally, the absolute amount of the PTM isoform in the total cellular protein is calculated from Eq. 6 using total molar amount of the protein of interest (Ptot) and PTM occupancy at the site of protein of interest (Rocc) (Fig. 1).

4

Notes 1. Quantitation of PTM isoforms should be performed under conditions that can retain the in vivo PTM states and minimize any possible in vitro change on the PTM. According to this rationale, (a) denaturing buffers (including protein extraction buffer, protein resuspension buffer) should be used; (b) prefractionation and purification approaches that can be applied under denaturing conditions, such as SDS-PAGE gel and TAP using polypeptide tag, are preferred choices; and (c) in-gel, onbead, or on-column digestion may be more suited to AQUIP than in-solution digestion. Moreover, for the PTM of high reactivity such as reversible oxidation of cysteine, additional steps (i.e., thiol alkylation) are necessary to capture the in vivo PTM states. 2. A PTM site-independent peptide IS curated should be (a) unique in its sequence, (b) free of any known PTM, and (c) present in mass spectrometry data as a signal of a good reproducibility and a high resolution. Moreover, use of multiple peptide IS (n ³ 2 ) can not only improve the accuracy of the quantitation, but also offer an additional validation of the results, which may be incorrect due to unexpected effects such as unknown PTM on the peptide(s) selected or identical peptide(s) derived from unknown protein(s).

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3. Combination of 14N-coded synthetic peptide IS and 15N-labeled biological samples is more suitable to autotrophic organisms than other strategies. 4. Dividing the sample at the peptide level is helpful to eliminate the possible artifacts from digestion. 5. To fit the two parameters (aj and bj) of a linear equation (Eqs. 9 and 10), at least two pairs of experimental data are needed. 6. The molar amounts of peptide IS applied should be decided based on a priori estimation of the molar amounts of the targeted peptides in the sample to avoid the MS signals (i.e., molecule ion counts in MS data) of endogenous peptides and that of peptide IS mask each other and ensure the comparable accuracies of quantitation for both endogenous peptides and peptide IS. A series of IS amounts, usually ranging within one order of magnitude around the estimated molar amounts of the targeted peptides, are spiked (e.g., a range of IS from 100 to 1,000 μM used to quantify ~400 μM targeted peptide). 7. The purity of targeted protein in purified protein samples depends on the purification methods and should be estimated case by case. 8. Use of q ( q ³ 2 ) in different amounts of the highly purified protein samples and peptide IS ensures that a standard curve can be built for each of the n PTM site-independent peptides digested from the highly purified protein samples. Then the molar amounts of the endogenous PTM site-independent peptides are calculated from these standard curves. However, the data from peptide IS spiked into the total cellular protein can also be used here because the conversion parameters aj and b j ( j = 1, ¼, n ) depend only on the ionization of the peptide analyzed and detection of the mass spectrometer used. 9. The total protein extract and the highly purified protein samples should go through the identical sample preparation processes to ensure that the assumption k j = k jpu holds for the results.

Acknowledgment This work was supported by grants 16101114, 661613, CAS10SC01, HKUST10/CRF/10, HKUST12/CRF/11G, GMGS14SC01, NMESL11SC01, PD13SC01, 13142510 and the National Natural Science Foundation of China (Grant No. 31370315).

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Chapter 9 Phosphorylation Stoichiometry Determination in Plant Photosynthetic Membranes Björn Ingelsson, Rikard Fristedt, and Maria V. Turkina Abstract This chapter describes different strategies for the study of phosphorylation dynamics and stoichiometry in photosynthetic membranes. Detailed procedures for the detection, large-scale identification, and quantification of phosphorylated proteins optimized for plant thylakoid proteins are given. Key words Protein phosphorylation, Immunological detection, Phosphorylation stoichiometry, Mass spectrometry

1

Introduction Reversible protein phosphorylation is a widespread modification which plays a key role in regulatory signaling pathways of all cells. In plant thylakoid membranes a large number of proteins undergo rapid environmentally dependent phosphorylation and dephosphorylation. Many essential photosynthetic thylakoid proteins are found to be reversibly phosphorylated in plants: PSII subunits D1, D2, CP43, PsbH, and TSP9; extensively studied LHCII proteins and minor light harvesting protein CP29; PSI subunits PsaP and PsaD [1–4]; and kinases and phosphatases responsible for thylakoid phosphorylation-dephosphorylation are identified [5–8]. During recent years it was clearly demonstrated that environmentally dependent phosphorylation regulates the whole structure of thylakoids. Studies of the thylakoid kinase mutant stn8 and the double mutant stn7/8 demonstrate a central role of protein phosphorylation for structural macroscopic alterations in thylakoid membranes [7, 9–12]. Despite this progress, detection, correct annotation, and quantification of protein phosphorylation are

Dedicated to the memory of Professor Alexander Vener Waltraud X. Schulze (ed.), Plant Phosphoproteomics: Methods and Protocols, Methods in Molecular Biology, vol. 1306, DOI 10.1007/978-1-4939-2648-0_9, © Springer Science+Business Media New York 2015

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challenging technical issues and usually require protein purification and affinity enrichment of phosphopeptides because of their low abundance. Moreover, quantitative measurements of phosphorylation levels in vivo under particular environmental conditions remain challenging tasks due to lability of protein phosphorylation during sample purification steps. The stoichiometry of protein phosphorylation in photosynthetic membranes of plants and algae has been studied by both polyacrylamide gel electrophoresis-based and gel-free techniques. Conventional electrophoretic protein separation is still widely and successfully used in combination with immunological analyses with specific anti-phosphoamino acid antibodies or fluorescence staining of gels with Pro-Q Diamond phosphoprotein reagent [8, 12, 13]. During the last decade gel-free analytical strategies based on enrichment of phosphopeptides and subsequent liquid chromatography coupled with highly sensitive mass spectrometry (LC-MS) have been established as powerful tools for large-scale characterization of in vivo protein phosphorylation. Both stable isotope labeling and label-free MS-based approaches have been developed for quantitative phosphoproteomic analysis [11, 13–15]. This chapter describes commonly used proteomic strategies and detailed protocols for comprehensive plant thylakoid phosphoproteomic analysis.

2

Materials Use only ultrapure water for all solutions and washing steps.

2.1 SDSPolyacrylamide Gel Electrophoresis

1. SDS protein sample buffer: To make 1 mL of a 4× stock mix 0.1 g SDS, 0.4 g sucrose, 50 μL 1 M Tris–HCl pH 6.8, 10 μL 100 mM EDTA, 400 μL H2O, 200 μL 14.7 M β-mercaptoethanol, and bromophenol blue. 2. 10× Laemmli electrophoresis running buffer: Dissolve the following components in 1,000 mL H2O: 30.0 g Tris, 144.0 g glycine, and 10.0 g SDS. No pH adjustment is required. Store the running buffer at room temperature and dilute ten times before use. 3. Separating buffer: 1.5 M Tris–HCl, pH 8.8; 0.4 % SDS. 4. Stacking buffer: 0.5 M Tris–HCl, pH 6.8; 0.4 % SDS. 5. Fresh 10 % ammonium persulfate (APS) water solution. 6. Casting of two separation 14 % acrylamide mini-gels with 6 M urea: 3.6 g urea, 1.18 mL H2O, 2.5 mL separation buffer, 3.5 mL of 40 % acrylamide, 6 μL N,N,N′,N′tetramethylethylenediamine (TEMED), 60 μL 10 % APS.

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7. Casting of 6 % acrylamide stacking gel for two mini-gels: 3.6 g urea, 3.6 mL water, 2.5 mL, stacking buffer, 1.5 mL acrylamide, 8 μL TEMED, 80 μL 10 % APS. 8. Prestained protein molecular weight markers, broad range. 2.2 Immunoblotting with PhosphoThreonine Antibody

1. Immobilon-P polyvinylidene fluoride (PVDF) membrane (Millipore). 2. Transfer buffer: 48 mM Tris, 39 mM glycine, 0.0375 % (w/v) SDS, and 20 % methanol (v/v). 3. Albumin from bovine serum (BSA) fatty acid free for blocking. 4. Nonfat dried milk for blocking. 5. Tris-buffered saline (TBS): 20 mM Tris–HCl, pH 7.5, 500 mM NaCl. 6. TBS-T: TBS containing 0.05 % Tween-20. 7. Primary antibody: Anti-phosphothreonine (P-Thr) polyclonal antibody from New England Biolabs, Cell Signaling (Ipswich, MA, USA), or Zymed Laboratories Inc. (San Francisco, CA, USA). 8. Secondary anti-rabbit IgG horseradish peroxidase-conjugated antibody. 9. Enhanced chemiluminescence (ECL) western blotting detection reagent (GE Healthcare). 10. Imaging equipment: Chemiluminescence detection using, e.g., a cooled charge-coupled device (CCD) camera.

2.3 Fluorescent Phosphoprotein Staining with Pro-Q Diamond

1. Pro-Q Diamond phosphoprotein gel stain (Invitrogen). 2. Fix solution: 45 % methanol, 5 % acetic acid. 3. Destaining solution: 4 % acetonitrile, 50 mM sodium acetate, pH 4.0. 4. SYPRO Ruby for total protein stain (Invitrogen). 5. Wash solution: 10 % methanol, 7 % acetic acid. 6. Rotary shaker. 7. Visible-light scanning instrument or a transilluminator.

2.4 Phosphopeptide Enrichment and Mass Spectrometry

1. Thylakoid resuspension solution: 25 mM NH4HCO3 with 10 mM NaF. 2. Sequencing-grade modified trypsin. 3. 2 N methanolic HCl: Add 80 μL of acetyl chloride dropwise to 0.5 mL of anhydrous methanol with stirring; the procedure should be carried out under the laboratory chemical fume hood.

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4. 2 N methanolic DCl: Add 80 μL of acetyl chloride dropwise to 0.5 mL of anhydrous d3-methyl d-alcohol (deuteriumcontaining methanol) with stirring; the procedure should be carried out under the laboratory chemical fume hood. 5. Chelating Sepharose Fast Flow (GE Healthcare). 6. IMAC wash solution A: 0.1 % acetic acid. 7. IMAC activation solution: Freshly prepared 100 mM FeCl3. 8. IMAC peptide reconstitution solution: H2O:acetonitrile: methanol (1:1:1). 9. IMAC wash solution B: 0.1 % trifluoroacetic acid, 20 % acetonitrile. 10. IMAC wash solution C: 20 % acetonitrile. 11. IMAC elution buffer: 20 mM Na2HPO4, 20 % acetonitrile. 12. ZipTip C18 pipette tips (Millipore). 13. 50 % Acetonitrile. 14. ZipTip equilibration solution: 0.1 % trifluoroacetic acid. 15. ZipTip elution solution: 50 % acetonitrile, 0.1 % formic acid. 16. Alkaline phosphatase. 17. 25 mM NH4HCO3. 18. Liquid system.

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Methods We present here four different approaches to determine the stoichiometry of thylakoid protein in vivo phosphorylation which allow identification of the phosphoproteins and estimation of their amount: (1) the gel-based approach with phospho-specific antibody detection, (2) phosphoprotein gel staining with Pro-Q Diamond reagent, (3) stable isotope labeling combined with phosphopeptide enrichment coupled with mass spectrometry for relative quantitation, and (4) label-free phosphopeptide quantification by mass spectrometry.

3.1 Relative Quantitation of Thylakoid Phosphoproteins Using PhosphoThreonine Antibodies

Electrophoretic protein separation in combination with immunoblotting is the traditional method widely used in plant proteomics to measure phosphorylation stoichiometry. Differentially phosphorylated and non-phosphorylated forms of the same protein can be separated by SDS-PAGE (see Note 1) and their quantities are estimated using antibodies. For the quantitative analyses phosphoand protein-specific antibodies should be used.

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Use freshly prepared (see Note 2) thylakoid membranes [16]. 1. SDS-PAGE is run generally according to [17]. Prepare 0.75 mm thick polyacrylamide mini-gels using 14 % acrylamide with 6 M urea in the separation gel. 2. Solubilize thylakoid proteins in 1× SDS protein sample buffer by incubation for 15 min at 55 °C. 3. Load the thylakoid sample corresponding to 0.75 μg of chlorophyll in each well. 4. Run the gels at 10 mA/gel through the stacking gel and increase to 25 mA/gel when the samples have entered the separation gel (see Note 3). 5. Transfer the proteins to a PVDF membrane using a standard tank, semidry blotting transfer or vacuum-driven blot system according to the manufacturer’s instructions.

3.1.2 Immunodetection of Phosphoproteins with Phospho-Threonine Antibodies

1. Block the membrane with 5 % BSA in TBS for 1.5 h at room temperature (see Note 4). Use a rotary shaker platform rotating at 1 revolution/second for all washing, blocking, and incubating steps. 2. Incubate the membrane with primary P-Thr antibody (antibody dilution: Zymed: 1:3,000; Cell Signaling: 1:20,000 in 1 % BSA prepared in TBS-T) at 4 °C overnight. 3. Wash the membrane five times for 5 min with TBS-T. 4. Incubate the membrane with secondary antibody (dilution 1:10,000 in 1 % BSA prepared in TBS-T) for 2 h at room temperature. 5. Wash the membrane five times for 5 min with TBS-T and two times for 5 min with TBS. 6. Develop the immunoblots using ECL reagent according to the manufacturer’s instructions and detect phosphoproteins in a CCD camera. 7. Make relative quantification of the density of appearing bands with western blot analysis software. The signal intensity correlates with the number of phosphorylated threonine residues (see Note 5) (Fig. 1).

3.1.3 Immunodetection with Protein-Specific Antibodies

1. Block the membrane with 5 % fat-free milk in TBS for 1.5 h. 2. Incubate the membrane with primary antibody (for D1 antibody dilution 1:8,000, 1 % fat-free milk in TBS-T) overnight at 4 °C. 3. Continue as described under Subheading 3.1.2, steps 3–7. Relative differences in protein amounts can be assessed by the signal intensity as it typically correlates to the protein amount in the blot.

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Fig. 1 Typical thylakoid protein phosphorylation pattern obtained by immunoblotting with p-Thr antibodies from Zymed (a) and Cell Signaling (b). Thylakoids were isolated from Arabidopsis thaliana wild-type (ecotype Columbia) leaves exposed for 4 h to normal light. Positions of the phosphorylated thylakoid proteins and molecular mass markers are indicated. Figure adapted from [11] ProQ

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Fig. 2 Detection of thylakoid proteins and their phosphorylation of Arabidopsis thylakoids from wild-type plants exposed to normal light for 4 h. Pro-Q Diamond phosphoprotein stain and SYPRO Ruby total protein staining of the same SDSPAGE gel are shown. Positions of the phosphorylated thylakoid proteins and molecular mass markers are indicated. Figure adapted from [11]

3.2 Relative Quantitation of Thylakoid Phosphoproteins by Pro-Q Diamond Stain

This useful method allows direct in-gel detection and relative quantitation of phosphoproteins: Pro-Q Diamond stain signal intensity correlates with the extent of phosphorylation. A combination to the following SYPRO Ruby protein stain for total protein allows normalization to the total amount of protein (see Note 6) (Fig. 2). Besides this, all reagents are fully compatible with mass spectrometry and gel bands (spots) of interest can be subjected to in-gel digestion in order to identify the particular protein. The method is run generally according to the manufacturer’s protocol; all steps are performed at room temperature. 1. Prepare gels as described in Subheading 3.1.1. 2. Load the thylakoid sample corresponding to 2 μg of chlorophyll per well and run gels as described in Subheading 3.1.1. 3. Fix the gels with 100 mL fix solution for 30 min. Change the fix solution and continue fixation overnight. 4. Rinse the gels 3 × 10 min with 100 mL of H2O.

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5. Incubate gels in 60 mL of Pro-Q Diamond solution for 90 min in darkness. 6. Destain gels in darkness by incubation 3 × 30 min with 100 mL of destaining solution (see Note 7). 7. Wash the gel 2 × 5 min with H2O. 8. Visualize thylakoid phosphoproteins using a laser scanner. Optimal excitation wavelengths are within the range of 532 nm to 560 nm and it is recommended to use a ~580 nm long-pass or 600 nm band-pass emission filter for signal detection (emission maximum: ~585 nm). 9. Rinse the gel two times for 5 min with H2O. 10. Incubate the gel in 60 mL of SYPRO Ruby overnight in darkness. 11. Transfer the gel to a clean container and wash the gel in 100 mL of wash solution for 30 min in darkness. 12. Rinse the gel two times for 5 min with H2O. 13. Visualize the total thylakoid proteins using a laser scanner with a suitable excitation wavelength (450, 473, 488, or 532 nm) and detect emission at ~610 nm. 3.3 Quantitative Mass Spectrometric Analyses of StableIsotope-Labeled Thylakoid Phosphoproteins

Protein phosphorylation stoichiometry can also be measured by mass spectrometry but this task is challenging due to low abundance of phosphopeptides. Thus, a lot of efforts were focused on developing technologies for enriching phosphopeptides for further MS identification and quantitation. Since phosphorylation is restricted to the surface-exposed domains of membrane proteins and to peripheral proteins attached to the membrane surface we employ the “vectorial proteomics” strategy [18, 19]: proteolytic shaving of the hydrophilic domains exposed to the surface of uniformly oriented membrane vesicles. The trypsin treatment of isolated thylakoids cleaves phosphorylated peptides of all major phosphorylated proteins from the membrane [3, 20, 21]. This approach does not require any detergents for membrane solubilization and is therefore perfectly compatible with subsequent mass spectrometry analysis. The relatively short soluble peptides can be easily collected in the supernatant after centrifugation and then phosphorylated peptides are isolated from more abundant unmodified peptides by immobilized metal affinity chromatography (IMAC). A combination of IMAC with stable isotope labeling allows successful identification, sequencing, and relative quantitation of phosphopeptides by LC-MS [14, 20, 22, 23]. The methods described below outline the preparation of the surface-exposed peptides from thylakoid membrane proteins, enrichment of the phosphopeptides by IMAC using columns charged with Fe3+ ions (see Note 8), and quantitative analysis of protein phosphorylation using liquid chromatography coupled to mass spectrometry.

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3.3.1 Isolation of the Surface-Exposed Peptides from Thylakoid Membranes

Use freshly prepared thylakoid membranes [16]. 1. Resuspend thylakoids in thylakoid resuspension solution (see Note 9) to a final chlorophyll concentration of 3 mg/mL. 2. Incubate the thylakoid suspension with sequence-grade modified trypsin in a ratio of 5 μg trypsin per 1 mg of chlorophyll at 22 °C for 3 h in the dark (see Note 10). 3. Freeze the sample in liquid nitrogen, thaw, and centrifuge at 15,000 × g for 20 min. 4. Transfer the soluble peptides in supernatant into a new tube, resuspend the pellet in the same volume of H2O, and centrifuge at 15,000 × g for 20 min. 5. Pool the supernatants and centrifuge at 100,000 × g for 20 min. 6. Collect the supernatant with released peptides.

3.3.2 Stable Isotope Labeling by Esterification of Isolated Peptides

Changes in thylakoid protein phosphorylation can be quantitatively measured by labeling of the control and experimental samples with stable isotopes. The peptides isolated from control and experimental thylakoid samples as described in Subheading 3.3.1 can be differentially labeled during the esterification step prior to IMAC enrichment. Methyl esterification is blocking of peptide carboxylic groups of aspartic and glutamic acids, as well as C-terminal carboxylic groups of all peptides, thus preventing unspecific binding of these groups to immobilized metal ions at the IMAC step and resulting in more effective enrichment. 1. Dry peptides completely in a centrifugal vacuum evaporator (e.g., SpeedVac) (see Note 11). 2. Add 250 μL methanolic HCl and DCl to lyophilized peptides isolated from 50 μg of chlorophyll from control and experimental samples, respectively (see Note 12). 3. Incubate the reaction mixture at room temperature for 2.5 h. 4. Mix the differentially heavy or light stable isotope-labeled peptides 1:1 and dry in a centrifugal vacuum evaporator.

3.3.3 Enrichment of Labeled Phosphopeptides by Immobilized Metal Ion Affinity Chromatography

1. Gently resuspend Chelating Sepharose Fast Flow beads to homogeneity and pack a microcolumn in a gel loader pipette tip with 3–4 μL (7.5 μL of 50 % slurry). 2. Wash the column with 40 μL of IMAC wash solution A. 3. Charge the column with 80 μL of IMAC activation solution. 4. Wash the unbound salts two times with 20 μL IMAC wash solution A. 5. Reconstitute labeled peptides in 10 μL peptide resuspension solution and load the peptides onto the column.

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6. Wash the column with 40 μL of wash solution A, 40 μL of wash solution B, and, finally, 40 μL of wash solution C. 7. Elute phosphopeptides with 40 μL of elution buffer and dry in a centrifugal vacuum evaporator. Phosphorylated peptides enriched by IMAC should be desalted using ZipTip C18 before mass spectrometry analyses (see Note 13). 1. Prewet ZipTip C18 with 50 % acetonitrile in water: slowly aspirate and dispense solution into waste tube three times, 10 μL each. 2. Equilibrate ZipTip C18: Aspirate and dispense ZipTip equilibration solution into waste tube three times, 10 μL each. 3. Reconstitute sample in 10 μL of ZipTip equilibration solution and load the peptides by pipetting ten times back into the same tube. 4. Wash the ZipTip with ZipTip equilibration solution: Aspirate and dispense solution into waste tube five times, 10 μL each time. 5. Put 10 μL of elution buffer into a clean tube and elute peptides by aspirating and dispensing eluent through ZipTip ten times. The obtained differentially labeled phosphopeptides are then separated by liquid chromatography and analyzed typically by electrospray ionization mass spectrometer in positive mode (see Note 14). The peptide esterification with light and heavy isotope gives the mass increment of 14 Da and 17 Da per one modification, respectively. Signals for differentially esterified phosphopeptides appear as pairs of peaks with mass difference n(3 Da)/z, where n is the number of carboxylic groups in the peptide and z is the charge on the peptide (Fig. 3). The difference in ion intensity (or peak area) of two signals in each doublet reflects the ratio in phosphorylation degree of a particular site in particular peptide between the control and experimental sample (see Note 15). 3.4 Label-Free Phosphopeptide Quantification by Mass Spectrometry

The label-free quantitation by mass spectrometry implies measurement of ion signal intensities of phosphorylated and corresponding non-phosphorylated peptides. This approach requires comparison of separate LC-MS runs for separate samples, which limits the accuracy of the method due to systematic errors, and sample and run-to-run differences. Moreover, the MS-ionization efficiency usually is different for phosphorylated/non-phosphorylated form of the same peptide. To measure the absolute phosphorylation stoichiometry and obtain accurate quantitative data the normalization procedure accounting for the differences in ionization/detection efficiencies of phosphorylated and corresponding non-phosphorylated peptides should be used. The normalization strategy initially published by Steen et al. [24] which we adopted for plant thylakoid proteins

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Fig. 3 Relative quantitation of phosphopeptides using affinity chromatography and stable isotope labeling. (a) The scheme of a differential stable isotope labeling from wild-type and mutant plants. Typical LC-MS ion chromatograms for one of the differentially labeled phosphopeptides are shown. (b) Ion intensity peaks indicated with continuous line correspond to peptide from WT plants and labeled by light h3-methanol. Ion intensity peaks indicated with dashed line correspond to the same peptide obtained from the mutant plants and labeled by heavy d3-methanol. (c) Ion intensity peaks as in A except that the peptides from the mutant plants were labeled with light isotope (continuous line) and the peptides from the WT plants were labeled with heavy isotope (dashed line)

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Fig. 4 Label-free quantification of PSII core protein phosphorylation. LC-MS extracted ion chromatograms of the phosphorylated and non-phosphorylated N-terminal peptide from the CP43 protein of Arabidopsis thaliana after incubation of the peptide mixture with alkaline phosphatase for different time, as indicated. The ratios of phosphorylated to non-phosphorylated peptide intensities at each time point were used for calculations of CP43 phosphorylation stoichiometry [11]

[11, 13] employs the idea that the decrease of amount of phosphopeptide would increase the amount of corresponding dephosphorylated peptide. These changes in ion signal intensities of peptides before and after controlled enzymatic dephosphorylation with alkaline phosphatase can be measured by LC-MS. During the phosphatase treatment the aliquots can be taken at different time points in order to generate samples with different phosphorylation stoichiometries. MS measurements of ion signal intensities should be done for peptide/phosphopeptide pairs from proteins and their flyability ratios should be calculated using different time points (Fig. 4). These ratios are used later to correct the signal intensities of the phosphorylated peptides in measurements of the protein phosphorylation states by calculation of peptide and corresponding phosphopeptide ratios for each particular protein of interest. 3.4.1 Stable Isotope-Free Quantitation of Protein Phosphorylation Stoichiometry by Mass Spectrometry

1. Obtain the surface-exposed peptides from thylakoid membranes as described in Subheading 3.3.1. 2. Add alkaline phosphatase to a final concentration of 100 milliunits/μL and perform a controlled dephosphorylation in a 25 mM NH4HCO3 at 37 °C. 3. Take aliquots after 0, 10, 20, and 40 min of incubation and stop the dephosphorylation reactions by 1 % formic acid (pH 4.0) to obtain samples with varying degrees of phosphorylation. 4. Analyze each sample by LC-MS in positive ionization mode at least two times to measure the signal intensity of each peptide ion of interest (see Note 16). 5. Calculate the flyability ratio for each peptide/phosphopeptide pair as (IB − IA)/(IpA − IpB), where IpA and IA are the ion

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intensities of the phosphorylated and non-phosphorylated peptides at 0-min time point, and IpB and IB are the corresponding ion intensities at the 10-, 20-, or 40-min time points. An average flyability ratio for each peptide/phosphopeptide pair is calculated from the data corresponding to all time points of alkaline phosphatase treatment. Use these ratios to calculate peptide/phosphopeptide ratios for each particular protein. 6. Calculate the stoichiometry of protein phosphorylation from the ratios of ion currents for phosphorylated to nonphosphorylated peptide pairs from the proteins of interest according to [11].

4

Notes 1. The electrophoretic separation of phosphorylated and nonphosphorylated forms of the same protein does not occur for all proteins. 2. Freezing and storage of thylakoids at −20 °C may lead to irreversible aggregation of proteins. 3. Do not run urea-containing gels in cold room (4 °C) because of urea precipitation. 4. Use BSA for membrane blocking and antibody dilutions; do not use milk. Milk-derived blocking solutions contain phosphoproteins. 5. The anti-phospho-threonine antibody may cross-react with phospho-serine-containing proteins. 6. Abundant non-phosphorylated proteins can exhibit nonspecific staining with Pro-Q Diamond phosphoprotein stain and can be distinguished from less abundant phosphorylated proteins by careful comparison to SYPRO Ruby total-protein stain. 7. A too long destaining time will result in a decreased number of detected phosphoproteins. 8. Phosphopeptides can also be enriched by using titanium dioxide (TiO2) columns. Titansphere Phos-TiO kits are available from GL Sciences Inc. (Torrance, CA, USA) generally according to the manufacturer’s protocol. 9. 10 mM NaF is added to all preparation buffers to avoid dephosphorylation of thylakoid phosphoproteins. 10. Do not perform proteolytic treatment of thylakoids at 37 °C because some thylakoid phosphoproteins are rapidly dephosphorylated by the heat-shock-activated membrane protein phosphatases. 11. The peptides should be dried completely because any traces of water will lead to incomplete esterification of peptide.

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12. 2 N methanolic HCl is explosive upon contact with water; let the excess of the reagent to evaporate under the hood. 13. Alternatively, use the TopTip C-18 columns (Glygen Corp, Columbia, MD, USA). 14. Typically the collision-induced dissociation (CID) is used as peptide fragmentation technique, but electron transfer dissociation (ETD) can be very useful for analysis of multiple phosphorylation sites within one peptide. 15. Perform the reverse labeling of peptides as an internal control for more accurate and more robust data. 16. The label-free approach requires quantitative comparison of separate LC-MS runs for separate samples, which limits the accuracy of the method. Thus, MS analysis of each sample should be repeated at least two times.

Acknowledgements Alexander was a mentor, friend, and inspiration to the authors. He was working for many years in the field of quantitative phosphoproteomics and played a key role in the development of the methods described in this chapter. In particular, Alexander made significant contributions to the understanding of how these posttranslational modifications regulate the dynamic photosynthetic membranes inside the chloroplast. The authors will continue to explore and emulate the work of Alexander to unravel the mysteries around photosynthetic control using mass spectrometry and proteomics; in this way we hope to serve his memory well. This work was supported by grants from the Swedish Research Council and the Swedish Research Council for Environment, Agriculture and Spatial Planning. References 1. Vener AV (2007) Environmentally modulated phosphorylation and dynamics of proteins in photosynthetic membranes. Biochim Biophys Acta 1767:449–457 2. Hansson M, Vener AV (2003) Identification of three previously unknown in vivo protein phosphorylation sites in thylakoid membranes of Arabidopsis thaliana. Mol Cell Proteomics 2:550–559 3. Vener AV, Harms A, Sussman MR et al (2001) Mass spectrometric resolution of reversible protein phosphorylation in photosynthetic membranes of Arabidopsis thaliana. J Biol Chem 276:6959–6966

4. Vener AV, Ohad I, Andersson B (1998) Protein phosphorylation and redox sensing in chloroplast thylakoids. Cur Opin Plant Biol 1:217–223 5. Vainonen JP, Hansson M, Vener AV (2005) STN8 protein kinase in Arabidopsis thaliana is specific in phosphorylation of photosystem II core proteins. J Biol Chem 280:33679–33686 6. Bellafiore S, Barneche F, Peltier G et al (2005) State transitions and light adaptation require chloroplast thylakoid protein kinase STN7. Nature 433:892–895 7. Rochaix JD, Lemeille S, Shapiguzov A et al (2012) Protein kinases and phosphatases

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12.

13.

14.

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Björn Ingelsson et al. involved in the acclimation of the photosynthetic apparatus to a changing light environment. Philos Trans R Soc Lond Series B Biol Sci 367:3466–3474 Shapiguzov A, Ingelsson B, Samol I et al (2010) The PPH1 phosphatase is specifically involved in LHCII dephosphorylation and state transitions in Arabidopsis. Proc Natl Acad Sci U S A 107:4782–4787 Fristedt R, Willig A, Granath P et al (2009) Phosphorylation of photosystem II controls functional macroscopic folding of photosynthetic membranes in Arabidopsis. Plant Cell 21:3950–3964 Herbstova M, Tietz S, Kinzel C et al (2012) Architectural switch in plant photosynthetic membranes induced by light stress. Proc Natl Acad Sci U S A 109:20130–20135 Fristedt R, Granath P, Vener AV (2010) A protein phosphorylation threshold for functional stacking of plant photosynthetic membranes. PLoS One 5:e10963 Samol I, Shapiguzov A, Ingelsson B et al (2012) Identification of a photosystem II phosphatase involved in light acclimation in Arabidopsis. Plant Cell 24:2596–2609 Fristedt R, Wasilewska W, Romanowska E et al (2012) Differential phosphorylation of thylakoid proteins in mesophyll and bundle sheath chloroplasts from maize plants grown under low or high light. Proteomics 12:2852–2861 Turkina MV, Klang Arstrand H, Vener AV (2011) Differential phosphorylation of ribosomal proteins in Arabidopsis thaliana plants during day and night. PLoS One 6:e29307 Ingelsson B, Vener AV (2012) Phosphoproteomics of Arabidopsis chloroplasts reveals involvement of the STN7 kinase in phosphorylation of nucleoid protein pTAC16. FEBS Lett 586:1265–1271

16. Kieselbach T, Hagman Å, Andersson B et al (1998) The thylakoid lumen of chloroplasts. Isolation and characterization. J Biol Chem 273:6710–6716 17. Laemmli UK (1970) Cleavage of structural proteins during assembly of the head of bacteriophage T4. Nature 227:680–685 18. Aboulaich N, Vainonen JP, Stralfors P et al (2004) Vectorial proteomics reveal targeting, phosphorylation and specific fragmentation of polymerase I and transcript release factor (PTRF) at the surface of caveolae in human adipocytes. Biochem J 383: 237–248 19. Vener AV, Stralfors P (2005) Vectorial proteomics. IUBMB Life 57:433–440 20. Turkina MV, Vener AV (2007) Identification of phosphorylated proteins. Methods Mol Biol 355:305–316 21. Turkina MV, Kargul J, Blanco-Rivero A et al (2006) Environmentally modulated phosphoproteome of photosynthetic membranes in the green alga Chlamydomonas reinhardtii. Mol Cell Proteomics 5:1412–1425 22. Ficarro S, Chertihin O, Westbrook VA et al (2003) Phosphoproteome analysis of capacitated human sperm. Evidence of tyrosine phosphorylation of a kinase-anchoring protein 3 and valosin-containing protein/p97 during capacitation. J Biol Chem 278: 11579–11589 23. He T, Alving K, Feild B et al (2004) Quantitation of phosphopeptides using affinity chromatography and stable isotope labeling. J Am Soc Mass Spectrom 15:363–373 24. Steen H, Jebanathirajah JA, Springer M et al (2005) Stable isotope-free relative and absolute quantitation of protein phosphorylation stoichiometry by MS. Proc Natl Acad Sci U S A 102:3948–3953

Chapter 10 Phosphopeptide Immuno-Affinity Enrichment to Enhance Detection of Tyrosine Phosphorylation in Plants Sharon C. Mithoe and Frank L.H. Menke Abstract Tyrosine (Tyr) phosphorylation plays an essential role in signaling in animal systems, but the relative contribution of Tyr phosphorylation to plant signal transduction has, until recently, remained an open question. One of the major issues hampering the analysis is the low abundance of Tyr phosphorylation and therefore underrepresentation in most mass spec-based proteomic studies. Here, we describe a working approach to selectively enrich Tyr-phosphorylated peptides from complex plant tissue samples. We describe a detailed protocol that is based on immuno-affinity enrichment step using an anti-phospho-tyrosine (pTyr)-specific antibody. This single enrichment strategy effectively enriches pTyr-containing peptides from complex total plant cell extracts, which can be measured by LC-MS/MS without further fractionation or enrichment. Key words Immuno-affinity enrichment, Tyrosine, Posttranslational modifications, Phosphorylation, Arabidopsis, Metabolic labeling, Mass spectrometry

1

Introduction Protein phosphorylation is considered to be a central mechanism for regulation of growth and cellular signaling in eukaryotes. Tyrosine (Tyr) phosphorylation has been extensively studied in most eukaryotes and has been described to have an important role in developmental processes and human diseases [1–3]. Relatively little is known about the role of Tyr phosphorylation in plant systems, but recent evidence may suggest that, similar to animal systems, Tyr phosphorylation may play an important role in regulation of receptor kinases [4, 5]. However, the analysis of Tyr phosphorylation can be a challenging quest, due to the low abundance of tyrosine-phosphorylated (pTyr) residues, which is between 0.5 and 3 % of the total phosphoproteome [6, 7]. By comparison, the levels of phosphorylated threonine (pThr) contribute about 15–20 % and phosphorylated serine (pSer) about 70–75 % of the phosphoproteome. Therefore, initial studies of the plant phosphoproteome

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that used suboptimal phosphopeptide enrichment methods detected mostly peptides phosphorylated on Ser and Thr residues [8–10]. More recently, modified and targeted phosphopeptide enrichment approaches have shown that the levels of tyrosine phosphorylation in plants are similar to those in animal systems [11–13]. A targeted approach based on immunoprecipitation (IP) of tyrosine-phosphorylated peptides with phospho-tyrosine (pY)-specific antibodies was introduced for the use in animal cells [1, 14, 15]. The use of pY antibodies has also been shown to detect tyrosine phosphorylation in plant protein samples [16]. A number of enrichment techniques are reviewed by Thingholm et al. [17] including TiO2 and immobilized metal affinity chromatography (IMAC); however these methods for enrichment are more effective for general enrichment of all kinds of phosphorylation, including very abundant phosphopeptides. They may not be selective enough to detect pTyr due to the low stoichiometry of these modifications. Here we describe an approach for the targeted analysis of Tyr phosphorylation in plants, using an immune-affinity purification strategy. Our protocol is robust and simple and can be combined with a metabolic labeling strategy of plant cells or post-extraction labeling that allows direct comparison of changes in Tyr phosphorylation in a complex cell extract. With the recent improvements of effective 15N metabolic labeling of entire seedlings [18] this protocol can also be applied to detect pTyr signaling in intact seedlings and plants.

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Materials Prepare all solutions using double-deionized water (MilliQ). Prepare and store all solutions at room temperature (RT), unless indicated otherwise. Clean all glassware and tubes suitable for Sorvall SW34 rotor centrifuge with an anionic detergent such as Alconox, to prevent protein degradation. Rinse several times with demineralized (demi) water and air-dry (see Note 1). The chemicals used in this protocol can be purchased from Sigma-Aldrich, unless indicated otherwise.

2.1 Plant Growth and Harvest

1. Arabidopsis cell culture medium. Dissolve 3.2 g Gamborg’s B5 Basal medium with minimal organics (Sigma G5893 or Duchefa G0210) in 800 ml of MilliQ water, add 30 g sucrose, and add 25 μl 40 mM 1-naphthaleneacetic (1-NAA, final concentration 1 μM). Adjust pH to 5.8 with KOH and adjust volume to 1,000 ml. Aliquot 50 ml medium in 250 ml conical flasks, cap with aluminum foil, and sterilize at 121 °C for 20 min (see Note 2).

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2. Seedling growth medium. Dissolve 2.2 g Murashige and Skoog basal salts with vitamins (Sigma M5524) in 800 ml of MilliQ water, add 10 g sucrose, adjust pH to 5.8 with KOH, and adjust volume to 1,000 ml. Aliquot 50 ml medium in 250 ml conical flasks, cap with aluminum foil, and sterilize at 121 °C for 20 min. 3. Arabidopsis cell cultures. Grow 50 ml cell suspension cultures of Arabidopsis thaliana cells with shaking at 125 rpm and 8 h of light at 22 °C. Subculture the cells by transferring 6–8 ml cell culture into 50 ml fresh medium every 7 days. 4. Arabidopsis seedlings. Weigh 20 mg of Arabidopsis seeds (approximately 1,000 seeds) and transfer to 1.5 ml tube. Sterilize seeds for 10–15 min with 1 % hypochlorite and wash sterilized seeds four to five times with sterile water to remove the hypochlorite completely. Incubate the seeds in the last wash at 4 °C for 24–48 h to stratify. After stratification completely remove the water and resuspend in 1 ml of ½ Murashige and Skoog medium. Transfer the sterilized seeds to a conical flask with 50 ml of seedling growth medium and incubate for 7–10 days on a shaker at 22 °C in the light (see Note 3). 5. Büchner funnel or filtration device. 6. 30 μM Nitex Nylon mesh disks (Genesee Scientific, San Diego, CA, USA), cut to the diameter of Büchner funnel. 7. Büchner flask. 8. Vacuum pump. 9. KCl wash buffer: 20 mM KCl. Dilute 1 M KCl stock in MilliQ water and incubate on ice to cool prior to use. 2.2 Protein Extraction

1. MS extraction buffer: 8 M urea, 25 mM ammonium bicarbonate, 1 mM potassium fluoride (NaF), 1 mM sodium orthovanadate (Na3VO4), 5 mM sodium phosphate (NaH2PO4), 1 mM dithiothreitol (DTT), 1 mM phenylmethylsulfonyl fluoride (PMSF). Prepare buffer fresh just before use (see Note 4). Weigh 121.2 g urea (Sigma), transfer to a 500 ml glass cylinder, add water to a volume of 200 ml, and stir at RT. Weigh 494.1 mg ammonium bicarbonate and add to the urea solution. To prevent protein degradation and dephosphorylation also add 2.5 ml 100 mM potassium fluoride (NaF), 2.5 ml 100 mM sodium orthovanadate (Na3VO4), and 2.5 ml 100 mM sodium phosphate (NaH2PO4). Keep stirring the solution in the laminar flow. Finally add 1 ml 250 mM PMSF and 250 μl 1 M DTT. Add water to a final volume of 250 ml and use the buffer immediately (see Note 5).

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2. Potter-Elvehjem homogenizer. 3. 60 ml Potter-Elvehjem grinding vessel and pestle (Sartorius). 4. Sorvall centrifuge and SV34 rotor and tubes. 5. Bradford reagent (Biorad). 2.3

Trypsin Digestion

1. DTT stock: 1 M DTT: Dissolve 154 mg of DTT in total volume of 10 ml MilliQ water and store in aliquots at −20 °C for 3 months. 2. Ammonium bicarbonate buffer: 50 mM NH4HCO3: Dissolve 988.25 mg of ammonium bicarbonate in 250 ml of MilliQ water. Prepare fresh just before use. 3. Alkylation solution: 700 mM Iodoacetamide (IAA): Dissolve 129.26 mg of IAA in 1 ml of MilliQ water. Prepare fresh just before use, or aliquot and store at −20 °C for up to 3 months. 4. Sequencing-grade trypsin, 20 μg/50 μl (Promega).

2.4

Peptide Cleanup

1. C18 Sep-Pak columns (Waters, Milford, MA, USA). 2. 100 % acetonitrile. 3. 5–40 % Acetonitrile and 0.1 % acetic acid solutions. 4. 0.1 % Acetic acid solution. 5. 2–3 % Acetic acid solution.

2.5 Immunoprecipitation

1. IP buffer: 50 mM Tris pH 7.4, 150 mM NaCl, 1 % n-octyl-βD-glucopyranoside (NOG). Prepare IP buffer fresh from stock solutions. Cool the IP buffer on ice prior to use. 2. 40× Complete Mini: Dissolve one tablet of Complete Mini Protease inhibitor in 262.5 μl MilliQ water. 3. pTyr (PY99) antibody beads (Santa Cruz Biotechnology, CA, USA).

2.6 Concentration of Peptides

1. C18 Micro SpinColumn (Harvard Apparatus, Holliston, MA, USA; Catalogue number 74-4601). 2. MeOH. 3. 80 % Acetonitrile, 0.1 % trifluoroacetic acid (TFA). 4. 40 % Acetonitrile, 0.1 % TFA. 5. 2 % Acetonitrile, 0.1 % TFA. 6. 0.1 % FA in water.

3

Methods

3.1 Treatment and Harvesting Tissue

Use 4-day-old Arabidopsis cell cultures or 7–10-day-old liquidgrown seedlings. Treat or incubate with stimulus as appropriate (see Note 6). Pool three separately stimulus-treated cultures and

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harvest by vacuum filtration using a Büchner funnel fitted with a 30 micron mesh. Wash twice with ice-cold KCl buffer while vacuum is maintained on Büchner funnel. Transfer filtrated cell mass to aluminum foil, fold into packets, and freeze immediately in liquid nitrogen or proceed with homogenization immediately (see Note 7). 3.2 Isolation of Whole-Cell Extracts

1. Transfer the harvested cells or seedlings to the Potter-Elvehjem tube and add 25 ml ice-cold MS extraction buffer. Insert the Potter tube in the ice jacket and secure tightly. Insert the Potter pestle into the tube and grind for 8 min at 1,000 rpm (see Note 8). 2. Centrifuge the cell homogenate for 30 min at 10,000 × g (Sorvall SW34 rotor) at 4 °C to remove all particulate matter. 3. Pipette the supernatant, which contains the soluble proteins, carefully off, and transfer to a new tube. 4. Keep the supernatant on ice while the protein concentration is measured using the Bradford protein assay. 5. Dilute in MS extraction buffer to adjust to a final protein concentration of 6 mg/ml (see Note 9). 6. Snap freeze the protein samples in liquid nitrogen and store at −80 °C until use.

3.3

Trypsin Digestion

1. For each sample use 6 mg of protein extract as starting material (see Note 9). 2. Add 10 μl DTT stock solution to a final concentration of 10 mM and incubate for 30 min at 50 °C to reduce the proteins. 3. Cool to room temperature. 4. Add 80 μl iodoacetamide solution for a final concentration of 55 mM and leave for 30 min in the dark at RT to alkylate the cysteine residues, thus preventing reformation of the disulfide bonds. 5. Dilute the samples to a final concentration of less then 2 M urea with 5 ml of ammonium bicarbonate buffer. 6. Add 60 μg of trypsin to each sample for a ratio of 1:100 (trypsin:substrate). 7. Digest overnight at 37 °C with gentle shaking in the dark (see Note 10). 8. Add 2–3 % acetic acid to the digest to lower the pH to 2–3 to quench enzyme activity. 9. Centrifuge for 5 min at 2,000 × g to remove any precipitates, transfer supernatant to a new tube, and keep on ice.

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3.4 Desalting by C18 Solid-Phase Extraction

To purify the digest use C18 Sep-Pak columns (6 cc) and precondition the columns before loading the cleared digest containing peptides (see Note 11). 1. Use 5 ml 100 % acetonitrile to precondition the C18 column and allow to empty by gravity. 2. Equilibrate the column with a total of 7 ml (2 × 3.5 ml) of 0.1 % acetic acid. 3. Load the acidified peptide digest samples at a relatively low flow onto the column to ensure maximum peptide binding. Allow to empty by gravity. 4. Wash the column with a total volume of 12 ml (1, 5, 6 ml) of 0.1 % acetic acid. 5. Wash with 2.8 ml of 5 % acetonitrile/0.1 % acetic acid to remove further impurities. 6. Change to a new tube and elute the peptides stepwise with 1.4 ml of increasing organic solution each of 10 %, 15 %, 20 %, 25 %, 30 %, 35 %, and 40 % acetonitrile in 0.1 % acetic acid. 7. Collect the eluate in a single fraction, vortex the fraction, separate into two tubes of 5 ml each, snap freeze in liquid nitrogen, and lyophilize overnight.

3.5 Immunoprecipitation of pY Peptides

1. Remove the lyophilized peptides from the freeze-dryer and spin down briefly. 2. Resuspend peptides in 800 μl of ice-cold IP buffer and incubate for 10 min at RT. 3. Vortex the mixture gently and leave for an additional 20 min at RT with shaking to further dissolve the peptides. The pH of the sample influences the binding efficiency; therefore we suggest to test the pH of the peptide mixture and adjust to pH 7.4 if needed. 4. Meanwhile prepare the PY99 beads (see Note 12). Use a cut pipette tip to take out 45–50 μl of PY99 agarose beads and put into a 1.5 ml tube (see Note 13). 5. Equilibrate and wash by adding 1 ml of cold IP buffer to the PY99 beads; mix by hand to fully resuspend the beads and centrifuge for 1 min at 1,500 × g at 4 °C. 6. Remove supernatant and repeat the wash step twice (see Note 14). 7. Remove peptide mixture from the shaker and add the peptides into the tube containing the washed PY99 beads. 8. Mix the peptides and the antibody beads carefully, but thoroughly and incubate overnight with slow rotation at 4 °C (see Note 15).

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9. After overnight incubation, centrifuge the peptide-PY99 bead mixture at 1,500 × g for 1 min at 4 °C. Remove the supernatant, which contains mainly non-phosphorylated peptides. 10. Wash the beads with 1 ml of IP buffer to remove unspecifically bound peptides and centrifuge at 1,500 × g for 1 min at 4 °C. Remove supernatant and repeat the washing steps with IP buffer twice. 11. Wash the beads with 500 μl of cold MilliQ water to remove IP buffer components. Repeat MilliQ wash step once. 12. Remove the remaining supernatant carefully to prevent the loss of beads. 13. Elute pTyr peptides from the beads by adding 55 μl of 0.15 % TFA. Mix carefully and leave for 10 min at RT. 14. Spin down beads at 1,500 × g for 1 min and collect the supernatant into a fresh 1.5 ml tube. 15. Repeat elution with 45 μl of 0.15 % TFA as before, combine both collected supernatants in one tube, and mix carefully. 3.6 Concentration of Peptides

Prior to LC-MS\MS analysis the eluted peptides must be desalted and concentrated. The peptide concentration in the eluted fraction is very low, use columns with a small volume, e.g., C18 Stop and Go extraction (STAGE) tips or Micro SpinColumns. 1. Wash Micro SpinColumns twice with 200 μl methanol and spin for 1 min at 1,200 rpm. 2. Wash twice with 200 μl 80 % ACN and 0.1 % TFA and spin for 1–2 min at 1,400 rpm. 3. Equilibrate with 200 μl 2 % ACN and 0.1 % TFA and spin for 2 min at 1,400 rpm. Repeat four times. 4. Load the eluted 95 μl of pTyr peptides and spin for 2 min at 1,400 rpm. Reapply the flow through onto the Micro SpinColumn and spin as before. 5. Wash with 200 μl 2 % ACN and 0.1 % TFA and spin for 2 min at 1,400 rpm; repeat four times. 6. Elute twice with 100–150 μl of 40 % ACN and 0.1 % TFA and spin for 2 min at 1,600 rpm. 7. Combine the flow through from the first and second elutions and dry in speedvac. 8. Dry peptides to approximately 1–2 μl and resuspend in LC-MS loading buffer (1 % formic acid in 2 % methanol) to a total volume of 9 μl.

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3.7 Mass Spectrometry to Detect pTyr Peptides

The conditions and settings to detect pTyr peptides depend on the instrument that is used in the laboratory. Below is described how we performed the analyses of the pTyr experiments using our settings Mithoe et al. [12]. The pTyr peptides are analyzed by LC-MS/MS using an Agilent 1100 nanoflow system (Agilent Technologies, Waldbronn, Germany) connected to an LTQ-Orbitrap mass spectrometer (Thermo Electron, Fisher Scientific, Bremen, Germany). To load the samples we use an auto sampler at a flow rate of 5 μl/min on an in-house-packed 2 cm fused silica precolumn (100 μm inner diameter, 375 μm outer diameter, Aqua™ C18, 5 μm (Phenomenex, Torrance, CA)). Peptides are eluted sequentially by using a linear gradient for 3 h from Solvent A (0.6 % acetic acid in water) to 50 % Solvent B (80 % acetonitrile and 0.5 % acetic acid in water) over the precolumn coupled to an in-house-packed 40 cm resolving column (50 μm inner diameter, 375 μm outer diameter, Resprosil C18-AQ, 3 μm (Dr. Maisch, Ammerbuch, Germany). The mass spectrometer is operated in a data-dependent mode to automatically switch between MS and MS/MS. Survey of full-scan MS spectra (from m/z 300–1,500) is acquired in the Orbitrap at a resolution of 60,000 after accumulation to a target value of 500,000. The most intense ions are sequentially isolated for accurate mass measurements and subsequently fragmented in the linear ion trap using collision-induced fragmentation. The threshold for triggering an MS/MS event is set to 500 counts.

3.8 Database Search and Bioinformatics

All MS/MS spectra files from each LC-MS run are centroided and merged to a single file using Bioworks 3.3 (Thermo Electron). Protein identification is performed using the Mascot search engine (version 2.1.0, Matrix Science, UK) by searching against the publicly available Arabidopsis database (TAIR 10) using standard scoring. Searches are performed with trypsin as the proteolytic enzyme, and allowing up to two miscleavages. The mass tolerance for the precursor ions was set to 10 ppm, and the mass deviation for fragment ions was set to 0.9 Da for MS/MS. Carbamidomethylation of cysteines is chosen as a fixed modification, while oxidized methionine and phosphorylation of Ser, Thr, and Tyr are set as variable modifications. Peptides are assigned to the first protein hit by Mascot. Individual MS/MS spectra from the pY phosphopeptides that are further validated have to have a Mascot score > = 20 to reduce the probability of inclusion of false positives. Additionally, all identified Tyrphosphorylated peptides are manually validated Mithoe et al. [12].

4

Notes 1. The use of Alkanox detergent to clean glassware and tubes is recommended. Immerse in Alkanox solution o/n and next day rinse thoroughly with water. Rinsed glassware and tubes should

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then be immersed o/n in an acid rinse bath (0.1 % HCL) to remove any remaining detergent. Rinse the following day with deionized water several times and dry. Work with gloves as Alkanox and dilute HCl are corrosive substances and prevent/ limit contamination of glassware. 2. Arabidopsis cell culture medium can also be prepared from salts to facilitate metabolic labeling as described in Benschop et al. [8]. Stock solutions of all the individual components, except for the nitrate and ammonium salts, can be made and stored at 4 °C. 15N salts of ammonium sulfate and potassium nitrate can be bought from Spectra Stable Isotopes (Columbia, MD, USA). 3. Incubation of the seedlings in liquid medium on a shaker at 125 rpm in the light will not stress the seedlings according to the test we have done. Shaking ensures that all seedlings are equally exposed to media, light, and air. The seedling germinates in about 48 h in the liquid medium and initially floats around individually. After about 7–10 days the seedlings will have clumped together into a dark green ball. The advantage is that treatment and harvesting of the seedlings can be done more easily as compared to seedlings growing on plates. 4. Prepare stock solutions of phosphatase inhibitors NaF (100 mM), Na3VO4 (100 mM), and NaH2PO4 (100 mM) beforehand and store at RT. PMSF stock is made by dissolving PMSF in methanol and storing at −20 °C. Shield the PMSF stock from light by wrapping stock bottle in aluminum foil. DTT stock is made in water at a concentration of 1 M and stored in aliquots at −20 °C for 3 months. 5. PMSF is unstable in aqueous solutions and the buffer should be used as soon as possible after the addition of PMSF. 6. In our study we used flg22 as an elicitor; however this protocol is generic and can be easily applied to other stress conditions and responses to environmental changes to elucidate other signal transduction networks in plants. We treated for exactly 10 min with 1 μM flg22 and harvested cells by vacuum filtration. Cultured cells and young seedlings have relatively small vacuoles and produce higher protein yields as compared to Arabidopsis leave tissue. 7. Harvesting with a Büchner funnel or similar filtration device under vacuum will ensure complete removal of culture medium and maximize the protein content of the extract. Working quickly is essential to ensure that no additional stress is applied to the plant material. Washing with ice-cold KCl buffer suspends the cells in their current state and removes cell wall debris and death cells from the plant material prior to extraction. Replicate the procedure for all samples as identical as

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possible, including for the mock or control samples. When seedlings are used, remove as much liquid by tapping dry on tissue paper prior to freezing in liquid nitrogen. 8. The Potter-Elvehjem homogenizer uses a tube and pestle that tightly fit together and allows for gentle homogenization of samples. The tube is cooled by an ice jacket to prevent the friction of the grinding heating up the sample. Start homogenizing immediately, initially at lower speed to ensure that the pestle does not get stuck during the up and motion of the homogenization. Continue to homogenize for 8 min and ensure that all tissue is passed through space between the tube and the pestle several times. Frozen tissue may need more work to homogenize, especially when frozen cell culture tissue is used. Alternatively the tissue can be ground to a powder in liquid nitrogen first, before homogenization with the Potter. 9. It is essential to have a high concentration of protein in the extract prior to proceeding and convenient to use the samples all at the same concentration before starting with the digestion. Due to the low percentage of Tyr phosphorylation we found it necessary to start with a relatively large amount of input material for the IP. The suggested concentration of 6 mg/ml has been optimized in pTyr IP using tissue-cultured mammalian cells and was shown to work equally well with Arabidopsis total cell extracts. Extract from other plant species and or tissues will require further optimization. 10. The enzyme digestion step is crucial for successful immuneaffinity enrichment. Therefore we suggest to run a small digested aliquot and perform routine LC MS/MS analyses to ensure that the proteins have completely been digested. 11. Prior to incubating with pY antibodies it is recommended to desalt and purify the digest to ensure maximum antigenantibody affinity. 12. There are a variety of anti-phosphotyrosine antibodies available for IP purposes and we used the PY99 antibody (Santa Cruz biotechnology, CA, USA). PY99 beads were shown to be sensitive and more specific [1]. However it has also been shown that different pTyr antibodies can bring down partially overlapping sets of pTyr peptides [19]. 13. The amount of beads needs to be optimized for each experiment; we found that 45–50 μl of PY99 beads works best with 6 mg of protein as input. 14. Beads must be thoroughly washed in order to remove glycerol or other storing agents before adding the peptide mixture to the beads.

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15. Shorter incubation times at RT may be considered; however this may result in decreased specific binding of pTyr peptides and a relative increase in the background of non-phosphorylated peptides in the eluted sample.

Acknowledgement This work was partly financed through Gatsby Charitable Foundation. References 1. Boersema PJ, Foong LY, Ding VM, Lemeer S, van Breukelen B, Philp R, Boekhorst J, Snel B, den Hertog J, Choo AB, Heck AJ (2010) In-depth qualitative and quantitative profiling of tyrosine phosphorylation using a combination of phosphopeptide immunoaffinity purification and stable isotope dimethyl labeling. Mol Cell Proteomics 9(1):84–99 2. Ding VM, Boersema PJ, Foong LY, Preisinger C, Koh G, Natarajan S, Lee DY, Boekhorst J, Snel B, Lemeer S, Heck AJ, Choo A (2011) Tyrosine phosphorylation profiling in FGF-2 stimulated human embryonic stem cells. PLoS One 6(3):e17538 3. Del Rosario AM, White FM (2010) Quantifying oncogenic phosphotyrosine signaling networks through systems biology. Curr Opin Genet Dev 20(1):23–30. doi:10.1016/j. gde.2009.12.005 4. Lemmon MA, Schlessinger J (2010) Cell signaling by receptor tyrosine kinases. Cell 141(7): 1117–1134 5. Macho AP, Schwessinger B, Ntoukakis V, Brutus A, Segonzac C, Roy S, Kadota Y, Oh MH, Sklenar J, Derbyshire P, Lozano-Duran R, Malinovsky FG, Monaghan J, Menke FL, Huber SC, He SY, Zipfel C (2014) A bacterial tyrosine phosphatase inhibits plant pattern recognition receptor activation. Science 343(6178):1509–1512 6. Mithoe SC, Menke FLH (2011) Phosphoproteomics perspective on plant signal transduction and tyrosine phosphorylation. Phytochemistry 72(10):997–1006 7. de la Fuente van Bentem S, Hirt H (2009) Protein tyrosine phosphorylation in plants: More abundant than expected? Trends Plant Sci 14(2):71–76. doi:10.1016/j.tplants. 2008.11.003 8. Benschop JJ, Mohammed S, O’Flaherty M, Heck AJR, Slijper M, Menke FLH (2007) Quantitative Phosphoproteomics of Early

9.

10.

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12.

13.

14.

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Elicitor Signaling in Arabidopsis. Mol Cell Proteomics 6(7):1198–1214 Nuhse TS, Stensballe A, Jensen ON, Peck SC (2004) Phosphoproteomics of the Arabidopsis plasma membrane and a new phosphorylation site database. Plant Cell 16(9):2394–2405 de la Fuente van Bentem S, Anrather D, Dohnal I, Roitinger E, Csaszar E, Joore J, Buijnink J, Carreri A, Forzani C, Lorkovic ZJ, Barta A, Lecourieux D, Verhounig A, Jonak C, Hirt H (2008) Site-specific phosphorylation profiling of Arabidopsis proteins by mass spectrometry and peptide chip analysis. J Proteome Res 7(6):2458–2470 Sugiyama N, Nakagami H, Mochida K, Daudi A, Tomita M, Shirasu K, Ishihama Y (2008) Large-scale phosphorylation mapping reveals the extent of tyrosine phosphorylation in Arabidopsis. Mol Syst Biol 4 Mithoe SC, Boersema PJ, Berke L, Snel B, Heck AJ, Menke FL (2012) Targeted quantitative phosphoproteomics approach for the detection of phospho-tyrosine signaling in plants. J Proteome Res 11(1):438–448 Grimsrud PA, den Os D, Wenger CD, Swaney DL, Schwartz D, Sussman MR, Ane JM, Coon JJ (2010) Large-scale phosphoprotein analysis in Medicago truncatula roots provides insight into in vivo kinase activity in legumes. Plant Physiol 152(1):19–28. doi:10.1104/ pp.109.149625 Zhang Y, Wolf-Yadlin A, Ross PL, Pappin DJ, Rush J, Lauffenburger DA, White FM (2005) Time-resolved mass spectrometry of tyrosine phosphorylation sites in the epidermal growth factor receptor signaling network reveals dynamic modules. Mol Cell Proteomics 4(9): 1240–1250 Rikova K, Guo A, Zeng Q, Possemato A, Yu J, Haack H, Nardone J, Lee K, Reeves C, Li Y, Hu Y, Tan Z, Stokes M, Sullivan L, Mitchell J, Wetzel R, Macneill J, Ren JM, Yuan J,

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Bakalarski CE, Villen J, Kornhauser JM, Smith B, Li D, Zhou X, Gygi SP, Gu TL, Polakiewicz RD, Rush J, Comb MJ (2007) Global survey of phosphotyrosine signaling identifies oncogenic kinases in lung cancer. Cell 131(6):1190–1203 16. Oh MH, Wang X, Kota U, Goshe MB, Clouse SD, Huber SC (2009) Tyrosine phosphorylation of the BRI1 receptor kinase emerges as a component of brassinosteroid signaling in Arabidopsis. Proc Natl Acad Sci U S A 106(2):658–663

17. Thingholm TE, Jensen ON, Larsen MR (2009) Analytical strategies for phosphoproteomics. Proteomics 9(6):1451–1468 18. Bindschedler LV, Palmblad M, Cramer R (2008) Hydroponic isotope labelling of entire plants (HILEP) for quantitative plant proteomics; an oxidative stress case study. Phytochemistry 69(10):1962–1972 19. Lind SB, Artemenko KA, Pettersson U (2012) A strategy for identification of protein tyrosine phosphorylation. Methods 56(2):275–283

Chapter 11 The Peptide Microarray ChloroPhos1.0: A Screening Tool for the Identification of Arabidopsis thaliana Chloroplast Protein Kinase Substrates Anna Schönberg and Sacha Baginsky Abstract We designed the peptide microarray ChloroPhos1.0 to screen for substrates of chloroplast protein kinases. The peptides represented on the microarray were selected from phosphoproteomics data, and the identified chloroplast phosphopeptides were spotted as 15-mers on a glass slide with the phosphorylation site centered. Altogether, 905 distinct peptides from chloroplast proteins are present on the array. Here we describe how the array can be used to identify the target protein spectrum of chloroplast kinases. We present the method and discuss limitations and challenges associated with the determination of phosphorylation activity on peptide substrates in vitro. Key words Chloroplast, Peptide array, Kinase substrate, Plastid, In vitro kinase assay, Substrate specificity

1

Introduction One of the challenging tasks of contemporary large-scale phosphorylation network analysis is the identification of protein kinase substrates. While this is a straightforward task for a kinase with its anticipated substrates, the situation is more complicated when dealing with a new protein kinase for which no substrates are known or anticipated [1]. In this case, large-scale screening methods are required that allow analyzing kinase motif preference and substrate identities. In recent years, several such methods have become available. Among these are comparative phosphoproteomics methods that allow determining the phosphorylation status of proteins in vivo for example in a kinase mutant compared to wild type. This way, insights into the in vivo phosphorylation activity of selected kinases are obtained. The in vitro identification of kinase targets at large scale is easier to achieve and does not need sophisticated mass spectrometry equipment. For example, kinase

Waltraud X. Schulze (ed.), Plant Phosphoproteomics: Methods and Protocols, Methods in Molecular Biology, vol. 1306, DOI 10.1007/978-1-4939-2648-0_11, © Springer Science+Business Media New York 2015

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activity can be analyzed on several dozen to hundred substrates in parallel on protein or peptide microarrays. In recent years, peptide arrays have become a successful screening method because of improvement and simplification of peptide syntheses. Peptide Arrays assay the activity of a kinase on several hundred putative phosphorylation targets in parallel using synthetic peptides with a length of around 12–18 amino acids. Such peptides are offered to the kinase either in solution as in the “Kinase Client (KIC)-assay” in which phosphorylated peptides are identified and quantified by mass spectrometry [2], or immobilized on a glass slide. The primary determinant of kinase substrate specificity is the catalytic cleft, e.g., its depth, hydrophobicity, and charge distribution. Apart from the phosphorylated residue, the amino acids that are directly neighboring the phosphorylation site are the most important specificity determinants for the recognition of a substrate by its kinase. It was shown, that the active site of a kinase interacts roughly with four amino acids up- and downstream of the phosphorylation site [3]. Thus, peptides are suitable substrates for in vitro assays of kinase activity. With some limitation, phosphorylation of a peptide will correctly reflect the specificity of a protein kinase. Among these limitations is the fact the interaction of a kinase with its substrate is not correctly reflected, because of docking sites or scaffold proteins that are not represented in a short peptide. Thus, using peptides as kinase targets may result in an underestimation of phosphorylation rates. Using the phosphopeptide array reported here, we could not observe phosphorylation signals with crude extracts, supporting the lower phosphorylation activity of kinases on peptide substrates. The same holds true for scaffold proteins that are required to establish a productive kinase/substrate interaction in vivo. Both cases result in “false negative” assignments, i.e., peptides may not be phosphorylated in vitro although the proteins they originate from may be in vivo targets of the kinase under investigation. In every in vitro experiment, the concentration ratio between kinase and its substrate that exists in vivo is neglected. This may result in “false positive” assignments because protein concentrations are a potential regulator of kinase activity in vivo, operating for example by substrate competition [3].

2

Materials

2.1 Generating the Peptide Microarray

Peptide synthesis and their spotting on the microarray were outsourced to Steinbeis GmBH. For details on chemicals, material for peptide synthesis and microarray production we refer the reader to [4].

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2.2 Kinase Activity Assays on the Microarray ChloroPhos1.0

1. Blocking buffer: 50mM Tris–HCl pH 8.0, 150 mM NaCl, 3 % bovine serum albumin (w/v).

2.2.1 Constructing the Humidity Chamber

3. Tissues.

2. Plastic box with sealing 150 mm × 150 mm × 50 mm).

lid

(minimum:

4. Disposable petri dish. 5. Microarray slide (“ChloroPhos1.0”) and dummy slide (glass slide without spotted peptides). 6. Plastic spacers (30 mm × 3 mm × 0.8 mm). 7. Tweezers.

2.2.2 Incubation of Active Kinase Preparations on the Microarray Preparation of the Nonradioactive Incubation Mixture

1. Active protein kinase preparation (max. 300 μL for radioactive experiment and max. 600 μL for Pro Q diamond experiment) (see Note 1). 2. 100 mM MgCl2 solution. 3. 10× kinase activity buffer (KAP-buffer): 500 mM Tris–HCl pH 7.5, 500 mM NaCl, 10× PhosStop (Roche) and 1 % protease inhibitor cocktail (Sigma P9599). 4. Double distilled water. 5. 1 M ATP stock solution (pH 7.5). 6. Cellulose acetate filter (pore size: 0.2 μm) (see Note 2).

Preparation of the Radioactive Incubation Mixture 2.2.3 Washing of the Microarray

1. [γ-33P] ATP (20–80 pmol). 2. 1.5 mL reaction tubes with safe-lock cap (see Note 3). 1. Two tweezers. 2. Beaker (diameter: 100 mm; height: 150 mm). 3. 0.5 L 1× TBS, pH 7.5. 4. Plastic box (minimum: 80 mm × 80 mm × 50 mm). 5. Shaker. 6. 0.5 L H3PO4, pH 2.0 (see Note 4). 7. Double distilled water. 8. 0.5 L methanol (see Note 5).

2.3 Phosphorimaging

2.3.1 Conducting a Microarray Experiment with Signal Visualization by Pro Q Diamond®

1. Tissues. 2. Phosphorimager (pixel size < 50 μm; e.g., Fujifilm BAS-1800), screen and cassette (see Note 6). 1. Thermoblock. 2. Pro Q diamond® staining solution. 3. Pro Q diamond® destain solution: 50 mM Na-acetate, pH 4.0, 20 % acetonitrile, 5 % methanol.

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4. Double distilled water. 5. Styrofoam box with sealing lid for light protected incubations (see Note 7). 6. Shaker. 7. Microarray fluorescence scanner (e.g., Molecular Devices Axon GenePix 4000B). 2.4

Data Analysis

1. Image processing program (e.g., Adobe Photoshop, Gimp). 2. Microarray analysis software (e.g., Molecular Devices GenePix® Pro 6.1). 3. Spreadsheet.

3

Methods

3.1 Generating the Peptide Microarray ChloroPhos 1.0

A phosphopeptide library based on published phosphoproteomics data was assembled for the generation of ChloroPhos1.0. Phosphoproteomics data from Arabidopsis thaliana were filtered for chloroplast proteins as described [4]. Altogether, 376 chloroplast phosphoproteins (as of January 2012) were identified from which we extracted 15 mer peptides with the phosphorylation site centered. In case the phosphorylation site could not be unambiguously localized within the peptide, we synthesized alternative peptides with the different hydroxylated amino acid in the center position. In total, the peptide library comprises 905 different chloroplast 15-mer sequences from Arabidopsis thaliana and several peptides that serve as standard controls. Peptide synthesis and microarray generation was performed as described earlier [4–6]. Every peptide was spotted in triplicates next to each other and the entire peptide set was spotted in three identical subarrays on the peptide chip (Fig. 1) (see Note 8).

3.2 Kinase Activity Assays on the Microarray ChloroPhos1.0

The incubation of the peptide chip during the microarray analysis should occur in a humid environment to avoid evaporation of the reaction buffer. This is a potential problem because the reaction mix is incubated between the two glass slides (microarray and dummy) with a large surface and without any sealing. In order to construct a humidity chamber place some watered tissues in a plastic box with sealing lid (see Note 9).

3.2.1 Constructing the Humidity Chamber

1. Block the microarray, the dummy slide and all plastic tips and tubes for 1h at RT in the blocking buffer. Wash afterwards in double distilled water and let them dry. 2. Place a petri dish onto these tissues and deposit a microarray dummy slide on top of this construction (Fig. 2) (see Note 10). 3. Put plastic spacers on the dummy slide, 4 mm distant to the broadside margins. Place the microarray slide with the peptides

Fig. 1 Design of the peptide microarray. (a) Peptides are spotted in triplicate three times on the array. Thus, every peptide is represented nine times on the microarray, and signal intensity is only considered for analysis in case the triplicates give comparable results. (b) Result of a peptide microarray incubated with an active kinase and visualized by either ProQ-diamond® left and middle image or by autoradiography (image on the right). The image on the left represents a mock kinase incubation, and the image in the middle represents the active kinase incubation. The image on the right represents the autoradiography result of the same microarray used for the ProQ-diamond® stain (middle image)

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3 4

6 SL

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WT Fig. 2 Assembly of the microarray sandwich and the humidity chamber. (1) Place the dummy slide at the edge of a petri dish. (2) Place plastic spacers 4 mm distant to each broadside margin. (3, 4) Place the microarray slide with the peptides facing down onto the spacers. Allow for a slight torsion towards the dummy slide. (5) Pipette the reaction mixture into the ledge and correct the position of the microarray slide so that both slides are parallel to each other. (6) This microarray sandwich (MS) and the petri dish (PD) is placed onto the watered tissue (WT) in a humidity chamber. Close the box during the kinase incubation with a sealing lid (SL)

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facing down onto these spacers with a slight torsion towards the dummy slide (see Note 11). A scheme of such a microarray sandwich is depicted in Fig. 2. 3.2.2 Incubation of an Active Kinase Preparation on the Microarray

If you like to perform this microarray experiments without radioactive chemicals and use the phosphoprotein stain ProQ diamond® for signal visualization, omit the following steps and proceed with Subheading 3.2.5. 1. Use BSA coated plastic tips and tubes for the incubation mixture. 2. Prepare an incubation mixture consisting of the active kinase preparation, 40 μL of a 100 mM MgCl2 solution and 40 μL 10× KAP-buffer. 3. Add double distilled water to a volume of 390 μL (see Note 12). Prepare a 100 μM ATP working solution. Filter the incubation mixture and the ATP working solution by a 0.2 μm cellulose acetate filter (see Note 2). 4. This subsequent step must be carried out in an environment with permission for radioactive work. Add 8 μL of the ATP working solution (final concentration: 2 μM) and 20 pmol [γ-33P] ATP (final concentration: 50 nM) to the incubation mixture. The latter should be mixed with the ATP working solution prior to adding it to the active kinase. In our case, this was done by adding 2 μL [γ-33P] ATP to the 8 μL working solution, but the volume ratios may be different depending on the molarity of the [γ-33P] ATP solution. 5. While the volume may differ, it is important to maintain a chemical concentration ratio between radioactive and nonradioactive ATP of >1:40. After adding the ATP, the reaction mixture should be mixed and quickly placed onto the microarray sandwich. Start pipetting the reaction mix carefully between the slides and allow capillary forces to distribute the solution between the two glass slides. 6. Align the upper and lower glass slides so that their orientation is perfectly parallel (see Note 13). Close the humidity chamber entirely and incubate the reaction on the microarray for 2 h at RT (~22 °C).

3.2.3 Washing of the Microarray

1. After incubation, transfer the microarray sandwich with two tweezers grabbing the broadside margins to a beaker filled with 1× TBS pH 7.5. Separate the two slides with the tweezers and transport the microarray slide to a plastic box filled with 1× TBS pH 7.5. Place the microarray inside this box with the peptides facing up and let it wash for 5 min with slow agitation. 2. Change the solution once and repeat this washing step. Afterwards perform two washing steps with H3PO4 pH 2, two

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further washing steps with double distilled water and two washing steps with methanol (see Note 14) [7]. 3. Dry the microarray slide for 3 min at RT. 3.2.4 Phosphorimaging

1. Place the microarray with the peptide surface facing up in a Phosphorimager cassette. Place the Phosphorimager screen with the sensitive surface facing down directly on the microarray. In order to stabilize the assembly, it may be necessary to place tissues on the backside of the screen. Afterwards, close the cassette (see Note 15) and expose the microarray on the screen for at least 4 days (see Note 16). 2. Develop the Phosphorimager screen. Save the image without information loss as a 16-bit .tif file. Assess the image quality by considering the number of unwanted background signals (see Note 17), signal resemblance among the replicate spots (see Note 18) and signal intensity. 3. If your image quality is sufficient for data analysis, proceed with Subheading 3.3.1.

3.2.5 Conducting a Microarray Experiment with Signal Visualization by Pro Q Diamond®

Omit the radioactive part of the kinase incubation on the microarray in case you like to use the Pro Q diamond® stain for signal visualization (see Note 19) [8, 9]. 1. Perform the kinase incubation on the microarray as described under Subheading 3.2.2, but without the addition of radioactive ATP. Perform a parallel mock control experiment on a second microarray. 2. Follow the washing instructions as Subheading 3.2.3 after the incubation.

described

under

3. Stain both microarray slides in the dark with Pro Q diamond® for 30 min. Destain the slides three times for 30 min in the Pro Q diamond® destain solution and wash the microarrays two times for 5 min with double distilled water. Keep the slides during all these staining and destaining steps in the dark. 4. Dry the slides and keep them further protected of light until the fluorescence readout on a microarray fluorescence scanner. 3.3

Data Analysis

3.3.1 Image Processing

3.3.2 Assigning Signal Spots to Peptide Locations

Open the autoradiogram in 16-bit grayscale mode in an image processing program and save the microarray image as a 16-bit .tif file. 1. Open the microarray image in a microarray analysis software, e.g., GenePix® Pro. 2. Set the photomultiplier tube (pmt) gain in a way that the strongest signal does not reach the maximum of the grayscale intensity (see Note 20).

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3. Assign the corresponding peptide sequences to the spot signals by aligning the .gal file (see Note 21). Adjust the spot size and position to the actual signal if necessary (see Note 22). If signal artifacts are overlaying the spot signals, exclude such spots from further analysis by flagging them. Save the raw data as .gpr file (see Note 23). 3.3.3 Signal Intensity Analysis

Use the grayscale intensity median after background subtraction of the spots for further analysis (see Note 24). 1. Average those values over all spots per peptide. Categorize the resulting values for further signal quantification. Therefore, set the strongest signal to 100 % and define for example the following four categories (see Note 25): 3—strong signal >50 % of the maximum signal. 2—medium signal >20 %; 10 %; 1 to the number of representative gene models listed in TAIR10 (arabidopsis.org) suggests that 80 % (21,764 proteins) can be phosphorylated, but currently predicted results by far exceed experimental evidence. 8. For each kinase–target pair the relationship is defined based on knowledge from the original paper. Knowledge of the relationships between kinase and target protein can be very specific (“phosphorylation”, “autophosphorylation”), less specific (“activation”, “interaction”) or rather unspecific (“pathway”) if only the context of involvement is known. For kinase–substrate relationships, the database now hosts about 6,000 relationships covering about 300 kinases. 9. As an option a user can create an account for possible private data handling. 10. The database has mapped the protein identifiers to various formats, and it supports the search from many identifier types, such as AGI codes, Uniprot Accessions, Ensembl, RefSeq, and IPI. 11. This protein chart is used to show the physicochemical properties overlaid with the phosphorylation sites, such as protein disorder score, hydrophobicity, and domain annotations. 12. The phosphorylation sites can be filtered by different studies/ references from the drop-down list. In this way, the user can compare phosphorylation sites discovered by different papers. 13. “NR” means nonredundant. The same peptide can be identified from different studies or even from the same study in multiple replicates. The same peptide only appears once in the peptide list, and then it shows all the different copies in the peptide page and links to all the spectra if available. The peptide can also be filtered by different studies, by selecting the specific study from the drop-down list. 14. This is a hierarchical framework, which provides double links between protein page, phosphosite page, phosphopeptide page, and spectra page. The user can browse the data back and forth freely. There are more information in the phosphoprotein page, such as protein structure, protein–protein interactome, gene ontology annotations, kinase phosphatase information, domain annotation, and user’s comments. 15. If the protein IDs are provided by the user, it has to be separated by space in between. When there is no search result for any of the protein IDs, or the protein ID is incorrect, it will be automatically ignored. 16. The list shows the direct interaction between the bait (the input proteins) and the target (the first tier of neighbors).

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17. The connectivity-maximizing algorithm applies the double-way shortest path to find the existing connections between two proteins. After applying this calculation, all the proteins in the graph can be connected with each other if the connection exists, no matter how long the connection is. This is a way to show the potential interaction pathway between two proteins. References 1. Altelaar AF, Munoz J, Heck AJ (2013) Nextgeneration proteomics: towards an integrative view of proteome dynamics. Nat Genet 14(1):35–48 2. Chung HJ, Sehnke PC, Ferl RJ (1999) The 14-3-3 proteins: cellular regulators of plant metabolism. Trends Plant Sci 4(9):367–371 3. Yaffe MB (2002) Phosphotyrosine-binding domains in signal transduction. Nat Rev Mol Cell Biol 3(3):177–186 4. Pawson T (2004) Specificity in signal transduction: from phosphotyrosine-SH2 domain interactions to complex cellular systems. Cell 116(2):191–203 5. Pawson T, Gish GD (1992) SH2 and SH3 domains: from structure to function. Cell 71:359–362 6. Camoni L, Iori V, Marra M, Aducci P (2000) Phosphorylation-dependent interaction between plant plasma membrane H(+)ATPase and 14-3-3 proteins. J Biol Chem 275(14): 99919–99923 7. Hrabak EM, Chan CW, Gribskov M, Harper JF, Choi JH, Halford N, Kudla J, Luan S, Nimmo HG, Sussman MR, Thomas M, WalkerSimmons K, Zhu JK, Harmon AC (2003) The Arabidopsis CDPK-SnRK superfamily of protein kinases. Plant Physiol 132(2): 666–680 8. Xue L, Wang P, Wang L, Renzi E, Radivojac P, Tang H, Arnold R, Zhu JK, Tao WA (2013) Quantitative measurement of phosphoproteome response to osmotic stress in arabidopsis based on Library-Assisted eXtracted Ion Chromatogram (LAXIC). Mol Cell Proteomics 12(8):2354–2369 9. Umezawa T, Sugiyama N, Takahashi F, Anderson JC, Ishihama Y, Peck SC, Shinozaki K (2013) Genetics and phosphoproteomics reveal a protein phosphorylation network in the abscisic acid signaling pathway in Arabidopsis thaliana. Science Signaling 6(270):rs8 10. Wang X, Goshe MB, Sonderblom EJ, Phinney BS, Kuchar JA, Li J, Asami T, Yoshida S, Huber SC, Clouse SD (2005) Identification and functional analysis of in vivo phosphorylation sites

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of the Arabidopsis Brassinosteroid-insensitive 1 receptor kinase. Plant Cell 17:1685–1703 Wu X, Sanchez-Rodriguez C, Pertl-Obermeyer H, Obermeyer G, Schulze WX (2013) Sucroseinduced receptor kinase SIRK1 regulates a plasma membrane aquaporin in Arabidopsis. Mol Cell Proteomics 12(10):2856–2873 Wang P, Xue L, Batelli G, Lee S, Hou YJ, Van Oosten MJ, Zhang H, Tao WA, Zhu JK (2013) Quantitative phosphoproteomics identifies SnRK2 protein kinase substrates and reveals the effectors of abscisic acid action. Proc Natl Acad Sci U S A 110(27):11205–11210 Lan P, Li W, Wen TN, Schmidt W (2012) Quantitative phosphoproteome profiling of iron-deficient Arabidopsis roots. Plant Physiol 159(1):403–417 Reiland S, Messerli G, Baerenfäller K, Gerrits B, Endler A, Grossmann J, Gruissem W, Baginsky S (2009) Large-scale Arabidopsis phosphoproteome profiling reveals novel chloroplast kinase substrates and phosphorylation networks. Plant Physiol 150(2):889–903 Nakagami H, Sugiyama N, Mochida K, Daudi A, Yoshida Y, Toyoda T, Tomita M, Ishihama Y, Shirasu K (2010) Large-scale comparative phosphoproteomics identifies conserved phosphorylation sites in plants. Plant Physiol 153: 1161–1174 Li H, Wong WS, Zhu L, Guo HW, Ecker J, Li N (2009) Phosphoproteomic analysis of ethylene-regulated protein phosphorylation in etiolated seedlings of Arabidopsis mutant ein2 using two-dimensional separations coupled with a hybrid quadrupole time-of-flight mass spectrometer. Proteomics 9(6):1646–1661 Chen Y, Höhenwarter W, Weckwerth W (2010) Comparative analysis of phytohormoneresponsive phosphoproteins in Arabidopsis thaliana using TiO2-phosphopeptide enrichment and MAPA. Plant J 63(1):1–17 Engelsberger WR, Schulze WX (2012) Nitrate and ammonium lead to distinct global dynamic phosphorylation patterns when resupplied to nitrogen starved Arabidopsis seedlings. Plant J 69(6):978–995

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19. Niittylä T, Fuglsang AT, Palmgren MG, Frommer WB, Schulze WX (2007) Temporal analysis of sucrose-induced phosphorylation changes in plasma membrane proteins of Arabidopsis. Mol Cell Proteomics 6(10): 1711–1726 20. Kline KG, Barrett-Wilt GA, Sussman MR (2010) In planta changes in protein phosphorylation induced by the plant hormone abscisic acid. Proc Natl Acad Sci U S A 107(36):15986–15991 21. Hardin SC, Larue CT, Oh MH, Jain V, Huber SC (2009) Coupling oxidative signals to protein phosphorylation via methionine oxidation in Arabidopsis. Biochem J 422(2):305–312 22. van Noort V, Seebacher J, Bader S, Mohammed S, Vonkova I, Betts MJ, Kühnert S, Kumar R, Maier T, O'Flaherty M, Rybin V, Schmeisky A, Yus E, Stülke J, Serrano L, Russell RB, Heck AJ, Bork P, Gavin AC (2012) Cross-talk between phosphorylation and lysine acetylation in a genome-reduced bacterium. Mol Syst Biol 8:571 23. Hunter T (2007) The age of crosstalk: phosphorylation, ubiquitination, and beyond. Mol Cell 28:730–738 24. Thomas SN, Cripps D, Yang AJ (2009) Proteomic analysis of protein phosphorylation and ubiquitination in Alzheimer's disease. Methods Mol Biol 566:109–121 25. Van Wijk KJ, Friso G, Walther D, Schulze WX (2014) Meta-analysis of Arabidopsis thaliana phospho-proteomics data reveals compartmentalization of phosphorylation motifs. Plant Cell 26(6):2367–2389 26. Hummel J, Niemann M, Wienkoop S, Schulze W, Steinhauser D, Selbig J, Walther D, Weckwerth W (2007) ProMEX: a mass spectral reference database for proteins and protein phosphorylation sites. BMC Bioinformatics 8(1):216–223 27. Sun Q, Zybailov B, Majeran W, Friso G, Olinares PD, van Wijk KJ (2008) PPDB, the Plant Proteomics Database at Cornell. Nucleic Acids Res 37:D969–D974 28. Heazlewood JL, Durek P, Hummel J, Selbig J, Weckwerth W, Walther D, Schulze WX (2008) PhosPhAt: a database of phosphorylation sites in Arabidopsis thaliana and a plant-specific phosphorylation site predictor. Nucleic Acids Res 36:D1015–D1021 29. Durek P, Schmidt R, Heazlewood JL, Jones A, MacLean D, Nagel A, Kersten B, Schulze WX (2010) PhosPhAt: the Arabidopsis thaliana phosphorylation site database. An update. Nucleic Acids Res 38:D828–D834

30. Zulawski M, Braginets R, Schulze WX (2013) PhosPhAt goes kinases—searchable protein kinase target information in the plant phosphorylation site database PhosPhAt. Nucleic Acids Res 41(D1):D1176–D1184 31. Joshi HJ, Hirsch-Hoffmann M, Bärenfaller K, Gruissem W, Baginsky S, Schmidt R, Schulze WX, Sun Q, van Wijk KJ, Egelhofer V, Wienkoop S, Weckwerth W, Bruley C, Rolland N, Toyoda T, Nakagami H, Jones AME, Briggs SP, Castleden I, Tanz SK, Millar H, Heazlewood JL (2011) MASCP Gator: an aggregation portal for the visualization of Arabidopsis proteomics data. Plant Physiol 155(1):259–270 32. Gao J, Agrawal GK, Thelen JJ, Xu D (2009) P3DB: a plant protein phosphorylation database. Nucleic Acids Res 37:D960–D962 33. Yao Q, Ge H, Wu S, Zhang N, Chen W, Xu C, Gao J, Thelen JJ, Xu D (2014) P3DB 3.0: from plant phosphorylation sites to protein networks. Nucleic Acids Res 42:D1206–D1213 34. Huang Y, Houston NL, Tovar-Mendez A, Stevenson SE, Miernyk JA, Randall DD, Thelen JJ (2010) A quantitative mass spectrometry-based approach for identifying protein kinase-clients and quantifying kinase activity. Anal Biochem 402(1):69–76 35. Gao J, Thelen JJ, Dunker AK, Xu D (2010) Musite, a tool for global prediction of general and kinase-specific phosphorylation sites. Mol Cell Proteomics 9(12):2586–2600 36. Grimsrud PA, den Os D, Wenger CD, Swaney DL, Schwartz D, Sussman MR, Ane JM, Coon JJ (2010) Large-scale phosphoprotein analysis in Medicago truncatula roots provides insight into in vivo kinase activity in legumes. Plant Physiol 152(1):19–28 37. Rose CM, Venkateshwaran M, Grimsrud PA, Westphall MS, Sussman MR, Coon JJ, Ane JM (2012) Medicago PhosphoProtein Database: a repository for Medicago trunculata phosphoprotein data. Frontiers in Plant Science 3:122 38. Duan G, Walther D, Schulze WX (2013) Reconstruction and analysis of nutrientinduced phosphorylation networks in Arabidopsis thaliana. Front Plant Sci 4:540 39. Stecker KE, Minkoff BB, Sussman MR (2014) Phosphoproteomic analyses reveal early signaling event sin the osmotic stress response. Plant Physiol 165(3):1171–1187 40. Schwartz D, Gygi SP (2005) An iterative statistical approach to the identification of protein phosphorylation motifs from large-scale data sets. Nat Biotechnol 23(11):1391–1398

Chapter 17 Phosphorylation Site Prediction in Plants Qiuming Yao, Waltraud X. Schulze, and Dong Xu Abstract Protein phosphorylation events on serine, threonine, and tyrosine residues are the most pervasive protein covalent bond modifications in plant signaling. Both low and high throughput studies reveal the importance of phosphorylation in plant molecular biology. Although becoming more and more common, the proteome-wide screening on phosphorylation by experiments remains time consuming and costly. Therefore, in silico prediction methods are proposed as a complementary analysis tool to enhance the phosphorylation site identification, develop biological hypothesis, or help experimental design. These methods build statistical models based on the experimental data, and they do not have some of the technical-specific bias, which may have advantage in proteome-wide analysis. More importantly computational methods are very fast and cheap to run, which makes large-scale phosphorylation identifications very practical for any types of biological study. Thus, the phosphorylation prediction tools become more and more popular. In this chapter, we will focus on plant specific phosphorylation site prediction tools, with essential illustration of technical details and application guidelines. We will use Musite, PhosPhAt and PlantPhos as the representative tools. We will present the results on the prediction of the Arabidopsis protein phosphorylation events to give users a general idea of the performance range of the three tools, together with their strengths and limitations. We believe these prediction tools will contribute more and more to the plant phosphorylation research community. Key words Phosphorylation site prediction, PhosPhAt, Musite, Support vector machines, Machine learning

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Introduction Protein phosphorylation is the most well-known protein posttranslational modification event, which plays important role for plant growth, cell death, and innate immunology response through altering the signaling pathways or protein functionalities [1–3]. High-throughput techniques especially like mass spectrometry make it more systematic and more practical to conduct proteomics study in plant sciences. With the growing number of the proteomewide studies for plants, phospho-proteome-wide study [4] has also become more and more popular in the past decade for hacking the plant signaling events as a whole, by high-resolution screening of

Waltraud X. Schulze (ed.), Plant Phosphoproteomics: Methods and Protocols, Methods in Molecular Biology, vol. 1306, DOI 10.1007/978-1-4939-2648-0_17, © Springer Science+Business Media New York 2015

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the phosphorylated peptides and sites. These studies contribute to the dramatic increase of phospho-proteomics data in just a few years, which led to the establishment of several high-quality web resources of plant phospho-proteomics, e.g., P3DB [5] and PhosPhAt [6]. On the other hand, although high-throughput experimental studies using mass spectrometry can identify large amounts of modified peptides and sites at one time, it is still expensive in terms of cost and running time. The experimental approach is not problem-free either. More and more arguments have been raised to address the reliability of database search and technical variations, such as the enrichment step which is needed in most approaches but potentially cause more bias in peptide identification. Besides, it is even more problematic in ambiguity resolving in phosphosite localization. Furthermore, while some of the model organisms like Arabidopsis and rice are relatively well studied, most other plant species still lack of phosphorylation data from any of the existing experimental records. While researchers can still infer phosphorylation sites from the homologous proteins in species with experimental data, it is very limited, especially as plants often have multiple paralogs whose phosphorylation sites may vary. Besides, the extent of conservation of the phosphorylation events across homologs in different species is still a challenging question [7]. Therefore, in silico prediction of phosphorylation sites is an attractive alternative for single protein prediction or even proteomewide annotation. To conduct general plant phosphorylation site prediction more accurately and with more confidence, it is important to utilize the explosive experimental data effectively. The tools for phosphorylation prediction in plants build comprehensive statistical model to address the inference from these data. Most of existing tools not only use the sequence similarities to known phosphorylation peptides, but also other intrinsic characteristics of protein sequence, including evolutionary patterns across the species [7]. Since the statistical or machine learning methods tend to approach the true classification with sufficient data sets, it is believed that the rapid growth in plant experimental phosphorylation data will bring high predictive power and confidence level in either intra- or crossspecies prediction. Because these methods or tools bridge the information from the known to the unknown, and they are fast, cheap, and scalable, the in silico prediction is becoming an active research topic in the plant phosphorylation community. This chapter will provide a general review or guidance of the current popular plant phosphorylation prediction tools, including Musite [7, 8], PhosPhAt [6], and PlantPhos [9]. PhosphAt specifically trains and infers the data from Arabidopsis, based on its own comprehensive Arabidopsis phosphorylation database. PlantPhos is a plant-specific phosphorylation tool using the

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maximum dependence decomposition (MDD) to resolve the feature dependencies. Musite is a machine learning based tool by applying feature selection and support vector machine to conduct the training and prediction for phosphorylation. Although Musite was not specifically designed for plant, it can pair up with a plant specific database like P3DB and obtain the plant-specific models after being trained with specific datasets. The results on the Arabidopsis phosphorylation prediction will be discussed to provide the user a general idea for applying these three algorithms based on their own specialties.

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Overview of the Prediction Methods In this section, an overview of the methodology for Musite and PhosphAt is provided to give the reader a brief idea about the prediction procedure.

2.1 Methodology in Musite

The method for building prediction models in Musite consists of data preprocessing step and the machine learning step with feature extractions.

2.1.1 Data Processing and Sampling

Plant specific predictor Musite is trained specifically by the plant phosphorylation data sets from P3DB [5] as well as from Uniprot-KB [10]. The data from both resources were merged into a single dataset. More specifically, if a phosphorylated site is observed in either source, the correspondent peptide is considered as the positive data. The proteins without any known phosphorylation of the correspondent organism are used as the negative data. The phosphorylated proteins may also contain the non-phosphorylated sites for serine, threonine, or tyrosine residue, in which case these peptides centered by non-phosphorylated sites are considered as negative data too. Generally, there are more negative data than the positive data, and this imbalance issue is not only at the protein level but also amplified to several magnitudes at the site/peptide level. The sequence redundancy was removed in order to avoid potential bias in the machine-learning training process, because some proteins or protein families are artificially over-studied, as well as their homologs. CDHit [11] was used in this process and proteins with more than 50 % (or based on user settings) sequence identity were removed. The unbalancing issue between the phosphorylated sites and non-phosphorylated sites still exists after removing the redundancy. In order to address this problem, the resampling for obtaining a balanced set composed of positive and negative data is performed by the bootstrapping procedure, and is repeated thousands of times to explore the whole space of the original data set. These are the strategies on generating the training data in Musite before conducting any machine learning methods.

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2.1.2 Feature Selection and Machine Learning

The phosphorylation prediction is formulated as a binary classification problem, i.e., distinguishing the serine threonine, or tyrosine centered peptide as either “can be phosphorylated” or “cannot be phosphorylated”. This is modeled and solved by a machinelearning framework in Musite. K-Nearest Neighbor (KNN) scores, disorder scores, and amino acid frequencies were used as the main features for the training purpose. The serine, threonine, or tyrosine centered flanking sequences were used as peptide samples to extract these features. Practically, the length of the flanking sequences is not fixed and can be set by users. Different sizes of the flanking peptides represent different scales of the local information, which can be informative in identifying the intrinsic properties. KNN score is the ratio between the numbers of positive and negative peptides around the candidate peptide. It is understandable that if the neighborhood contains more hits from the positive data, this peptide is more likely phosphorylated. The range of neighborhood for the measurement of KNN needs to be predefined, which is set as a certain percentage of the total positive and negative peptides (the training population). The neighboring peptides around the candidate peptide is calculated, sorted and ranked based on the similarity score between the candidate peptide and all others in the training population, taking into account the amino acid similarity described by BLOSUM matrices (usually BLOSUM62 is selected as the default). The default settings for the range of the neighborhood are 0.25 %, 0.5 %, 1 %, 2 %, and 4 % of the overall population, respectively; therefore, five different KNN scores are obtained for each candidate peptide. The length of the flanking sequence in calculating KNN score is set to 13, with 6 amino acids at each side of the centered residue. Disorder score is a feature to measure the stability of the local structure. It needs to be precalculated by VSL2B [12], which is a predictor of protein disorder from sequence only. The disorder feature of a given peptide is calculated as the average disorder score over its all amino acids to smoothen the nearby information. Musite often uses the sequence length of the disorder calculation with 1, 5, and 13, so that there are three disorder scores for each candidate peptide. Amino acid frequency reflects the amino acid composition or preference in phosphorylated peptides [13] compared to the nonphosphorylated ones. It is represented as a vector of length of 20 given that there are 20 different amino acids, and it contains the normalized counts for each. The length of the peptide for calculating amino acid frequency is usually 13 in Musite. As mentioned above, bootstrapping is applied to solve the unbalanced problem of the positive and negative sites. Since the negative sites dominate the whole dataset, each time only the same amount of the negative sites were randomly sampled and formed a balanced set with the positive data for training. Then support

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vector machine (SVM) is used to train on each sampled dataset. The final prediction score is aggregated by averaging the outputs of all the SVM classifiers. 2.2 Methodology in PhosPhAt

3

The prediction tool under PhosPhAt is based on a training data set of experimentally identified phosphorylation sites. Nonphosphorylated peptides were used as a negative control. To avoid abundance bias towards the negative class (unphosphorylated serine sites), the raw set of 49,314 true negative serine sites was reduced by randomly eliminating sites from the set until the negative set was no more than twice as large as the positive set. This final datasets served as the true-negative set. We used the svm-light package developed by Joachims and coworkers [14]. The feature vector (FV) used for the Support Vector Machines consisted of the sequence of amino acids and their chemical-physical properties. The sequence information part was represented by a vector consisting of 240 elements (12 × 20 with 6 residues on either side of the central serine and 20 amino acid types). Each component of the vector was set to 1 in case of an occurrence of the particular amino acid type in the respective position. For the amino acid property part of the FV, we utilized data from the collection of 530 commonly used indices provided by the AAindex database [15] including hydrophobicity, solvent accessibility preferences, secondary and tertiary structure preferences, polarity, volume, solvent accessibility, as well as structural disorder indices. The resulting vector consisted of 530 × 12 elements representing every index and position around the central serine. Optimal parameters for the kernel decision function, as judged by the highest obtained CC value, have been determined by using the built-in Leave-One-Out (LOO) test for all possible parameter combinations for degree of the polynomial function with degrees ranging from 2 to 4 and error weighting values (cost factor) ranging from 1 to 2.5 in 0.25 increments (21 possible parameter combinations) [16].

Prediction Tools and Function Modules In this section, we will describe the usage and functionalities of three current prediction tools: Musite, PhosphAt, and PlantPhos.

3.1

Musite

3.1.1 Desktop Version

The Musite desktop version is written in Java and needs to be installed in a Java running environment (JRE). It can be run on both Mac and PC. It provides standalone prediction, which does not need Internet connection. The pre-trained models are stored in the package. The user can also train a prediction model based on his/her own dataset. The installment package and the source code

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are available at http://musite.sourceforge.net/. The following shows the steps to run Musite: 1. Go through “Tool → Feature Extraction → Disorder Prediction” to get the disorder score for the samples (see Note 1). 2. Train your model by open the training panel through “Tool → Prediction Model Training”. The data can be uploaded in the Musite XML format or FASTA format (see Note 2). The PTM type and related amino acid need to be set (see Note 3). 3. Click the button of “Advanced Options”, and then the user can customize the features that are needed and the training settings for bootstrap (see Note 4). 4. After the model is properly trained, the user should be able to see and select the model in the drop list of “Select a Model File”, and conduct predictions for query sequences. 5. The user can type in or paste the FASTA sequences in the edit box, or upload a FASTA file or a Musite XML file (see Note 5). 6. After the prediction is done, a new panel showing the results will be popped out (see Note 6). The results can be saved to a Musite XML format, which contains the prediction score at every candidate position. In the desktop version, Musite provides several powerful tools, which can help preprocess the data as well as obtain some statistics for the analysis. Musite provides functionalities to convert multiple file formats to or from the standard Musite XML format. It also can help collect all the accession numbers from the training dataset, and pile up the flanking peptides in the query sequences. Musite can be used to filter the protein data set by organism, accessions, PTM type, and other annotations. Furthermore, Musite provides strong statistical tool sets to count sites or overlap of sites among samples. Remember that in the data preprocessing procedure, a nonredundant dataset is usually needed with below 50 % of the sequence identify, which can also be done by using Musite. In many cases when having multiple data resources, Musite can be used to merge them based on sequence comparison. Musite is an open-source tool, which can be changed or customized for a specific purpose. The users can customize the data set for training purposes usually from their own wet-lab experiment to provide a unique prediction model for specific species or data application. In addition, any users who are familiar with the Java programming language can download the source code and refine the statistical methods or improve the feature extraction. The main modules in the codes include the multiple data module (which defines the data structures of prediction and prediction results), the input/output (IO) module (which supports reading

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from or writing to files of various formats including FASTA, Musite XML, and UniProt XML), the feature extraction module (which defines the feature types and extraction procedure), the classifier module (which defines the binary classifier), the user interface (UI) module (which provides a friendly graphical user interface (GUI) to various functionalities) and the utility module (which defines some common usage functions). The open-source framework allows researchers in the bioinformatics or plant phosphorylation community to make contribution to and improve Musite. 3.1.2 Web Interface

A Web application is available for Musite at http://musite.net. This web implementation does not provide customized training, and it is for prediction only. Specific models for plant phosphorylation are provided at the website, and they are pre-trained systematically with the P3DB database and thus have high quality. This plant specific phosphorylation prediction tool can also be accessed through the P3DB toolkits (http://p3db.org/prediction.php). The procedure of using the Musite website is described as follows: 1. Submit the protein list by providing the Uniprot Accessions directly, or paste a set of FASTA-formatted sequences in the edit box. 2. Select a prediction model and click “submit”. 3. The result panel will appear as a new tab. Multiple sequences will be separated by stacked bars in the panel. Click one of the bars to obtain the detailed results (see Note 7). 4. Download the result for all the predicted sites or with a filtering setting. The website provides an API (Application Programming Interface) for other websites to access our prediction services. An example format of the API link is http://musite. net/?seq=%s&model = overall.sixplant.ser.thr.model (where %s is the protein sequence, and “model = overall.sixplant.tyr.model” represents the tyrosine phosphorylation prediction model). Our in-house P3DB database has links to Musite in this way.

3.2

PhosPhAt

1. Go to the “Prediction” field in the left tab. 2. Paste either an AGI code or a protein sequence into the respective query field and submit. 3. The protein prediction tab will open and show the protein sequence with highlighted phosphorylation sites. Mouse-over will give the respective prediction scores. Score >0 indicates a positive prediction. 4. Predictions can be exported using the “export” function in the upper right.

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PlantPhos

PlantPhos is available at http://csb.cse.yzu.edu.tw/PlantPhos/ Predict.html. A prediction can be done with the following procedure: 1. Paste the FASTA sequence or upload a FASTA file with sequences. 2. Select the target amino acid type for the prediction. 3. The results will be listed as a table showing the amino acids at different positions with different scores of possible phosphorylation. If the score is negative, it is non-phosphorylated site; if it is positive, it is a potential phosphorylation site. If the score is close to zero, there is more uncertainty of the prediction. This prediction model is pre-calculated by HMM (Hidden Markov Model) and shows the matched motifs.

4

Performance of the Different Prediction Tools We compared the performance of the above three software tools (Musite, PhosPhAt, and PlantPhos) using a benchmark dataset that we created. The dataset is combined from P3DB (version 3.5) and Uniprot-KB (June 2014) for Arabidopsis. The redundant proteins are removed at a 50 % of the sequence identify by the CD-Hit clustering. The data are divided into the training and testing sets. The training set contains 4,875 phosphorylated proteins out of 16,668 proteins for the training set and we created a balanced testing set with 484 phosphorylated proteins out of 968 total proteins. The number of sites (S, T, Y) are even more imbalanced and thus we implemented 2,000 bootstraps in our Musite training. The same testing set is also used to evaluate the performance based on the ROC curves for Musite, PhosPhAt and PlantPhos. A curve located more at the top-left side indicates better performance, in which the specificity and the sensitivity are larger. Figure 1 shows that overall Musite outperforms PhosPhAt, and PhosphAt outperforms PlantPhos. But in some cases, such as for the tyrosine prediction, when the specificity is low, the sensitivity of Musite is lower than PhosPhAt and PlantPhos. Needless to say, this comparison is based only on specificity and sensitivity for the assessment. Other factors should be considered too. For example, PhosPhAt is especially fcoused on Arabidopsis and integrates extensive information (e.g., domain identification) with the prediction tool. PlantPhos provides the HMM motif matching as an additional output information. So these tools provide complementary values with each other and users can choose any of them depending on particular application and need.

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Conclusions With improvement of large-scale phosphorylation identification, in-silico prediction tools are also becoming more and more popular. Multiple plant protein prediction tools (Musite, PhosPhAt, and PlantPhos) are illustrated in this chapter, and their procedures, results, and specialties are also described. Although different tools have different performance and features, the general methodology and user experience are similar. By applying these tools, the phosphorylation site identification can be enhanced and the whole proteome-wide study will be more practical with little cost or time. For example, proteome-wide prediction of phosphorylation sites was applied in a study of the effect of single nucleotide polymorphisms on protein phosphorylation sites [17]. A similar study was conducted for tissues affected by different disease in human cells [18]. These examples indicate the value of prediction data in largescale biology.

6

Notes 1. If the disorder score is already included in the Musite XML format, there is no need to run the disorder prediction beforehand. The disorder results will be automatically written into the standard Musite XML format, and then it can be directly used for training purpose. 2. For the training from the FASTA file, because there is no way to include the disorder score in this FASTA format, the user can upload a disorder score prediction result file separately. 3. The user can select multiple amino acid types together, and they will be predicted together in a single model. Usually serine and threonine are selected together and trained into a single model for phosphorylation prediction, while tyrosine is trained in a different model. This is due to the fact that the serine and threonine phosphorylation events share major similarities from sequence-based information, kinase characteristics, and functionalities. 4. This provides much flexibility in training without changing the code. The parameters will control the peptide length, KNN calculation, and other properties. Parameters can also be tuned for the SVM training. 5. We recommend using the Musite XML format in this tool because it is more convenient to see the results and features, and much easier to do other operations. 6. The results panel will show the prediction results with colorcoded background on each residue that needs to be predicted.

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The color gradient shows the prediction under certain specificity. The scroll bar at the bottom can be tuned and different numbers of sites will be seen as phosphorylated under different specificity threshold. A common setting is at the 95 % specificity. 7. This is an interactive interface. The potential phosphorylation sites are highlighted with different colors coded for specificity values under a specificity threshold. The user can choose to hide or show the sequence and the specificity scroll bar. By clicking the “graph”, the specificity levels of the candidate amino acids at different positions are shown with colors coded for specificity. By clicking the “table”, the results are shown in a tabular format. References 1. Pawson T (2004) Specificity in signal transduction: from phosphotyrosine-SH2 domain interactions to complex cellular systems. Cell 116(2):191–203 2. Pawson T, Gish GD (1992) SH2 and SH3 domains: from structure to function. Cell 71: 359–362 3. Wang H, Chevalier D, Larue C, Ki Cho S, Walker JC (2007) The protein phosphatases and protein kinases of Arabidopsis thaliana. Arabidopsis Book 5:e0106. doi:10.1199/tab.0106 4. Grimsrud PA, den Os D, Wenger CD, Swaney DL, Schwartz D, Sussman MR, Ane JM, Coon JJ (2010) Large-scale phosphoprotein analysis in Medicago truncatula roots provides insight into in vivo kinase activity in legumes. Plant Physiol 152(1):19–28 5. Yao Q, Ge H, Wu S, Zhang N, Chen W, Xu C, Gao J, Thelen JJ, Xu D (2014) P3DB 3.0: from plant phosphorylation sites to protein networks. Nucleic Acids Res 42:D1206–D1213 6. Zulawski M, Braginets R, Schulze WX (2013) PhosPhAt goes kinases—searchable protein kinase target information in the plant phosphorylation site database PhosPhAt. Nucleic Acids Res 41(D1):D1176–D1184 7. Yao Q, Gao J, Bollinger C, Thelen JJ, Xu D (2012) Predicting and analyzing protein phosphorylation sites in plants using musite. Front Plant Sci 3:186. doi:10.3389/fpls.2012.00186 8. Gao J, Thelen JJ, Dunker AK, Xu D (2010) Musite, a tool for global prediction of general and kinase-specific phosphorylation sites. Mol Cell Proteomics 9(12):2586–2600

9. Lee TY, Bretana NA, Lu CT (2011) PlantPhos: using maximal dependence decomposition to identify plant phosphorylation sites with substrate site specificity. BMC Bioinformatics 12:261. doi:10.1186/1471-2105-12-261 10. UniProt: a hub for protein information (2014) Nucleic Acids Res. doi:10.1093/nar/gku989 11. Fu L, Niu B, Zhu Z, Wu S, Li W (2012) CD-HIT: accelerated for clustering the nextgeneration sequencing data. Bioinformatics 28(23):3150–3152. doi:10.1093/bioinformatics/bts565 12. Obradovic Z, Peng K, Vucetic S, Radivojac P, Dunker AK (2005) Exploiting heterogeneous sequence properties improves prediction of protein disorder. Proteins 61(Suppl 7):176–182. doi:10.1002/prot.20735 13. Iakoucheva LM, Radivojac P, Brown CJ, O'Connor TR, Sikes JG, Obradovic Z, Dunker AK (2004) The importance of intrinsic disorder for protein phosphorylation. Nucleic Acids Res 32(3):1037–1049. doi:10.1093/nar/gkh253 14. Joachims T (1999) Making large-scale SVM learning practical. In: Advances in kernel methods—support vector learning. MIT Press, Boston 15. Kawashima S, Kanehisa M (2000) AAindex: amino acid index database. Nucleic Acids Res 28(1):374 16. Durek P, Schmidt R, Heazlewood JL, Jones A, MacLean D, Nagel A, Kersten B, Schulze WX (2010) PhosPhAt: the Arabidopsis thaliana phosphorylation site database. An update. Nucleic Acids Res 38:D828–D834

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17. Riano-Pachon DM, Kleessen S, Neigenfind J, Durek P, Weber E, Engelsberger WR, Walther D, Selbig J, Schulze WX, Kersten B (2010) Proteome-wide survey of phosphorylation patterns affected by nuclear DNA polymorphisms in Arabidopsis thaliana. BMC Genomics 11(1):411

18. Ren J, Jiang C, Gao X, Liu Z, Yuan Z, Jin C, Wen L, Zhang Z, Xue Y, Yao X (2010) PhosSNP for systematic analysis of genetic polymorphisms that influence protein phosphorylation. Mol Cell Proteomics 9(4): 623–634

INDEX A

F

Abscisic acid (ABA) ................. 11, 29, 32, 33, 35, 48, 63, 68 Absolute quantitation .............................................. 105–116 Absolute quantitation of isoforms of posttranslationally modified protein (AQUIP) .................. 107–111, 115 AGC kinases ....................................................... 6, 9, 11–12 Aluminum hydroxide (Al(OH)3)..................... 84, 86, 87, 96 Antibody..................................................... 48, 98, 122–126, 132, 136, 138, 140, 144, 164, 168, 175, 184 AQUIP. See Absolute quantitation of isoforms of posttranslationally modified protein (AQUIP) Arabidopsis........................................ 2, 6–16, 26–37, 48–51, 54, 61, 71, 78, 82–85, 102, 107, 108, 126, 136–138, 142–144, 147–157, 159, 160, 168–170, 172, 181, 183, 185, 208–210, 218, 219, 224 Aurora kinase..................................................................... 13

False-discovery rate (FDR) ........................... 78, 92, 93, 102 FLS2 ................................................................................... 4

B Bioinformatic ............... 48, 50, 106, 142, 202, 208, 212, 223 Bioworks.......................................................................... 142 BRI1 ............................................................... 2, 4, 33, 34, 71

C C18 ................ 64, 66, 74–77, 88, 90, 100–102, 138, 140–142 Cascade ......................... 2, 4, 6–7, 54, 97, 159, 177, 180, 186 Casein kinase ..................................................................... 14 Cell culture .............................................. 136–138, 143, 144 Cereal .......................................................................... 47–54 ChloroPhos1.0 ........................................................ 147–157 Chloroplast ........................................... 30, 34, 133, 147–157 Column ................................................... 77, 88–90, 93, 102, 127–129, 132, 133, 138, 140–142, 210, 211, 213 Computational identification .................................. 195–204 Correlation ..................................... 72, 95, 96, 179, 180, 185

G Gel...................................................................... 51, 68, 112, 115, 122–128, 167–171, 174 Green fluorescent protein (GFP) .............................. 98, 100 Group-based prediction system (GPS)........... 182, 196–198, 200–201, 203

H Hidden Markow Model (HMM)................... 182, 196–200, 202, 203, 224 Histidine kinase ....................................................... 1, 25, 97 HPLC ....................................................................... 77, 101 Hydroxyl acid-modified metal oxide chromatography (HAMMOC) ............................................ 63, 64, 66

I Immobilized metal affinity chromatography (IMAC) .............................................. 48–50, 60, 82, 124, 127–129, 136, 196 Immuno-affinity enrichment................................... 135–145 Immunological detection ................................................. 122 Immunoprecipitation (IP) .............................. 100, 136, 138, 140–141, 144, 167 In-solution ............................................................... 112, 115 Internal standard (IS) .............................. 107, 108, 110–116 In vitro phosphorylation ........... 64–65, 67–68, 160, 162–163 Ion intensity ................................. 76, 78, 111, 115, 129, 130 Isoforms................................ 4, 9, 29, 33, 105–116, 167–169 Isotope labeling .................. 65, 107, 112, 122, 124, 127–130

K D Database ................................................ 7, 47, 48, 50, 64, 67, 71, 78, 92–94, 99, 102, 106, 142, 181, 183, 184, 186, 196–198, 201, 207–215, 218, 219, 221, 223 Desalting ............................ 75, 77, 84, 87–88, 102, 103, 140

E Enrichment ....................... 47–50, 60, 72, 75–76, 82–87, 89, 107, 122–124, 127–129, 135–145, 155, 196, 218 Escherichia coli (E. coli) ................................... 64, 67, 68, 106

Kinase activity ................................................... 1, 14, 29, 61, 62, 97–103, 148–153, 162, 164 family ................................................ 1–8, 10, 13–16, 182 substrate..................................... 59–69, 72, 98, 147–157, 160, 178, 182, 183, 196, 202, 209–211, 214 substrate identification ................................ 72, 159–165 target......................... 7, 48, 147, 148, 154, 186, 210, 214 Kinome ...................................................... 1–16, 50, 54, 210 Knock-out mutant ................................................. 59–69, 72

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PLANT PHOSPHOPROTEOMICS: METHODS AND PROTOCOLS 230 Index L Label-free quantitation ......................................... 63, 65, 67, 72, 76, 92, 102, 129, 131 Leaf ............................................................. 28, 49, 162, 169 Light harvesting complex (LHC)...................................... 37 Liquid chromatography (LC) ..................................... 89, 90, 122, 127, 129, 144 LRR receptor kinase. See Receptor kinase

M Magnetic beads.......................................... 98, 100, 101, 103 Maize............................................................... 48, 49, 51–54 MAP kinase................................................... 6–7, 27, 28, 72 MAP kinase cascade ............................................ 4, 6–7, 159 Mapping ................................. 91–93, 95, 156, 183–184, 214 Mascot ............................................ 64, 93–95, 108, 142, 213 Mass spectrometry (MS) ................................. 47, 48, 50, 71, 76, 77, 82, 89–91, 94, 98, 101–103, 106–112, 114–116, 122–124, 126–132, 140, 142, 147, 148, 167, 169, 178, 182, 209, 213, 217, 218 MaxQuant .................................. 64, 67, 78, 91, 92, 102, 109 Medicago ..................................................... 35, 50, 208, 212 Medicago PhosphoProtein ...................................... 208, 212 Metabolic labeling ........................ 82–84, 108, 136, 143, 168 Metal oxide affinity chromatography (MOAC).................................................... 48, 81–96 Microarray .......................... 60, 147–157, 159–165, 178, 179 Microsomes ................................................. 74, 77, 100, 102 MPK3...................................................................... 7, 27, 28 MPK6................................................................ 7, 27, 28, 35

N Network........................................................... 38, 54, 60, 72, 82, 143, 147, 177–187, 198, 210–212 reconstruction .................................................... 177–187 15 N labeling.................................................. 81–96, 108, 116

Phospho-relay system .................................................... 1, 16 Phosphorylation cascade. See Cascade Phosphorylation network .......................... 60, 147, 177–187 Phosphorylation stoichiometry.................... 91, 93, 121–133 Phosphoserine. See Serine phosphorylation Phosphosite mapping .................................................. 91–93 Phosphothreonine. See Threonine phosphorylation Phosphotyrosine. See Tyrosine phosphorylation Phylogeny ..................................................... 3, 5–7, 9, 10, 12 Plasma membrane .......................... 2, 4, 6, 11, 49, 51, 74–75 Post-translational modification ............... 1, 47, 82, 105–116, 167–169, 178, 181, 195, 207, 208, 217, 222 Potter ....................................................................... 139, 144 Prediction ................................................. 50, 154, 181–183, 186, 196–203, 209, 210, 213, 214, 217–227 Profiling.......................................... 50, 71–78, 179, 186, 207 Pro-Q Diamond .............................. 122–124, 126–127, 132 Protein family ............................................. 2, 6, 47, 208, 219 Protein microarray ..................................... 60, 159–165, 178 Protein phosphorylation ....................................... 25, 47, 48, 50, 51, 54, 60, 61, 63, 71, 82, 93–97, 121, 122, 126–128, 131–132, 135, 167–176, 178, 181–183, 186, 195, 208, 213, 214, 217, 226 Protein–protein interaction (PPI).................... 30, 33, 34, 50, 183–184, 186, 197, 198, 201, 204, 210, 211, 214 Purification ................. 98, 101, 107, 108, 114–116, 122, 136

Q Quantitative proteomics ....... 64, 72, 106, 107, 182, 184–186

R Receptor kinase ............ 1–4, 11, 34, 53, 71–78, 98, 135, 159 Receptor-like kinase. See Receptor kinase Relative quantitation ........................... 76–77, 124–127, 130 Rice ......................................................... 48–51, 53, 54, 218 Root....................... 12, 15, 28, 29, 32, 36, 49, 51, 52, 68, 212

S O Occupancy ............................................... 108, 109, 113–115 Orbitrap.................................................... 77, 90, 91, 94, 142 Oxophytodienoic acid reductase (OPR3) ........ 168–170, 172

P Pathway ................................................. 1, 3–7, 9–15, 27, 30, 34–36, 47, 48, 50, 51, 54, 59–61, 63, 71, 72, 97, 121, 159, 183–186, 207, 208, 214, 215, 217 P3DB ........................................................ 50, 198, 200, 208, 210–212, 218, 219, 223, 224 Peptide microarray ................................................... 147–157 PhosPhAt .................................................. 7, 48, 71, 99, 181, 208–210, 212, 213, 218, 221, 223–226 Phosphatase family ...................................................... 26–31 Phosphoproteome......... 50–52, 54, 61, 81–96, 135, 182, 198

Screening ........................................ 59–69, 91, 147–157, 217 SDS-polyacrylamide gel electrophoresis (SDS-PAGE) .................................... 64, 68, 98, 112, 115, 124–126, 168–171, 174 Seedlings ............... 11, 27, 29, 65, 68, 83, 136–139, 143, 144 Sepharose .......................................................... 64, 124, 128 Serine phosphorylation............................................ 108, 208 Signaling............................ 34–36, 47, 48, 50–54, 59, 60, 63, 71, 72, 82, 97, 122, 123, 125, 126, 135, 136, 159, 160, 168, 177, 182, 183, 185–187, 207, 208, 217 Signaling cascade. See Cascade Snf1-related kinase (SnRK) ............................ 4, 6, 8, 10–11, 14–15, 35, 63 SnRK2 .......................................................... 6, 14, 15, 35, 63 Soluble kinase .................................................. 2, 4, 6, 7, 100 Soluble protein .................................................... 74–75, 139

PLANT PHOSPHOPROTEOMICS: METHODS AND PROTOCOLS 231 Index Specificity .......................................... 31, 32, 35, 38, 97–103, 106, 148, 182, 184, 196, 199, 203, 204, 224, 227 Spot ................... 126, 153–157, 160, 163, 164, 168, 169, 175 Standard peptide ............................................................. 107 STN kinase............................................................ 15, 36, 37 Stoichiometry ................................. 82, 91, 93, 121–133, 136 Synthetic peptide .................... 60, 67, 97–103, 107, 116, 148

T Tandem-affinity purification ........................................... 108 T-DNA insertional mutant ............................................... 29 Threonine phosphorylation ..................................... 224–226 Thylakoid ................................................ 121–129, 131, 132

Titanium dioxide (TiO2) ...................................... 48, 49, 75, 82, 85, 89, 132, 136 Transporter ............................................... 4, 6, 11, 28, 51, 53 2D-gel. See Gel Tyrosine phosphatases ..................................... 26, 27, 30, 31 Tyrosine phosphorylation ........................ 136–145, 223, 225

W Western blot ..................................... 123, 125, 164, 167–169 Wheat.............................................................. 48, 49, 53, 54

Y Yeast-two hybrid ............................................................... 27