Plant Phosphoproteomics: Methods and Protocols (Methods in Molecular Biology, 2358) 1071616242, 9781071616246

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Methods in Molecular Biology 2358

Xu Na Wu 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, UK

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

For over 35 years, biological scientists have come to rely on the research protocols and methodologies in the critically acclaimed Methods in Molecular Biology series. The series was the first to introduce the step-by-step protocols approach that has become the standard in all biomedical protocol publishing. Each protocol is provided in readily-reproducible step-by step fashion, opening with an introductory overview, a list of the materials and reagents needed to complete the experiment, and followed by a detailed procedure that is supported with a helpful notes section offering tips and tricks of the trade as well as troubleshooting advice. These hallmark features were introduced by series editor Dr. John Walker and constitute the key ingredient in each and every volume of the Methods in Molecular Biology series. Tested and trusted, comprehensive and reliable, all protocols from the series are indexed in PubMed.

Plant Phosphoproteomics Methods and Protocols

Edited by

Xu Na Wu State Key Laboratory of Conservation and Utilization of Bio-Resources in Yunnan and Center for Life Science, School of Life Sciences, Yunnan University, Kunming, China

Editor Xu Na Wu State Key Laboratory of Conservation and Utilization of Bio-Resources in Yunnan and Center for Life Science School of Life Sciences Yunnan University Kunming, China

ISSN 1064-3745 ISSN 1940-6029 (electronic) Methods in Molecular Biology ISBN 978-1-0716-1624-6 ISBN 978-1-0716-1625-3 (eBook) https://doi.org/10.1007/978-1-0716-1625-3 © Springer Science+Business Media, LLC, part of Springer Nature 2021, corrected publication 2021 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, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Humana imprint is published by the registered company Springer Science+Business Media, LLC part of Springer Nature. The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.

Preface Protein phosphorylation is a widespread post-translational modification that regulates cellular signaling processes. Today, mass spectrometry (MS)-based phosphoproteomics has become a routine procedure to globally study the dynamics of phosphorylation in various biological contexts. However, many challenges still exist, particularly in plants, including reduced efficiency of plant extraction due to the plant cell wall and the generally low efficiency of phosphopeptide enrichment. Therefore, the design of the proteomics experiment should be strongly based on the experimental question. For this purpose, suitable protocols for every stage of the experiment from protein isolation and phosphopeptides enrichment to large-scale data analysis need to be considered. This volume on Plant Phosphoproteomics provides phosphoproteomics techniques currently developed for use in plants, which describe detailed experimental protocols, offering a variety of methodologies to analyze different types of plant phosphoproteomic data. The aim is to provide a useful protocol book for scientists working in the plant phosphoproteomics field. Kunming, China

Xu Na Wu

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

1 Phosphorylation Site Motifs in Plant Protein Kinases and Their Substrates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lin Xi, Zhaoxia Zhang, Sandra Herold, Sarah Kassem, Xu Na Wu, and Waltraud X. Schulze 2 Protein Phosphorylation Response to Abiotic Stress in Plants . . . . . . . . . . . . . . . . Rebecca Njeri Damaris and Pingfang Yang 3 Protein Phosphorylation in Plant Cell Signaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ping Li and Junzhong Liu 4 Phosphoproteomics Profiling of Receptor Kinase Mutants . . . . . . . . . . . . . . . . . . . Dandan Lu, Ting Gao, Lin Xi, Leonard Krall, and Xu Na Wu 5 Phosphoproteomic Analysis of Soybean Roots Under Salinity by Using the iTRAQ Labeling Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuchen Qian, Jia Xu, and Erxu Pi 6 Universal Sample Preparation Workflow for Plant Phosphoproteomic Profiling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chuan-Chih Hsu, Justine V. Arrington, and W. Andy Tao 7 Mapping Plant Phosphoproteome with Improved Tandem MOAC and Label-Free Quantification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yanmei Chen and Xinlin Liang 8 SILIA-Based 4C Quantitative PTM Proteomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . Emily Oi Ying Wong and Ning Li 9 Phosphoproteomics Analysis of Plant Root Tissue . . . . . . . . . . . . . . . . . . . . . . . . . . Zhe Zhu, Shubo Yang, Shalan Li, Xiaolin Yang, and Leonard Krall 10 Plant Phosphopeptides Enrichment by Immobilized Metal Ion Affinity Chromatography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiahe Huang, Yuanya Zhang, Haitao Ge, Dandan Lu, and Yingchun Wang 11 Two-Dimensional Gel Electrophoresis and Pro-Q Diamond Phosphoprotein Stain-Based Plant Phosphoproteomics . . . . . . . . . . . . . . . . . . . . . . Yuan Li and Dongtao Ren 12 2-D DIGE Combined with Pro-Q Diamond Staining for the Identification of Protein Phosphorylation for Chlamydomonas reinhardtii: A Successful Approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Li Li 13 Plant Phosphopeptide Identification and Label-Free Quantification by MaxQuant and Proteome Discoverer Software. . . . . . . . . . . . . . . . . . . . . . . . . . . Shalan Li, Haitao Zan, Zhe Zhu, Dandan Lu, and Leonard Krall

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PhosPhAt 4.0: An Updated Arabidopsis Database for Searching Phosphorylation Sites and Kinase-Target Interactions . . . . . . . . . . . . . . . . . . . . . . . Lin Xi, Zhaoxia Zhang, and Waltraud X. Schulze 15 Computational Phosphorylation Network Reconstruction: An Update on Methods and Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Min Zhang and Guangyou Duan 16 The Application of an R Language-Based Platform cRacker for Phosphoproteomics Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mingjie He and Zhi Li 17 Kinase Activity Assay Using Unspecific Substrate or Specific Synthetic Peptides . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jiahui Wang, Xiaolin Yang, Lin Xi, and Xu Na Wu Correction to: Plant Phosphopeptide Identification and Label-Free Quantification by MaxQuant and Proteome Discoverer Software. . . . . . . . . . . . . . . . . . Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Contributors JUSTINE V. ARRINGTON • Department of Chemistry, Purdue University, West Lafayette, IN, USA; Roy J. Carver Biotechnology Center, University of Illinois, Urbana-Champaign, Urbana, IL, USA YANMEI CHEN • State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing, China REBECCA NJERI DAMARIS • State Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Science, Hubei University, Wuhan, China GUANGYOU DUAN • Energy Plant Research Center, School of Life Sciences, Qilu Normal University, Jinan, People’s Republic of China TING GAO • State Key Laboratory of Conservation and Utilization of Bio-Resources in Yunnan and Center for Life Science, School of Life Sciences, Yunnan University, Kunming, China HAITAO GE • State Key Laboratory of Molecular Developmental Biology, The Innovation Academy of Seed Design, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China MINGJIE HE • Department of Plant Systems Biology, University of Hohenheim, Stuttgart, Germany SANDRA HEROLD • Department of Plant Systems Biology, University of Hohenheim, Stuttgart, Germany CHUAN-CHIH HSU • Department of Biochemistry, Purdue University, West Lafayette, IN, USA; Department of Plant Biology, Carnegie Institute for Science, Stanford, CA, USA XIAHE HUANG • State Key Laboratory of Molecular Developmental Biology, The Innovation Academy of Seed Design, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China SARAH KASSEM • Department of Plant Systems Biology, University of Hohenheim, Stuttgart, Germany LEONARD KRALL • State Key Laboratory of Conservation and Utilization of Bio-Resources in Yunnan and Center for Life Science, School of Life Sciences, Yunnan University, Kunming, China LI LI • College of Life Sciences, Capital Normal University, Beijing, China NING LI • Division of Life Science, The Hong Kong University of Science and Technology, Hong Kong, SAR, China; Shenzhen Research Institute, The Hong Kong University of Science and Technology, Hong Kong, SAR, China PING LI • State Key Laboratory of Conservation and Utilization of Bio-Resources in Yunnan and Center for Life Science, School of Life Sciences, Yunnan University, Kunming, China SHALAN LI • State Key Laboratory of Conservation and Utilization of Bio-Resources in Yunnan and Center for Life Science, School of Life Sciences, Yunnan University, Kunming, China

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YUAN LI • State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing, China ZHI LI • Department of Plant Systems Biology, University of Hohenheim, Stuttgart, Germany XINLIN LIANG • School of Pharmaceutical Sciences, South-Central Minzu University, Wuhan, China JUNZHONG LIU • State Key Laboratory of Conservation and Utilization of Bio-Resources in Yunnan and Center for Life Science, School of Life Sciences, Yunnan University, Kunming, China DANDAN LU • State Key Laboratory of Crop Stress Adaptation and Improvement, School of Life Sciences, Henan University, Kaifeng, China ERXU PI • College of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, People’s Republic of China YUCHEN QIAN • College of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, People’s Republic of China DONGTAO REN • State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing, China WALTRAUD X. SCHULZE • Department of Plant Systems Biology, University of Hohenheim, Stuttgart, Germany W. ANDY TAO • Department of Biochemistry, Purdue University, West Lafayette, IN, USA; Department of Chemistry, Purdue University, West Lafayette, IN, USA JIAHUI WANG • Department of Plant Systems Biology, University of Hohenheim, Stuttgart, Germany YINGCHUN WANG • State Key Laboratory of Molecular Developmental Biology, The Innovation Academy of Seed Design, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China EMILY OI YING WONG • Division of Life Science, The Hong Kong University of Science and Technology, Hong Kong, SAR, China; Shenzhen Research Institute, The Hong Kong University of Science and Technology, Hong Kong, SAR, China XU NA WU • State Key Laboratory of Conservation and Utilization of Bio-Resources in Yunnan and Center for Life Science, School of Life Sciences, Yunnan University, Kunming, China LIN XI • Department of Plant Systems Biology, University of Hohenheim, Stuttgart, Germany JIA XU • College of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, People’s Republic of China PINGFANG YANG • State Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Science, Hubei University, Wuhan, China SHUBO YANG • State Key Laboratory of Conservation and Utilization of Bio-Resources in Yunnan and Center for Life Science, School of Life Sciences, Yunnan University, Kunming, China XIAOLIN YANG • State Key Laboratory of Conservation and Utilization of Bio-Resources in Yunnan and Center for Life Science, School of Life Sciences, Yunnan University, Kunming, China HAITAO ZAN • State Key Laboratory of Conservation and Utilization of Bio-Resources in Yunnan and Center for Life Sciences, School of Life Sciences, Yunnan University, Kunming, China MIN ZHANG • Department of Neurology, The First Affiliated Hospital of Shandong First Medical University, Jinan, People’s Republic of China

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YUANYA ZHANG • State Key Laboratory of Molecular Developmental Biology, The Innovation Academy of Seed Design, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China ZHAOXIA ZHANG • Department of Plant Systems Biology, University of Hohenheim, Stuttgart, Germany ZHE ZHU • State Key Laboratory of Conservation and Utilization of Bio-Resources in Yunnan and Center for Life Science, School of Life Sciences, Yunnan University, Kunming, China

Chapter 1 Phosphorylation Site Motifs in Plant Protein Kinases and Their Substrates Lin Xi, Zhaoxia Zhang, Sandra Herold, Sarah Kassem, Xu Na Wu, and Waltraud X. Schulze Abstract Protein phosphorylation is an important cellular regulatory mechanism affecting the activity, localization, conformation, and interaction of proteins. Protein phosphorylation is catalyzed by kinases, and thus kinases are the enzymes regulating cellular signaling cascades. In the model plant Arabidopsis, 940 genes encode for kinases. The substrate proteins of kinases are phosphorylated at defined sites, which consist of common patterns around the phosphorylation site, known as phosphorylation motifs. The discovery of kinase specificity with a preference of phosphorylation of certain motifs and application of such motifs in deducing signaling cascades helped to reveal underlying regulation mechanisms, and facilitated the prediction of kinase-target pairs. In this mini-review, we took advantage of retrieved data as examples to present the functions of kinase families along with their commonly found phosphorylation motifs from their substrates. Key words Kinase families, Phosphorylation motifs, Phosphorylation, Biological function

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Introduction Protein phosphorylation is a posttranslational modification (PTM) involved in the regulation and maintenance of most biological processes. Modern mass spectrometry enables the rapid and direct discovery of hundreds of phosphorylation sites in a single experiment [1]. Notably, an in-depth study of a mass spectrometry-based draft of the Arabidopsis proteome recently identified about 18,210 proteins out of the 27,655 protein-coding genes (66%) annotated in Araport11 [2]. Among them, 8577 phosphoproteins were identified at the tissue-specific level, and at least 7603 nonredundant phosphoproteins were found by accumulated experimental data [3– 5]. This result suggested that in Arabidopsis plants, at least 41% of the expressed proteome was phosphorylated under some condition. Although analytical challenges remain, the bottleneck in the study of phosphorylation is shifting toward functional characterization of the phosphorylation events [6].

Xu Na Wu (ed.), Plant Phosphoproteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2358, https://doi.org/10.1007/978-1-0716-1625-3_1, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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The nature of phosphorylation is reversible. Kinases trigger the phosphorylation on the specific amino acids, commonly serine, threonine, and tyrosine, while phosphatases are responsible for dephosphorylation of the target proteins. The positions of the targeted amino acids are referred to as the phosphorylation sites. The common patterns surrounding phosphorylation sites, which are viewed as the guidance rules for different kinases to recognize their corresponding protein substrates, are termed phosphorylation motifs. In Arabidopsis thaliana, there are ~1000 kinases, constituting one of the largest protein families [7]. Furthermore, kinasemediated protein phosphorylation affects multiple process in a plant’s adaptation to abiotic stresses, immunity responses, developmental process, and the regulation of metabolism. Therefore, understanding the character of kinases and their substrates’ motif patterns is fundamental in understanding the biological functions of phosphorylation and the hierarchy of signaling cascades. Here, within this chapter, we present an overview of kinases and their targeted phosphorylation motifs, which could aid in understanding their biological functions and the signaling cascades to which they belong.

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Relationship Between Kinases Families and Biological Functions Phylogenetic analysis at a kinome scale classified Arabidopsis kinases into two major clades: the membrane-located receptorlike kinases and soluble cytosolic kinases [7]. According to the similarity of the kinase domain structures, the families and subfamilies of kinases were distinguished in Arabidopsis (Table 1). Efforts were also made to map the biological functions of the kinase to phylogeny clusters or gene duplication events. Comparison of kinase phylogenies among different plant species point to a functional conservation of kinases throughout the plant kingdom [18–21]. The closer the kinases phylogeny, the more conserved their biological function. For example, in the membrane-located receptor-like kinases clade, one of the largest groups is the receptor-like kinases (RLKs). Structurally, RLKs consist of an extracellular region, a single membrane-spanning domain, and an intracellular kinase domain. They generally function by recognizing various signals from outside the cell in response to environmental cues [22]. Among these RLKs, the subfamily containing leucine-rich repeats (LRR) in their extracellular domain comprises the largest kinase group with over 200 members in Arabidopsis [8, 23]. Functional conservation among kinases within phylogenetic subclades has been observed. For example, in one subfamily within the LRR-RLKs, CLAVATA3 insensitive receptor kinases (CIKs), all four members of the same phylogeny cluster were found to

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Table 1 Kinase families by phylogeny and their related functional pathwaya Kinase family

Description

Functional pathways

Receptor-like kinase families RLKs (e.g., LRR-RK)

Receptor-like kinases

Hormone signaling, peptides signaling, development, and (a)biotic stress [8]

RLCKs

Receptor-like cytosolic kinases

Hormone signaling, peptides signaling, development, and (a)biotic stress [9]

Soluble cytosolic kinase families CDKs

Cyclin-dependent kinases

Cell cycle regulation, development and morphogenesis, biotic stress, circadian rhythm, transcription, and splicingb

CKLs

Casein-like kinases

Cytoskeletal dynamics and cell communicationb

NIMA/NEK

Never in mitosis-related kinases

Cell growth, cytoskeletal dynamics, and ethylene signalingb

CK II

Casein kinases II

Circadian rhythm, photochromic signaling, translation regulation, and development [10]b

WNKs

With-no-lysine kinases

Circadian rhythm, flowering timeb

CDPKs

Calcium-dependent protein kinases

Hormone (ABA/JA/SA) signaling, (a)biotic stress, ethylene synthesis, auxin transport, and developmentc

CRK/CDPK-RK

CDPK-related kinases

N metabolism, auxin transport, and heat stressc

MAPKs

Mitogen-activated protein kinases

Hormone (ABA/JA/GA, auxin) signaling, (a)biotic stress signaling, development, and cell cyclec

MAP2Ks

Mitogen-activated protein kinases

Hormone signaling, (a)biotic stress, and developmentc

MAP3Ks

Mitogen-activated protein kinases

Hormone signaling, (a)biotic stress, and developmentc

MAP4Ks

Mitogen-activated protein kinases

Hormone signaling, (a)biotic stress, and development

SnAKs

SnRK1-activating kinases

Sugar signaling, biotic stress, and plant growthb

SnRK1

Snf1-related protein kinases 1

Calcium signaling, sugar signaling, plant growth, and phosphate metabolismb

SnRK2

Snf1-related protein kinases 2

ABA signaling, abiotic stress, and flowering timeb (continued)

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Table 1 (continued) Kinase family

Description

Functional pathways

SnRK3/CIPK

Snf1-related protein kinases 3/CBLinteracting kinases

ABA signaling, abiotic stress, K+ metabolism, and transport

AGC

cAMP-dependent protein kinase A, cGTP-dependent protein kinase G, and phospholipiddependent protein kinase C

Auxin transport, blue light signaling, plant defense, ABA signaling, and abiotic stressc

AURORA

AURORA kinases

Cell division processes,b development [11]

SLK/GSK3

Shaggy-like kinases/glycogensynthetase kinases3

Development, hormone signaling

STNs

Thylakoid kinases

Photosynthesis

WAKs

Wall-associated kinases

Development, biotic stress [12, 13]

HKs

Histidine kinases

Hormone (cytokine, ethylene) signaling, osmotic stress, development, photochromic signaling [14–16]

a

Modified or updated based on [17] Kinases family with specific (proven) function c Kinases family proved in different signaling pathways [17] b

determine cell fate specification during Arabidopsis early anther development [24]. As well, in the Arabidopsis soluble cytosolic kinases clade, functional conservation can be found. For example, casein-like kinases (CKLs) have 13 members located within a phylogeny branch and are known to regulate cortical microtubule origination and cell-to-cell communication via plasmodesmata [2, 7]. A recent publication showed that these CLKs were also involved in the circadian clock [25]. Another family, with-no-lysine kinases (WNKs) with 11 members, has been implicated in circadian rhythm and flowering time [26]. However, functional conservation for a kinase family is not the rule. Individual kinases with functions in different pathways, such as WNK8 in modulation of ABA responses [27], provided cumulated cases for functional diversity within a kinase family. Besides, biological functions are determined by more factors than their ancestor relationships [7, 23, 28, 29]. The LRR-RKs can be taken as an example; although most LRR-RKs work in a heterodimer mechanism in responding to external stimuli and mediate the signal transduction into the cells, their biological output is diversely defined by the types of ligands, and their domain(s) structure, including the kinase domains’ phosphorylation state. Not to mention the mitogen-activated protein kinase family (MAPK), which hierarchically cascade the cellular signaling network [30] in response to hormone signaling, and

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both biotic and abiotic stress, to control development and cell cycle regulation [17]. The functional diversities among the same kinase family indicates that other factors are involved in biological pathway determinations. Therefore, the patterns of the phosphorylation target/substrate for the kinases are extremely crucial in understanding the role of kinases within specific signaling pathways.

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Phosphorylation Motifs and Functional Classification in Arabidopsis The advances in mass spectrometry techniques allow rapid and directed discovery of hundreds of phosphorylation sites (p-sites) in a single experiment [31], which greatly increased the number of identified plant p-sites. At the same time, several databases were designed to integrate these cumulated data sets for customized retrieval [4, 32–34]. The residues surrounding phosphorylated serine/threonine/tyrosine are specifically recognized by these modular phosphoprotein-binding domains of the kinases that are referred to as phosphorylation motifs or p-motifs [31, 35]. Specifically, it can now be determined which p-motifs were de facto “overrepresented” in phosphorylation patterns of known targets to different kinases. Based on this concept, several algorithms have been developed to calculate the fitted motif from phosphopeptides. These programs were packed as either web tools or downloadable applets, such as Motif-x, MMFPh, F-Motif, Motif-All, MoMo, or PTMphinder [35–40]. By meta-analysis, functions of p-sites or phosphorylation motifs were drawn to the community systematically [41]. In principle, the same p-site can be targeted by different kinases or phosphatases while the p-site is within two different motifs.

3.1 P-Motifs in Kinase Substrates

The relationship between kinases and their substrates has been indispensable in understanding the recognition mechanisms and their biological function. The common question asked is: “Which kind of p-motifs could be phosphorylated by certain kinds of kinases?” Previously, the significantly found phosphorylation motifs have been categorized into two major types: pS types and pT types. Hierarchical clustering nicely separated the pS types into seven clades: glycine-rich, glutamate-rich, DS with basic residues upstream or acidic residues downstream, pS with acid residues, SP motif, pS with basic residues, and serine-rich pS motifs. The pT types were classified into seven clades, namely, glutamate-rich, aspartate-rich, proline-rich (2), arginine-rich (2), and lysinerich pT [41]. Among them, G-S and S-G were glycine-rich motifs, and S-P was one typical SP-type motif. Acidic S-type motifs included S-[DE], S-E, S-X-[DE], S-X-X-[DE], while common basic S-type motifs with arginine contained [RK]-X-X-S, [RK]-XS, and R-S. Within the T motif, T-P was the most commonly found

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pT motif. The basic T-type motifs were also with arginine, e.g., [RK]-X-X-T, while the acidic T-type motifs included aspartate or glutamate acid, e.g., X-T-X-[ED]. We used the annotation from the PhosPhAt4.0 database and extracted the known kinase-target relationships to organize the found p-motifs in common overlapping substrates of different kinase families. The Fisher’s exact test was applied, and the resultant p-values were presented in the network view as the width of edges. Obviously, the substrates of different kinases contain different p-motifs. G-S motifs were both overrepresented in substrates of CDPK, AGC, and SnRK3, while the S-G motif was overrepresented in substrates of the CDK, CKII, and MAPK families. The MAPK, CDPK, AGC, and LRR-RLK were found to be all as an S-P motif (Fig. 1a). Recent studies have revealed that about one-third of identified p-motifs in substrates of MAPK were T/S-P [42, 43], and the S-P motif was found as one potential substrate of AGC1 in the determination of seed size [44]. As for the acidic S-type motifs, CDKs are strongly associated substrates containing S-E, S-[DE], and S-X-[DE]. RLCK was found to be selective for the S-X-[DE] motif (Fig. 1b). Substrates of LRR-RK, SnRK1, SnRK2, and SnRK3 were found to be overrepresented with basic S-motifs (Fig. 1c). Within the T-motifs group, T-P was found in substrates of multiple kinase families, such as CDK, CDPK, SnRK1, SnRK3, and CKII (Fig. 1d). These correspondence relationships could be of help in predicting the kinases which could phosphorylate proteins containing the specific p-motifs. For example, identification of CDK targets, S/T-P-X-K/ R, on eIF4A1 and eIF4A2 placed the kinase CDK into an eIF4Amediated cell cycle pathway [45]. Clearly, despite the enrichment of specific p-motifs among their substrates, the same kinase family can perform multisite targeting which may determine a different biological output. For example, in mitosis research, CDK1 (CDK family) was known as the prolinedirected kinase, phosphorylating substrates on the minimal consensus motif Ser/Thr-Pro (S/T-P) [46]. At the same time, CDK was also confirmed in targeting different motifs to contribute to the timing control of the cell cycle [47]. Studies also demonstrated that RLKs are generally basophilic kinases, with a preference for basic residues at 3 (His, Arg), +2 (Arg, Lys, His), or +6 (Lys, His) [48], which corresponded with our observation that LRR-RLK was highly associated with the basic T-motif [RK]-X-X-T. Yet the autophosphorylation S-motifs of LRR-RLK were more likely HxSxxV, KxxxxxxSxV, and [KRH]xxxxS [48]. Therefore, the knowledge of substrate motifs is a key factor in the determination of a kinase’s biological functions and the role of phosphorylation sites in determining specificity in signaling pathways. 3.2 Frequently Identified P-Motifs Functions

In order to gather relationships between biological processes and p-motifs, functional overrepresentation was carried out using the MapMan 4.0 enrichment tool for these most identified motifs

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Fig. 1 Kinase-substrate motifs network by Fisher exact test. (a) Kinase families and substrates of G-S, S-G, S-P motifs; (b) Kinase families and substrates of acidic S-type motifs; (c) Kinase families and substrates of basic S-type motifs; (d) Kinase families and substrates of T motifs. Square nodes represented p-motifs. Eclipse nodes represented kinase families. Width of the edges is represented by the log ( p-value) from the Fisher exact test

[49]. Interestingly, different motifs are enriched in proteins with certain functional properties. The proteins containing glycine-rich motifs G-S and S-G were found significantly enriched for functions in protein modification (bin 18) and vesicle trafficking (bin 22), including clathrin-coated vesicle machinery (bin 22.1). Other than that, G-S was highly linked with the pathway of external stimuli response (bin 26), and S-G was found overrepresented in protein degradation (bin 19) and cytoskeleton (bin 20) in particular. Proteins containing the S-P motif were significantly enriched for proteins with functions in protein biosynthesis (bin 17), cell wall (bin 21), vesicle trafficking (bin 22), and RNA biosynthesis (bin 15)

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Fig. 2 Functional overrepresentation of the most identified p-motifs. Numbers represent bin number from MapMan 4.0. bin1:Photosynthesis, bin3:Carbohydrate metabolism, bin5:Lipid metabolism, bin7:Coenzyme metabolism, bin9:Secondary metabolism, bin10:Redox homeostasis, bin11:Phytohormones, bin12:Chromatin organization, bin13: Cell cycle, bin14: DNA damage response, bin15:RNA biosynthesis, bin16: RNA processing, bin17: Protein biosynthesis, bin18: Protein modification, bin19: Protein degradation, bin20: Cytoskeleton, bin21: Cell wall, bin22: Vesicle trafficking, bin23: Protein translocation, bin24: Solute transport, bin26: External stimuli response, bin35: not assigned

(Fig. 2a). The S-acidic clades members, S-X-X-[DE], S-X-[DE], S-[DE], and S-E motifs, were functional enriched and demonstrated together by radar plot (Fig. 2b). The functional pattern of proteins containing these motifs contained large overlapping areas of functions in cell wall (bin 21), RNA process (bin 16), chromatin organization (bin12), and vesicle trafficking (bin 22). There proteins with S-[DE] were enriched in proteins of carbohydrate metabolism (bin 3); the S-X-X-[DE] and S-X-[DE] were highly enriched in proteins with functions in secondary metabolism (bin 9) (Fig. 2b). Similarly, proteins with basic S-type motifs also shared similar biological pathways, such as RNA process (bin 16), RNA splicing (bin 16.4) practically, and cytoskeleton (bin 20), while [RK]-X-X-S was particularly enriched in vesicle trafficking including clathrin-coated vesicle machinery (bin22.1) while R-S was in soluble transport including V-ATPase complex (bin 24.1.1) (Fig. 2c). As for proteins containing pT motifs, typical prolinedirected TP motif (T-P), acidic pT motif (X-T-X-[ED]), and basic pT motif ([RK]-X-X-T) were enriched in different protein

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functions. T-P motifs were enriched in proteins involved in regulation of phytohormones (bin 11) regarding brassinosteroid perception and signal transduction (bin 11.3.2), cell cycle (bin 13), protein biosynthesis (bin 17), cytoskeleton (bin 20), and soluble transport (bin 24). X-T-X-[ED] was highly enriched in proteins of carbohydrate metabolism (bin 3), protein modification (bin 18), cytoskeleton (bin 20), and protein translocation (bin 23). The [RK]-X-X-T motif was overrepresented relatively higher in proteins of phytohormone biosynthesis and signaling (bin 11) (Fig. 2d). Besides the motifs mentioned above, the SQ motif, belonging to glutamine S-type motif, was found on the SUPPRESSOR OF GAMMA RESPONSE1(SOG1) protein for the regulation of DNA damage responses in Arabidopsis [50]. A previous study using PANTHER GO-slim ontology enrichment on proteins with specific p-motifs to compare rice and Arabidopsis generally confirmed our observation. In addition, by analysis of co-occurrence of shared p-motifs between Arabidopsis and rice, it was found that these motifs were associated with the same biological processes [51]. Meanwhile the study confirmed that the SP motif was especially highly enriched in proteins of RNA metabolic processes, mRNA processing, nitrogen compound metabolic processes, and vesicle-mediated transport. Proteins with SD motifs (SDXD, SXD) were found highly enriched in RNA metabolism, and nucleobasecontaining compound metabolic and cellular process [51]. Based on our observations, there are also certain overlaps of pathways across different types of p-motifs. Multiple types of p-motifs were always identified simultaneously under the same condition. The p-motifs S-P, S-D-X-E, and R-X-X-S were found to be highly enriched under cold stress [52], and it was also found that S-P and R-X-X-S were found enriched in phosphorylation responses upon ABA-mediated drought, seeds imbibition process, and osmotic stress [53–55].

4

Frequently Identified Motifs in Kinase Families Many protein kinases are themselves regulated by phosphorylation, either through autophosphorylation or by upstream kinases [41]. In order to gain an overview of these p-motifs on kinases, we extracted the experimental data from the PhosPhAt4.0 database, and plotted the frequency of the p-motifs across the different kinases families (Table 2). We observed that the commonly identified 11 p-motifs only occupied about one-third of the identified frequency in the LRR-RLK, RLCK, CDK, CDPK, SnRK1, SnRK2, and SnRK3 families (30–40%), but account for nearly half of the sites identified in the MAPK and AGC kinase families (Table 2). Thus, the soluble cytosolic kinases themselves contributed a very high proportion of identified phosphorylation motifs. This is seen

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Table 2 Frequency distribution for each p-motif across kinase familiesa p-Motifs

LRR-RLK

RLCK

CDK

CKII

WNK

CDPK

MAPK

SnRK1

SnRK2

SnRK3

AGC

(35%)a

(38%)a

(42%)a

(20%)a

(29%)a

(43%)a

(50%)a

(41%)a

(31%)a

(38%)a

(49%)a

G-S

6.2

9.4

7.3

0.0

0.5

25.6

11.1

30.2

3.0

0.4

6.2

S-G

11.0

4.8

1.4

0.0

0.2

21.1

8.3

22.5

2.1

0.3

28.4

S-P

4.4

14.7

25.7

0.0

0.3

2.1

0.2

34.2

0.8

2.3

15.4

[RK]-X-S

2.5

2.4

1.3

0.0

0.0

25.8

0.0

23.3

0.0

0.4

44.2

[RK]-X-X-S

0.9

17.8

0.4

0.0

0.3

12.5

52.8

0.0

0.1

0.0

15.3

R-S

2.4

9.4

0.0

0.0

0.5

0.0

0.0

0.0

0.0

0.0

87.8

S-[DE]

1.6

2.8

2.6

0.1

0.0

1.8

79.9

0.0

2.9

0.1

8.2

S-E

1.3

2.4

3.7

0.0

0.0

1.0

86.1

0.0

0.0

0.2

5.4

S-X-[DE]

10.1

10.5

5.9

0.0

0.2

8.0

14.7

0.0

0.1

0.1

50.3

S-X-X-[DE]

1.9

5.3

0.3

0.0

0.1

1.0

78.5

0.0

0.2

0.3

12.3

T-P

1.5

17.4

1.4

0.0

0.0

2.5

43.1

20.0

6.0

1.9

6.2

[RK]-X-X-T

0.4

2.7

0.0

0.0

0.0

2.4

74.7

0.0

15.4

0.9

3.5

X-T-X-[ED]

0.9

2.2

4.1

0.0

0.0

0.3

87.1

0.0

4.3

0.3

0.8

T-D

1.0

1.2

0.8

0.0

0.0

2.0

93.8

0.0

0.0

0.0

1.1

* Numbers indicate the percentage of the p-motif identified under each kinase family a

Identified frequency proportion for these 11 p-motifs out of 70 p-motifs by Phosphat4.0.

Kinases and Substrates Phosphorylation Function

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because soluble kinases in signaling cascades are very often downstream of other kinases, making them substrates of the upstream kinases. By looking at enrichment of phosphorylation motifs in kinase families, we can learn about potential activation motifs, and also use this information to construct hierarchies in signaling pathways. Signaling pathway hierarchies are especially well known for the MAPKs, which are at the end of established kinase cascades and operate at the core of eukaryotic signal transduction networks. Their phosphorylation is organized hierarchically and involved in nearly every aspects of the plant responses (Table 1). Therefore, it is not surprising that the most identified motifs were found in MAPKs. Here, we observed that the MAPK family has a high frequency of phosphorylation at [RK]-X-X-S, S-[DE], S-E, S-XX-[DE], and all T motifs (>40%). However, the exact function of these p-motifs on MAPKs still needs to be characterized. These motifs were partially identified from experiments regarding pollen development, seed maturation, or stress responses [56–58]. Therefore, tracing back to the experiment in the database may be of use in understanding the p-motifs potential function. For example, we found that the 93.8% of the T-D motifs from the cumulative data were actually found in MAPKs, as part of the well-known double phosphorylation motif T-E/D-Y for activating the MAPKs [59] (Table 2). As well, the AGC family was found to be preferentially phosphorylated at [RK]-X-S, R-S, S-P, and S-X-[DE] (>40%). AGC kinases are involved in many signaling pathways (Table 1) and constitute a core of the plant’s signaling network. The S-P motif was detected more often in the CDK (26%) and SnRK1 (34%) families. The members of the SnRK1 family were also found to be more frequently phosphorylated at the G-S, S-G, and [RK]-X-S motifs compared to other kinase families. The membrane kinases of the RLCKs accounted for 17.8% of [RK]-X-X-S and 17.4% of the T-P motif. RLCKs are known as substrates for different kinases including LRR-RLKs and GSK/SLKs [9, 60]. It has been shown that the [RK]-S-X-S motif is a target for RLK, which prefers motifs with a preponderance of basic amino acids [41]. An example for such a signaling hierarchy found in the p-motif distribution is that BSK1 can be activated at a K-S-X-S motif (S230) by the LRR-RLK BRI1 [61]. Another new kinase-target relationship was revealed by the discovery of the GSK/SLKs catalytic motif S/T-X-X-X-S/T on BSK3. Later, it was discovered that the corresponding kinase was BIN2 from the GSK3 family [60]. Therefore, the information on the phosphorylation motifs within kinases could theoretically be used to systematically construct a kinase’s immediate signaling partners and perhaps the entire network. Multisite phosphorylation of multiple kinases can control enzymatic activity via orthostatic or allosteric regulation [26]. We have

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shown that different kinase families are enriched for different p-motifs in Arabidopsis (Table 2). Previous research presented Thr172 phosphorylation as an important site of allosteric activation of AMP-activated protein kinase in rat [62]. In plants, coupling with phosphorylation, the activation of kinases resulted in conformational changes in the kinase domain [22, 63]. The idea of such activating phosphocodes was later used to cluster kinase phosphorylation patterns combined with other specified kinase features. For example, BAK1 is a well-described co-receptor to multiple ligandbinding LRR-RKs involved in different biological pathways. In its kinase domain, phosphorylation at Y403 was identified as determinant for a role in plant defense instead of in plant growth. In other LRR-RKs, there may be functional analogous p-sites around T-Y motifs, which could control pathway specificity [64]. Hence, different p-motifs may be linked with distinct functional pathways. With motif comparison among different plant species, or even more generally among eukaryotic species, similar substrate motif preferences or kinase activation motifs can be found. Examples are the motif R-X-X-S [65, 66] among AGC or CDPK substrates, or the MAPK activation motif T-E/D-Y [67]. Despite the evolutionary distance, it was demonstrated that S-P and R-X-X-S were both highly enriched in response to drought stress in barley, Ammopiptanthus mongolicus and the moss Physcomitrella patens [53, 68]. Thus, p-motifs seem to have conserved functions across different species.

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Conclusion In this mini-review, we organized the known kinase families and their experimentally established substrates and extracted the most identified p-motifs from the PhosPhAt4.0 database to ascertain patterns in p-motif distributions within substrates and kinases. Multi-motif phosphorylation for a kinase family itself could be highly related to its functional diversity. We provided different p-motifs which are overrepresented among proteins with distinguished biological functions. At the same time, as the phosphorylation substrates, different p-motifs were enriched in different kinase families, while in turn, certain kinase families seemed to be enriched for certain p-motifs in their substrates. So far, the information about functional p-motifs in plant research are limited. Here, we provided these examples by taking advantage of phosphoproteomic data or retrieved data to conclude kinase-substrate relationships. Using functional p-motifs in large-scale studies can be of great significance in systematically predicting and understanding the biological role of phosphorylation.

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Chapter 2 Protein Phosphorylation Response to Abiotic Stress in Plants Rebecca Njeri Damaris and Pingfang Yang Abstract Plants are an important part of nature because as photoautotrophs, they provide a nutrient source for many other living organisms. Due to their sessile nature, to overcome both biotic and abiotic stresses, plants have developed intricate mechanisms for perception of and reaction to these stresses, both on an external level (perception) and on an internal level (reaction). Specific proteins found within cells play crucial roles in stress mitigation by enhancing cellular processes that facilitate the plants survival during the unfavorable conditions. Well before plants are able to synthesize nascent proteins in response to stress, proteins which already exist in the cell can be subjected to an array of posttranslation modifications (PTMs) that permit a rapid response. These activated proteins can, in turn, aid in further stress responses. Different PTMs have different functions in growth and development of plants. Protein phosphorylation, a reversible form of modification has been well elucidated, and its role in signaling cascades is well documented. In this minireview, we discuss the integration of protein phosphorylation with other components of abiotic stress–responsive pathways including phytohormones and ion homeostasis. Overall, this review demonstrates the high interconnectivity of the stress response system in plants and how readily plants are able to toggle between various signaling pathways in order to survive harsh conditions. Most notably, fluctuations of the cytosolic calcium levels seem to be a linking component of the various signaling pathways. Keywords Posttranslation modification, Abiotic stress, Phytohormones, Phosphorylation

1

Introduction Plants are an important component of almost every biome because, in addition to being the basis of most food chains, they facilitate the storage of carbon, fix nitrogen from the atmosphere, and produce oxygen, all processes which support other living organisms. Especially for crop plants, which have been domesticated based on the selection of suitable traits to ensure robust growth, the sessile nature of all plants can expose them to stress conditions, such as pest invasion, increased global temperature, or increased salinity and drought that produce an unfavorable environment for optimal growth and survival. These stress conditions limit their potential growth and development and ultimately negatively affect their yield

Xu Na Wu (ed.), Plant Phosphoproteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2358, https://doi.org/10.1007/978-1-0716-1625-3_2, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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[1]. Plants have thus evolved intricate strategies to respond to a large variety of stresses, both biotic and abiotic. These strategies include regulation of the cell at different molecular levels for ensuring their survival. These levels range from protein posttranslational modification to broad transcriptional control, to chromatin rearrangement. For example, a study conducted by Sun et al. [2] showed that heat stress induces rearrangement of chromatin organization as well as activation of transposable elements and that the heat-induced transposable element activation was accompanied by heterochromatin decompensation [3]. Similarly, condition-specific stress genes have been shown to be expressed in response to stress in the plant which include transcription factors [4], such as the cold-responsive inducer of CBF (C-repeat-binding factor) expression 1 (ICE1) that enhances the transcription of CBF and the coldresponsive genes (COR) [5]. Proteins are biopolymers that are composed of amino acids and are essential for all living things. Active proteins and protein complexes are the ultimate products of most protein-coding genes. Proteins have a wide range of functions in plants such as the regulation of transcription (for example, expression of GA-responsive genes, GAMYB that is regulated by GAMYBBINDING proteins [6]), enzymes [7], cell-receptors, and as components of connective tissues, as well as many other functions. Proteins are responsible for almost every task of cellular life such as product manufacture, waste cleanup, and routine cellular maintenance. Different proteins, such as the membrane proteins and enzymes, have different structures and functions which are mainly determined by the sequence of the amino acids and tertiary folded shape, respectively [8, 9]. Enzymes play crucial roles in the cell including the mobilization of stored reserves [7] and enzymatic endosperm loosening for radical protrusion [10] during seed germination. Under environmental stress, plants require adjustments to both their metabolism and gene expression in order to attain a balance between growth, development, and survival. For instance, to survive from biotic and abiotic stress, plants have developed an intricate defense mechanism regulated by complex signaling pathways. Proteins are directly involved in these mitigative pathways, and posttranslational modification (PTM) is the first modification to the already present proteins as a response at the onset of stress (Table 1). Protein translation is the process of translating the message encoded by the RNA into a protein. Nascent polypeptides are generated by ribosomes with the majority of the synthesized proteins being subjected to PTMs. Taken all together, this results in the mature “protein-machinery” that ultimately forms the proteome [11]. Throughout their life cycle, proteins can undergo different PTMs which can be placed either on the carboxy or amino terminus or on the amino acid side chains of the protein. These

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Table 1 List of several proteins that are involved in the various protein phosphorylation cascades during abiotic stress Kinase

Stress

Reference

SnRK2

Drought, salt, osmotic, cold

[90, 171]

MPK3

Osmotic,

[171]

MPK4

Salt, cold,

[172, 173]

MPK6

Drought, salt, cold, wounding

[174]

CDPK

Osmotic

[70, 71, 79]

SCaBP

Osmotic

[41, 50]

PKS5

Salt, osmotic

[41, 50]

RBOH

Osmotic

[78, 79]

PTMs diversify the chemical properties of the 20 standard amino acids by either modifying existing functional groups or introducing new ones. PTMs include phosphorylation, ubiquitination, glycosylation, nitrosylation, methylation, acetylation, or lipidation. These modifications play key roles in the proteins function by specifying the localization, specific activity, or signaling status, or by marking the proteins for increased stability or rapid degradation. Different PTMs have varying functions in plants, for example: (a) N-termini protein modification, which include N-terminal methionine excision (NME), N-α-acetylation (NTA), N-myristoylation (MYR), and peptide cleavage, influences the folding, activity, complex associations, localization, and protein half-life [12, 13]. In plants, NME is associated with protein stability, and by integrating thiol status with proteolysis, cytoplasmic NME obtains a crucial role in plant growth and development [14]. It has been reported that MYR ensures the proper targeting of the myristoylated proteins to the plasma membrane. For example, studies on Arabidopsis thaliana (referred to as “Arabidopsis” henceforth) thioredoxins indicated that MYR alone localizes proteins to the endomembrane [15]. (b) Glycosylation, an enzymatic controlled covalent linkage of sugar to protein, accounts for one of the most complex PTMs studied. Being a form of co-translational modification, glycosylation adds a simple sugar or an elaborate oligosaccharide, ultimately affecting the role, location, and function of plant proteins. For example, the N-linked glycans play an important role in protein folding within the endoplasmic

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reticulum (ER), and recent evidence suggests a role in endomembrane sorting and trafficking [16]. (c) Reversible protein phosphorylation is one of the most predominant PTMs with one-third of eukaryotic proteins thought to be phosphorylated [17]. In this mini-review, involvement of protein phosphorylation in abiotic stress will be discussed, with specific focus on phytohormones and ion and osmotic homeostasis.

2

Protein Phosphorylation Protein phosphorylation is a well-studied PTM which plays a major role in signal transduction by altering protein conformation, activity, localization, and stability, thus increasing their functional diversity [18, 19]. Catalyzed by kinases, phosphorylation is a reversible modification achieved by transferring a phosphoryl group from adenosine triphosphate (ATP) by forming an ester bond with the hydroxyl group of specific serine (80–85%), threonine (10–15%), or tyrosine (0–5%) residues within the target protein [20, 21]. Phosphatases are the enzymes responsible for dephosphorylating the phosphorylated residues in the modified proteins. Several methods have been employed in identifying protein phosphorylation in plants which include radioactive ATP, gel retardation, phosphoantibody and fluorescence staining, chromatographic methods (HPLC (high-performance liquid chromatography), TLC (thinlayer chromatography)), electrophoresis, Edman sequencing, and mass spectrometry which works by detecting the phosphorylated protein and the exact phosphorylated amino acid(s) [22]. The radiolabeling method for detecting candidate phosphorylated proteins uses sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE) to separate the proteins from either in vivo or in vitro labeled proteins with either 32 [P]phosphate or [g-P]ATP. The dried gel is exposed to X-ray film to locate the phosphorylated proteins via autoradiography [22]. For the phosphor-antibodies phosphorylated protein detection, the SDS-PAGE fractionated proteins are subjected to Western blot analysis using specific antibodies for the various residues, including Ser(P), Thr(P), and Tyr (P) [22]. A recent review by Ajadi et al. [23] offers an in-depth detail on phosphoproteomics highlighting important advances in this field. However, the heterogenicity of protein phosphorylation poses a great challenge during phosphoproteome studies due to phosphorylation potentially being on more than one residue. Identification of the proteome by mass spectrophotometer (MS) is challenging due to a number of factors that include: (1) phosphopeptides being negatively charged while the MS system utilizes a positive mode of electrospray, (2) phosphopeptides being

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hydrophilic and thus not binding efficiently to the purification columns, (3) phosphopetides are not observed in intense peaks, especially in the presence of other non-phosphorylated peptides due to ionic suspension, and (4) if the proteins produces peptides fragment that are too small or too large, they might not be in the spectrum [24, 25] Additionally, detection of phosphorylated serine, threonine, or tyrosine residues using MS is more challenging as residues of phosphoserine and phosphothreonine are unstable while those of tyrosine are more labile [24]. Therefore a combination of phosphorylation detection methods has been utilized as reviewed by Mann et al. [25], to complement the deficits of each method individually. In the plant genome, there are approximately twice as many kinases as compared to the mammalian genome [26], with the genome of the model plant Arabidopsis containing 1052 protein kinases and 162 phosphatases [27] while rice contains 1512 (Japonica) and 1403 (Indica) kinases [28]. Approximately three million phosphorylation sites have been predicted in rice [29]. This indicates the vital role of protein phosphorylation in regulation of cellular processes in the life cycle of plants. Over the years, several studies have identified numerous phosphoproteins and their respective phosphopeptides as well as specific phosphorylation sites. The database of phosphorylation sites in plants, using experimentally identified phosphorylation and integrating datasets, reports 82,175 phosphorylation sites in 31,012 proteins from 20 different plants [30]. In response to cadmium (Cd), Zhong et al. [31] identified a total of 2454 phosphosites from 1244 proteins in an analysis of the quantitative phosphoproteome from rice seedlings. From these, 482 were differentially phosphorylated in response to Cd stress. Ten heat-responsive phosphoproteins from 42  C heat-stressed rice seedlings were reported by Chen et al. [32], while there were 12 upregulated and 1 downregulated phosphoprotein in response to cold stress [33]. A total of 27 and 13 phosphorylation sites from 20 and 8 proteins from-salt treated Arabidopsis suspension plants and rice shoots, respectively, have been reported by Chang et al. [34]. Being one of the predominant modifications, and given its vast modification ability, protein phosphorylation is therefore reported to be involved in the mitigation of various abiotic stresses in plants. Some of the pathways that utilize protein phosphorylation include ion stress signaling, such as the salt-overly-sensitive (SOS) pathway, osmotic stress signaling, such as the ROS homeostasis, cold and heat stress signaling, as well as others that are discussed below.

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Ion Stress Signaling

3.1 Protein Phosphorylation and the SOS Pathway

To understand the involvement of protein phosphorylation in salt stress in plants requires in-depth analysis of the conserved SOS pathway, which regulates sodium ion homeostasis under salt stress [35, 36]. The major components of the SOS pathway have been well studied, particularly in Arabidopsis. These major players consist of the EF-hand calcium binding protein, SOS3 and the SOS3-like calcium-binding protein 8 (SCaBP3), the SOS2 protein kinase, and the plasma membrane Na+/H+ antiporter SOS1 (PM Na+/H+ antiporter) [37–40]. When plants are growing under normal condition, PKS5 (protein kinase SOS2-like 5) phosphorylates SOS2 Ser294 enhancing the interaction of 14-3-3 proteins binding to SOS2. This interaction results in an inhibition of SOS2 kinase activity, thus reducing the activity of the SOS1 Na+/H+ antiporter [41]. Under salt stress, the calcium sensor proteins, including 14-33, SOS3, and SCaBP3, perceive and decode the calcium signals resulting from changes of the cytosolic-free Ca2+ [41]. When plants are exposed to NaCl and mannitol stress, they exhibit a rapid increase in cytosolic calcium concentration ([Ca2+]cyt) within seconds, originating from the roots [42]. It is still unclear whether Na+/as ions specifically trigger Ca2+ independently, or if this phenomenon is based on osmotic impact [43]. Additionally, the exposure to salt stress could accelerate the 26s-proteosome-mediated degradation of the two kinds of 14-3-3 proteins [44]. This leads to repression of PKS5 activity which reduces phosphorylation of SOS2 Ser294 and thus relieves the repression of SOS2 activity by 14-3-3 protein [41] (Fig. 1). The liberated SOS2 is recruited to the plasma membrane where it phosphorylates and activates the SOS1 Na+/H+ antiporter. When combined with the reduced activity of PKS5, the activated SOS1 activates the PM H+-ATPase by binding to 14-3-3ω. This provides SOS1 with the proton gradient needed to drive Na+ transportation [41] (Fig. 1). Therefore, it is clear that the integrity of the plasma membrane is crucial for the general wellbeing of the cell during salt or osmotic stress conditions. The plasma membrane H+-ATPase plays a crucial role in maintenance of membrane potential particularly under saline conditions, where it is required for maintenance of negative membrane potential by providing Na+ exclusion via the Na+/H+ exchanger [45]. Nonselective cation channels and high-affinity K+ transporters (HKTs) allow the entry of Na+ under saline conditions. The positively charged Na+ ions depolarize the plasma membrane which leads to immediate K+ leakage. The depleted cytosolic K+ pool impairs cell metabolism which can potentially lead to programed cell death [46]. During salt stress, H+-ATPase activity is upregulated via protein phosphorylation, resulting in higher rate of membrane potential repolarization, and thus enabling better retention

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Fig. 1 Simplified events during salt-responsive SOS singling pathway. On the left are events during normal growth condition while on the right are events during salt stress. P represents phosphorylated status while Pi represents inorganic phosphate. [Ca2+]cyt means cytoplasmic calcium ions

of K+ (Fig. 1). This salt-responsive protein phosphorylation was confirmed at the molecular level by Bose et al., who showed that there was no difference at the transcription level between mutant and the wild-type H+-ATPases (AHA1/2/3) [47]. Through mass spectrometry experiments, the H+-ATPases AHA1, AHA2, AHA3, and AHA4/11 were shown to have 10 different phosphorylation sites distributed at the N-termini, P-Domain, and as well at the C-terminus [48]. Under normal conditions, the activity of PM H+ATPase is maintained at a relatively low level. However, the activity shows a dramatic increase after saline-alkali stress exposure in Arabidopsis [49]. This increased activity is vital for driving transporters in response to abiotic stress [50]. It has been reported that a Ca 2+ sensor modulates the plasma membrane H+-ATPase. In response to saline-alkali stress, the Ca2+ sensor SCaBP3 interacts with the C-terminal region 1 domain of the PM H+-ATPase which in turn facilitates the intermolecular interaction of the AHA2 C-terminus with the central loop region of the PM H+-ATPase causing the autoinhibition of the PM H+ATPase activity [50] (Fig. 1). In the same research conducted by Yang et al., [50], they demonstrated a direct interaction between SCaBP3 and the protein kinase PKS5, which resulted in the stability of the kinase–ATPase complex with the resulting phosphorylation

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by the PKS5 inhibiting the ATPase activity of AHA2. Abiotic stress causes cellular energy deficiency by, among other means, inhibiting photosynthesis and energy-releasing catabolic reactions [36]. A putative plasma membrane H(+)-transporting ATP synthase, which couples proton transport, transportation of hydrogen ions, and ATP synthesis through a rotational mechanism, was reported to be phosphorylated in response to heat stress in rice leaves [32]. The ATP-β subunit, by associating with a 14-3-3 protein (containing a casein kinase II phosphorylation motif), is phosphorylated by casein kinase II and ultimately reduces ATP synthase activity [32]. During phosphorylation, the γ-phosphoryl group from adenosine triphosphate (ATP) or guanosine triphosphate (GTP) is transferred by a protein kinase forming a covalent bond with the hydroxyl group of a specific amino acid (serine, threonine, or tyrosine) within the target protein [51] (Fig. 1). 3.2 Protein Phosphorylation for Ion Homeostasis Is Crucial for Plant Response to Abiotic Stress

Plants, in addition to water and sunlight, require mineral nutrients that are assimilated mainly as cations or anions from their surroundings. For the vascular plants, this is usually via the roots. Nutrients are classified as macro or micronutrients depending on how much of the nutrients is required. The macronutrients include potassium, nitrogen, phosphorous as well as others while the micronutrients consist of iron, copper, and zinc to name but a few. Due to their sessile nature, plants utilize the sensing and signaling ability in their root systems to detect the availability, or lack thereof, of nutrients in the soil. Under nutrient deficiency, various plant signaling pathways are utilized such as activation of responsive genes/transcription factors aimed at maintaining nutrient homeostasis [52]. Phosphorous plays an important role in the plants life cycle as it is a structural element of many organic molecules including nucleic acids, ATP, and phospholipids. It is therefore a crucial macromolecule for plant growth and development. Phosphate is absorbed from the soil in the form of inorganic phosphate (Pi) [53]. In plants, phosphate 2 (PHO2), encodes a ubiquitin-conjugating (E2) enzyme (UBC24) that directly and/or indirectly degrades high-affinity phosphate transporters, which take up Pi from the soil, and PHO1, which loads Pi into the xylem for distribution from root to shoot [54–56], thus modulating Pi homeostasis. In Arabidopsis, mutation of PHO2 leads to an increased accumulation of Pi in leaves under Pi-replete condition [57, 58]. Detected mainly in the vascular tissue [59], OsPH02/OsLTN1, the AtPHO2 homologue in rice plays a crucial role in Pi homeostasis downstream of miR399 regulating several Pi starvation responses in rice [59, 60]. Similar to the Arabidopsis mutant, Ospho2/ltn1 mutants exhibits enhanced Pi uptake displaying leaf tip necrosis as a result of increased Pi translocation from roots to shoot [59]. Protein phosphorylation plays an important role in Pi homeostasis in rice with ER-located casein kinase 2 (OsCK2) holoenzyme (OsCK2α3/β3) phosphorylating

Protein Phosphorylation Response to Abiotic Stress in Plants

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the phosphate transporter OsPT8 at Ser-517 under Pi-sufficient conditions, and thus preventing it from interacting with phosphate transporter traffic facilitator 1 (OsPHF1) [61]. However, Pi-deficient conditions cause OsCK2β3 subunit degradation, and the unphosphorylated OsPT8 is transported to the plasma membrane with the help of OsPHF1, resulting in an increased Pi uptake and thus maintaining cellular Pi homeostasis [61]. Additionally, OsCK2β3 was shown to interact with OsPHO2 phosphorylating it at Ser-841 via an atypical CK2 recognition motif resulting in a rapid degradation of the phosphorylated OsPHO2 compared to the non-phosphorylated OsPHO2 [62]. This function was confirmed by knockdown of OsCK2α3 or OsCK2β3 exhibiting significantly higher Pi concentrations in shoots and roots than in wildtype cv. Nipponbare [61]. Another macronutrient, potassium (K+) is crucial in plants as it is involved in numerous physiological processes such as the activation of enzymes, the maintenance of membrane potential, and osmotic regulation as well as others [63]. The high-affinity K+ transporter, HAK5, is the main K+ adsorption channel in Arabidopsis. Low potassium stress induces the expression of the HAK5 gene as well as rapid phosphorylation of Auxin Response Factor 2 (ARF2). The phosphorylation of ARF2 blocks its ability to bind to the HAK5 promoter and thus allowing for HAK5 expression [64]. Expression of HAK5 facilitates HAK5-mediated high-affinity K+ uptake enabling the plant to survive under K+ deprived conditions [64].

4

Phosphorylation and the ROS-Mediated (Osmotic) Stress Response Reactive oxygen species (ROS), which include hydrogen peroxide (H2O2), the superoxide radical (O2 ), the hydroxyl radical (OH ), and singlet oxygen (1O2), are harmful by-products of basic cellular metabolism of aerobic organisms and are a result of excitation or incomplete reduction of molecular oxygen [65, 66] (Fig. 2). Apart from their harmful effects, ROS have been reported as signaling molecules that regulate plant development and response to biotic and abiotic stress [65, 67]. The most well-elucidated enzymatic source of ROS are the plant NADPH oxidases, also known as respiratory burst oxidase homologs (RBOHs). In addition to other domains, RBOHs have a cytosolic N-terminal extension that possesses the regulatory regions which includes calciumbinding EF-hands and phosphorylation target sites. These domains are important for the function and regulation of plant NADPH oxidases [68, 69]. The ROS produced during abiotic stress triggers Ca2+ accumulation in the cytosol, and these changes are perceived and transduced by the calcium-dependent protein kinases (CDPKs) and CIPKs through phosphorylation of downstream target l

l

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Fig. 2 ROS signaling cascade during salt abiotic stress. Abiotic stress causes ROS burst that leads to the increase in the cytosolic calcium ([Ca2+]cyt) that is perceived by various calcium sensors that induces the calcium signaling pathway. P stands for phosphorylated status while Pi indicates inorganic phosphorus. The increased ROSs in the cell activates the MAPKs that leads to transcription regulation of stress-responsive genes

proteins [70, 71] (Fig. 1). The downstream components of calcium signaling pathways are regulated by the CDPKs. The CDPKs consist of a variable N-terminal domain and several functional domains such as a protein kinase domain, an autoinhibitory region, as well as a calmodulin-like domain [72]. The binding of Ca2+ to the calmodulin-like domain activates the CDPKs which in turn regulates downstream components of calcium signaling [73, 74]. The CDPKs constitute a large multigene family consisting of 29 genes in Oryza sativa [75] and 34 genes in Arabidopsis [76, 77]. In potato, CDPK4 and CDPK5 have been shown to induce the phosphorylation of StRBOH, thus regulating the oxidative burst during pathogen defense [78]. Dubiella et al. [79] also reported ROBH phosphorylation by calcium-dependent protein kinase 5 (CPK5) during pathogen defense, with this reaction occurring within a very short period (15 minutes). RBOHs are reported to be phosphorylated by the open stomata 1 (OST1) in Arabidopsis at Ser 174 and Ser 13 during ABA-dependent stomata closure [80]. Abiotic stress was reported to increase the activity of RBOHs, and the proteins’ high-temperature stability and alkaline-philic feature demonstrates its crucial role in abiotic/drought stress [81].

Protein Phosphorylation Response to Abiotic Stress in Plants

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The mitogen-activated protein kinases (MAPKs) cascade also plays an important role in ROS signaling. For instance, the ROS are not only the trigger but also the consequence of activation of MAPK signaling in Arabidopsis [82–84] (Fig. 2). MAPKs are serine-threonine kinases whose cascades transduce signals involved in biotic and abiotic stress, hormone signaling, development, and cell cycle regulation pathways. The phosphorylative nature of these kinases is crucial in abiotic stress signaling in plants. For example, MAPKs, specifically the mitogen-activated protein kinase-3 (MPK3) and MPK6, mediate phosphorylation of proteins that control freezing tolerance in Arabidopsis [85]. Similar to other forms of abiotic stress, ROS are involved in response to cold stress such as the observed increase of H2O2 [86]. Exogenous application of H2O2 led to activation of components of the MAPK cascade [87]. Similarly, protoplast activation of AtMPK6 and AtMPK3 utilizes ANP1, (an MAPKKK) that is activated by H2O2 [82]. It is thought that the primary role of H2O2 is to disturb ROS homeostasis via redox relay metabolism [88]. These disturbances result in a rapid rise in intracellular levels of ROS, and thus transferring the oxidation target to MAPK cascades. This mechanism has been well described in mammalian cells, where H2O2 induces the oxidation of thioredoxin (Trx) which leads to a conformational change resulting in the disruption of interaction between Trx and ultimately leads to apoptosis. This results in activation of signal-regulating kinase 1 (ASK1) and subsequent phosphorylation of its substrate, p38 MAPK [89]. In Arabidopsis, MAPKs are also involved in the negative regulation of ICE1. In this case, MPK3 and MPK6 interact with and phosphorylate ICE1, rendering it unstable and affecting its transcriptional activity. That is, during cold stress, the phosphorylated ICE1 is marked for ubiquitin-mediated degradation thereby affecting the expression of downstream genes [85]. CBF, a well-known transcription factor that is induced by cold stress, activates the cold-responsive gene (COR) by binding on its promoter. Phosphorylation of ICE1 by SnRK2.6, a cold activated gene which is discussed under the ABA signaling pathway, suppresses HOS1-mediated ICE1 degradation, allowing it to bind to the cis-element of the CBF promoter, thereby positively regulating its expression ultimately leading to freezing tolerance [90]. However, this is in opposition to the actions of MPK3/MPK6 phosphorylation, which was shown to negatively regulate the ICE1-CBF-COR cascade in response to cold stress [85]. This is an example of the cross talk in plant stress responses to allow both rapid response to a stress and also to fine-tune the response to prevailing conditions.

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Phytohormone-Linked Protein Phosphorylation

5.1 ABA-Responsive Genes

With its structure determined in 1965, abscisic acid (ABA) is one of the major plant hormones that play pivotal roles in various aspects of plant growth and development including mediating response to environmental stresses such as drought, high salinity, and low temperature [91]. In response to salt and osmotic stress, signal transduction pathways including ABA-independent [92] and ABA-dependent pathways (Fig. 3) [92, 93] are enhanced. In the latter, the signal is initially perceived by ABA receptors with these two pathways leading to the induction of the basic leucine zipper (bZIP) transcription factors. When phosphorylated at multiple serine residues, bZIP transcription factors exhibit enhanced DNA-binding properties [94]. ABA levels increase in plants under high-salinity conditions where it is involved in plant stress responses by adjusting the gene expression profile and cellular responses [95– 97]. Qiu et al. [98] reported 2271 phosphosites in response to ABA. bZIP is one of the major transcription factors which have a highly conserved domain consisting of the basic region that is involved in DNA-binding and nuclear import, and the leucine zipper domain for dimerization [99]. This class of transcription factors participates in the regulation of light, stress and hormone response pathways, pathogen defense, as well as others [100, 101]. In rice, several members of the bZIP transcription factor family are involved in salt stress along with other abioticresponsive transcription factors. For example, Liu et al. [102] showed that OsbZIP71 confers salt and drought stress resistance. OsHBP1b was also reported to be induced by salt stress in rice seedlings [103] as well as imparting resistance for other abiotic stresses in rice [104]. In addition to the two bZIP conserved domains, OsbZIP46, which belongs to the ABF/ABB15 subfamily of bZIP transcription factors, contains five conserved motifs that were predicted to contain five phosphorylation sites. This implied that phosphorylation by protein kinase of members of the ABF/AB15 subfamily is the means by which they respond to stress or ABA signaling [105]. ABI5 contains at least three phosphorylatable regions (amino acid 31–50, 158–159, and 195–208) that were revealed by mass spectrometry analysis [106]. Sucrose nonfermenting 1(SNF1)-related protein kinase subfamily 2 (SnRK2s) were the first kinases reported as involved in the ABA-dependent activation of ABF (ABRE-binding factor) [107–109]. The transcript of OsRK1, a Ser/Thr self-phosphorylating protein kinase of the SnRK2 family, showed a significant increase in response to salt stress and ABA, suggesting that its regulation is dependent on hyperosmotic stress and ABA signals with a preference for bZIP proteins [110].

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Fig. 3 ABA signaling cascade in response to salt stress. The ABA-dependent pathway ends up in the activation of bZIP transcription factors that shows increased stability after phosphorylation where P stands for phosphorylated status

The OsABF1 (also known as OsABI5 ,OREB1, OsbZIP10) protein was shown to be differentially phosphorylated in response to exogenous application of ABA in rice shoots [98]. In addition to two kinase recognition motifs (RXXS/T and S/TXXE/D) within different functional domains, OsABF1, being a rice ABRE-binding factor, possesses multiple highly conserved phosphorylation domains, namely C1, C2, C3, and C4 [111]. It has been demonstrated that OsRK1 phosphorylates OREB1 on all its four domains with varying level of phosphorylation [110] and that S158 and/or T159 are essential for the kinase activity. Examples of phosphorylated ABF in response to abiotic stress include phosphorylation of rice TRAB1 which was shown to be phosphorylated in an ABA-dependent manner [112]. Potato StABF1 is phosphorylated in response to both ABA and salt stress, with StCDPK2, a potato CDPK isoform being reported to phosphorylate StABF1 in vitro [113]. In Arabidopsis, ABF4 is phosphorylated by AtCPK32 with the same kinase acting at ABF4 at serine 110 [114]. Thr-128, Ser-134, and T451 of AtABF3 were reported as CDPK phosphorylation sites with ABF3 being phosphorylated by recombinant CDPK3 and CDPK16 in vitro [115].

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Similarly, ABA-induced protein phosphorylation functions in the stomata closure during drought stress. In the guard cells, the ABA network involves SnRK2.6 (also known as open stomata 1 (OST1)). The drought-induced increase in ABA avails it for binding to the components of the ABA receptor. This resultant hormone–receptor complex binds and inhibits Protein Phosphatase 2C (PP2C) (which in absence of ABA, binds and inhibits the kinase activity of OST1), thus releasing it [116–118] (Fig. 3). The kinetically active OST1 then phosphorylates an assortment of substrates which include respiratory burst oxidase homologue (RBOH) [80, 119], slow anion channel-associated 1 (SLAC1) [120], quickly activating anion channel 1 (QUAC1) [121], K+ inward rectifying channel (KAT1) [122, 123], and membrane water channel plasma membrane intrinsic protein 2;1 (PIP2;1) [124]. Consequently, the phosphorylated substrates lead to degradation of ROS, enhanced water and anion efflux, and the inhibition of K+ influx, all of which induces stomata closure, thus avoiding water loss via evaporation. 5.2 GA-Responsive Genes

Gibberellic acids (GAs) were one of the first phytohormones to be discovered. They are involved in crucial aspects of plant biology, such as seed germination, leaf expansion, and stem and root elongation, to mention but a few examples [125, 126]. In the recent past, GA has been reported to control several biological processes in response to stress [127–130]. GA-promoted vegetative growth occurs through regulation of cell division, expansion, and differentiation [126]. DELLA proteins are repressors of GA signaling in plants [131]. The GA signal is perceived by the nuclear receptor GA-insensitive dwarf 1 (GID1) which promotes interaction of GID1 with DELLA proteins thus tagging it for rapid degradation via the ubiquitin–proteasome pathway mediated by the ubiquitin E3 ligase SCFSLY1/GID2 [125] (Fig. 4). However, it was reported that abiotic stress, particularly salt stress, reduces the endogenous bioactive GA content in the cell [132, 133] and that phosphorylation of DELLA protein increases its stability (Fig. 4). For example, Dai et al. [134] reported that in rice, a casein kinase 1, earlier flowering 1 (EL1) represses GA signaling by enhancing DELLA stability. The DELLA protein structure consists of a conserved N-terminal DELLA domain where it interacts with GID1, a diverse region rich in serine and threonine residues known as poly S/T, and a conserved C-terminal GRASS domain where interaction with transcription factors occurs [135, 136]. For example, phosphorylation of SLR1, (a rice DELLA protein) at the C-terminus by EL1, sustains its active form and phosphorylation at the N-terminus suppresses GID1-GA-mediated degradation which ultimately results in suppressed GA response/s. Similarly, salt stress induces gene expression of a GA receptor ring E3 ubiquitin ligase (GARU) that in turn binds to the GID1A (a GID1 homologue in Arabidopsis) which leads to its ubiquitin-dependent proteasome

Protein Phosphorylation Response to Abiotic Stress in Plants

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Fig. 4 GA-mediated DELLA degradation. On the left, shows normal growth condition while on the right shows response to salt stress that leads to DELLA stability. Ub stands for ubiquitin-tagged protein for degradation

degradation. This results in stabilization of DELLAs and inhibition of the GA response [137]. However, the GARU–GID1A interaction is regulated by phosphorylation of Try321 residue on GARU by TAGK2 kinase, with phosphorylation of GARU rendering it unavailable for interaction with GID1A, and this leads to the destabilization of DELLAs and activation of the GA response [137]. The reduced or halted GA response can be attributed to stabilized DELLA which can conveniently bind with transcriptional factors making them unavailable for binding to the promoter region of their target genes, ultimately modulating gene expression. GAMYB is an important transcription factor in the GA signaling pathway that is crucial for seed germination and plant growth and development [7]. Phosphorylation of GAMYB enables its nuclear translocation which in turn upregulates the expression of GA-responsive genes. As a transcription factor, its nuclear localization is of paramount importance for it to be able to regulate the expression of its target gene. cGMP-dependent protein kinase (PKG), a salt-responsive kinases, interacts with and phosphorylates GAMYB at Ser 6 resulting in modulation of its nucleocytoplasmic distribution in response to GA [138]. PKG has dual activity as both kinase and a phosphatase which is regulated by cGMP. GA causes production of cGMP that binds to a specific domain in the PKG resulting in enhancement of PKG kinase activity as well as inhibition of PP2C phosphatase activity [138]. As mentioned earlier, salt stress reduces the available endogenous GA content in the cell [132, 133], this, in turn, causes a decrease or complete lack of cellular cGMP [138], leading to a less-activated PKG kinase activity and less inhibition of PP2C phosphatase. The activated PP2C phosphatase of PKG dephosphorylates GAMYB causing its

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cytoplasmic localization and an overall downregulation of GA-responsive genes [138]. This may provide an explanation (among other factors) for the stunted/reduced growth during salt stress resulting from the decreased GA levels as well as promotion of DELLA stability via phosphorylation (Fig. 4). Similarly, DELLAs are among the core regulators that enhance plant survival in different stress environments by integrating internal and external signals [139–141]. Verma et al. [142] showed that quadrupleDELLA mutant seedlings were inhibited less by salt stress compared to the wildtype, implying that the slow growth during salt stress is partially by a DELLA-dependent mechanism and that salt enhances DELLA function. Reduced or halted growth during abiotic stress is crucial for plant survival, as much of the energy and resources are channeled to pathways that facilitate mitigation of the negative effects caused by stress. Therefore, under stressful conditions, phosphorylation of DELLAs acts as the necessary evil that reduces growth and saves limited resources. This reveals just how intricate and interconnected the stress/growth signaling pathways and plant adaptability are, and how plant systems can be modulated via protein phosphorylation to favor the existing conditions. 5.3 BrassinosteroidsResponsive Genes

Plant receptor-like protein kinases (RLKs), the largest family of plant transmembrane signaling proteins, constitute about 610 and 1132 members in Arabidopsis and rice, respectively [143]. This group contains unique protein kinases that are positive regulators of plant tolerance to abiotic stress such as salt and cold [144]. RLKs can convert an external signal into an intracellular cytoplasmic signal through phosphorylation and dephosphorylation of the Ser/Thr residues on itself [145, 146]. In Arabidopsis, the 610 members encode for about 400 receptor kinases and 200 receptor-like cytoplasmic kinases (RLCKs) [147]. Brassinosteroid perception by brassinosteroid insensitive 1 (BRI1) and BRI1associated receptor kinase 1 (BAK1) discussed below utilize the RLKs signal perception network [148]. Brassinosteroids (BRs) are polyhydroxylated steroidal phytohormones, which act through complex signaling pathways resulting in regulation of a wide range physiological processes including plant growth and development, plant immunity as well as mitigation of abiotic stresses such as cold [149–151]. Signal transduction in the BR pathway relies heavily on protein phosphorylation. Using label-free and an MS-based phosphoproteomics approach, Hou et al. [152] reported a total of 4043 phosphosites belonging to 1900 proteins in response to BR treatment in rice seeds. Exogenous application of BRs resulted in cold tolerance in Arabidopsis, while mutation of BRI1 (bri1) resulted in plants exhibiting increased cold tolerance as well as a dwarfism phenotype [150].

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Fig. 5 The BR signaling pathway showing BR signal perception in the cell membrane, the respective phosphorylation events, and the eventual expression of BR-responsive genes

In the well-elucidated BR signaling model from Arabidopsis, [153, 154], BRs are sensed by the extracellular domain of the membrane receptor BRI1 and BAK1 [155, 156] (Fig. 5). In the presence of BRs, BRI1, which encodes a putative leucine-rich repeat receptor kinase in Arabidopsis, directly binds with BRs forming a distinctive protein structure that favors the binding of co-receptor BAK1. It is within this BRI1–BR–BAK1 complex where BRI1 is activated via the autophosphorylation and BAK1mediated transphosphorylation of its kinase domain, thus enabling it to phosphorylate both BSK1 (brassinosteroid-signaling kinase 1) and CDG1 (constitutive differential growth 1) [152, 157]. Consequently, the phosphorylated BSK1 phosphorylates BSU1 (BRI1 suppressor 1), a phosphatase that dephosphorylates BIN2 (brassinosteroid insensitive 2) thus suppressing its kinase activity [158, 159]. The inactivation of BIN2 results in dephosphorylation of BZR1/2 (brassinazole-resistant 1/2 with BZR2 also known as BES1) by PP2As (phosphatase 2A), leading to their accumulation in the nucleus, where they function in regulating the expression of BR-responsive genes (Fig. 5). When BRs are absent, the activated BIN2 phosphorylates BZR1/2, inhibiting their nuclear localization and DNA-binding ability resulting in a blocked BR signaling pathway [160–162].

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BRs signaling in rice is composed of several similar components including OsGSK1 (glycogen synthase kinase 3-like 1) which belongs to the plant GSK3/SHAGGY-like protein kinase family and it has been reported to be a close homologue of the AtBIN2 [163]. In absence of BRs, BIN2 is phosphorylated by the upstream components of the BR signaling pathway, activating its kinase activity and thus catalyzing the phosphorylation of BZR1. Phosphorylation of the latter promotes its interaction and binding to a 14-3-3 protein, with this complex facilitating the shuttling of BZR1 from the nucleus to the cytoplasm, hence rendering OsBZR1 nonfunctional. However, in the presence of BRs, BIN2 is dephosphorylated, thus losing its kinase activity. Consequently, a PP2A dephosphorylates BZR1, disassociating it from the 14-3-3 protein complex which allows it to remain in the nucleus [164, 165]. Knockout mutants of OsGSK1exhibited enhanced tolerance to abiotic stress, while overexpression caused stunted growth phenotype similar to the BR-deficient mutants, [163]. Taken together, this suggests that the differential phosphorylation of the rice BRs signaling pathway components might serve as negative regulator of the BRs signaling pathway similar to that observed in Arabidopsis [152]. Phytohormones play crucial roles in regulating plants tolerance to abiotic stress [166]. Thus, the accumulation of BRs during abiotic stress leads to the expression of BR-related genes which causes the accumulation of phytohormones, particularly ABA [167], which then follows the phosphorylative ABA signaling pathway as discussed above. This again leads to the existence of highly interconnected phytohormone mechanisms working in plant growth, development, and the mitigation of biotic and abiotic stress.

6

Nonstress Factor That Affects Protein Phosphorylation Single nuclear polymorphisms (SNPs) are widely utilized in functional genomics-related studies. They can be classified as either synonymous or non-synonymous (nsSNPs), with the latter being capable of causing changes in the amino acid sequence as well as having the potential to influence protein phosphorylation status [168, 169]. In the rice genome, almost four million SNPs have been reported, among which 9.9% were denoted as nsSNPs, and 39.1% of these nsSNPs could potentially influence protein phosphorylation [29]. This observation led to the speculation that nsSNPs may regulate protein phosphorylation dynamics and thus affect various biological pathways. An example has been shown with rice G-protein. This protein, when phosphorylated, results in ABA and drought signal transduction relaying the message between the cell and the environment [29]. Several nsSNPs were reported that potentially influenced heterotrimeric G-protein phosphorylation

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status. This was confirmed by Trusov and Botella [170] who reported that several of the phosphorylated residues inactivates the G-protein subunit rendering it unable to restore the wild phenotype in the respective Arabidopsis mutants [29, 170].

7

Conclusion Abiotic stresses are an inevitable dilemma for plants due to their sessile nature and the ever-changing environment in which they live. Regardless of the specific stress, certain components of the stress-responsive pathway seem to be in common, such as fluctuations in cytosolic calcium appearing to be universal for all the abiotic stresses. From this review, it appears that phytohormones, ROSs, and osmolytes of the ion homeostasis pathway all integrate at some point to fine-tune the response to the stress to aid in plant survival. It seems there is no single, or solitary response to a particular stress, neither cold, drought, salinity nor heat, and it will be of great benefit when more interconnecting components of stress-responsive pathways are unveiled. Protein phosphorylation, especially the kinases, appears to be primary integrators as they work in most if not all the signaling pathways. However, if a “master regulator” can be identified through further research, then there can be an all-in-one approach in tackling the stress response in plants.

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Chapter 3 Protein Phosphorylation in Plant Cell Signaling Ping Li and Junzhong Liu Abstract Owing to their sessile nature, plants have evolved sophisticated sensory mechanisms to respond quickly and precisely to the changing environment. The extracellular stimuli are perceived and integrated by diverse receptors, such as receptor-like protein kinases (RLKs) and receptor-like proteins (RLPs), and then transmitted to the nucleus by complex cellular signaling networks, which play vital roles in biological processes including plant growth, development, reproduction, and stress responses. The posttranslational modifications (PTMs) are important regulators for the diversification of protein functions in plant cell signaling. Protein phosphorylation is an important and well-characterized form of the PTMs, which influences the functions of many receptors and key components in cellular signaling. Protein phosphorylation in plants predominantly occurs on serine (Ser) and threonine (Thr) residues, which is dynamically and reversibly catalyzed by protein kinases and protein phosphatases, respectively. In this review, we focus on the function of protein phosphorylation in plant cell signaling, especially plant hormone signaling, and highlight the roles of protein phosphorylation in plant abiotic stress responses. Keywords Protein phosphorylation, Signal transduction, Stress responses, Protein phosphatase, Hormone

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Introduction Protein phosphorylation is one of the most common posttranslational modifications (PTMs), which is dynamically catalyzed by protein kinases [1] and reversed by protein phosphatases [2]. The Arabidopsis thaliana genome encodes 940 protein kinases, which, according to the subcellular localization, are divided into the membrane-located or membrane-associated receptor-like kinases, and the soluble cytosolic kinases [3]. Only about 150 protein phosphatases have been identified in Arabidopsis. These are considered “housekeeping enzymes” that execute the dephosphorylation activity [2]. At least 7603 nonredundant phosphorylated proteins have been experimentally identified and many proteins have more than one phosphorylation site [4]. Protein kinases predominantly catalyze the transfer of a phosphoryl group from ATP to the hydroxyl groups of specific amino acid serine (Ser, S), threonine

Xu Na Wu (ed.), Plant Phosphoproteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2358, https://doi.org/10.1007/978-1-0716-1625-3_3, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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(Thr, T), or tyrosine (Tyr, R) within the target proteins. Metaanalysis of large-scale phosphoproteomic data reveals that Ser is the most frequently (80–85%) phosphorylated residue in plants, while the frequency of phosphorylation on Thr and Tyr is 10–15% and 0–5%, respectively [5]. Histidine (His, H) and aspartic acid (Asp, D) residues can also be phosphorylated in plants. Phosphorylation of His and Asp residues has been reported in two-component signaling systems, such as cytokinin signaling and phytochrome B signal transduction [6]. Although arginine (Arg, R) phosphorylation plays a central role in protein quality control in bacteria [7], phosphoarginine is rarely reported in plants. The phosphorylation motifs have been identified within different subcellular compartments, which may be associated with the localized kinase activity [5]. The reversible protein phosphorylation executed by protein kinases and phosphatases plays vital roles in plant growth and development, biotic and abiotic stress responses, metabolism and hormonal signaling through regulating protein activities, protein interactions, subcellular localization, and signaling cascades [4]. During their sessile life, plants need to adjust their growth, development, reproduction, and immunity in response to the complicated internal growth signals and environmental cues. Plants have evolved complicated receptors to perceive the diverse endogenous and exogenous signals. Most receptors are plasma membranebound cell surface receptors, including receptor-like kinases (RLKs) and receptor-like proteins (RLPs), and some receptors are cell wall or intracellular cytoplasmic/nuclear receptors. Both RLKs and RLPs contain various extracellular domains for specific perception of distinct ligands. RLPs are distinguished from RLKs by their lack of a cytoplasmic kinase domain that conveys the signal. Upon activated by the ligands, RLKs induce their dimerization and autophosphorylation, and then the downstream signaling proteins are phosphorylated [8]. RLPs often form hetero-dimerization with their associated RLKs to activate or attenuate signal perception and transduction [9]. A subgroup of RLKs, known as the somatic embryogenesis receptor kinases (SERKs), are converging hubs of some RLK and RLP signaling pathways and function as shared co-receptors [10]. Receptor-like cytoplasmic kinases (RLCKs), which lack the extracellular ligand-binding domains, associate with various RLK complexes and function as signal transmitters in the receptor complexes [10, 11]. RLCKs can phosphorylate multiple substrates such as NADPH oxidases, hetero-trimeric G-proteins, and mitogen-activated protein kinase (MAPK) kinase kinases (MAPKKKs) [11]. Downstream of the receptor complexes, the signals converge at some key signaling pathways, such as MAPK cascades, G-protein signaling, and calcium signaling. Each MAPK cascade consists of an MAPKKK or MKKK, an MAPK kinase (MAPKK or MKK), and an MAPK or MPK. These signaling pathways regulate multiple cellular and physiological processes either by

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direct binding or through phosphorylation of different downstream target proteins [11]. Plant hormones are signaling molecules produced within plants that have profound effects on a wide variety of biological processes ranging from growth, development, and reproduction to biotic and abiotic stress responses. Plant growth, development, and immunity are regulated by various hormones, including auxins, cytokinin (CK), brassinosteroids (BRs), gibberellins (GA), ethylene (ET), abscisic acid (ABA), and strigolactones (SLs) [12]. As well, the phytohormones jasmonic acid (JA) and salicylic acid (SA) have been recognized as the most important hormones in plant defense against pathogens and herbivores [13]. These hormones signaling pathways are initiated with the binding of hormones to their receptors in different subcellular localizations. BRs and CK are perceived by the plasma membrane (PM)-localized receptors brassinosteroidinsensitive 1 (BRI1) and PM/endoplasmic reticulum (ER)localized Arabidopsis histidine kinases (AHK2/3/4), respectively [14, 15]. The receptors of ethylene ETR1/2, ERS1/2, and EIN4 predominantly reside in the ER membrane [16]. SL perception occurs through the α/β-hydrolase superfamily protein dwarf 14 (D14) in an irreversible non-canonical receptor-ligand manner [17]. ABA is sensed by the nucleocytoplasmic pyrabactin resistance 1 (PYR1)/PYR1-like (PYL)/regulatory components of the ABA receptor (RCAR) family of proteins [18]. In the nucleus, auxin binds to the F-box-containing transport inhibitor resistant1/ auxin signaling F-box (TIR1/AFB) proteins [19], and GAs bind to the gibberellin-insensitive dwarf 1 (GID1) receptor [20]. JA is perceived by the inositol-phosphate-potentiated coronatineinsensitive 1-jasmonate zim domain (COI1-JAZ) co-receptor [21]. Perception of SA is mediated by receptors of the non-expressor of pr gene (NPR) family [22, 23]. The perceived hormonal signal is relayed through signal transduction processes, which then lead to an appropriate cellular response. It is worth noting that the signal transduction of auxin, JAs, GAs, and SLs are based upon hormone-activated proteolysis [17, 19–21]. In this review, we summarize the common theme that underlies protein phosphorylation in plant signal transduction, with a focus on plant hormone signaling. We further highlight the role of protein phosphorylation in plant abiotic stress responses.

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Protein Kinases and Phosphatases in Plants The Arabidopsis thaliana genome encodes 561 membrane-located receptor kinases and 381 soluble kinases [3]. The membranelocated receptor kinases are either RLKs or RLCKs. Based on the kinase domain sequence, the receptor kinases can be divided into a large number of families [3]. The functions of the majority of RLKs

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remain obscure. The most well-studied RLKs belong to the leucine-rich repeat (LRR) family. Many members of LRR-RLKs play crucial roles in signaling pathways to regulate plant growth, development, and immunity. For example, an LRR-RLK HAESA can sense the peptide hormone IDA and regulate floral abscission [24]. The LRR receptor-like Ser/Thr kinase ERECTA is a pleiotropic regulator of cell patterning, organ development, and plant responses to environmental stimuli, such as pathogens and heat [25, 26]. 149 and 379 RLCKs belonging to 17 subgroups have been reported in Arabidopsis and rice, respectively [27]. Most RLCKs have only a Ser/Thr kinase domain, while the rest contain additional LRR, WD40, or transmembrane domain(s) [28]. These RLCKs can be activated by RLKs and second messengers such as oscillating calcium signals, cyclic nucleotides, metabolites, and hormones. In concert with PLKs, RLCKs function as central players in diverse physiological processes like plant immunity, growth, development, and reproduction [11]. Soluble kinases are mainly localized in the nucleus or cytoplasm. In contrast to RLKs, many soluble kinase families have been functionally characterized, such as four families of the MAPK signaling cascade (MAPK, MAP2K, Raf-like, and Ste-like MAP3K), calcium-dependent kinases (CDPK), CBL-interacting kinases (CIPK/SnRK3), cyclindependent kinases (CDK), the casein and casein-related kinases (CKII and CKL), and the regulators of circadian rhythm CKII and WNK, SnRK1 and SnRK2 [1, 3]. These soluble kinases regulate multiple pathways involved in hormone signaling, biotic and abiotic stresses, plant growth, and development. According to the targeted phospho-amino acids, primary protein sequence and catalytic mechanism, the protein phosphatases in plants can be classified into four groups: phosphoprotein phosphatases (PPPs), metal-dependent (magnesium or manganese) protein phosphatases (PPM), protein tyrosine phosphatases (PTPs), and aspartate-based phosphatases [2]. PPPs, PPM, and aspartatebased phosphatases mainly catalyze the dephosphorylation of phosphor-Ser/Thr, while PTPs target either phospho-Tyr or both phosphor-Ser/Thr and phospho-Tyr (the dual specificity phosphatases [DSPs]) [2]. The PTP superfamily contains a catalytic signature (C[X]5R), and most PTPs in Arabidopsis are DSPs [29]. Among these DSPs, five MAPK phosphatases (MKPs) that dephosphorylate MAPKs have been reported to play important roles in plant biotic and abiotic stresses [2]. Based on the sequence and structural relationship, the 26 PPPs in Arabidopsis are grouped into Ser/Thr protein phosphatase type one (PP1), PP2 (PP2A), PP3 (PP2B), PP4, PP5, PP6, PP7, protein phosphatases with kelch-like repeat domains (PPKLs/BSU1) and Shewanella-like protein (SLP) phosphatases [30]. The roles of PPPs in auxin and BR signaling, phototropism, and cell stress responses have been well reviewed [30]. PPMs, also known as PP2C-type phosphatases, are

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Mg2+- or Mn2+-dependent enzymes. 80 PPMs belonging to 12 clusters (A-L) are predicated in Arabidopsis [31]. Nine PP2Cs of clade A function as co-receptors of ABA signaling, and clade B PP2Cs regulate MAPK activities. The roles of clade C PP2Cs in plant development, clade D and F in PP2Cs plant immunity, and clade E PP2Cs in stomatal signaling have been elucidated [31]. The aspartate-based phosphatases are characterized by a catalytic signature DXDXT/V. 23 aspartate-based phosphatases have been predicted in Arabidopsis, including 19 FCP (TFII-interacting RNA Pol II CTD protein phosphatase)-like enzymes and 4 haloacid dehalogenase (HAD) superfamily (3 chronophins and 1 EYA member) [29]. The FCP-like members CTD phosphatase-like protein phosphatases (CPL)1–5 are involved in hormone signaling, stress response, and development [2]. The functions of many other aspartate-based phosphatases in plants remain to be elucidated.

3

Protein Phosphorylation in Plant Hormone Signaling Protein phosphorylation plays multiple roles in plant hormone signaling, including (1) modulating the biosynthesis and transport of hormones; (2) activating the receptor complexes that perceive hormone signals; (3) transmitting and amplifying hormone signals through second messengers and phosphorelay systems; and (4) integrating information from diverse hormone signaling pathways. We briefly summarize the distinct roles of protein phosphorylation in diverse hormone signaling as follows.

3.1 Protein Phosphorylation Regulates the Biosynthesis and Signaling of Auxin, Ethylene, and Gibberellins

In the auxin biosynthesis pathway, the phosphorylation at threonine 101 (T101) of a key enzyme tryptophan aminotransferase of Arabidopsis (TAA1) acts as a switch to control TAA1-dependent auxin biosynthesis [32]. The T101 phosphorylation of TAA1 can be induced by auxin itself particularly via transmembrane kinase 4 (TMK4), suggesting a self-regulatory loop in the regulation of auxin concentration [32]. In addition to the biosynthesis of auxin, TMKs have been reported to mediate auxin signaling in parallel to the canonical auxin pathway based on the TIR1/AFB receptors. At the cell surface, high levels of auxin mediate the C-terminal cleavage of TMK1, and then the cytosolic and nucleus-translocated C-terminus of TMK1 specifically phosphorylates two transcriptional repressors of the auxin or indole-3-acetic acid (Aux/IAA) family (IAA32 and IAA34), thereby regulating ARF transcription factors [33]. TMK1/4 directly and specifically interacts with and phosphorylates MKK4/5, which connects auxin signaling to MAPK cascades [34]. The polar localization and auxin transport activity of pin-formed (PIN) auxin efflux transporters are regulated by Ser/Thr protein kinase-mediated phosphorylation [35, 36].

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In the ethylene biosynthesis pathway, CDPK phosphorylates and regulates the turnover of 1-aminocyclopropane-1-carboxylic acid synthase (ACS), which is the rate-limiting enzyme [37]. The ET receptors ETR1 and ERS1 have histidine kinase activity, which modulates the ethylene responses [38]. In the absence of ET, the receptors of ethylene (ETR1/2, ERS1/2, and EIN4) activate a Ser/Thr kinase CTR1, and CTR1 inactivates the positive regulator EIN2 through directly phosphorylating its C-terminal end. Upon the binding of ethylene, the inactivated receptors switch off CTR1, which prevents the phosphorylation of EIN2. Ethylene triggers the proteolytic cleavage at the C-terminal of EIN2, resulting in the ERto-nuclear translocation of the C-terminal EIN2 fragment (EIN2C). The nuclear-localized EIN2-C triggers 26S proteasomal degradation of the F-box proteins EBF1/2, which releases the master transcription factors EIN3/EIL1. EIN3/EIL1 activates a transcriptional cascade that results in the activation of ethylene response genes [39]. Intriguingly, MAPK cascades play bifurcate roles in ethylene signaling. EIN3 contains two phosphorylation sites (T174 and T592) with opposite functions. Without ethylene, CTR1 inactivates MKK9-MPK3/6 and probably activates downstream MAPKs to phosphorylate T592 to promote EIN3 degradation. Ethylene inactivates CTR1 to activate MKK9-MPK3/6 signaling and triggers T174 phosphorylation to stabilize EIN3 [40]. The GAs levels in plant cell are delicately refined by the feedback regulation of several GA biosynthetic enzymes, such as GA 2-oxidase, GA 3-oxidase, and GA 20-oxidase. In tobacco (Nicotiana tabacum), calcium-dependent protein kinase, NtCDPK1, catalyzes the phosphorylation on S114 of the transcription factor repression of shoot growth (RSG) and promotes the translocation of RSG from the nucleus to the cytoplasm [41]. NtRSG modulates the homeostasis of GAs through feedback regulation of NtGA20ox1 by directly binding to its promoter [42]. In GA signaling, the binding of GAs to the receptor GID1 initiates the proteasome-dependent degradation of DELLA proteins, which act as central repressors in GA signaling pathway [20]. A type-one protein phosphatase (TOPP4) directly binds and dephosphorylates DELLA proteins RGA and GAI and promotes their degradation [43]. In rice (Oryza sativa), cGMP-dependent protein kinase (PKG) possesses both protein kinase and phosphatase activities, and cGMP stimulates its kinase activity but inhibits its phosphatase activity. In response to GA, cGMP activates PKG to phosphorylate GAMYB, a transcription factor in GA signaling, at Ser6 and modulates the nucleocytoplasmic distribution of GAMYB to upregulate the expression of GA-responsive genes [44].

Protein Phosphorylation in Plant Cell Signaling

3.2 Protein Phosphorylation Modulates ABA, CK, and BR Signaling as Well as Their Crosstalk

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Posttranslational control of ABA signaling through protein phosphorylation and dephosphorylation has been well reviewed [45]. ABA is mainly perceived by PYR/PYL/RCAR receptors and inhibits the activity of PP2A and PP2C phosphatases, thereby relieving Snf1-related protein kinases (SnRKs) from PP2C phosphatasesmediated repression. SnRKs phosphorylate downstream transcription factors such as ABI5 that regulate the transcription of various stress-responsive genes [46]. The SnRK2-mediated ABA signaling is also reciprocally regulated by target of rapamycin (TOR) kinase to balance growth and stress responses. Under nonstress conditions, the growth-promoting TOR kinase phosphorylates PYL receptors to prevent SnRK2-mediated activation of stress responses. Under stress conditions, ABA-activated SnRK2 phosphorylates and inactivates the TOR complex to prevent growth [47]. A transcription factor HAT1, a substrate of SnRK2, represses the expression of ABA biosynthesis genes ABA3 and NCED3, but positively regulates several PP2C genes expression, which in turn negatively regulates the ABA synthesis and signaling [48]. The phosphorylation status of SnRK2 is also regulated by other kinases, such as casein kinase 2 (CK2), and ABA and abiotic stress-responsive Raf-like kinase (ARK) [45]. In addition to SnRKs, calcineurin B-like interacting protein kinase 26 (CIPK26) interacts with and phosphorylates ABI5 to activate downstream ABA responses [49]. In BR signaling, in the absence of BRs, a glycogen synthase kinase-3 family member BIN2 acts as a negative regulator by phosphorylating BZR1 (brassinazole-resistant 1) and BES1 (BRI1-EMSsuppressor 1), thereby targeting them for degradation. BRs are perceived by BRI1 receptors and induce the autophosphorylation of BRI1 and interaction between BRI1 with an RLCK brassinosteroidinsensitive 1-associated receptor kinase 1 (BAK1), creating an active receptor complex that initiates an intracellular phosphorylation relay cascade. The cascade inactivates BIN2, thereby dephosphorylating and consequently activating BZR1 and BES1 in the nucleus, which regulate the transcription of thousands of BR-responsive genes [50]. Interestingly, protein phosphorylation regulates BR-ABA crosstalk during plant stress responses. BIN2 can also directly phosphorylate and activate SnRKs and ABI5, while PP2C phosphatases are able to inactivate BIN2. ABI5 is also a direct target of BZR1, which represses its transcription to negatively regulate stress-responsive gene expression [50]. Like the ET signaling, CK signaling involves receptors with His kinases activity and multistep His-to-Asp phosphorelay system similar to the two-component signaling system that found in bacteria. The core cytokinin signaling circuitry in Arabidopsis comprises AHK2/3/4, Arabidopsis His phosphotransfer proteins (AHPs), and Arabidopsis Asp-containing response regulators (ARRs) [15]. The PM and ER membrane-localized AHK2/3/4 perceive CK and initiate signaling by autophosphorylation at a conserved His residue in the sensor-kinase domain, which is subsequently

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relayed to a conserved Asp residue within the receiver domain. The phosphate is subsequently transferred to the downstream cytoplasmic AHP1-5 to mediate the cytoplasmic to nuclear signal transfer. In the nucleus, AHP1-5 phosphorylates the Asp residue of type-A and type-B ARRs and activates them to regulate the expression of cytokinin target genes and negative-feedback regulators [15]. Protein phosphorylation also regulates CK-ABA crosstalk during plant stress responses. Under normal conditions, CK signaling antagonizes ABA signaling by repressing the kinase activity of SnRK2.6 via the type-B ARRs ARR1/11/12. Upon drought stress, ABA-activated SnRK2s directly phosphorylate type-A ARR5, a negative regulator of cytokinin signaling. The phosphorylation of ARR5 Ser residues by SnRK2s enhanced ARR5 protein stability. Such antagonistic actions of ABA and CK signaling pathways shed new light on how plants coordinate growth and drought stress response [51]. 3.3 Protein Phosphorylation in Defense Phytohormone (JA and SA) Signaling

In SA signaling, SA inhibits the transcriptional repression activities of NPR3/NPR4 receptors, but stimulates NPR1, all together increasing the expression of downstream defense genes [23]. SA promotes NPR1 phosphorylation at Ser11/15 residues in the nucleus, and then facilitates its recruitment to CUL3-based ubiquitin ligase, which results in proteasome-mediated NPR1 degradation. The turnover of phosphorylated NPR1 is required for the establishment of systemic-acquired resistance (SAR), a broad-spectrum plant immune response involving global transcriptional reprogramming for defense [52]. The sumoylation of NPR1 by SUMO3 activates defense gene expression. Phosphorylation of NPR1 at Ser11/15 enhances its sumoylation, whereas phosphorylation at Ser55/59 inhibits its SUMO modification, which keeps NPR1 stable and quiescent [53]. NPR1 is phosphorylated and activated by SnRK2.8, which is necessary for its nuclear transport [54]. Moreover, high levels of SAs directly bind to A subunit of protein phosphatase 2A (PP2A) and suppress its dephosphorylation activity on the auxin efflux carrier PIN2, which results in the decrease in auxin export and attenuation of growth [55]. Interestingly, SA signaling integrates with ABA signaling via the Ca2+/CPK-dependent phosphorylation of slow-type anion channel SLAC1 at Ser 59/120 to regulate the stomatal closure in guard cells [56]. In JA signaling, JA elicits the formation of COI1 (an F-box protein)-JAZ co-receptor complex, which subsequently promotes the degradation of JAZ proteins through the 26S proteasome pathway. The downstream basic helix-loop-helix (bHLH) transcription factors, such as MYC2, are released from JAZ-mediated repression and regulate the expression of JA-responsive genes [57]. MYC2 is a master regulator of JA signaling pathway and the crosstalk between JA signaling and the signaling pathways of ABA, SA, GAs, and auxin in Arabidopsis [58]. MYC2 protein can be phosphorylated and destabilized by FERONIA receptor kinase,

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thereby suppressing JA signaling [59]. The phosphorylation at Thr328 residue of MYC is required for its function. Interestingly, MYC phosphorylation also facilitates its turnover by proteolysis, which is important to fine-tune the JA-mediated plant immunity [60]. During plant innate immunity, an RLCK BIK1 is phosphorylated by an immune receptor EFR at S89 and T90 to activate JA and SA signaling through interaction with WRKY transcription factors in the nucleus [61]. Recently, a novel JAV1-JAZ8-WRKY51 (JJW) complex has been reported to regulate JA biosynthesis to defend against insect attack. Under nonstress conditions, JAV1 and WRKY51 target the transcriptional repressor JAZ8 to the promoter of JA biosynthesis genes to represses JA biosynthesis to ensure plant growth. When plants are injured by herbivores, injury rapidly induces the elevation of cytosolic calcium to activate CaM-dependent phosphorylation of JAV1. Phosphorylated JAV1 protein is degraded through the ubiquitin-26S proteasome pathway, which disintegrates JJW complex and activates JA biosynthesis, initiating the rapid burst of JA for plant defense [62].

4

Protein Phosphorylation in Plant Stress Responses As sessile organisms, plants are exposed to diverse biotic and abiotic stresses. The biotic stresses are induced by bacteria, fungi, nematodes, virus, and insects, while the abiotic stresses include extreme temperatures (cold and heat), drought, salinity, osmotic, high light and other environmental extremes. To survive in the stressed circumstance, plants initiate multilayer stress responses including the activation of stress-responsive genes, the maintenance of RNA, protein, metabolism and ROS homeostasis, and the adjustment of growth and development. As mentioned above, plant hormones signaling play vital roles in plant stress resistance, especially ABA signaling in salt and drought stresses and JA and SA in biotic stresses. Abiotic stress responses have been summarized in detail [63, 64]. Protein phosphorylation is recognized as one of the major mechanisms in plants stress signaling transduction. Many phosphoproteome analyses in plants stress responses have been reported and summarized [65–67]. Protein phosphorylation in plant immunity is already described and is well reviewed [9, 11, 68, 69]. Here, we summarize the main roles of protein phosphorylation in sensing and signal transduction of the four major abiotic stresses: drought (water deficiency), salinity (salt), heat and cold stresses.

4.1 Protein Phosphorylation of Abiotic Stress Sensors in Different Organelles

Sensing the stress signals is the primary step to initiate plant stress responses. The sensors perceive the advert environmental cues and convert them to cellular signaling by altering the structure and/or activity of the sensors, or their interaction with other molecular components to trigger downstream physiological or morphological responses. Unlike the classical ligand perception, the physical and

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chemical stress signals cause multiple changes in the cellular components and activities, which may be sensed by stress sensors everywhere in the cell. Several stress sensors have been identified, such as hyperosmotic stress sensor OSCA1 (reduced hyperosmolalityinduced calcium increase 1) [70], putative cold stress sensor COLD1 [71], putative heat sensor phyB (phytochrome B) [72, 73], CNGCs (cyclic nucleotide-gated Ca2+ channels) [74] and histone variant H2A.Z [75], and putative salt sensor GIPC (glycosyl inositol phosphorylceramide) sphingolipids [76]. These stress sensors are localized to different subcellular organelles, suggesting that plant stress perception is complex and sophisticated. PhyB responds to the elevated ambient temperature by changing its form from the active Pfr to the inactive Pr in Arabidopsis [72, 73]. The phosphorylation of S86 on N-terminal extension (NTE) of phyB particularly accelerates the thermal reversion rate of the phyB Pfr-Pr heterodimer [77]. In Arabidopsis thaliana, MUT9P-LIKE-KINASE (MLK4) phosphorylates histone H2A on serine 95, a plant-specific modification in the histone core domain. The MLK4-mediated H2AS95 phosphorylation is involved in deposition of the histone variant H2A.Z, as loss of MLK4 function leads to the attenuated accumulation of H2A.Z at some genes [78]. The roles of protein phosphorylation in regulating the subcellular location, stability, or activity of OSCA1 and COLD1 remain to be elucidated. Extreme temperature stresses induce changes in membrane fluidity, which may be sensed by RLKs and Ca2+ channels. CNGC6 is activated by heat stress to trigger an influx of calcium into the cell, which promotes the expression of heat shock transcription factors (HSFs) and increases thermotolerance [79]. CNGC2 and CNGC4 have been reported to be phosphorylated and activated by the effector kinase botrytis-induced kinase 1 (BIK1) upon pathogen attack [80]. Besides CNGCs, glutamate receptor-like (GLR) channels also play important roles in initiating cytosolic Ca2+ signals in plant stress signaling [81]. Whether CNGC6 and GLRs are phosphorylated upon abiotic stress remains to be investigated. The stress information encoded with the Ca2+ signals is decoded and relayed by Ca2+-binding proteins, such as CaMs, CDPKs/CPKs, and CIPK/SnRK3, which regulate the transcription or phosphorylation of target genes [82]. For example, CBL1/9-CIPK26 complexes interact with and phosphorylate NADPH oxidase respiratory burst oxidase homologue f (RBOHF) to enhance ROS production, which is involved in the systemic signaling of stresses [83]. CPK3, which is activated by salt stress, phosphorylates the vacuolar two-pore K+ channel 1 (TPK1) at S42 to enhance salt stress adaptation [84].

Protein Phosphorylation in Plant Cell Signaling

4.2 Protein Phosphorylation Modulates the Signal Transduction of Abiotic Stresses 4.2.1 Heat Stress

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HSFs and heat shock proteins (HSPs) are central players in the acquired thermotolerance in plants, which refers to the ability to survive in lethal heat stress after acclimatization to mild high temperatures [85]. HSFs are the central regulators responsible for the expression of HSP genes, chaperon genes, and other genes that are implicated in protein biosynthesis and processing, signaling, metabolism, and transport [86]. Many HSFs undergo phosphorylation, which modulates their stability and activity and contributes to thermotolerance and sometimes tolerance against other stresses. In heat-stressed Arabidopsis, mitogen-activated protein kinase AtMPK6 phosphorylates the major heat stress transcription factor AtHsfA2 on T249 and changes its intracellular localization [87]. CaM-binding protein kinase 3 (AtCBK3) can phosphorylate AtHSFA1, which promotes the binding of AtHSFA1 to the heatshock elements (HSEs) and the transcription of HSP genes [88]. HSFA4A can be phosphorylated by MPK3/4/6 at Ser309, which contributes to plant responses to combined heat and salt stresses [89]. Moreover, MPK3/6 can also phosphorylate and activate other transcription factors including C2H2-type zinc finger transcription factor ZAT10, which is required for the tolerance of plants to salinity, heat and osmotic stress [90], and Lyst-interacting protein 5 (LIP5), a positive regulator of multivesicular body biogenesis and plant responses to heat and salt stresses [91]. However, MPK3/6-mediated phosphorylation inactivates speechless (SPCH) to control stomatal development under acute heat stress [92]. In the upstream, HSP90s interact with YODA mitogenactivated protein kinase kinase kinases (MAPKKKs) and modulate the phosphorylation of MPK3 and MPK6 [92]. In tomato (Solanum lycopersicum), heat-activated MAPK phosphorylates and activates SlHSFA3 [93]. SlMPK1 can directly phosphorylate a serineproline-rich protein homolog SlSPRH1 at Ser44 and mediate antioxidant defense mechanism to negatively regulate thermotolerance in tomato plants [94]. The phosphorylation of other transcription factors also plays important roles in thermotolerance, such as FUSCA3 (FUS3) and dehydration-responsive element-binding protein 2a (DREB2A). The transcription factor FUS3, a major regulator of seed maturation, is phosphorylated by the SnRK1 catalytic subunit AKIN10/SnRK1α1 at Ser 55/56/57. FUS3 phosphorylation positively regulates embryogenesis, seed yield, and plant growth at high temperature [95]. Heat inhibits the phosphorylation-mediated proteasomal degradation of DREB2A, a key transcriptional activator that induces many heat- and drought-responsive genes, thereby promoting thermotolerance in Arabidopsis [96].

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4.2.2 Cold Stress

The PM and ER-localized cold sensor COLD1 interacts with the G-protein to activate the Ca2+ channel for sensing low temperature and to accelerate G-protein α GTPase activity [71]. Like heat stress, the cold-induced cytosolic Ca2+ signal can be mediated by CDPKs/ CPKs and CIPKs to activate MAPK cascade. The activated MPKs can regulate transcription factors such as inducer of CBF expression 1 (ICE1) to induce the expression of CBFs, which then active the expression of more than 2000 cold-responsive (COR) genes [64]. ICE1 is one of the central regulators of the plant cold response, and its protein levels are fine-tuned. Cold-activated MPK3/6 phosphorylates ICE1 protein to reduce its stability and transcriptional activity, which consequently negatively regulates freezing tolerance, whereas MPK4 constitutively suppresses the activity of MPK3/6 [97, 98]. The protein kinase BIN2 also phosphorylates ICE1 to promote the degradation of ICE1 under prolonged cold stress [99]. In contrast, the protein kinase open stomata 1 (OST1), a key component in ABA signaling, phosphorylates and stabilizes ICE1 under cold stress to facilitate the CBF-COR pathway [100]. The kinase activity of OST1 is negatively regulated by EGR2 phosphatase [101]. In rice, OsICE1/ OsbHLH002 is phosphorylated by OsMAPK3 under chilling stress to prevent its ubiquitination by the E3 ligase OsHOS1 [102]. Besides ICE1, CBFs are transcriptionally and posttranscriptionally regulated by multiple upstream transcription factors and several enzymes. BTF3 and BTF3L (BTF3-like), β-subunits of a nascent polypeptide-associated complex (NAC), are phosphorylated by OST1 to positively regulate CBF stability [103]. MYB15, a key negative regulator of CBFs, is targeted by U-box E3 ligases PUB25/26 for degradation under cold stress, and OST1 can phosphorylate PUB25/26 to enhance their E3 activity [104]. MYB15 is phosphorylated at Ser168 by MPK6, which modulates its transcriptional repression activity [105]. In addition to the calcium signaling, the cold-activated PM-localized protein kinase CRPK1 phosphorylates 14-3-3 proteins, which are imported from the cytosol to the nucleus and promote the degradation of CBF proteins to fine-tune CBF-dependent cold signaling [106].

4.2.3 Salt Stress

High salinity usually increases both ionic strength (mainly Na+ ions) and osmotic pressure, which pose osmotic and ionic stresses to plants. For osmosensing, OSCA1, which forms hyperosmolalitygated calcium-permeable channels, mediates osmotic-stress-evoked Ca2+ increases in Arabidopsis [70]. For sensing of ionic stress, GIPCs bind to Na+ ions and depolarize the cell-surface potential to gate Ca2+ influx channels [76]. The salinity-induced softening of the cell wall can be sensed by a malectin-like receptor kinase Feronia (FER), which is phosphorylated and activated to elicit cell-specific Ca2+ transients that maintain cell-wall integrity during salt stress [107]. The salt stress-induced increase in cytosolic-free Ca2+ is

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decoded by different CaMs, CDPKs, and calcineurin B-like proteins (CBLs) to induce downstream MAPK cascades, calcium signaling, and hormone signaling. As mentioned above, ABA signaling pathway is central to salt and drought stress responses in plants. Many phosphorylation and dephosphorylation events in ABA signaling also contribute to plant salt and drought stress [63, 64]. The salt-overly-sensitive (SOS) pathway is the first established abiotic stress signaling pathway in plants [108]. The major components of the SOS pathway comprise the Na+/H+ antiporter SOS1, the protein kinase SOS2/CIPK24, and the Ca2+ sensor SOS3/ CBL4 and SOS3-like calcium-binding protein 8 (SCaBP8). Upon the perception of salt-induced Ca2+ signals, SOS3 interacts with and recruits SOS2 to PM to phosphorylate SOS1, which increases Na+ efflux [64]. SOS2 can also be activated by SCaBP8 and in turn, SOS2 phosphorylates SCaBP8 at its C-terminus to stabilize their protein complex to regulate salt tolerance [109]. In the absence of salt stress, SOS2 is phosphorylated at Ser294 by SOS2-like protein kinase 5 (PKS5) and interacts with 14–3-3 protein λ and κ, which repress the kinase activity of SOS2 [110, 111]. Salt stress promotes the interaction between 14-3-3 proteins and PKS5, repressing its kinase activity and releasing inhibition of SOS2 [110]. Moreover, SOS2 phosphorylates a putative calcium-permeable transporter AtANN4 under salt stress, which represses the activity of AtANN4 and alters calcium transients and signatures to optimize plant salt response [112]. Besides SOS2, SOS1 can be phosphorylated by MPK6 in response to salt stress. The second messenger phosphatidic acid (PA) promotes the PM-localization of MKK7/9 to phosphorylate and activate MPK6 [113]. Under salt stress, the phosphatidic acid (PA) also regulates Pinoid (PID) kinase in the phosphorylation of PIN2 to mediate auxin redistribution [114]. In addition to the SOS3/CBL4 and SOS2/CIPK24, other protein kinases (RLCKs, CBLs, CIPKs, and CDPKs) and phosphatases also play important roles in plant salt responses. For example, a glutamate receptor-like protein AtGLR3.7 is phosphorylated by a CDPK on Ser860, which is important for the interaction between AtGLR3.7 and 14-3-3ω protein in plant salt stress responses [115]. In rice, salt tolerance receptor-like cytoplasmic kinase 1 (STRK1) phosphorylates catalase C (CatC) at Tyr 210 and activates CatC to regulate H2O2 homeostasis and improve salt tolerance [116]. In cotton (Gossypium spp.), salt stress induces the phosphorylation of an annexin protein GhANN8b, which is subsequently dephosphorylated by GhDsPTP3a [117]. In apple (Malus domestica), a sucrose transporter MdSUT2.2 is phosphorylated at Ser 254 in response to salt, which may be mediated by the protein kinase MdCIPK13 [118].

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4.2.4 Drought Stress

Under drought stress, ABA signaling plays vital roles to induce stomatal closure and reduce transpirational water loss. Key players in ABA synthesis and signaling mentioned above such as HAT1, PYLs, and PP2Cs help plants fine-tune their drought responses [48, 119]. During drought stress, protein phosphatase 2C Highly ABA-Induced 1 (HAI1) directly dephosphorylates the S313/314 of DNA-binding protein AT-hook-like 10 (AHL10) to regulate plant growth and development [120]. Besides ABA signaling, other hormones signaling pathways also function in plant drought responses. BIN2, a negative regulator in the BR pathway, phosphorylates and stabilizes the stress-responsive NAC transcriptional factor RD26 to potentiate drought stress responses, while ABI1, a negative regulator in ABA signaling, dephosphorylates and destabilizes BIN2 to inhibit BIN2 kinase activity [121]. The stressinducible AP2/ERF transcription factor TINY is also phosphorylated by BIN2 to activate the expression of drought-responsive genes, such as RD29A, COR15A, and COR414 [122]. Moreover, BIN2 phosphorylates a ubiquitin receptor protein DSK2 and enhances its interaction with ATG8, a ubiquitin-like protein directing autophagosome formation and cargo recruitment. During drought and starvation stress, BES1, a master regulator of BR signaling, is targeted by DSK2-ATG8 for degradation via the autophagy pathway, thereby balancing plant growth and survival [123]. The antagonistic actions of SnRK2s in ABA signaling and ARR5 in CK signaling pathways mediate drought stress response [51]. MAPK cascade and calcium signaling are essential for plant drought resistance [124]. However, the detailed roles of protein phosphorylation in drought-induced MAPK cascade and calcium signaling remain largely unclear. In cotton, two MAPK cascades, consisting of GhMAP3K15-GhMKK4-GhMPK6-GhWRKY59GhDREB2 and GhMAP3K14-GhMKK11-GhMPK31, respectively, have been identified to control the drought responses [125, 126]. In rice, OsMPKK10.2 physically interacts with and phosphorylates OsMPK3, which positively regulates drought tolerance [127]. A Raf-like MAPKKK gene OsDSM1 functions in rice drought resistance through ROS scavenging, but its direct phosphorylation targets remain unclear [128]. In Arabidopsis, MAPKKK18-MAPKK3 cascade and overexpression of ZmMAPK1 from maize (Zea mays) positively regulate drought stress resistance [129, 130]. An MAPK-like protein ZmMPKL1 positively regulates seedlings drought sensitivity by altering ABA biosynthesis and signaling [131]. AtCIPK11 can phosphorylate Di19–3, a C2H2type zinc-finger transcription factor, and functions as a negative regulator in drought stress response [132]. Interestingly, the sucrose transporter MdSUT2.2 in apple can also be phosphorylated at Ser 381 by protein kinase MdCIPK22 in response to drought [133]. AtCPK1 in Arabidopsis, OsCPK10 in rice, and HvCPK2a in

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barley (Hordeum vulgare L.) also regulate the responses to drought [134–136], but their downstream targets remain to be deciphered.

5

Conclusion and Perspective Protein phosphorylation plays vital roles in the perception of environmental stimuli and endogenous growth and development cues in plants. As shown in Table 1, the direct substrates of protein kinases and phosphatases are largely unidentified. Recently, a quantitative atlas of the phosphoproteomes of 30 tissues of Arabidopsis has identified more than 43,000 phosphorylated sites [137]. It will be a dramatic challenge to identify the corresponding kinases and phosphatases for each site and to elucidate the roles in diverse biological processes. Novel cross-linking technologies, such as proximity-dependent biotin identification (BioID), may accelerate the identification of substrates of diverse kinases and phosphatases. The dynamic, reversible, and comprehensive protein phosphorylation have been widely studied in plant hormone signaling. These findings help us understand the mechanisms by which protein phosphorylation regulates plant growth, development, and stress responses. The identification of primary abiotic stress sensors is the holy grail of plant stress signaling, which is very challenging owing to the complex cellular stress responses and sophisticated abiotic stress conditions. Many gaps remain in our knowledge concerning the sensing of plant stresses and the role of protein phosphorylation in them. How the combination of abiotic stresses is perceived and their downstream signaling is integrated remain to be investigated. In different plant abiotic stress signaling, MAPK cascade and calcium signaling function as common themes, using natural phosphopeptides as the substrate pool, more than 5000 putative sites have recently been identified as targets of nine protein kinases (CDPK11, SOS1, MPK6, SnRK2.4/2.6, CIPK23, etc) involved in plant biotic and abiotic stress responses [138]. With the development of protein interactomics and phosphoproteomics, we may unravel the proteome-wide targets of protein kinases and phosphatases in plant stress responses in near future. So far, most studies regarding the role of protein phosphorylation in plant cell signaling are limited to the model plant Arabidopsis (Table 1). It is urgent to elucidate the vital roles of protein phosphorylation in regulating the agronomic traits of crops, such as rice, wheat, maize, and potato. The combination of emerging gene editing tools and knowledges on protein phosphorylation in plant cell signaling will benefit the breeding of stress-resistant crops.

Group

Transmembrane Kinase family

Transmembrane Kinase family

MAPK cascade

AGCVIII kinase family

Calcium-dependent protein kinase

Histidine kinase

Raf-like MAPKKK

MAPK cascade

Calcium-dependent protein kinase

Type-one protein Phosphatases

cGMP-dependent protein kinase

Snf1-related protein kinase

Species

Arabidopsis thaliana

Arabidopsis thaliana

Arabidopsis thaliana

Arabidopsis thaliana

Arabidopsis thaliana

Arabidopsis thaliana

Arabidopsis thaliana

Arabidopsis thaliana

Nicotiana tabacum

Arabidopsis thaliana

Oryza sativa

Arabidopsis thaliana

Protein(s)

TMK1

TMK4

MKK7-MPK6

D6PK, PID

CDPKs

ETR1, ERS1

CTR1

MKK9–MPK3/6

NtCDPK1

TOPP4

PKG

SnRKs

TFs such as ABI5 And ABFs, HAT1, TOR complex, ARR5

GAMYB

DELLA

NtRSG

EIN3

EIN2, MKK9

Unknown

ACS

PINs

PIN1

MKK4/5

TAA1, IAA32/34, MKK4/5

Substrates

Table 1 Overview of protein phosphorylation and dephosphorylation discussed in this review

[44]

[43]

[41]

[40]

[39, 40]

[38]

Reviewed in [37]

[36, 114]

[35]

[34]

[32–34]

Reference(s)

ABA, CK signaling, and abiotic Reviewed in stresses [45, 46] and recently reported in [47, 48, 51]

GA signaling and salt stress

GA signaling

GA biosynthesis

Ethylene signaling

Ethylene signaling

Ethylene signaling

Ethylene biosynthesis

Auxin efflux and salt stress signaling

Auxin efflux

Auxin signaling

Auxin biosynthesis and signaling

Function

60 Ping Li and Junzhong Liu

Target of rapamycin (TOR) kinase

Calcineurin B-like interacting protein kinase

Glycogen synthase kinase-3 family

Receptor-like cytoplasmic kinases

His kinases

His phosphotransfer proteins

Snf1-related protein kinase

CDPKs

Feronia receptor kinase MYC2 and other unknown protein

Pattern recognition receptors (PRRs)

Effector kinase

Arabidopsis thaliana

Arabidopsis thaliana

Arabidopsis thaliana

Arabidopsis thaliana

Arabidopsis thaliana

Arabidopsis thaliana

Arabidopsis thaliana

Arabidopsis thaliana

Arabidopsis thaliana

Arabidopsis thaliana

Arabidopsis thaliana

TOR kinase

CIPK26

BIN2

BAK1

AHK2-4

AHP1-5

SnRK2.8

CDPK3/6

FER

EFR

BIK1

CNGC2/4

BIK1

SLAC1

NPR1

Type A and type B ARRs

AHP1-5

BSU1, BIK1, PBLs,

BZR1, BES1, SnRKs, ABI5, ICE1, RD26, TINY, DSK2

ABI5

PYL ABA receptors

SnRK2 kinases

Casein kinase, Raf-like kinase

Arabidopsis thaliana

CK2, APK

SnRK2 kinases, BIN2, PIN2

Type 2 protein phosphatases

Arabidopsis thaliana

PP2As and PP2Cs

[49]

[47]

Reviewed in [45]

Plant immunity

JA and SA signaling

JA and salt stress signaling

ABA and SA signaling

SA signaling

CK signaling

CK signaling

BR signaling and plant immunity

[80]

[61]

[59, 107]

[56]

[54]

Reviewed in [15]

Reviewed in [15]

Reviewed in [11, 50]

(continued)

BR and ABA signaling, cold Reviewed in and drought stress signaling [50] and recently reported in [99, 121–123]

ABA signaling

ABA signaling and abiotic stresses

ABA signaling and abiotic stresses

ABA, auxin, BR, GA, ethylene, Reviewed in and JA signaling [45, 46, 50, 55]

Protein Phosphorylation in Plant Cell Signaling 61

HSFA3 and SPRH1 HSFA1

MUT9P-like kinase

CBL-interacting protein kinase

Calcium-dependent protein kinase

MAPK

MAPK kinase kinase

Snf1-related protein kinase

Snf1-related protein kinase

PP2C-type phosphatases

MAPK

Arabidopsis thaliana

Arabidopsis thaliana

Arabidopsis thaliana

Solanum MAPK lycopersicum

Calmodulin-binding protein kinase

Arabidopsis thaliana

Arabidopsis thaliana

Arabidopsis thaliana

Arabidopsis thaliana

Arabidopsis thaliana

Arabidopsis thaliana

Oryza sativa

MLK4

CIPK26

CPK3

MPK3/4/6

MPK1

CBK3

YODA

AKIN10

OST1

EGR2

OsMAPK3

OsICE1

OST1

ICE1, BTF3, BTF3L, PUB25/26

FUSCA3

MPK3/6

HSFA2, HSFA4A, ZAT10, LIP5, SPCH, ICE1, MYB15, SOS1

TPK1

RBOHF

H2A

JAV1

Calmodulins

Arabidopsis thaliana

CaM

Substrates

Group

Species

Protein(s)

Table 1 (continued)

Cold stress signaling

Cold stress signaling

ABA and cold stress signaling

High temperature adaption

Heat stress signaling

Heat stress signaling

Heat stress signaling

Heat, cold, salt, and osmotic stress signaling

[102]

[101]

[100, 103, 104]

[95]

[92]

[88]

[93, 94]

[87, 89–92, 97, 98, 105, 113]

[84]

[83]

Ca2+ signaling and ROS signaling Salt stress adaption

[78]

[62]

Reference(s)

Deposition of the H2A.Z

JA biosynthesis and signaling

Function

62 Ping Li and Junzhong Liu

GhANN8b MdSUT2.2

SOS2-like protein kinase

RLCK

Clade A protein phosphatase 2C

MAPK cascade

MAPK cascade

MAPK kinase

Oryza sativa

Gossypium spp. Protein phosphatase

Calcineurin B-like interacting protein kinases

Arabidopsis thaliana

Malus domestica

Arabidopsis thaliana

Gossypium hirsutum

Gossypium hirsutum

Oryza sativa

PKS5

STRK1

GhDsPTP3a

CIPK13, CIPK22

HAI1

GhMAP3K15GhMKK4GhMPK6

GhMAP3K14GhMKK11GhMPK31

MPKK10.2

MPK3

Unknown

GhWRKY59

AHL10

CatC

SOS2

SOS1, SCaBP8, ANN4,

SnRK3s family

Arabidopsis thaliana

SOS2/CIPK24

14–3-3 proteins

CRLK family

Arabidopsis thaliana

CRPK1

[110]

[109, 112]

[106]

Drought stress signaling

Drought stress signaling

Drought stress signaling

Drought stress signaling

Salt and drought stress signaling

Salt stress signaling

[127]

[126]

[125]

[120]

[118, 133]

[117]

H2O2 homeostasis in salt stress [116] signaling

Salt stress signaling

Salt stress signaling

Cold stress signaling

(continued)

Protein Phosphorylation in Plant Cell Signaling 63

Group

Raf-like MAPKKK

MAPKKK

MAPK-like protein

Calcineurin B-like interacting protein kinases

Calcium-dependent protein kinases

Calcium-dependent protein kinases

Calcium-dependent protein kinases

Species

Oryza sativa

Arabidopsis thaliana

Zea mays

Arabidopsis thaliana

Arabidopsis thaliana

Oryza sativa

Hordeum vulgare L.

Protein(s)

DSM1

MAPKKK18

MPKL1

CIPK11

CPK1

CPK10

CPK2a

Table 1 (continued)

Unknown

Unknown

Unknown

Di19–3

Unknown

MAPKK3

Unknown

Substrates

Drought stress signaling

Drought stress signaling

Salt and drought stress signaling

Drought stress signaling

Drought stress signaling

Drought stress signaling

Drought stress signaling

Function

[136]

[135]

[134]

[132]

[131]

[129]

[128]

Reference(s)

64 Ping Li and Junzhong Liu

Protein Phosphorylation in Plant Cell Signaling

65

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Chapter 4 Phosphoproteomics Profiling of Receptor Kinase Mutants Dandan Lu, Ting Gao, Lin Xi, Leonard Krall, and Xu Na Wu Abstract The transmembrane receptor kinase family is the largest protein kinase family in Arabidopsis. Many members of this family play critical roles in plant signaling pathways. However, many of these kinases have yet uncharacterized functions and very little is known about the direct substrates of these kinases. We have developed the “ShortPhos” method, an efficient and simple mass spectrometry (MS)-based phosphoproteomics protocol to perform comparative phosphopeptide profiling of knockout mutants of receptorlike kinases. Through this method, we are able to better understand the functional roles of plant kinases in the context of their signaling networks. Key words Plant receptor kinases, Phosphoproteomics, Mass spectrometry

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Introduction Protein phosphorylation is a widespread posttranslational modification that regulates cellular signaling processes. This modification is conducted by protein kinases [1, 2], and the phosphate group is generally removed by phosphatases [3]. The Arabidopsis genome encodes approximately 1000 protein kinases. Among these kinases, the receptor kinases make up the largest clade with 427 transmembrane receptor kinases and 145 receptor-like cytoplasmic kinases which lack an extracellular domain [1]. Plant receptor kinases have key roles in the perception of external signals in several well-studied membrane-based signaling pathways [4–7]. For example, the BRI1/BAK1 receptor complex phosphorylates each other sequentially to completely activate BRI1, and then BRI1 is able to phosphorylate downstream targets (e.g., BSU1, BZR1) [8–11]. The receptor kinase QSK1 enhanced the SIRK1 kinase’s ability to interact with its substrate, the water channel PIP2F, in a sucroseinduced condition, which is involved in regulation of water transport [12, 13]. Therefore, the phosphorylation status of kinases and their substrates is of high interest in studying signaling cascades.

Xu Na Wu (ed.), Plant Phosphoproteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2358, https://doi.org/10.1007/978-1-0716-1625-3_4, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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However, for many kinases, the substrates are still unknown, and for many substrates, the respective kinases are also not clear. Mass spectrometry (MS)-based phosphoproteomics has been used to study the dynamics of global phosphorylation for more than a decade in plants in the context of nutrient stimulation [14– 20], defense [21], primary metabolism [22, 23], and hormone signaling [24, 25]. Comprehensive information of Arabidopsis protein phosphorylation sites can be found in the PhosPhAt database [26, 27]. Identification of kinase-target relationship under distinct stimuli through phosphoproteomics helps researchers create, visualize, and understand condition-dependent cellular signaling networks [28]. For example, in previous studies using a kinase mutant, identification of the kinase-substrate was determined. We found that the water channel PIP2F is substrate of SIRK1 through phosphoproteomic analysis of a sirk1 mutant [13]. By using the sucrose nonfermenting 1 (SNF1)-related protein kinase 2s (SnRK2s) triple mutant snrk2.2/2.3/2.6, many downstream targets related with ABA action were identified, such as flowering time regulators [29]. A similar method was used with the QISK platform, to analyze cytoplasmic substrates of specific kinases proteome-wide, by using an ATP-analogue-sensitive kinase mutant [30]. In this chapter, we describe an MS-based label-free quantitation phosphoproteomics workflow to profile receptor kinase mutants (Fig. 1). We used the lab-optimized protocol, “ShortPhos” [31], to extract high-quality plant membrane protein and follow this with highly efficient phosphopeptide enrichment prior to mass spectrometric analysis.

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Materials

2.1 Buffers and Solutions

1. Homogenization buffer (HB): 330 mM sucrose, 100 mM KCl, 1 mM EDTA, 50 mM Tris-MES, pH 7.5. Add 5 mM DTT (see Note 1), protease inhibitor cocktail (50 μl/10 ml HB), phosphatase inhibitor cocktail 2 (50 μl/10 ml HB), and phosphatase inhibitor cocktail 3 (50 μl/10 ml HB) immediately before use. 2. 10 mM Tris–HCl, pH 8.0. 3. 8 M UTU: 6 M urea and 2 M thiourea in 10 mM Tris–HCl, pH 8.0. 4. DTT reduction buffer: 1 μg/μl DTT in water. Dissolve 10 mg DTT into 10 ml of deionized water (see Note 2). 5. Alkylation buffer (IAA): 5 μg/μl iodoacetamide (IAA) in water. Dissolve 50 mg iodoacetamide in 10 ml deionized water (see Note 2).

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Fig. 1 Workflow of comparative experiments using WT and a receptor kinase mutant (RLK mutant)

6. Enrichment buffer: 1 M glycolic acid in 80% acetonitrile (ACN) and 6% trifluoroacetic acid (TFA) (see Note 3). 7. Wash buffer: 80% ACN and 1% TFA. 8. Elution buffer: Freshly prepared 5% ammonium hydroxide solution (NH4OH, stock concentration 25%). 9. Acidified buffer: 10% formic acid. 10. 2% trifluoroacetic acid (TFA). 11. Solution A: 0.1% TFA and 5% ACN. 12. Solution B: 0.1% TFA and 80% ACN. 13. Solvent A: 0.5% acetic acid. 14. Solvent B: 0.5% acetic acid and 80% ACN. 2.2

Other Materials

1. 10 cm3 Teflon-glass grinder. 2. 50 ml falcon tubes. 3. Bradford assay reagents. 4. Speed vacuum concentrator. 5. Methanol (Gradient grade, 99.9%). 6. C18 StageTip: 200 μl tip containing two discs with C18 material (see Note 4). 7. 0.5 μg/μl Lys-C: Sequencing-grade.

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8. 0.5 μg/μl trypsin: Sequencing-grade, modified. 9. 5 μm titanium dioxide (TiO2) beads. 2.3 Specific Equipment

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1. Nanoflow Easy-nLC 1000 HPLC system. 2. Orbitrap hybrid mass spectrometer (Q Exactive plus).

Methods

3.1 Sample Preparation

1. Sow 10 mg sterilized Arabidopsis seeds (WT/mutant separately) into a 250 ml flask, put the flask under continuous light (20  C, 130 μE/s m2) in half-strength MS medium. 2. After 11 days, treat seedlings with stimuli (such as hormones). 3. Harvest plant materials and fragment a total of 1–1.5 g frozen plant material (fresh weight) for each of three biological replicates (stored in a 80  C freezer until needed) into small pieces with a hammer or a pestle (see Note 5). 4. Carefully put the plant materials into a 10 cm3 Teflon-glass grinder (Dounce homogenizer) and homogenize with 10 ml HB buffer. The plant material should be incubated in the HB for 10 min on ice before grinding (see Note 5). 5. Perform at least 100 pestle strokes per sample for complete homogenization (see Note 5). 6. Filter the homogenate through four layers of miracloth into a 50 ml falcon tube (see Note 5). 7. Centrifuge the homogenate at 7500  g for 15 min at 4  C. 8. Carefully transfer the supernatant into a new 50 ml falcon tube and centrifuge at 48,000  g for 75 min at 4  C. 9. Resuspend the microsomal (MF) pellet in 150–200 μl UTU on ice (see Note 6). 10. Measure the protein concentration by the Bradford assay. 1 μl MF is mixed with 24 μl deionized water and 200 μl Bradford reagent. Incubate the mixture for 10 min at room temperature in darkness. Measure the absorbance at 590 nm. 11. Aliquot 100 μg MF into 1.5 ml Eppendorf tubes and store at 80  C (see Note 7).

3.2 In-Solution Trypsin Digestion

1. Add 1 μl/50 μg protein of reduction buffer into the sample and incubate the mixture for 30 min at room temperature. 2. Add 1 μl/50 μg protein of alkylation buffer into the sample and incubate the mixture for 20 min in darkness at room temperature (see Note 8). 3. Add 1 μl/50 μg protein of Lys-C into the sample and incubate the mixture at room temperature for 3 h.

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4. Dilute the sample with four volumes of 10 mM Tris–HCl, pH 8.0. 5. Add 1 μl/50 μg protein of trypsin into the sample and incubate the mixture at 37  C overnight. 6. Add equal volume of enrichment buffer, mix well (see Note 9). 3.3 TiO2 Phosphopeptide Enrichment

1. Add TiO2 beads (10:1 ratio TiO2 beads/protein) into 100 μl methanol (Gradient grade, 99.9%). Centrifuge at 2500  g for 2 min at room temperature. Carefully remove and discard the supernatant. 2. Wash the TiO2 beads once with 100 μl of elution buffer for 10 min with vortex mixing and centrifuge at 2500  g for 2 min at room temperature. Discard the supernatant. TiO2 beads are equilibrated with 100 μl of enrichment buffer for 1 min and centrifuged at 2500  g at room temperature for 2 min. Carefully remove and discard the supernatant. Repeat this step two times. 3. Add the digested peptides from Subheading 2.2, step 6, into the tube with the equilibrated TiO2 beads for 30 min with continuous mixing on a shaker or a vortex mixer. Centrifuge at 2500  g for 2 min at room temperature. Transfer the supernatant into a new tube (see Note 10) and keep the TiO2 beads pellet to continue. 4. Wash the phosphopeptides and TiO2 beads mixture once with 100 μl enrichment buffer with continuous mixing on vortex for 30 s. Centrifuge at 2500  g at room temperature for 2 min. Carefully remove and discard the supernatant. 5. Wash the phosphopeptides and TiO2 beads mixture with 200 μl wash buffer with continuous mixing for 2 min. Centrifuge for 2 min at 2500  g at room temperature. Carefully remove and discard the supernatant. Repeat this step two times. 6. Incubate the phosphopeptides and TiO2 beads mixture with 80 μl elution buffer for 15 min and centrifuge at 2500  g for 2 min at room temperature. Collect the supernatant into a new Eppendorf tube. 7. Repeat step 7 twice more. 8. Immediately acidify the eluates with 60 μl acidified buffer (see Note 11). 9. Desalt the eluated samples directly or store them in the 20  C for the short term or 80  C for a longer time.

3.4 Peptide Desalting Using C18 StageTips

1. The preparation of C18 StageTips has been previously described [32] (see Note 4).

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2. Condition the C18 StageTips by adding 50 μl of solution B. Centrifuge at 1500  g for 5 min at room temperature. Discard the flow-through. 3. Equilibrate the C18 StageTips by adding 100 μl of solution A. Centrifuge at 1500  g for 5 min at room temperature. Discard the flow-through. Repeat this step once. 4. Load the eluate from Subheading 3.3, step 10, into the equilibrated C18 StageTip. Centrifuge at 1500  g for 5 min at room temperature. Discard the flow-through. 5. Wash the C18 StageTip by adding 100 μl of solution A. Centrifuge at 1500  g for 5 min at room temperature. Repeat this step once. 6. Elute the phosphopeptides by adding 20 μl of solution B into the C18 StageTip. Centrifuge at 1500  g for 5 min at room temperature. Collect the flow-through into a new 1.5 ml Eppendorf tube. Repeat this step one time and pool the eluates. 7. Dry the desalted eluates in a speed vacuum concentrator at 45  C. 3.5 LC-MS/MS Analysis and Peptide Identification

3.6 Label-Free Relative Quantification Analysis

Resuspend the dried peptides in 5 μl of solution A at room temperature. The enriched peptide mixture, consisting mainly of enriched phosphopeptides, is analyzed via LC-MS/MS (see Note 12). The parameters used for the LC-MS/MS analysis are listed in Table 1. 1. MS raw output data are processed using MaxQuant [33, 34] (see Note 13, details in Chapter 13). The parameters used for searching label-free phosphoproteomics data using MaxQuant are described in Chapter 13. The LFQ data found in the “Phospho (STY) Sites” table can be used for future data analysis. 2. Comparative analysis between WT and RLK mutants can be further performed with Perseus [35], cRacker (details in Chapter 16), or other suitable software. Generally, calculate fold changes of phosphosite levels between WT and RLK mutants, then set the criteria for selecting of phosphosites that are the possible substrates of receptor kinase (see Note 14).

4

Notes 1. Heat the MES solution in a microwave oven for a short time to easily dissolve the MES. DTT is oxygen sensitive and should be freshly prepared. 2. It is recommended to aliquot 10 ml DTT solution and 10 ml iodoacetamide into 1.5 ml tubes and store them at 20  C.

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Table 1 The parameters used for the LC-MS/MS analysis Instrument

Parameter

Setting

LC system

Sample loading

Ion source

Column heater temperature ( C) Spray voltage (kV) Capillary temperature ( C)

50 2.2 320

Mass spectrometer: full MS

Resolution (at m/z 200) Automatic gain control target (%) Maximum injection time (ms) Scan range (m/z)

60,000 300% 20 300–1500

Mass spectrometer: dd-MS2

Resolution (at m/z 200) Automatic gain control target (%) Maximum injection time (ms) Isolation window (m/z) Cycle time (sec) First mass (m/z) Scan range Normalized collision energy

15,000 75% 22 1.6 2 120 Define first mass 30%

Mass spectrometer: general

Polarity Intensity threshold Include charge state (s) Dynamic exclusion (s) Expected LC peak width (s)

Positive 5000 2–6 40 15

At maximum pressure 800 bar Gradient length (min) 130 Gradient flow rate (nl/min) 250 Linear gradient (percentage of LC solvent B in LC 8–28% for 85 min solvent A) 28–35% for 35 min 35–98% for 5 min 98% for 5 min

3. It is recommended to freshly prepare this buffer before use. 4. 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 StageTip manufacturing are described elsewhere [32]. In general, desalting over C18 can, in principle, also be done using alternative products such as ZIP-TIPs or C18 cartridges. 5. Plant materials are harvested with aluminum foil and broken immediately when removed from the 80  C freezer without the use of liquid nitrogen. We use Teflon-glass grinders (Dounce homogenizer) for maintaining the membrane integrity and increasing the efficiency of membrane fraction extraction.

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6. Resuspension of the MF should be very slow to avoid many bubbles. At this step, the MF can be stored at 20  C for shortterm storage or 80  C for long-time storage. 7. We use 100–200 μg MF for phosphopeptide enrichment from 1 g starting plant materials. You may need to “scale-up” the trypsin digestion and phosphopeptide enrichment protocols if using more MF, as in the case of searching for a known low-abundance protein. 8. IAA is light sensitive; this step should be performed in darkness. 9. Dissolved peptides from Subheading 2.2, step 6, are suggested to be spun down, and the supernatant should be transferred carefully to the Eppendorf tube. 10. The supernatant contains the un-phosphorylated peptides. Here, we suggest to keep the supernatant (in storage) in case it is necessary to compare between un-phosphorylated peptides and phosphorylated peptides from the same sample. These supernatants should be stored at 80  C for long-term storage. 11. A very basic solution can induce the loss of phosphate groups from peptides, it is crucial to add acidified buffer immediately. Enriched peptide samples can be stored at 20  C for shorttime storage or 80  C for long-time storage. 12. A C18 analytical capillary column is used for eluting peptides and spaying them directly into the mass spectrometer (EASYSpray analytical column, 25 cm  75 μm inner diameter). 13. Default settings in MaxQuant are suggested to be left unchanged. For the analysis of modifications, such as phosphorylation, variable modifications need to be defined. In-depth information about MaxQuant settings is described in [34]. 14. In the case, the criteria was p value 0.05 and fold change downregulation in RLK mutant/WT  1 in at least three replicates. References 1. Zulawski M, Schulze G, Braginets R et al (2014) The Arabidopsis kinome: phylogeny and evolutionary insights into functional diversification. BMC Genomics 15(1):548 2. Zulawski M, Schulze WX (2015) The plant kinome. Methods Mol Biol 1306:1–23. https://doi.org/10.1007/978-1-4939-26480_1 3. Schweighofer A, Meskiene I (2015) Phosphatases in plants. Methods Mol Biol 1306:25–46.

https://doi.org/10.1007/978-1-4939-26480_2 4. Kim J, Choi JN, John KM et al (2012) GC-TOF-MS- and CE-TOF-MS-based metabolic profiling of cheonggukjang (fastfermented bean paste) during fermentation and its correlation with metabolic pathways. J Agric Food Chem 60(38):9746–9753. https://doi.org/10.1021/jf302833y

Phosphoproteomics Profiling of Receptor Kinase Mutants 5. Kim TW, Guan S, Sun Y et al (2009) Brassinosteroid signal transduction from cell-surface receptor kinases to nuclear transcription factors. Nat Cell Biol 11(10):1254–1260 6. Marshall A, Aalen RB, Audenaert D et al (2012) Tackling drought stress: receptor-like kinases present new approaches. Plant Cell 24 (6):2262–2278 7. Osakabe Y, Yamaguchi-Shinozaki K, Shinozaki K et al (2013) Sensing the environment: key roles of membrane-localized kinases in plant perception and response to abiotic stress. J Exp Bot 64(2):445–458 8. Wang X, Kota U, He K et al (2008) Sequential transphosphorylation of the BRI1/BAK1 receptor kinase complex impacts early events in brassinosteroid signaling. Dev Cell 15 (2):220–235. https://doi.org/10.1016/j. devcel.2008.06.011 9. Wang ZY, Nakano T, Gendron J et al (2002) Nuclear-localized BZR1 mediates brassinosteroid-induced growth and feedback suppression of brassinosteroid biosynthesis. Dev Cell 2(4):505–513 10. Ryu H, Kim K, Cho H et al (2010) Predominant actions of cytosolic BSU1 and nuclear BIN2 regulate subcellular localization of BES1 in brassinosteroid signaling. Mol Cells 29(3):291–296. https://doi.org/10.1007/ s10059-010-0034-y 11. Pascual J, Canal MJ, Escandon M et al (2017) Integrated physiological, proteomic, and metabolomic analysis of ultra violet (UV) stress responses and adaptation mechanisms in Pinus radiata. Mol Cell Proteomics 16 (3):485–501. https://doi.org/10.1074/mcp. M116.059436 12. Schwacke R, Ponce-Soto GY, Krause K et al (2019) MapMan4: a refined protein classification and annotation framework applicable to multi-omics data analysis. Mol Plant 12 (6):879–892. https://doi.org/10.1016/j. molp.2019.01.003 13. Wu XN, Sanchez Rodriguez C, PertlObermeyer H et al (2013) Sucrose-induced receptor kinase SIRK1 regulates a plasma membrane aquaporin in Arabidopsis. Mol Cell Proteomics 12(10):2856–2873. https://doi.org/ 10.1074/mcp.M113.029579 14. Niittyl€a T, Fuglsang AT, Palmgren MG et al (2007) Temporal analysis of sucrose-induced phosphorylation changes in plasma membrane proteins of Arabidopsis. Mol Cell Proteomics 6 (10):1711–1726 15. Tran HT, Plaxton WC (2008) Proteomic analysis of alterations in the secretome of Arabidopsis thaliana suspension cells subjected to

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nutritional phosphate deficiency. Proteomics 8. https://doi.org/10.1002/pmic. 200800292 16. 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 17. Lan P, Li W, Wen TN et al (2012) Quantitative phosphoproteome profiling of iron-deficient Arabidopsis roots. Plant Physiol 159 (1):403–417 18. Douglas P, Morrice N, MacKintosh C (1995) Identification of a regulatory phosphorylation site in the hinge 1 region of nitrate reductase from spinach (Spinacea oleracea) leaves. FEBS Lett 377(2):113–117 19. Wu X, Sanchez-Rodriguez C, Pertl-Obermeyer H et al (2013) Sucrose-induced receptor kinase SIRK1 regulates a plasma membrane aquaporin in Arabidopsis. Mol Cell Proteomics 12 (10):2856–2873 20. Wu X, Sklodowski K, Encke B et al (2014) A kinase-phosphatase signaling module with BSK8 and BSL2 involved in regulation of sucrose-phosphate synthase. J Proteome Res 13(7):3397–3409 21. Benschop JJ, Mohammed S, O’Flaherty M et al (2007) Quantitative phospho-proteomics of early elicitor signalling in Arabidopsis. Mol Cell Proteomics 6(7):1705–1713 22. Reiland S, Finazzi G, Endler A et al (2011) Comparative phosphoproteome profiling reveals a function of the STN8 kinase in finetuning of cyclic electron flow (CEF). Proc Natl Acad Sci U S A 108(31):12955–12960 23. Reiland S, Messerli G, Baerenf€aller K et al (2009) Large-scale Arabidopsis phosphoproteome profiling reveals novel chloroplast kinase substrates and phosphorylation networks. Plant Physiol 150(2):889–903 24. Chen Y, Ho¨henwarter W, Weckwerth W (2010) Comparative analysis of phytohormone-responsive phosphoproteins in Arabidopsis thaliana using TiO2-phosphopeptide enrichment and MAPA. Plant J 63 (1):1–17 25. Zhang H, Zhou H, Berke L et al (2013) Quantitative phosphoproteomics after auxinstimulated lateral root induction identifies an SNX1 protein phosphorylation site required for growth. Mol Cell Proteomics 12 (5):1158–1169 26. Durek P, Schmidt R, Heazlewood JL et al (2010) PhosPhAt: the Arabidopsis thaliana phosphorylation site database. An update. Nucleic Acids Res 38:D828–D834

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27. Heazlewood JL, Durek P, Hummel J et al (2008) PhosPhAt: a database of phosphorylation sites in Arabidopsis thaliana and a plantspecific phosphorylation site predictor. Nucleic Acids Res 36:D1015–D1021 28. Duan G, Walther D, Schulze WX (2013) Reconstruction and analysis of nutrientinduced phosphorylation networks in Arabidopsis thaliana. Front Plant Sci 4:540 29. Rappsilber J, Mann M, Ishihama Y (2007) Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat Protoc 2 (8):1896–1906. https://doi.org/10.1038/ nprot.2007.261 30. Morandell S, Grosstessner-Hain K, Roitinger E et al (2010) QIKS-quantitative identification of kinase substrates. Proteomics 10:2015–2025 31. Wu XN, Xi L, Pertl-Obermeyer H et al (2017) Highly efficient single-step enrichment of low abundance phosphopeptides from plant membrane preparations. Front Plant Sci 8:1673. https://doi.org/10.3389/fpls.2017.01673

32. 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 33. 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. https://doi.org/10. 1038/nbt.1511 34. Su W, Huber SC, Crawford NM (1996) Identification in vitro of a post-translational regulatory site in the hinge 1 region of Arabidopsis nitrate reductase. Plant Cell 8(3):519–527. https://doi.org/10.1105/tpc.8.3.519 35. Tyanova S, Temu T, Sinitcyn P et al (2016) The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat Methods 13(9):731–740. https://doi.org/ 10.1038/nmeth.3901

Chapter 5 Phosphoproteomic Analysis of Soybean Roots Under Salinity by Using the iTRAQ Labeling Approach Yuchen Qian, Jia Xu, and Erxu Pi Abstract Protein phosphorylation is one of the most important posttranslational modifications. The phosphorylation and dephosphorylation of proteins regulate almost every cellular process, and the understanding of their functions can provide insights into the regulation of living systems at the molecular level. In recent years, both the rapid developments of enrichment approaches for phosphoproteins and MS techniques have improved the research scope and depth of phosphoproteomics. Using NaCl-treated soybean roots as the experimental materials, this chapter introduces the protein extraction, digestion with filter-aided sample preparation (FASP), eight-plex iTRAQ labeling, TiO2-based enrichment of phosphopeptides, LC-MS/MS analysis, as well as bioinformatic methods and protocols. Keywords Glycine max, Phosphoproteomics, FASP , Eight-plex iTRAQ, Q exactive hybrid quadrupole-orbitrap mass spectrometer

1

Introduction Phosphorylation of proteins is a reversible posttranslational modification that plays an important role in organisms. Chemical modification by phosphorylation occurs mainly on serine, threonine, and tyrosine residues. Havelund et al. [1] predict that there are 1003 protein kinases in the Arabidopsis thaliana proteome, accounting for 4% of all proteins [2]. This indicates that protein phosphorylation plays important roles in various plant biological processes. However, the absolute number of phosphoproteins currently identified from species is still very limited. Therefore, the exploration and discovery of new phosphoproteins, phosphorylation sites, and the regulation of their functions have become the focus of many researchers’ attention. Phosphoproteomic techniques are currently evolving rapidly. Enrichment of phosphoproteins is a prerequisite for the successful identification of phosphoproteins by mass spectrometry. The enrichment of phosphoproteins greatly increases their abundance

Xu Na Wu (ed.), Plant Phosphoproteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2358, https://doi.org/10.1007/978-1-0716-1625-3_5, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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for analysis and fundamentally promotes the development of phosphoproteomic research. Immobilized metal affinity chromatography (IMAC) [3, 4], immunoprecipitation [5–7], metal oxide affinity chromatography (MOAC) [8, 9], and strong cation exchange chromatography (SCE) are the most commonly used methods for enrichment of phosphoproteins [10]. The phosphoproteins or phosphopeptides are directly identified after enrichment by using liquid chromatography mass spectrometry (LC-MS/ MS) [11]. The development of techniques, including the detection of phosphopeptides, along with the localization and quantitative analysis of phosphorylation sites, has made it possible to conduct largescale and systematic studies on phosphopeptides. Because of the properties and characteristics of phosphoproteins, the methods and techniques for quantitative analysis of the phosphoproteome are different from analyzing the whole proteome [12]. Phosphoproteomic research should not only overcome the technical problems inherent in proteomics but also solve the specific problems caused by the chemical characteristics of phosphoproteins. Protein phosphorylation is transient, highly dynamic, and usually low abundance. These facts produce serious challenges for the quantitative study of phosphoproteins at the level [13]. Proper phosphoproteomic research requires one to, based on the characteristics of phosphopeptides, select an appropriate quantitative method and an appropriate detection method. Computer software is used to automate the analysis and assist in quantification. Development of research methods and technologies for analysis of the proteome and phosphoproteome is continuous. Techniques such as specific enrichment of phosphoproteins and phosphopeptides, more mature and stable labeling technology to ensure the accuracy of localization, and more sensitive and accurate mass spectrometry technology to identify and analyze phosphoproteins and phosphopeptides, are areas of the most intense focus. It is believed that the difficulties faced in analyzing the phosphoproteome will be overcome in the near future [13]. This chapter briefly describes the enrichment and analysis techniques that have been published in our laboratory for the detection and identification of phosphoproteins in soybeans under salt stress [8, 14, 15].

2

Materials

2.1 Plant and Plant Growth Materials

1. Glycine max cultivar Union85140. 2. 5% bleach solution (NaClO). 3. Filter paper. 4. Fahr€aeus medium: [16] Macronutrients consist of 0.5 mM MgSO4, 0.7 mM KH2PO4, 0.8 mM Na2HPO4, 50μM ferric

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citrate; Micronutrients contain 0.1μg/L MnSO4, 0.1μg/L CuSO4, 0.1μg/L ZnSO4, 0.1μg/L H3BO3, 0.1μg/L Na2MoO4, and 1 mM CaCl2. Adjust pH to 7.5 before autoclaving. 5. 200 mM NaCl. 6. Pearlite and sphagnum peat soil (v:v ¼ 1:3). 2.2 Protein Extraction

1. SDT lysis buffer: 4% SDS, 100 mM Tris–HCl pH 7.6, 1 mM DTT, 1 mM PMSF, including 1 PhosSTOP phosphatase inhibitor mixture. 2. Mortar and pestle. 3. Liquid nitrogen. 4. Trichloroacetic acid (TCA). 5. Acetone. 6. 50 ml conical bottom Falcon centrifuge tube. 7. Vacuum system. 8. BCA (bicinchoninic acid). 9. Spectrophotometer.

2.3 Protein Digestion with Filter Aided Sample Preparation (FASP)

1. 100 mM DTT. 2. UA buffer (8 M Urea, 150 mM Tris–HCl pH 8.0). 3. Microcon filtration devices (with an MWCO of 10 kDa). 4. 50 mM iodoacetamide (IAA). 5. Digestion buffer (10μl 0.5μg/μl of trypsin solution for every 1.5 mg aliquot of dissolved protein).

2.4 Eight-Plex iTRAQ Labeling and Phosphopeptide Enrichment

1. The eight-plex iTRAQ labeling reagent. 2. DHB buffer: 3% 2,5-dihydroxybenzoic acid (DHB), 80% ACN, and 0.1% TFA. 3. TiO2 beads (10μM in diameter). 4. 10μl pipette tips. 5. Wash solution I: 20% acetic acid, 300 mM of octanesulfonic acid sodium salt, and 20 mg/ml DHB. 6. Wash solution II: 70% water and 30% acetonitrile. 7. ABC buffer: 50 mM of ammonium phosphate, pH 10.5. 8. 0.1% TFA solution.

2.5 NanoRPLC-MS/ MS Analysis of Phosphopeptides

1. EASY-nLC1000 system. 2. Q exactive hybrid quadrupole-orbitrap mass spectrometer (Thermo Scientific). 3. NanoLC separation system.

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4. Mobile phase A solution: 0.1% formic acid in water. 5. Mobile phase B solution: 84% acetonitrile and 0.1% formic acid. 6. Thermo EASY SC200 100 mm  75μM).

trap

column

(RP-C18,

3μM,

7. Thermo Scientific EASY column (2 cm  100μM 5μM-C18).

3

Methods

3.1 Seed Sterilization (See Note 1)

1. Sterilize soybean seeds in 5% NaClO for 5 min. 2. Then rinse with sterile distilled H2O three times for 5 min. 3. Use tweezers to evenly distribute the seeds on filter paper, each seed approximately 2 cm apart, and germinate at room temperature (about 22  C) with 40–60% humidity. After 2 days of cultivation, the seeds should germinate.

3.2 Plant Growth (See Note 2)

1. Transplant the seedlings into pearlite and sphagnum peat soil (v:v ¼ 1:3). 2. Irrigate the seedlings with deionized water every 2 days and irrigate with 1/4 strength Fahr€aeus medium every 4 days. At the trifoliolate stage, the seedlings are treated with 200 mM NaCl for 24 h (see Note 3). 3. Collect samples and store at 80  C until further use.

3.3 Protein Extraction (See Note 4)

1. Grind approximately 5 g of frozen root tissue in a mortar to powder with liquid nitrogen. 2. Quickly add all the powder to 45 mL of precooled TCA/acetone (v:v ¼ 1:9) in a 50 ml conical bottom Falcon centrifuge tube and mix completely to suspend the powder. Place the suspension at 20  C overnight. 3. Centrifuge the homogenate at 7000  g at 4  C for 20 min. Carefully remove the TCA/acetone and discard. 4. Wash the pellet with 40 ml acetone. Centrifuge again at 7000  g at 4  C for 20 min. Carefully remove the acetone and discard. Repeat this step three times. 5. After the third wash, carefully remove the upper layer of acetone. Then remove the remaining acetone by speed vacuum. 6. Take 50 mg of the powder and resuspend it in 1 ml of SDT lysis buffer. 7. Place this solution in boiling water for 15 min. 8. Sonicate for 100 s. 9. Centrifuge at 14,000  g at 4  C for 15 min.

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10. Take the top clarified solution. The protein is present in this supernatant. 11. Measure the protein concentration by the BCA method. 3.4 Protein Digestion with FASP

1. Take an aliquot of approximately 1.5 mg of dissolved protein in a new 1.5 ml tube. 2. Add DDT to the protein solution to final concentration of 100 mM. 3. Boil for 5 min. 4. Add 200μl UA buffer into 25μl aliquot of sample. Load the mixture into a Microcon filtration device. 5. Centrifuge at 14,000  g for 15 min. 6. Add 200μl UA buffer to dilute the concentrate in the device. 7. Centrifuge at 14,000  g for 15 min. 8. Bring the volume of concentrate to 100μl with UA buffer supplemented with 50 mM iodoacetamide (IAA). 9. Shake the sample at 600 rpm for 1 min. 10. Incubate the sample at room temperature for 30 min. 11. Dilute the sample with 40μl of digestion buffer. 12. Shake the mixture at 600 rpm for 1 min. 13. Incubate the sample at 37  C for 16–18 h. 14. Pass the peptide solution through a Microcon filtration device (MWCO 10 kDa) by centrifuging. 15. Measure the peptide spectrophotometer.

3.5 Eight-Plex iTRAQ Labeling and Phosphopeptide Enrichment

concentration

at

OD280

by

1. Add AB Sciex iTRAQ labeling reagent to label an aliquot containing 100μg of digested peptides according to manufacturer’s instructions. 2. Add 50μl DHB buffer to acidify the labeled peptides. 3. Incubate with 150μl acidized TiO2 phosphobind buffer (consisted with 50μg/μl DHB and 25μg TiO2 beads) for 40 min at room temperature. 4. Spin down the TiO2 beads at 3000  g, at room temperature, for 3 min. 5. Wash the peptide-TiO2 beads with 20μl of wash solution I and centrifuge at 3000  g at room temperature for 3 min. Repeat three times. 6. Wash the peptide-TiO2 beads with 20μl of wash solution II and centrifuge at 3000  g at room temperature for 3 min. Repeat three times.

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7. Elute the peptides with freshly prepared ABC buffer and centrifuge at 3000  g at room temperature for 3 min. The supernatant is transferred to a new tube. Repeat this step and combine these supernatants. 8. Lyophilize the enriched phosphopeptide solution and redissolve in 20μl 0.1% TFA solution and store at 20  C for further analysis. 3.6 NanoRPLC-MS/ MS Analysis of Phosphopeptides (See Note 5)

1. Pre-equilibrate the Thermo EASY SC200 trap column with 95% mobile phase A for 30 min before peptide loading. 2. Transfer the phosphopeptides to the Thermo Scientific EASY column. 3. The phosphopeptides were initially transferred to the SC001 column (150μm  20 mm, RP-C18) using 0.1% formic acid solution. The peptides were then separated via the trap column using a gradient of 0–55% mobile phase B for 220 min with a flow rate of 250 nl/min followed by an 8 min rinse with 100% of mobile phase B. 4. Rinse the columns with 100% mobile phase B for 8 min and reequilibrate to the initial conditions for 12 min. The flow rate of the above procedures is 0.25μl/min. 5. Collect the MS data of sample for 300–1800 m/z at the resolution of 70k. 6. Dissociate the 20 most abundant ions from each MS scan subsequently by higher energy collisional dissociation (HCD) in alternating data-dependent mode. 7. Acquire MS/MS spectra generated by the HCD with a resolution less than 17,500. In nanoLC separation system, mobile phase A solution contains 2% acetonitrile (ACN) and 0.1% formic acid in water, and mobile phase B solution contains 84% ACN and 0.1% formic acid. The Thermo EASY SC200 trap column (RP-C18, 3μm, 100 mm  75μm) was pre-equilibrated with mobile phase A before peptides loading. The trap column was re-equilibrated to the initial conditions for 12 min.

3.7 Phosphopeptide Identification and Quantitative Analysis

Analyze the raw HCD data by Mascot 2.2 and Proteome Discoverer 1.4. To identify the phosphopeptides, the Mascot data is searched against the 74,305 entries curated in the peptide database of soybean (http://www.uniprot.org/on). For every eight-plex set, a pooled sample was obtained by combing two groups of samples representing seven time points (a control and six salt treatments). These pooled samples serve as normalizing reference for the peptide content in samples from all the tested eight-plex sets. The quantitative values of phosphopeptides at different treatment points are normalized and converted to

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log2 value of fold change. Phosphopeptides pass the cutoff (Log2 value is greater than 1) and are detected in at least two replicates to assess significant changes under NaCl stress. Two statistical methods are used for significance analysis. Cox and Mann describe in the report that the “significant A” value is suitable for assessing changes between treated (sample) and untreated (control, T0) root tissue in each biological replicate which consists of three technical replicates [17]. A Student’s t-test is to assess the global variability of all test samples using the standard deviation of the pooled samples (standard) between different biological replications [18]. The phosphopeptides that pass both Significance A 10). 7. Vacuum concentrator.

2.6 Nano-RPLC-MS Analysis

1. Q-Exactive Orbitrap hybrid mass spectrometer. 2. C18 reverse-phase column. 3. Solvent B: 80% acetonitrile and 0.5% formic acid. 4. Nano-electrospray ion source.

3 3.1

Methods Plant Growth

1. Arabidopsis seeds are germinated and grown in JPL-agar medium [6] in a 12 h day/night cycle (22, 110 uE/s m2). 2. After 1 month of growth, harvest Arabidopsis seedlings and roots at room temperature for protein extraction. All experiments are performed on three or more independently grown pools of seedlings.

3.2 Protein Extraction

1. Weigh 5 g of fresh tissue and add 5 ml of protein extraction buffer, homogenize the plant materials at 4
C with a Stabilizor T1 (Cayman chemicals, https://www.caymanchem.com/prod uct/18634) (see Note 1). 2. Transfer the entire homogenate into pre-chilled 15 ml tubes, and add three volumes of chilled phenol to each tube (see Note 2). 3. Mix sample on an over-end rotor for 30 min at 4
C. 4. Centrifuge at 3126  g (4000 rpm) for 8 min at 4
C in a tabletop centrifuge (see Note 3). 5. The phenolic phase will separate from the aqueous phase. Transfer the upper phenolic phase into a new 50 ml tube. 6. Add 15 volumes of ice-cold acetone, and store at 20
C overnight to precipitate the proteins.

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7. Centrifuge at 3126  g for 10 min at 4
C, discard the supernatant. 8. Wash the pellets three times with ice-cold methanol, recentrifuge the protein samples, and discard the wash. 9. Collect the precipitated protein pellet, dissolve the pellets with 8 M urea, 50 mM NH4HCO3 using a Precellys tissue homogenizer, and store at 80
C for future use or measure protein concentration (see Note 4). 3.3 Al(OH)3-Based MOAC Enrichment of Phosphoproteins

1. Weigh 50 mg Al(OH)3 in a 2 ml tube, equilibrate with 2  1 ml MB, centrifuge 1 min at 8536  g, and remove the supernatant. 2. Resuspend 1 mg protein pellet/lysate (max ~100 μl) (from Subheading 3.2, step 7) in 800 μl MB, and transfer the protein solution into the tube containing prewashed Al(OH)3 (from step 1). 3. Rotate sample at room temperature for ~30 min. Centrifuge 15 min at 4
C with 8536  g, and discard the supernatant. 4. Wash three times with 2 ml MB (resuspend Al(OH)3, centrifuge each time). 5. Add 500 μl PB and rotate for 30 min at 4
C to elute the proteins. 6. Centrifuge 15 min at 4
C with 8536  g, and retain supernatant into a clean tube. 7. Add 500 μl more PB and rotate for 30 min at 4
C. 8. Centrifuge 15 min at 4
C with 8536  g, and pool supernatant with previous eluate (step 6). 9. Concentrate sample with a Biomax 5k NMWL concentrator at 10,000  g until sample is ~50 μl (see Note 5). 10. Dissolve protein pellet in digestion buffer, spin down the solution at 4
C with 8536  g, and retain the supernatant. 11. Measure the protein concentration using Coomassie Plus® protein assay reagent according to the manufacturer’s instructions.

3.4 In-Solution Digestion

1. Take 100 μg of the enriched phosphoproteins and predigest for 3 h with endoproteinase Lys-C (1/100 w/w) at room temperature. 2. Dilute the sample with four volumes of dilution buffer, then add Poroszyme® immobilized trypsin (1/100 w/w) to the samples and digest overnight at 37
C. 3. Spin down the protein digestion for 15 min at room temperature with 8536  g, retain the supernatant, and desalt using

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SPEC C-18 column according to the manufacturer’s instructions. 3.5 TiO2-Based MOAC Enrichment of Phosphopeptides

1. Weigh 10 mg TiO2 beads into a 1.7 ml tube. 2. Condition the beads by adding 200 μl IB and gently pipetting up and down. 3. Spin down the beads at room temperature with a speed of 4529  g, and discard the supernatant and retain the beads. 4. Resuspend the digested peptides from Subheading 3.4, step 3 in 100 μl IB (see Note 6), and transfer these dissolved peptides to the beads. Rotate for 30 min at room temperature. 5. Spin down at room temperature with a speed of 4529  g, and discard the supernatant and retain the beads. 6. Wash the beads two times with WB1 and two times with WB2 at room temperature. 7. Bound peptides are eluted from the beads one time with 200 μl EB1, one time with 200 μl EB2, and one time with 200 μl EB3. Combine all eluates immediately and neutralize by addition of 5% TFA. 8. Dry down the samples to an approximate volume of 5 μl in a vacuum concentrator.

3.6 Nano-RPLC-MS Analysis

1. The peptide mixture is separated by nanoscale C18 reversephase liquid chromatography online with a Q Exactive Orbitrap hybrid mass spectrometer using a 50 μm inner diameter monolithic column together with a 15-cm fused silica emitter. 2. Samples are normally injected onto the C18 reverse-phase column with a flow rate of 800 nl/min for 30 min. Elute the peptides with a flow rate of 250 nl/min with a gradient inclining from 5% to 30% solvent B (80% acetonitrile, 0.5% formic acid) in 101 min, 30% to 60% solvent B in 10 min, 60% to 90% solvent B in 6 min, and isocratic flow of 90% solvent B for 3 min. 3. The eluate is electrosprayed online into the mass spectrometer via a nano-electrospray ion source.

3.7 Peptide Identification and Label-Free Quantification (LFQ)

1. Load all the mass spectrometric raw data files into the MaxQuant environment (version 1.6.0.16), and use the integrated Andromeda search engine to identify peptides and proteins [7]. 2. Use the MS/MS peak lists to search the UniProt database with taxonomy Arabidopsis (35,386 entries, 2017). The identification error rate is controlled using the target-decoy database FDR model with significance threshold α ¼ 0.01 at the protein, peptide, and modification levels.

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3. Specify trypsin and Lys-c as the protease, and up to two missed cleavages allowed. Oxidized methionine (M), acetylation of protein N-termini, and phosphorylation of serine, tyrosine, and threonine (SYT) are tolerated as variable modifications. Set carbomidomethylation of cysteine (C) as a fixed modification. 4. Set the mass tolerance of precursor ions to 20 ppm, and the mass tolerance of fragment ions to 0.5 Da for the database search. Use Perseus 1.6.0.7 [8] and the tool box COVAIN for multivariate analysis and statistical testing [9]. Set the minimum probability of 50% for PTM localization. 5. The peptide LFQ intensities in every sample are normalized by subtracting the median of all the intensities in each sample. For pairwise comparison of phosphopeptide intensities between biological conditions, a one-way ANOVA statistic implementing a permutation-based FDR significance threshold of α ¼ 0.05 and an s0 value of 2 is used. 6. Annotate proteins using UniProtKB, Gene Ontology (GO), and KEGG in Perseus. A minimum category size of at least four proteins is required, and significant pathway enrichment is determined with Fisher’s exact test employing a BenjaminiHochberg FDR significance threshold α of 0.02. Impute missing values on the basis of a normal distribution (width ¼ 0.3, downshift ¼ 1.8).

4

Notes 1. Phosphotase inhibitors are added immediately prior to use, and the stock solution can be stored at 20
C. 2. All buffers must be used within 24 h and can be stored at 4
C. 3. The solution is incubated with 50 ml Falcon tube, and centrifuge at 3126  g, too high speed will disrupt the phase separation. 4. Protein concentration has to be determined here, because this will be used for setting the Al(OH)3 amount in phosphoprotein enrichment, normally 80 mg Al(OH)3 for 1 g proteins. 5. It will take approximate 5–30 min, depending on the purity and concentration of protein solution. 6. Here will start the second enrichment step with digested peptides, which are from Al(OH)3-enriched phosphoproteins.

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References 1. Zou JJ, Li XD, Ratnasekera D et al (2015) Arabidopsis calcium-dependent protein kinase8 and catalase3 function in abscisic acid-mediated signaling and H2O2 homeostasis in stomatal guard cells under drought stress. Plant Cell 27 (5):1445–1460. https://doi.org/10.1105/tpc. 15.00144 2. McLoughlin F, Augustine RC, Marshall RS et al (2018) Maize multi-omics reveal roles for autophagic recycling in proteome remodelling and lipid turnover. Nat Plants 4 (12):1056–1070. https://doi.org/10.1038/ s41477-018-0299-2 3. Chen Y, Weckwerth W (2020) Mass spectrometry untangles plant membrane protein signaling networks. Trends Plant Sci. https://doi.org/10. 1016/j.tplants.2020.03.013 4. Chen Y, Hoehenwarter W (2015) Changes in the phosphoproteome and metabolome link early signaling events to rearrangement of photosynthesis and central metabolism in salinity and oxidative stress response in Arabidopsis. Plant Physiol 169(4):3021–3033. https://doi. org/10.1104/pp.15.01486. pp.15.01486 [pii] 5. Chen Y, Hoehenwarter W (2019) Rapid and reproducible phosphopeptide enrichment by

tandem metal oxide affinity chromatography: application to boron deficiency induced phosphoproteomics. Plant J 98(2):370–384. https://doi.org/10.1111/tpj.14215 6. Droillard M, Boudsocq M, Barbier-Brygoo H et al (2002) Different protein kinase families are activated by osmotic stresses in Arabidopsis thaliana cell suspensions. Involvement of the MAP kinases AtMPK3 and AtMPK6. FEBS Lett 527(1–3):43–50. S0014579302031629 [pii] 7. 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(5):698–705. https://doi.org/10. 1038/nprot.2009.36. nprot.2009.36 [pii] 8. Tyanova S, Temu T, Sinitcyn P et al (2016) The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat Methods 13(9):731–740 9. Weckwerth W, Wienkoop S, Hoehenwarter W et al (2014) From proteomics to systems biology: MAPA, MASS WESTERN, PROMEX, and COVAIN as a user-oriented platform. Methods Mol Biol 1072:15–27. https://doi.org/10. 1007/978-1-62703-631-3_2

Chapter 8 SILIA-Based 4C Quantitative PTM Proteomics Emily Oi Ying Wong and Ning Li Abstract To absolutely and relatively quantitate the alteration of a posttranslationally modified (PTM) proteome in response to a specific internal or external signal, a 15N-stable isotope labeling in Arabidopsis (SILIA) protocol has been integrated into the 4C quantitative PTM proteomics, named as SILIA-based 4C quantitative PTM proteomics (S4Quap). The isotope metabolic labeling produces both forward (F) and reciprocal (R) mixings of either 14N/15N-coded tissues or the 14N/15N-coded total cellular proteins. Plant protein is isolated using a urea-based extraction buffer (UEB). The presence of 8 M urea, 2% polyvinylpolypyrrolidone (PVPP), and 5 mM ascorbic acid allows to instantly denature protein, remove the phenolic compounds, and curb the oxidation by free radicals once plant cells are broken. The total cellular proteins are routinely processed into peptides by trypsin. The PTM peptide yield of affinity enrichment and preparation is 0.1–0.2% in general. Ion exchange chromatographic fractionation prepares the PTM peptides for LC-MS/MS analysis. The collected mass spectrograms are subjected to a target-decoy sequence analysis using various search engines. The computational programs are subsequently applied to analyze the ratios of the extracted ion chromatogram (XIC) of the 14N/15N isotope-coded PTM peptide ions and to perform the statistical evaluation of the quantitation results. The Student t-test values of ratios of quantifiable 14 N/15N-coded PTM peptides are normally corrected using a Benjamini-Hochberg (BH) multiple hypothesis test to select the significantly regulated PTM peptide groups (BH-FDR < 5%). Consequently, the highly selected prospect candidate(s) of PTM proteins are confirmed and validated using biochemical, molecular, cellular, and transgenic plant analysis. Keywords 15N-Stable isotope labeling in Arabidopsis (SILIA), SILIA-based 4C quantitative PTM proteomics (S4Quap), Posttranslational modification (PTM), SILIQUE-N/SQUA-N, Functional validation, Double in vivo substrate and kinase assay (DISKA)

1

Introduction Posttranslational modification (PTM) proteomics is the study of both chemical and enzymatic modification and processing of cellular proteins in a proteome-wide scale post synthesis. The posttranslationally modified proteins are well known to play key roles in nearly every major cellular event in plant cells, including DNA replication, RNA and protein synthesis and degradation, organelle biogenesis, as well as protein transport and protein–protein interactions [1–7]. As one of the powerful multi-Omics

Xu Na Wu (ed.), Plant Phosphoproteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2358, https://doi.org/10.1007/978-1-0716-1625-3_8, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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biotechnologies, PTM proteomics is able to integrate the advantages and technological advancements of chromatography, mass spectrometry, bioinformatics, transcriptomics, and genomics to profile and quantitate a large number of different types of protein modifications important to various biological responses [8–12]. To our knowledge, more than a thousand of different types of PTMs have been documented in PTM databases: RESID (https://pro teininformationresource.org/resid/publications.shtml) [13]; UNIMOD (http://www.unimod.org/unimod_help.html); and dbPTM [14], among which the phosphoproteome is one of the most extensively studied PTM, largely because of its ubiquitous and essential regulatory roles in cellular processes and its cost-effective enrichment method. Thus, targeting the cellular protein phosphorylation dynamics, numerous label-free and stable isotope chemical labeling-based quantitative PTM proteomics biotechnologies have been developed to identify the phosphor-relay-mediated events important for biological activity or signal transduction [15– 18]. These practical and successful proteomic methods and approaches provided exemplary workflows for identification and quantification of various PTM peptides. The new biotechnological advancements in quantitation of phosphopeptides can be modified to profile and quantitate other types of PTM peptides and proteins. The 15N-stable isotope labeling in Arabidopsis (SILIA) method has emerged as one of the more useful chemical labeling methods being incorporated into quantitative PTM proteomics, and it has been successfully applied in several functional and quantitative phosphoproteomics studies [10, 11, 19–21]. The nature of SILIA is the metabolic labeling of the entire society of nitrogen-containing molecules, including the whole proteome of an Arabidopsis plant with abundant and inexpensive heavy nitrogen isotope-coded salts (ammonium-15N Cl, ammonium-15N sulfate, and/or potassium nitrate-15N) given that the Arabidopsis autotrophs can grow on a solid agar medium under an axenic condition [19]. The stable isotope labeling by amino acids in cell culture approach (SILAC) [22], which is suitable for the labeling of heterotrophs of Animalia, was not considered as a choice of metabolic labeling for Arabidopsis simply because it is cost-prohibitive [23]. As well, it has a relatively lower labeling efficiency in Arabidopsis [24]. The global proteome labeling feature of SILIA is in fact suitable for labeling of both the vegetative and the reproductive organs of plants. This labeling method actually evolved from the previously well-established stable isotope metabolic labeling (SIML) methods, mostly performed either in axenic liquid medium [25–29] or in open hydroponic solutions (HILEP, the hydroponic isotope labeling of entire plants [30, 31]) or in soil (SILIP, the stable isotope labeling in planta [32]). Although SILIA is one type of SIML, the key advantage of SILIA, as compared to all other liquid solution and solid support-labeling methods [33], is its

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coherency and compatibility with the commonly performed Arabidopsis experiments for the phenotypic characterization and genetic study of mutants, plant–microbe interactions, tissue culture engineering, and physiology studies [34]. These Arabidopsis experiments are often performed on an axenic and solid agar medium in petri dish plates and jars. It is therefore anticipated that this in vivo labeling method-based PTM proteomics will be easily accepted and user-friendly to many plant biologists, who may have an intention to investigate a specific plant biological problem from a different approach which is complementary to the traditionally used molecular genetics, cell biology, biochemistry, and molecular biology approaches or even to genomics and transcriptomics. The workflow (Fig. 1) of PTM proteomics is unique to and different from proteomics in many ways. In the case of quantitative proteomics, the total cellular peptides can be subjected to LC-MS/ MS analysis directly without further enrichment. In contrast, the PTM peptides have to go through additional steps of affinity enrichment and chromatographic separation before being analyzed by LC-MS/MS (Fig. 1). The additional manipulation procedure frequently introduces multiple and huge variances into the results of MS measurement of PTM peptides from experiment to experiment and from sample to sample, and it is especially so if the labelfree approach would be adopted [35, 36]. By this approach, only a proper integration of the automation of the entire procedure of tissue-to-peptide-to-mass spectrometry with the randomized complete block designing of experiments [37, 38] are able to minimize the variation and to satisfy the requirement of statistics. More importantly, a higher number of biological replicates would need to be performed in this label-free approach and would lead to more costly proteomic experiments. Thus, to minimize the variances generated from protein isolation, peptide preparation, the affinity enrichment, and chromatography-MS/MS processes and at the same time to perform quantitative PTM proteomics economically, the mixing of the isotopic nitrogen-coded protein samples or plant tissues is adopted and used as the starting materials, which allows any pair of light and heavy isotope-coded PTM peptides to go through every step of manipulation in parallel. As a result, the parallel manipulation of the light and heavy nitrogen-coded PTM peptides conceivably eliminate those huge variations introduced from those multiple step processes [39]. Obviously, it also means that a lesser number of biological replicates need to be performed to achieve a similar acceptable significance in determining the significant regulation of PTM proteins as compared to the label-free quantification approach [40]. Lastly, the SILIA-based PTM quantitative proteomics approach is relatively economical because 15 N-coded salts are inexpensive [10, 19]. As a versatile and emerging powerful biotechnology, PTM proteomics has been applied to solve many plant biological

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SILIA-based 4C quantitative PTM proteomics 1st C 15N/Treat/Mut

2nd C Protein Peptides

Forward

PVPP, Urea

Affinity

3rd C

4st C

Fractionation

(mg)

(µg)

A

(µg)

B

A E

(mg)

14N/15N

(mg)

(µg)

B

(mL) 14N/control

(gram)

C

(gram) (mL) 14N/15N

Reciprocal

[Ca2+]

D

14N/Treat/Mut

(mg)

(mg)

(mg)

Urea, PVPP

(µg) Protein Peptides

Chemical labelling, Treatment/mutant

Affinity

G

(µg)

E

15N/control

F

C

LC-MS

SILIA

3x

D

(µg) Fractionation

Chromatography, Affinity and LC-MS

Computational analysis, Bioinformatics

Confirmation and validation, Structure and systems biology

Fig. 1 The workflow of SILIA-based 4C quantitative PTM proteomics (S4Quap). The chemical labeling (first C) means the 15N-stable isotope labeling in Arabidopsis (SILIA, or the stable isotope metabolic labeling of Arabidopsis [19]). The SILIA-labeled plants’ fresh weight often reaches dozens of grams, in which 14N and 15N stands for the light and the heavy nitrogen isotope, respectively. Forward mixing consists of the light nitrogencoded control or the wild-type plants (14N/control) and the treated or mutant plants labeled with the heavy nitrogen (15N/Treat/Mut). Reciprocal mixing consists of the heavy nitrogen-labeled control or the wild-type plants (15N/control) and the light nitrogen-labeled and -treated plants or mutants (14N/Treat/Mut). The pair of forward and reciprocal labeling and mixing experiments are usually performed three times (3
) to meet the minimum biostatistical requirement. The total cellular proteins of plant tissues are isolated from the mixtures of tissues using more than a hundred mL of the urea-based extraction buffer (UEB) [19]. The denatured total cellular protein is digested using trypsin and prepared for MS-based proteomic analysis. The second C means the utilization of chromatographic fractionation (or separation) and affinity enrichment methods to prepare hundreds of micrograms (μg) of PTM peptides, starting from hundreds of milligrams (mg) of the total cellular protein to hundreds of milligrams (mg) of trypsin-digested total cellular peptides before the highly enriched PTM peptides in micrograms (μg) being subjected to the mass spectrometry analysis. The third C stands for computational analysis of mass spectrograms (3C-A), phosphosite (PTM site) motif (3C-B), the ratio of the extracted ion chromatogram (XIC) of 14N/15N isotope-coded peptide ions (3C-C), statistics of quantitation results (3C-D), and bioinformatics of PTM proteins (3C-E). The outcome of the third C also suggests some candidate PTM proteins for performing subsequent functional analysis. The fourth C represents the confirmation PTM proteomics results and the functional validation of PTM proteins. The confirmation of PTM site can be done (4C-A) using the in vivo extracted plant kinase and the in vivo phosphoproteome-derived synthetic peptide substrates (or called double in vivo substrate-kinase assay, DISKA [47] as well as in planta transgenics coupled with the absolute quantitation (AQUA) proteomics approach, AQUIP (4C-B [20]). Validation of the selected PTM protein candidates is performed via a number of common life science research approaches, such as patch clump used to study the calcium ion flux activities of phosphorylated ion channels (4C-C [54]), transgenic plants, and loss-of-function mutants which are used to study the biological function of the genes

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problems [3, 41]. Especially, if one’s intention is to identify a few signal-regulated PTM sites from the large number of the total cellular PTM proteins for functional studies, the simple pairwise stable isotope labeling, such as SILIA, would become the ideal choice for the deep profiling and accurate quantitation of signalresponsive PTM peptides [41]. Furthermore, the application of the batch effect adjustment into the final quantification step and increasing the number of biological replicates can both provide some degrees of correction to the PTM peptide quantification result for the SILIA approach [10]. Nevertheless, the SILIAbased quantitative PTM proteomics may eliminate manipulation variations resulting from protein isolation, protease digestion, separation, and affinity enrichment (Fig. 1), and it is cost-effective. The overall workflow of the functional and quantitative PTM proteomics has been named as SILIA-based 4C quantitative PTM proteomics (S4Quap). The first C stands for the in vivo chemical labeling of entire plant PTM proteome or the in vitro chemical labeling of total cellular peptides digested from the total cellular protein or organellar protein (Fig. 1). The second C stands for the chromatographic separation, affinity enrichment, and mass spectrometry analysis of PTM peptides (Fig. 1). The third C stands for computational identification, quantitation, statistical evaluation, and bioinformatic analysis of PTM peptides and PTM proteins (Fig. 1). The fourth C represents the confirmation and validation experiments (Fig. 1), which include a wide spectrum of experiments such as immunoblot analysis [10, 42], paralleled reaction monitoring (PRM) [43], enzyme assays [42], or AQUIP [20], the use of molecular genetics, transgenic experiments [10, 20], or cell biology and physiology [44]. Eventually, one may perform the structural biology experiments on a few selected PTM protein isoforms and may build molecular system biology models for these PTM proteins [21, 44, 45]. In short, combination of SILIA with the quantitative and functional PTM proteomics produces the S4Quap approach [10, 11, 19, 20], one important feature of which is the integration of 15N-SILIA labeling with Siliamass, and Silique-N (or SQUA-N, SILIAMASS 2.0) quantitation software, and finally with the biochemical confirmation and functional validation of the selected candidate PTM proteins [10, 11, 20]. Another conspicuous feature of this protocol is the introduction of antioxidant ascorbic acid, antipolyphenol compound polyvinylpolypyrrolidone (PVPP), and the protein-denaturing compound urea into the plant total cellular ä Fig. 1 (continued) (4C-D [10]); the PTM protein-transformed plant cells are used to study the biological role of PTM protein regulating cellular activities (4C-E [44]), establishment of the mathematical models for the roles of PTM proteins in live cells (4C-F [44]), and the structural biology of the candidate PTM proteins (4C-G [45, 55])

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urea-based protein extraction buffer (UEB) [19]. These compounds in the UEB are able to fully denature all proteins immediately once the plant proteins are dissolved into UEB in a mortar. It prevents oxidation and other modification of PTM proteins during manipulation, consequently inhibiting the in vitro proteasemediated protein degradation, and especially preventing the nonspecific phosphorylation/dephosphorylation events or any other PTM events to occur during the manipulation of proteins. This urea-based protein solution presumably preserves the PTM moiety onto a PTM site throughout the protein purification [19]. In combination with a short period (less than 1 min) of signal induction, this S4Quap allows us to identify a number of key PTM protein components mediating early cell signaling in Arabidopsis, leading to gene expression and nuclear events [10]. It is envisaged that this S4Quap approach would become useful for the study of PTM networks, the roles of PTM in biological responses and molecular systems biology in plants. Hence, in the following protocol, we aim to elaborate the actual manipulation methods, reagents, chemicals, software, analytical tools, and technological know-how as meticulously as we can for readers.

2

Materials

2.1 SILIA Growth Medium

0.40 g/L NH4NO3, 1.90 g/L KNO3, 0.4 mM Ca5OH(PO4)3, 2 mM MgSO4, 1.3 mM H3PO4, 50 μM Fe-EDTA, 70 μM H3BO3, 14 μM MnCl2, 0.5 μM CuSO4, 1 μM ZnSO4, 0.2 μM Na2MoO4, 10 μM NaCl, 0.01 μM CoCl2, 10 g/L sucrose, 1 mg/L thiamine HCl, 0.1 mg/L pyridoxine, 0.1 mg/L nicotinic acid, 100 mg/L myo-inositol, and 0.8% bacteriological agar, pH 5.7.

2.2

Jars used for plant growth are 7.7-cm in diameter and 12.7-cm high with transparent white glass.

Growth Container

2.3 Urea-Based Protein Extraction Buffer (UEB)

8 M urea, 1.2% Triton-X100, 2% SDS, 20 mM EDTA, 20 mM EGTA, 50 mM NaF, 1% glycerol-2-phosphate, 5 mM DTT, 1 mM phenylmethanesulfonyl fluoride (PMSF), 0.5% phosphatase inhibitor cocktail 2, Complete EDTA-free protease inhibitors cocktail, 5 mM ascorbic acid, 2% polyvinylpolypyrrolidone (PVPP), and 150 mM Tris, pH 7.6 [19].

2.4 Precipitation Solution

Acetone: methanol ¼ 12:1.

2.5 Protein Resuspension Buffer (PRB)

6 M urea, 0.3% sodium dodecanoate, 5 mM DTT, 50 mM Tris– HCl, pH 8.0.

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2.6 Trypsin Digestion Solution (TDS)

25 mM ammonium bicarbonate with trypsin.

2.7 TiO2-Binding Solution

80% acetonitrile (ACN), 0.5% trifluoroacetic acid (TFA), and 2.8 M lactic acid.

2.8 TiO2-Washing Solution

80% ACN and 0.5% TFA.

2.9 IMAC-Binding Buffer

30% ACN and 250 mM acetic acid.

2.10 Kinase Extraction Buffer (KEB)

150 mM NaCl, 1% Triton X-100, 2.5 mM sodium pyrophosphate, 1 mM sodium fluoride, 1 mM sodium orthovanadate, 1 mM sodium molybdate, 1 mM glycerol-2-phosphate, and 1 mM phenylmethanesulfonyl fluoride (PMSF) protease inhibitors mix (Roche), and 20 mM HEPES, pH 7.5.

2.11 Kinase Assay Buffer (KAB)

45% glycerol, 2.5 mM ATP, 50 mM MgCl2, and 125 μg m/L bovine serum albumin.

2.12 SCX Column Loading Buffer (SLB)

10 mM ammonium formate, 20% ACN, and 0.1% formic acid (FA).

2.13 SCX Column Elution Buffer (SEB)

500 mM ammonium formate, 20% ACN, and 3% FA.

2.14 Affinity Beads for Phosphopeptide Enrichment

10 μm TiO2 Beads

3

Methods S4Quap (Fig. 1) is in principle a multidisciplinary research. Performing a large-scale PTM proteomics experiment ultimately aims to elucidate the molecular and cellular functions of PTM proteins regulating a specific biological response rather than simply describing changes of the PTM level associated with this biological response. Thus, the S4Quap starts from the biological treatment and isotope-labeling of plants, to performing PTM proteomics, and eventually to the functional analysis of the significantly regulated PTM proteins and determination of the roles of these proteins in a specific biological problem. The quantitative PTM proteomics approach in search for the key components of a biological response is equivalent to the genetic screen or map-based cloning of the key

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components in the same biological response. Both the proteomics and the genetics approach require molecular and cellular studies of the candidate PTM proteins in the end to complete the functional stories of these PTM proteins. However, quantitative proteomics is believed to be able to identify more candidates for functional studies, and especially in a case where two or more signaling components function in parallel and transduce signals to nuclear events, leading to a conspicuous phenotype. Quantitative proteomics can identify more related PTM proteins, but the phenotype-based genetic screen may not be as effective in this regard. That is why at the fourth C stage, it is highly recommended to perform the lossof-function gene analysis or the epistatic analysis of these mutants of PTM proteins as well as the site-directed mutagenesis of PTM sites to investigate the roles of the significantly regulated PTM proteins identified from quantitative PTM proteomics. Of course, to accomplish the complete set of experiments described in the S4Quap workflow (Fig. 1), one need to have some basic trainings in areas of phytochemistry, protein biochemistry, molecular biology, cell biology, genetics, transgenic, physiology, and bioinformatics. According to our experiences, a single graduate student, with a biology, physics, chemistry, or bioengineering undergraduate training background, can accomplish these experiments described in the 4C workflow within the 4–5 years’ timeframe of a Ph.D training program. 3.1 Labeling Plants with Heavy Nitrogen

1. Into each jar, pour 45–50 mL of growth medium (see Note 1). 2. Seeds of the wild-type Arabidopsis plant and mutant (or control and treated plants) are sown on solid agar medium. In each jar, there are 15–20 seeds sowed (see Note 2). 3. Plant containing jars are placed in a plant growth room at a temperature of 23.5  1.5  C, with a humidity of 35–65% and a light intensity of 140–200 μE/m2 s1. The stable isotopelabeled plants are grown to 16–21 days old. 4. Treat one of the two groups of plants with an experimental variable (see Note 3).

3.2 Tissue Harvesting

1. All frozen tissues are first ground into fine powder with a pre-chilled mortar/pestle. 2. The tissue powders prepared from both the control (14N-labeled) and the treated plants (15N-labeled) are mixed together to produce the forward (F) replicate sample whereas those of the control (15N-labeled) and the treated (14N-labeled) plant tissue powders are mixed to produce the reciprocal (R) replicate sample. 3. The mixing ratio of both tissues is 1: 1 in most experiments (see Note 4).

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1. The total cellular proteins are extracted from the frozen plant tissues using four volumes of urea-based protein extraction buffer (UEB). The frozen tissue powders are poured into the UEB (at room temperature) inside a mortar on the bench. 2. Grind the frozen tissue powders with a pestle gently in UEB buffer at room temperature (see Note 5). 3. The protein extract is centrifuged in centrifuge consequently at 1450
g for 10 min at room temperature, the supernatant of which is centrifuged again at 103,560
g for 1.5 h at 12  C to remove cell debris. 4. The supernatant is transferred to a fresh tube, and the proteins in the supernatant solution are precipitated with three volumes of precipitation solution. 5. Incubate the precipitates at 20  C overnight. 6. Bring down protein pellets by centrifuging the solutions at 12,298
g at 15  C for 20 min. Discard the supernatant gently and rinse the protein pellet with 50 mL of rinse solution (acetone: methanol: H20 ¼ 12:1:1.4) and discard the rinse solution. 7. Dry the protein pellet for 20–30 min in a fume hood, and resuspend the protein in 50 mL of PRB. 8. Repeat above protein precipitation and resuspension steps for two more rounds. 9. Measure protein concentration using the DC assay. Proteins can be stored in a 80  C freezer.

3.4 Peptide Preparation

1. All protein samples are incubated with 5 mM DTT for 1 h at room temperature to reduce disulfide bonds. 2. In addition, add iodoacetamide to a final concentration of 50 mM and incubate the solution in darkness at room temperature for 1 h to alkylate the cysteine of the proteins. 3. Prepare nine volumes of the pre-warmed (37  C) trypsin digestion solution (TDS) as the protein resuspension solution. 4. In the first tryptic digestion reaction, the protein solution is dissolved in TDS using a quick stirrer bar. The ratio of trypsin to protein is 1:20 (w/w). 5. Digest proteins for 12–16 h at 37  C. 6. In the second digestion reaction, add half of the amount (1:40 w/w) of the fresh trypsin protease and incubate the solution for additional 6 h. 7. Add formic acid to the digested peptides to a final concentration of 0.2% and acidify the solution to pH 3 in order to remove sodium dodecanoate.

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8. Centrifuge the solution at 103,560
g for 20 min at 14  C to spin down the precipitates. 9. Filtrate the solution using a 0.22 μM cellulose acetate membrane filter. 10. Desalt the digested peptides using a Sep-Pak C18 column. The peptides are eluted off the column using 40 mL of 80% acetonitrile (ACN) and 0.1% formic acid (FA). 11. Air-dry the peptides from 40 to 5 mL in a fume hood with an air blower. 3.5 TiO2 Bead Affinity Enrichment

1. Resuspend the desalted peptides in a mixture of 35 mL of TiO2-binding solution and 9.3 mL of lactic acid. 2. Mix one volume (1.25 mg of beads per mL, and particle size is 10 μm) of TiO2 beads with five bead volumes of TiO2-washing buffer and rotate it for 3 min, then centrifuge it at 344
g for 1 min to wash the beads. 3. Equilibrate one volume of TiO2 beads with five bead volumes of TiO2-binding buffer, and rotate it for 3 min and centrifuge at 344
g for 1 min. Remove the supernatant as waste. 4. Incubate the peptide samples with the pretreated TiO2 beads at a ratio of 1:4 for an hour at room temperature to allow the phosphopeptides to bind to the beads. 5. After adsorption, centrifuge beads at 1450
g for 1 min and store the flow-through solution at room temperature, which will be used for Fe+3IMAC enrichment. 6. Rinse the beads with five bead volumes of TiO2-binding buffer, rotate it for 3 min, and centrifuge at 344
g for 1 min. 7. Mix the beads again with five bead volumes of TiO2-washing buffer and centrifuge it at 344
g for 3 min to rinse the beads. 8. Elute phosphopeptides with 1.5 bead volumes of 5% ammonium hydroxide. 9. Acidify the eluent with the same volume of 20% TFA. 10. Incubate 1.5 bead volumes of 5% pyrrolidine solution with the TiO2 beads for another 5 min to elute phosphopeptides. 11. Acidify the eluent with the same volume of 20% TFA. Repeat TiO2 beads-based enrichment again on the flow-through solution (from step 5). 12. Combine the first and the second TiO2-enriched phosphopeptides into a single peptide solution and pass it through an HLB cartridge to desalt peptides. To further enrich phosphopeptides from the previous step, repeat one more TiO2 enrichment on the combined TiO2-enriched phosphopeptides.

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13. Resuspend the desalted peptide samples in TiO2-binding buffer. Mix the phosphopeptides with TiO2 beads at a ratio of 2:1 (w/w). 14. Repeat the TiO2 affinity enrichment procedure again. 15. Desalt the third round TiO2-enriched phosphopeptides in an HLB cartridge and reduce the phosphopeptide solution volume from 400 to 20 μL in a SpeedVac (see Note 6). 3.6 Fe+3IMAC Bead Affinity Enrichment

1. Combine all the flow-through solutions (20–40 mL) from the TiO2 bead enrichment (Subheading 3.5) and vaporize ACN under compressed air in a fume hood at room temperature. 2. Pass the peptide samples through a Sep-Pak C18 column and elute the peptide sample off the column using 40 mL solution of 80% ACN and 0.1% FA. 3. Vaporize the ACN and reduce the eluate solution from 40 to 5 mL under a constant compressed air blow. 4. Dissolve the peptide sample solution into the IMAC (immobilized metal affinity chromatography)-binding buffer. 5. Prepare the Fe3+-NTA IMAC resins by incubating 100 mM iron (III) chloride with Ni-NTA agarose (peptide to agarose ¼ 1 mg:10 μL) at room temperature for 2 h. 6. Wash the incubated Fe3+-NTA IMAC resins with 6% acetic acid for 4–5 times to remove excess Fe3+. 7. Equilibrate the resin with IMAC-binding buffer for two times. Incubate 10–20 mL of the phosphopeptides solution with Fe3 + -NTA IMAC resin at room temperature for 45 min to allow binding of phosphopeptides. 8. Spin the solution at 2576
g for 1 min using Sorvall™ ST 8 Small Benchtop centrifuge at room temperature, and collect the supernatant (flow-through). 9. Wash the Fe3+-NTA resin with the IMAC-binding buffer. 10. Spin beads at 2576
g for 1 min at room temperature in a centrifuge to remove the supernatant. 11. Repeat the beads washing using the IMAC-binding buffer for three times. 12. Repeat the last wash with water. 13. Elute the phosphopeptides using 0.5 mL of 5% ammonium hydroxide. 14. Acidify the eluent with 100 μL of formic acid. 15. Repeat the elution two more times and combine the eluents, store the flow-through solution in 80  C for the other PTM peptide enrichment.

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16. Desalt the affinity-enriched phosphopeptides using an HLB column as described above. 17. Elute the peptides using 150 μL of a solution containing 80% ACN and 0.1% FA for four consecutive times. 18. Concentrate the phosphopeptides from 600 to 20 μL in a SpeedVac. 3.7 SCX Chromatographic Separation

1. Equilibrate the column with 200 μL of washing solution (100% methanol or 100% ACN), and centrifuge the column at 55
g for 1 min at room temperature to remove the equilibrating solution. 2. Flush the column using 200–400 μL of pure double-distilled water and repeat once more. 3. Condition the Strong Cation Exchange (SCX) column (see Note 7). 4. Centrifuge the column for 1 min at 55
g to remove the conditioning solution. 5. Flush the column with 200–400 μL of pure double-distilled water and repeat it two more times. 6. Equilibrate the column with 200 μL of SLB for three times. 7. Resuspend either the forward or reciprocal PTM peptide sample in 50–200 μL of SLB. 8. Pass 50–200 μL of the PTM peptides solution through the column back and forth five times to enhance the binding, and wash the column with 200 μL of the SLB for two more times. 9. Elute PTM peptides into 15 fractions, 200 μL per fraction, with a step gradient of 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 60%, 70%, 80%, 90%, 100% of SEB. 10. Dry the PTM peptides fractions to 20–50 μL using a SpeedVac.

3.8 LC-MS/MS Analysis

1. The ZipTip® pipette tip-purified phosphopeptides are resuspended in Solvent A (0.1% formic acid) and analyzed on an ACQUITY nano-LC system coupled to an Orbitrap Fusion™ Lumos™ Tribrid™ Mass Spectrometer, run with a flow rate of 400 μL per min on an analytical C18 column with either a 95 or a 120 min gradient (see Note 8). 2. The UPLC gradient is configured as: 0–3 min 3–8% solvent B, 3–81 min 8–20% solvent B, 81–105 min 20–32% solvent B, 105–106 min 32–90% solvent B, 106–111 min 90–90% solvent B, 111–111.1 min 90–3% solvent B, and 111.1–120 min 3–3% solvent B, where solvent B is acetonitrile mixed with 0.1% (v/v) formic acid.

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3. The mass spectrometer runs under data-dependent acquisition (DDA) mode and is automatically switched under MS and MS/MS mode. 4. The mass spectrometer is set as: mass resolution 120 K with a m/z range of 375–1500, a filtered charge state of 2–7 for a maximum injection time of 50 ms, an AGC target value of 4e5 charges, MS/MS resolution set as 30 K with an AGC target value of 5e4 charges, and a maximum injection time of 100 ms under HCD collision mode (collision energy as 30). An isolation window of 1.6 m/z is employed (see Note 9). 3.9 Identification of PTM Peptides

1. The raw data files from LC-MS/MS machines are converted into Mascot generic format (xxx.mgf) and xxx.mzXML format files using MSconvert (ProteoWizard version: 3.0.18353 64-bit). 2. The xxx.Mgf files are searched against both target and decoy database using Mascot (version 2.6.0, 64-bit, Matrix Science). The target database for peptide identification is the Arabidopsis TAIR10 database (35,387 proteins, https://www.arabidopsis. org/download_files/Sequences/TAIR10_blastsets/TAIR10_ pep_20101214_updated) with two contaminant databases cRAP and protein contaminants. The decoy database, a reversed polypeptide sequence database, is built by random shuffling of the target peptide sequences using a PERL script decoy.pl.gz (Mascot). 3. The search parameters are set as the following: 10 ppm for MS precursor ions and 0.02–0.05 Da for MS/MS fragment ions with a maximum missed cleavage allowance of 2. Carbamidomethylation (57.021464 Da) on cysteine residues is set as a fixed modification whereas serine/threonine phosphorylation (S/T), tyrosine phosphorylation (Y) (79.9663304 Da), and oxidation (M) (15.994915 Da) are set as variable modifications. 4. The quantitation method “15N metabolic” is specified. 5. Once the Mascot generated the xxx.dat files of both target and decoy, these files are converted into xxx.pepXML files using the Institute for Systems Biology Trans-Proteomic Pipeline (version 5.0.0). 6. All xxx.mzXML, xxx.PepXML, and xxx.dat are subsequently run on a software Silique-N or Siliamass [10, 19] for quantification. 7. Mascot Percolator (version 3.1) is employed together with Silique-N or Siliamass software to estimate the False Discovery Rate (FDR) from xxx.dat file. False Discovery Rate (FDR) of 1% is set as a cutoff threshold to select peptide spectrum match (PSM) of phosphopeptides.

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8. A cutoff Mascot delta score greater than or equal to 10 is applied to filter all PSMs. 3.10 Quantitation of PTM Peptides

1. Quantification of phosphopeptides is accomplished by Siliamass or Silique-N [10, 19] (see Note 10). 2. Quantifiable phosphopeptides are selected from the total PTM peptides for quantification according to the following criteria: (1) the PSM of a PTM peptide should be identified at least twice (which is processed using the software mentioned above); (2) the PTM peptide is found from at least four experimental replicates (either from forward or reciprocal mixing); (3) the PTM peptide is found in both 14N- and 15N-coded peptides; (4) the PTM peptide is found from both the forward and reciprocal replicates; and (5) the PTM peptide is found from three biological replicates. 3. To quantify those selected PTM peptides, the ion chromatogram of monoisotopic peak of MS1 of the same pair of 14Nand 15N-coded PTM peptides is extracted and converted to the ratios of the treated to control PTM peptides. All binary logarithmic (log2) ratios are adjusted to median centered. 4. The statistical significance ( p-value) of the log2 ratio of the quantified PTM peptides is determined by two-tailed Student’s t-test, the results of which is corrected using BenjaminiHochberg (BH)’s multiple hypothesis test to produce a BH-FDR for each PTM peptide group [10]. 5. A cutoff value of BH-FDR  5% is often applied. 6. A final list of the significantly regulated (both up- and downregulated) PTM peptide groups are summarized (see Note 11).

3.11

Motif Analysis

1. Phosphorylation motifs of the significantly regulated phosphopeptides are constructed by searching for PTM peptide sequences against the Arabidopsis database (http://www.ncbi. nlm.nih.gov/BLAST/) (see Note 12). 2. Phosphopeptides of a length of 13 amino acid with 6 amino acids surrounding the phosphosite are blasted against the deduced proteome sequence of a plant [21, 46, 47]. 3. Those putative and blasted phosphopeptide sequences of 55.5% or greater homology to the MS-determined phosphopeptides (considered to be authentic PTM peptides here) are aligned using ClustalW (https://www.ebi.ac.uk/Tools/msa/ clustalw2/). 4. The aligned sequences are converted to motif format (with at least a stretch of 5-amino acid sequence between the MS-determined authentic phosphopeptide and several deduced polypeptide sequences).

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5. A number of homologs (>2) among different proteins are grouped as a PTM site motif. The motif figure is built using WebLogo (http://weblogo.berkeley.edu/logo.cgi) to show the multiple sequence alignment. 3.12

String Analysis

1. Phosphoproteins of significantly (BH-FDR value  10%) regulated (both up- and downregulated PTM proteins) are chosen for the String protein interaction network prediction (see Note 13). 2. The unique PTM site pattern (UPSP) is defined to be a specific PTM site of unique amino acid sequence surrounding it (see Note 14). 3. The networks are predicted based on interaction sources from experimental data, automatic text mining, database, gene fusion, co-occurrence, co-expression, and neighborhood. 4. The cutoff threshold for a minimum required Interaction score is set as 0.4. The disconnected nodes are hidden. 5. The protein interaction network from the String is loaded to Cytoscape (version 3.8.0, https://cytoscape.org/) to display a gradient of levels of the regulated phosphorylation alteration, in which the red and blue color stands for the upregulated and the downregulated phosphorylation, respectively [10].

3.13 Gene Ontology Analysis

1. The GO analysis is performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) ver6.8 (https://david-d.ncifcrf.gov/tools.jsp). 2. Those significantly (BH-FDR  10%) regulated phosphoprotein groups are processed by DAVID for enrichment using TAIR Arabidopsis thaliana database as reference. 3. The following filter settings were applied: Gene count  5, Fisher Exact p-value  1%, and FDR  10% (see Note 15). 4. The top ten categories sorted by the fold enrichment are selected to plot the GO figures (see Note 16).

3.14 Double In Vivo Substrate and Kinase Assay (DISKA)

1. Two different types of phosphosite motifs are constructed: one type of phosphosite motif having all members coming from in vivo phosphoproteome-derived phosphosites [10, 11] and the other type of motif having most members coming from a prediction using the significantly regulated phosphopeptides to align with the deduced proteome [46, 47]. 2. With the motif analysis results, a kinase assay is designed and performed [47], in which a number of 9–21 amino acid-long oligopeptides free of PTM are synthesized and used as the substrates (see Note 17).

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3. Both the in vivo phosphoproteome-derived and the motifpredicted synthetic PTM peptides are fused to a hexa-His tag at N-terminus to facilitate oligopeptide substrate purification after the kinase assay (see Note 18). 4. The kinase assay is performed in a kinase assay buffer (KAB) containing the plant kinase extracts and a single type or a mixture of synthetic oligopeptide substrates. 5. When the kinase activities of both the wild-type plant and a kinase-deficient mutant, or both control and treated plants, are compared, the kinase extracts from both genotypes (or treatments) of plants are extracted separately from the frozen powders of two plant tissues using a kinase extraction buffer (KEB). 6. The ratio of plant tissue powders to KEB is 1:3 (w/v). 7. The kinase and PTM peptide mixture are incubated on ice for an additional 10 min. 8. The plant kinase extracts from both genotypes of plants are consequently centrifuged at 12,740
g and 4  C for 10 min to remove cell debris. 9. Plant kinase extracts are activated by adding one-fourth volume of KAB. Each synthetic oligopeptide substrate is adjusted to a final concentration of 10 μM. 10. The substrate–kinase mixture is incubated at 30  C for 1 h. 11. His-tagged synthetic substrate peptides are purified using Ni2 + -NTA beads according to the manufacturer’s instruction. 12. Following an overnight trypsin digestion at 37  C to remove the HisTag, the purified peptides are desalted using the C18 Zip-tip columns and resuspended in 0.1% TFA. 13. The synthetic oligopeptides are further enriched using the TiO2 and Fe3+ IMAC beads to harvest the phosphorylated peptide substrates (see Note 19). 14. A chemical tag (such as iTRAQ or TMT) labeling-based quantitative PTM proteomics protocol is further applied to investigate the differentially regulated phosphorylation occurred in the DISKA [47] (see Note 20). 15. The isotope-labeled phosphopeptides are enriched again with TiO2 beads according to the manufacturer’s instructions, and after being desalted using C18 Zip-tip columns, they are resuspended in 0.1% TFA. 16. These iTRAQ-labeled PTM peptides are analyzed by mass spectrometry. 17. This type of chemical labeling experiment is repeated on the same pair of plant tissues three times using the reciprocal

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labeling to exclude the difference brought about by the differential labeling processes (see Note 21). 3.15 AQUIP Analysis of PTM Occupancy

1. A recombinant HisTag fusion protein (HBH tag, His8-BCCPHis8 [20]) is fused with a PTM protein of interest at either Nor C-terminus and expressed in a transgenic plant. 2. The entire proteome of this transgenic plant is labeled with 15N via the SILIA protocol [19]. 3. The 15N-coded total cellular proteins are isolated. Half of the 15 N-proteins are affinity enriched for the targeted recombinant protein using the HisTag and Biotin-Tag-based tandem affinity purification (TAP) approach. 4. Both the TAP-enriched recombinant 15N-protein and the other half of the 15N-total cellular proteins are resolved on SDS-PAGE gel simultaneously (see Note 22). 5. Consequently, both recombinant phosphoprotein bands, one coming from the total cellular protein separation and the other from TAP-enriched recombinant PTM proteins, are digested to produce two groups of 15N-coded tryptic peptides [20]. 6. To absolute quantitate (named as AQUA [48]), the 15N-coded tryptic peptides that contain both phosphosite peptides (15N-P) and non-phosphopeptides (15N-NP), four different 14 N-coded synthetic peptide standards, NP1, NP2, NP3, and P3, are synthesized according to three different aa positions of the deduced full-length sequence from the PTM protein, in which P3 is the phosphorylated isoform peptide (or PTM peptide) of NP3 peptide free of modification. 7. A series of synthetic peptide standards (100, 200, 400, 500, 600, 800, and 1000 fmol) are produced for each one of the four synthetic peptides. 8. These four sets of 14N-coded peptide standards (NP1, NP2, NP3, and P3) are separately spiked into two 15N-coded tryptic peptide samples digested from SDS-PAGE, followed by LC-MS/MS analysis. 9. According to both AQUA peptide and PTM peptide quantitation methods [48], both the molar amounts of 15N-coded non-phosphorylated peptides (NP1, NP2, and NP3) and the 15 N-coded phosphopeptide (P3) molar amount are quantified [20]. 10. The peptide molar amount-based occupancy Raqu (¼P3/ (NP3 + P3)) is consequently determined, which can be converted into the PTM protein’s occupancy: Risf (¼phosphoprotein molar amount/(non-phosphoprotein isoform molar amount + phosphoprotein molar amount)), and Risf ¼ Raqu.

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11. At the same time, the total molar amount of the phosphoprotein and its corresponding non-phosphorylated isoform is calculated according to the formula: Tisf ¼ Taqu ¼ (T1/k1 + T2/ k2)/2 (see Note 23). 12. Thus, the molar amount of the native phosphorylated protein of our interest, Pisf, is equal to Tisf
Risf (see Note 24). 3.16 Molecular and Cellular Biological Validation

1. The loss-of-function lines of some phosphoproteins should be obtained, including those T-DNA insertion [49] and RNA-interfering [50] mutants (see Note 25). 2. The candidate phosphoprotein genes can also be expressed ectopically to monitor the gain-of-function effects of these genes. 3. Substitution of a phosphosite Ser (S) with Asp (D) on a phosphoprotein is mimic of a phosphorylated isoform, whereas the substitution of Ser (S) with Ala (A) on a phosphoprotein may lead to a molecular function similar to that of the dephosphorylated isoform [10, 11, 20, 44] (see Note 26).

4

Notes 1. In the heavy nitrogen labeling agar media, the heavy nitrogencoded salts, 15NH4NO3 and K15NO3 of 15N-enriched isotope (99%), are often used to replace the light nitrogen (14N)-coded salts. 2. There are about 50 jars of Arabidopsis plants prepared for each plant group (either the control or the treated or a particular genotype) in each biological replicate. 3. A pair of wild type can be labeled simultaneously with light and heavy nitrogen-coded medium, and both groups of plants are separately harvested within 2 s either by pouring plants or soaking the entire aerial plant tissues with liquid nitrogen or both. 4. Sometimes, the ratio can be adjusted to 1 (14N-coded plants):1.2 (15N-coded plants) depending on the heavy nitrogen labeling efficiency. This mixing ratio is incorporated into the “dry-lab” quantitation process by computer programs [21] to calculate the significantly regulated PTM peptides. 5. The fine powder frozen tissue will bring down the temperature of buffer and mortar/pestle temporarily. Grinding friction increases the extraction solution’s temperature again so that the urea fully dissolves into the extraction solution. 6. The multiple rounds of enrichment is to increase the phosphopeptide yield in the peptide sample.

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7. The strong cation exchange (SCX) columns are often used to fractionate the PTM peptides, which include BioPureSPN MIDI HEM HIL-SCX Column with a binding capacity of 100–500 μg. Condition the column using 200 μL of the conditioning solution (0.2 M monosodium phosphate, 0.3 M sodium acetate) for at least 1 h prior to its initial use. 8. The ideal program of a gradient should be determined empirically according to the number and the nature of peptide samples and the running time constraint. 9. Again, the settings for MS and MS/MS are usually empirically determined according to the testing experiments. 10. The quantifiable PTM peptides are selected arbitrarily according to the following criteria: (1) the PTM peptide group should have a pair of high quality of XIC from both forward and reciprocal mixing replicates; (2) the number of ratios of log2 of the light and heavy stable isotope-labeled PTM peptides (either the treated/control or WT/loss-of-function mutant) should be larger or equal to 5. The PTM peptides are first grouped into a unique PTM peptide array (UPA) [10, 11]. 11. A final list of the significantly regulated (both up- and downregulated) PTM peptides are selected again according to 5% Benjamini-Hochberg (BH)-FDR and manual analysis of XIC [10, 11, 41]. 12. The significantly regulated phosphopeptides (BH-FDR < 5%, including both up- and downregulated PTM proteins) are often subjected to the motif prediction [10, 11, 21]. Since the number of MS-identified phosphopeptides is limited as compared to the theoretically predicted number [21, 51, 52], the prediction of putative phosphosites in plant proteomes using the inducible phosphorylation sites can be achieved using bioinformatics approach, which is expected to increase the probability of identifying additional phosphosites. 13. The predicted protein–protein interaction among the significantly (BH-FDR < 10%) regulated phosphoproteins [10, 41] is performed using the STRING software (http://stringdb. org/). 14. The unique PTM site pattern (UPSP) may be shared by several PTM peptide members of a UPA resulted from incomplete digestion or proteome sequence complexity or isoforms of gene family. The UPSP may also be shared by several PTM proteins, the group of which is called protein group. All members of the PTM protein group are usually chosen [10, 11, 41] to perform String analysis.

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15. The functional enrichment fold is calculated by comparing GO analysis results between the identified phosphoproteins and the proteome of Arabidopsis using the following equation: 0 Ri ¼ Log2((Ni/N)/(Ni0 /N0 )) [21], where N and N represents the total number of identified phosphoproteins in the experimental dataset and the total number of proteins from the total proteome of Arabidopsis, respectively. The Ni represents the number of the proteins from experimental dataset that can 0 be classified into a GO term category. The Ni represents the number of the proteins from the total proteome of Arabidopsis that can be classified into ith category of GO terms. 16. The ultimate objectives of the bioinformatic analysis of the significantly regulated PTM proteins are (1) to propose a cell signaling pathways mediated by these candidate proteins; and (2) select a few candidate PTM proteins for follow-up functional analysis. These selected PTM proteins are expected to play a role in the biological process from which the quantitative PTM proteomics was designed and started. 17. The highly conserved amino acid sequence surrounding a phosphosite may serve as the kinase’s recognition and docking site [53]. 18. The plant kinase extract contains a lot of degraded proteins and polypeptides. 19. In one type of kinase assay experiment where both the in vivo phosphoproteome-derived synthetic peptides and the in vivo plant cell-expressed kinases are used and coupled together to measure the phosphorylation changes on oligopeptide substrates, which is therefore called double in vivo substrate and kinase assay (DISKA). The criteria for selection of the phosphopeptides to perform the kinase assays include (1) level of regulation, (2) String analysis results, (3) GO analysis results, and (4) literature review. The choice of candidate phosphopeptide group for the functional analysis is largely subjective at this stage. 20. In this iTRAQ-based quantification, the HisTag-enriched and trypsin digested phosphopeptides coming from each DISKA are separately labeled using iTRAQ 4-Plex labeling reagents. 21. The ion intensity of the reporter ion of the air-treated (or the wild type) plant tissue sample is set as 1, whereas the changing level of the treated PTM peptides is calculated using the intensity of its reporter ion against that of the control plant (or the wild type [42]). 22. The highly enriched and known amount of recombinant 15N proteins serve to indicate the position of the targeted phosphoprotein on SDS-PAGE gel and at the same time it serves as a control for the in-gel digestion to calculate the peptide yield

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(km, which is defined as a ratio of the molar amount of tryptic peptides to the molar amount of recombinant PTM proteins loaded onto the gel). 23. T1 and T2 are the molar amounts of the two most abundant non-phosphopeptides derived from the tryptic digestion of the targeted phosphoprotein; k1 and k2 are the peptide yields determined from the in-gel digestion of TAP-enriched recombinant protein. 24. Assume that the HBH-tagged recombinant fusion protein serves as a probe to plant cell and has the same level of modification in plant as its corresponding native phosphoprotein, and assume that the recombinant fusion protein complements its loss-of-function mutation in its transgenic plants [20]. 25. This part of work (fourth C) is to further address the biological functions of the quantitative PTM proteomics and bioinformatics-selected candidate phosphoproteins in addition to DISKA and AQUIP analysis, and more molecular and cellular biological experiments need to be designed and performed. 26. The objective of this work is to validate the biological functions of phosphosites (or PTM sites), and phosphorylation- and dephosphorylation-mimic isoforms are introduced to the loss-of-function mutant background.

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SILIA-Based 4C Quantitative PTM Proteomics 33. Arsova B, Kierszniowska S, Schulze WX (2012) The use of heavy nitrogen in quantitative proteomics experiments in plants. Trends Plant Sci 17:102–112 34. Provart NJ, Alonso J, Assmann SM et al (2016) 50 years of Arabidopsis research: highlights and future directions. New Phytol 209:921–944 35. Zhao Y, Jensen ON (2009) Modificationspecific proteomics: strategies for characterization of post-translational modifications using enrichment techniques. Proteomics 9:4632–4641 36. Matros A, Kaspar S, Witzel K, Mock HP (2011) Recent progress in liquid chromatography-based separation and labelfree quantitative plant proteomics. Phytochemistry 72:963–974 37. Chen Y, Guenther JM, Gin JW et al (2019) Automated “cells-to-peptides” sample preparation workflow for high-throughput, quantitative proteomic assays of microbes. J Proteome Res 18:3752–3761 38. Al Shweiki MHDR, Mo¨nchgesang S, Majovsky P et al (2017) Assessment of label-free quantification in discovery proteomics and impact of technological factors and natural variability of protein abundance. J Proteome Res 16:1410–1424 39. Yates JR, Ruse CI, Nakorchevsky A (2009) Proteomics by mass spectrometry: approaches, advances, and applications. Annu Rev Biomed Eng 11:49–79 40. Stepath M, Zu¨lch B, Maghnouj A et al (2020) Systematic comparison of label-free, SILAC, and TMT techniques to study early adaption toward inhibition of EGFR signaling in the colorectal cancer cell line DiFi. J Proteome Res 19:926–937 41. Liu S, Yu F, Yang Z et al (2018) Establishment of dimethyl labeling-based quantitative acetylproteomics in Arabidopsis. Mol Cell Proteomics 17:1010–1027 42. Zhu L, Li N (2013) Quantitation, networking, and function of protein phosphorylation in plant cell. Front Plant Sci 3:302 43. Chen Q, Pan XD, Huang BF, Han JL (2017) Quantification of 16 β-lactams in chicken muscle by QuEChERS extraction and UPLC-QOrbitrap-MS with parallel reaction monitoring. J Pharm Biomed Anal 145:525–530 44. Qing D, Yang Z, Li M et al (2016) Quantitative and functional phosphoproteomic analysis reveals that ethylene regulates water transport

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via the C-terminal phosphorylation of aquaporin PIP2;1 in Arabidopsis. Mol Plant 9:158–174 45. Huai Q, Xia Y, Chen Y et al (2001) Crystal structures of 1-aminocyclopropane-1-carboxylate (ACC) synthase in complex with aminoethoxyvinylglycine and pyridoxal50 -phosphate provide new insight into catalytic mechanisms. J Biol Chem 276:38210–38216 46. Li H, Wai SW, Zhu L et al (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 9:1646–1661 47. Zhu L, Liu D, Li Y, Li N (2013) Functional phosphoproteomic analysis reveals that a serine-62-phosphorylated isoform of ethylene response factor110 is involved in arabidopsis bolting. Plant Physiol 161:904–917 48. Wu R, Haas W, Dephoure N et al (2011) A large-scale method to measure absolute protein phosphorylation stoichiometries. Nat Methods 8:677–683 49. Krysan PJ, Young JC, Tax F et al (1996) Identification of transferred DNA insertions within Arabidopsis genes involved in signal transduction and ion transport. Proc Natl Acad Sci U S A 93:8145–8150 50. Klink VP, Wolniak SM (2000) The efficacy of RNAi in the study of the plant cytoskeleton. J Plant Growth Regul 19:371–384 51. Heazlewood JI, Durek P, Hummel J et al (2008) PhosPhAt: a database of phosphorylation sites in Arabidopsis thaliana and a plantspecific phosphorylation site predictor. Nucleic Acids Res 36:1015–1021 52. Durek P, Schmidt R, Heazlewood JL et al (2010) PhosPhAt: the Arabidopsis thaliana phosphorylation site database. An update. Nucleic Acids Res 38:D828–D834 53. Jørgensen C, Linding R (2008) Directional and quantitative phosphorylation networks. Brief Funct Genomic Proteomic 7:1–7 54. Tian W, Hou C, Ren Z et al (2019) A calmodulin-gated calcium channel links pathogen patterns to plant immunity. Nature 572:131–135 55. Li J-F, Qu L-H, Li N (2005) Tyr152 plays a central role in the catalysis of 1-aminocyclopropane-1-carboxylate synthase. J Exp Bot 56:2203–2210

Chapter 9 Phosphoproteomics Analysis of Plant Root Tissue Zhe Zhu, Shubo Yang, Shalan Li, Xiaolin Yang, and Leonard Krall Abstract Plants absorb water and nutrients from soil through roots and transmit these resources through the xylem to the shoot. Roots therefore participate in information and material transduction as well as signal communication with the shoot. The importance of reversible protein phosphorylation in the regulation of plant growth and development has been amply demonstrated through decades of research. Here, we present a simple mass spectrometry-based shotgun phosphoproteomics protocol for Arabidopsis root tissue. Through this method, we can profile the Arabidopsis root phosphoproteome and construct signal networks of key proteins to better understand their roles in plant growth and development. Keywords Phosphoproteomics, Mass spectrometry, Total protein, Plant root tissue

1

Introduction Phosphorylation of proteins and other molecules is catalyzed by protein kinases [1] and the phosphorylation is removed by protein phosphatases [2], this process is the most well-studied reversible protein modification, especially with regard to its functional characterization. The roots extract water and nutrient resources from soil and allocate these critical resources to the shoot tissue. Many signaling pathways that control these processes work through phosphorylation [3–8]. Protein phosphorylation can function as a molecular switch in regulation of protein activities. NRT1.1 is a dual-affinity nitrate transporter [9], and the transition between the high- and low-affinity states is regulated by phosphorylation of NRT1.1 on threonine residue 101 (T101). NRT1.1 phosphorylation is high when plants are grown under nitrogen-limited conditions, but undetectable when plants are exposed to a high concentration of nitrogen [10]. Studies on the phosphorylation proteomics of root tissue can provide a comprehensive snapshot to understand the complex molecular interactions during root development and the root’s response to the environment [11].

Xu Na Wu (ed.), Plant Phosphoproteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2358, https://doi.org/10.1007/978-1-0716-1625-3_9, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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Most systematic studies of phosphorylation modifications by mass spectrometry consider the role of single modification types and provide detailed information of the altered sites in peptide sequences. Currently, in Arabidopsis, at least 7,603 nonredundant proteins have been experimentally identified as phosphorylated at 42,649 different sites, mainly by large-scale bottom-up mass spectrometry after enrichment by various methods [12, 13]. In this chapter, we describe a mass spectrometry-based shotgun phosphoproteomics protocol for Arabidopsis thaliana root tissue.

2

Materials

2.1 Buffers and Solutions

1. Protein extraction buffer (PEB): 6 M urea, 2 M thiourea, 2% w/v insoluble PVPP (polyvinylpolypyrrolidone), and 10 mM Tris–HCl pH 8.0. Dissolve 36.04 g urea, 15.22 g thiourea, and 2 g PVPP into 100 ml 10 mM Tris–HCl solution (pH 8.0). Immediately before use, add protease inhibitor cocktail (50 μl/ 10 ml PEB), and add the following protease and phosphatase inhibitors: 5 mM DTT (dithiothreitol), 1 mM PMSF (phenylmethylsulfonyl fluoride), 3 μM leupeptin, 25 mM NaF, 1 mM Na3VO4, 1 mM benzamidine, and mix well (see Note 1). 2. DTT: 1 M, weigh out 1.5425 g of DTT, add 9 ml water to dissolve, fill with water until 10 ml, and mix well. Aliquot into 1.5 ml tube, and store at 20  C for long-time use. 3. PMSF: 200 mM, weigh out 1.7419 g of PMSF, add water until 10 ml, and mix well. Aliquot into 1.5 ml tube, and store at 20  C for long-time use. 4. Leupeptin: 2 mM, weigh out 9.5 mg of leupeptin, add ethanol until 10 ml, mix well. Aliquot into 1.5 ml tube, and store at 20  C for long-time use. 5. NaF: 1 M, dissolve 2.0995 g of NaF into 50 ml water, and aliquot into 1.5 ml tube and store at 20  C for long-time use. 6. Na3VO4: 500 mM, dissolve 1.84 g of Na3VO4 in 20 ml of 50 mM Tris–HCl (pH 10). Depending on the pH of the solution, add either 1 M NaOH or 1 M HCl with stirring to adjust pH to 10.0 (see Note 2). 7. Benzamidine: 250 mM, weigh out 1.968 g of benzamidine, add water until 50 ml, mix well, aliquot, and store at 20  C for long-time use. 8. Tris–HCl: 10 mM, pH 8.0. Weigh out 0.605 g Tris base, add 450 ml water to dissolve, and adjust pH till 8.0 with 1 N HCl. Fill with water until 500 ml, mix well, and store at 4  C. 9. UTU: 6 M urea, 2 M thiourea, and 10 mM Tris–HCl pH 8.0. Dissolve 36.04 g urea and 15.22 g thiourea into 900 ml

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10 mM Tris–HCl solution (pH 8.0), and mix well. Fill Tris– HCl solution (pH 8.0) until 100 ml, mix thoroughly, aliquot to appropriate volume, and store at 20  C for long-time use. 10. Reduction buffer: 1 μg/μl DTT in water, 6.5 mM. Dissolve 10 mg DTT into 10 ml of deionized water. 11. Alkylation buffer: 5 μg/μl iodoacetamide in water, 27 mM. Dissolve 50 mg iodoacetamide into 10 ml of deionized water. 12. Phosphopeptides enrichment buffer: 1 M glycolic acid in 80% acetonitrile (ACN) and 6% trifluoroacetic acid (TFA). Mix 8 ml ACN, 0.6 ml TFA, and 1.4 ml deionized water well, and then dissolve 0.76 g glycolic acid into this mixture (see Note 3). 13. Wash buffer for phosphopeptide enrichment: 80% ACN and 1% TFA. Mix well 8 ml ACN, 0.1 ml TFA, and add water until 10 ml. 14. Elution buffer for phosphopeptide enrichment: 5% ammonium hydroxide solution (NH4OH, stock concentration 25%) in 40% ACN. Mix well 320 μl ACN and 480 μl water. Then add 200 μl 25% ammonium solution and mix well (see Note 4). 15. Acidified buffer: 10% formic acid. Mix well 1 ml formic acid and 9 ml deionized water. 16. 10% trifluoroacetic acid (TFA): Add 10 ml TFA into 90 ml water and mix well. 17. Solution A for resuspending dry eluates: 0.5% acetic acid; mix well 500 μl acetic acid into 99.5 ml water. 18. Wash buffer 1 for SDR stage-tip: 5% TFA in 60% isopropanol; add 60 ml isopropanol and 5 ml TFA, fill with water to 100 ml. 19. Wash buffer 2 for SDR stage-tip: 0.2% TFA in 5% ACN; add 200 μl TFA and 5 ml ACN, fill with water to 100 ml. 20. Elution buffer for SDR stage-tip: 5 μl ammonium solution in 1 ml 60% ACN; mix well 600 μl ACN and 400 μl water, and add 5 μl ammonium solution, mix well (see Note 4). 21. Resuspension buffer to resuspend dried peptides: 0.3% formic acid in 2% ACN; add 400 μl ACN and 60 μl formic acid, fill with water to 20 ml. 22. Solvent A for HPLC: 0.5% acetic acid; add 500 μl TFA into 99.5 ml HPLC-grade water, mix well (see Note 5). 23. Solvent B for HPLC: 0.5% acetic acid and 80% ACN; mix well 500 μl TFA and 80 ml ACN into 19.5 ml HPLC-grade water (see Note 5). 2.2

Other Materials

1. C8 stage-tip: 200 μl tips + two disc containing C8 materials (see Note 6).

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2. SDR (Styrene Divinyl Benzene, reversed-phase sulfonate) stage-tip: 200 μl tips + two disc containing SDR materials (see Note 6). 3. Lys-C: Sequencing-grade (0.5 μg/μl). 4. Trypsin: Sequencing-grade modified (0.5 μg/μl). 5. Titanium dioxide (TiO2) beads: 5 μm. 2.3 Specific Equipment

3

1. Nanoflow Easy-nLC 1200 HPLC system. 2. Orbitrap Exploris mass spectrometer.

Methods

3.1 Protein Extraction

1. Grind root tissue to powder under liquid nitrogen in a mortar. 2. Weigh approximately 1 g ground root tissue into a small precooled mortar. Add 4 ml of cold protein extraction buffer (PEB), and preform about 200 pestle strokes for complete homogenization (see Note 7). 3. Transfer the homogenate into a new 15 ml falcon tube and shake at 1000 rpm for 30 min at 4  C (see Note 8). 4. Centrifuge at 16,000  g at 4  C for 15 min, and collect the supernatant into a new 50 ml tube.

3.2 Protein Precipitation

1. Add 4 vol. of cold methanol to the supernatant (approximately 16 ml). Then add 1 vol. of cold chloroform (approximately 4 ml) to the methanol/supernatant mixture. Mix well by vortexing. 2. Add 3 vol. cold water (approximately 12 ml), vortex, and centrifuge at 16,000  g/4  C for 5 min. 3. Discard the upper layer as the protein precipitate is in the interphase. Add the same vol. of cold methanol as step 1 (approximately 16 ml), mix well by pipetting, and centrifuge at 16,000  g/4  C for 5 min. 4. Discard the supernatant and wash the precipitated protein at least once with cold methanol as step 3 and centrifuge at 16,000  g/4  C for 5 min. 5. Remove the supernatant after centrifugation, and air-dry the precipitated protein by opening the cap of the falcon tube for about 20 min. 6. Resuspend the precipitated protein in 400 μl UTU. 7. The protein concentration is measured with the Bradford assay. 1 μl sample + 24 μl H2O + 200 μl Bradford reagent is incubated at room temperature in darkness for 15 min, and the absorbance value is measured at 590 nm (see Note 9).

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8. 300–400 μg protein is aliquoted for in-solution digestion or can be stored at 80  C. 3.3 In-Solution Trypsin Digestion

1. Add ~6 μl reduction buffer (1 μl reduction buffer for every 50 μg of protein), mix well, and incubate for 30 min at room temperature (see Note 10). 2. Add ~6 μl alkylation buffer (1 μl alkylation buffer for every 50 μg protein), mix well, and incubate for 20 min at room temperature in the dark (see Note 10). 3. Add ~6 μl Lys-C (1 μl Lys-C for every 50 μg protein), mix well, and incubate for 3 h at room temperature. 4. Dilute sample with four volumes 10 mM Tris–HCl, pH 8.0 (see Note 11). 5. Add ~6 μl trypsin (1 μl trypsin for every 50 μg protein), mix well, and incubate overnight at 37  C. 6. Acidify the samples with 1/10 vol of 10% TFA to pH 2 (see Note 12).

3.4 Phosphopeptides Enrichment

1. To determine the amount of TiO2 beads to use, weigh out (10:1 ratio μg TiO2 beads/μg protein) of 5 μm TiO2 beads into a 2 ml Eppendorf tube, add enrichment buffer as 1 μl/mg beads, and aliquot into the required number of tubes depending on your number of samples (see Note 13). 2. TiO2 beads are equilibrated with 50 μl enrichment buffer and centrifuged at 1,700  g at room temperature for 2 min. The supernatant is then carefully removed and discarded. This step is repeated one time. 3. Add an equal volume of enrichment buffer into the digested peptides, and then mix with the equilibrated TiO2 beads for 30 min with continuous mixing on a vortex mixer at lowest speed. 4. Centrifuge at 1,700  g at room temperature for 2 min, and transfer the supernatant into a new tube for the detection of non-phosphorylated peptides (see Note 14). Resuspend peptides and TiO2 beads mixture with 100 μl enrichment buffer. 5. Centrifuge at 1,700  g at room temperature for 5 min, and add the supernatant into the tube with the non-phosphorylated peptides. 6. Resuspend peptides and TiO2 beads mixture with 100 μl wash buffer, centrifuge at 1,700  g at room temperature for 5 min, and also collect the supernatant into the tube for non-phosphorylated peptides. 7. Repeat this step one time.

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8. Resuspend peptides and TiO2 beads mixture with 100 μl wash buffer and then load into C8 stage-tip (see Note 15). Centrifuge at 2,200  g at room temperature for 8 min. 9. Elute phosphopeptides from TiO2-C8 tips into a fresh tube using 80 μl phosphopeptides elution buffer three times for a total of 240 μl. 10. Immediately acidify the sample using 80 μl 10% formic acid. 11. Dry eluates in a speed vacuum concentrator at 1,200  g at/ 45  C. The dried eluate samples can be either desalted directly or stored at 20  C for a short time or at 80  C for long-term storage. 3.5

Desalting

1. Place an Empore Styrene Divinyl Benzene disc on a flat, surface-cleaned plastic petri dish. Two layers of small Empore Styrene Divinyl Benzene discs are stacked into a 200-μl pipette tip (see Note 6). The ready-made tip can be then placed on a holder, and assembled together with a 2 ml tube for collecting the flow-through. 2. Resuspend the dry eluates with 100 μl solution A, load into the SDR stage-tip, and centrifuge at 2,200  g at room temperature for 5 min. 3. Wash the SDR stage-tips containing phosphopeptides two times with wash buffer 1. 4. Wash the SDR stage-tips containing phosphopeptides two times with wash buffer 2. 5. Elute the peptides by adding 30 μl of SDR-elution buffer and centrifuge at 2,200  g at room temperature for 5 min. Collect the flow-through into a new 1.5 ml Eppendorf tube. This step is repeated one time. The two eluates are combined together. 6. Dry the desalted eluates in a speed vacuum concentrator at 1,200  g/45  C.

3.6 LC-MS/MS Analysis

4

Resuspend the dried peptides with 5 μl resuspension buffer. Perform peptide analysis via LC-MS/MS according to the instrument performance and possibilities of the user (see Note 16).

Notes 1. Protease inhibitor cocktail and phosphatase inhibitor should be freshly added according to the instructions of the manufacturer. PVPP is added to adsorb and remove polyphenols and polysaccharides from plant tissues to improve the protein extraction efficiency.

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2. First dissolve 1.84 g of Na3VO4 in 15 ml of 50 mM Tris–HCl (pH 10), adding HCl will make the solution yellow, a large amount of HCl may need to be added here. Heat the solution in a microwave for 5–15 s; once boiled, the solution will be clear and colorless. Cool in the fume hood until the Na3VO4 solution reaches room temperature. At this point, if the pH is not at 10, add a small amount of 1 M NaOH or 1 M HCl by stirring to adjust pH to 10.0. After repeating 2–5 cycles of boiling, cooling, and adjusting the pH, the solution should reach a point where the pH is stabilized at 10. Adjust the volume to 20 ml. Aliquot and store at 20  C. 3. It is recommended to prepare the enrichment buffer freshly before use. 4. It is recommended to prepare freshly before use, and the prepared solution should be used within 1 h. 5. It is better to freshly prepare the solution A and solution B, or store the prepared solution A at 4  C for only a short time. Sonicate for more than 30 min to remove bubbles and cool until the solution reaches room temperature before use. Solution A and solution B should be changed every 1–2 weeks. 6. We use the Empore discs of C8 and Empore Styrene Divinyl Benzene, reversed-phase sulfonate (SDB-RPS, SDR) material and a blunt dermatological needle to punch out small discs [13] that can be stuffed into a 200 μl pipette tip. C8 for phosphopeptides elution and SDR for desalting. 7. Arabidopsis root materials are harvested and frozen in liquid nitrogen and stored at 80  C. Precool the mortar and protein extraction buffer to 4  C, and always keep on ice during the process of grinding. 8. We use a thermo shaker to keep the conditions of 1,000 rpm and 4  C, it may also be done using an alternative device such as a vortex to achieve the same efficiency. 9. Urea (concentration greater than 3 M) affects the bicinchoninic acid assay (BCA). It is recommended to use the Bradford assay to measure protein concentration when the protein is in resuspension buffer UTU. 10. The ratio of buffer (reduction buffer, alkylation buffer, Lys-C, and trypsin) (1 μl stock) to protein (50 μg) needs to be kept. Alkylation is light sensitive, therefore this step should be performed in darkness. 11. This step is important!!! high concentration of salt (urea) affects the efficiency of trypsin enzymatic activity. 12. After acidifying sample, use pH strips to check the pH.

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13. Keep the enrichment buffer and TiO2 well mixed during aliquoting to ensure an equal amount of TiO2. 14. The supernatant contains the non-phosphorylated peptides. Here, we suggest to keep the supernatant (in storage) in case it is necessary to compare between non-phosphorylated peptides and phosphorylated peptides from the same sample. These supernatants can be stored at 80  C for long-term storage. 15. Residual wash buffer will reduce the elution efficiency, and using a C8 stage-tip can avoid this problem. 16. There are some suggestions for equipment settings elsewhere in the current book. References 1. Moffett AS, Shukla D (2018) Using molecular simulation to explore the nanoscale dynamics of the plant kinome. Biochem J 475 (5):905–921. https://doi.org/10.1042/ BCJ20170299 2. Schweighofer A, Meskiene I (2015) Phosphatases in plants. Methods Mol Biol 1306:25–46. https://doi.org/10.1007/978-1-4939-26480_2 3. Liu KH, Niu Y, Konishi M et al (2017) Discovery of nitrate-CPK-NLP signalling in central nutrient-growth networks. Nature 545 (7654):311–316. https://doi.org/10.1038/ nature22077 4. Wang Q, Qin G, Cao M et al (2020) A phosphorylation-based switch controls TAA1mediated auxin biosynthesis in plants. Nat Commun 11(1):679. https://doi.org/10. 1038/s41467-020-14395-w 5. Vialaret J, Di Pietro M, Hem S et al (2014) Phosphorylation dynamics of membrane proteins from Arabidopsis roots submitted to salt stress. Proteomics 14(9):1058–1070. https:// doi.org/10.1002/pmic.201300443 6. Takahashi F, Shinozaki K (2019) Longdistance signaling in plant stress response. Curr Opin Plant Biol 47:106–111. https:// doi.org/10.1016/j.pbi.2018.10.006 7. Ota R, Ohkubo Y, Yamashita Y et al (2020) Shoot-to-root mobile CEPD-like 2 integrates shoot nitrogen status to systemically regulate

nitrate uptake in Arabidopsis. Nat Commun 11 (1):641. https://doi.org/10.1038/s41467020-14440-8 8. Tabata R, Sumida K, Yoshii T et al (2014) Perception of root-derived peptides by shoot LRR-RKs mediates systemic N-demand signaling. Science 346(6207):343–346. https://doi. org/10.1126/science.1257800 9. Liu KH, Huang CY, Tsay YF (1999) CHL1 is a dual-affinity nitrate transporter of Arabidopsis involved in multiple phases of nitrate uptake. Plant Cell 11(5):865–874. https://doi.org/ 10.1105/tpc.11.5.865 10. Liu KH, Tsay YF (2003) Switching between the two action modes of the dual-affinity nitrate transporter CHL1 by phosphorylation. EMBO J 22(5):1005–1013 11. Hochholdinger F, Marcon C, Baldauf JA et al (2018) Proteomics of maize root development. Front Plant Sci 9:143. https://doi.org/10. 3389/fpls.2018.00143 12. Millar AH, Heazlewood JL, Giglione C et al (2019) The scope, functions, and dynamics of posttranslational protein modifications. Annu Rev Plant Biol 70:119–151. https://doi.org/ 10.1146/annurev-arplant-050718-100211 13. 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

Chapter 10 Plant Phosphopeptides Enrichment by Immobilized Metal Ion Affinity Chromatography Xiahe Huang, Yuanya Zhang, Haitao Ge, Dandan Lu, and Yingchun Wang Abstract Protein phosphorylation plays important roles in the regulation of plant growth and development as well as adaption to changing environments. Large-scale identification of the phosphorylated proteins could provide both a global view of and specific targets involved in the mechanism underlying these processes. The progress of phosphoproteomic study for higher plants has lagged behind that of animals due to technical challenges, particularly the difficulty in solubilizing proteins from plant tissues with a rigid cell wall and the interference of the secondary metabolites, polysaccharides, and pigments throughout the whole processes of sample preparation and LC-MS analysis. Thus, it is critical to improve the efficiency of protein extraction and to remove the interfering metabolites before phosphopeptides enrichment. Here we describe a protocol for plant protein extraction and phosphopeptides enrichment by Fe3+-immobilized metal ion affinity chromatography (Fe3+-IMAC). Strong detergents such as SDS were used to extract proteins from plant tissues, and the secondary metabolites were removed by protein precipitation and washing of the pellets. The protein samples were digested and the resulting peptides were prefractionated. Phosphopeptides enriched from each fraction were combined before analysis with LC-MS. Keywords Phosphopeptide enrichment, Fe3+-IMAC, Mass spectrometry analysis

1

Introduction Protein phosphorylation is one of the most important posttranslational modifications, as it participates in many crucial biological processes. One of the major technical challenges in phosphoproteomic study is the relatively low abundance of phosphoproteins, especially in plants [1]. The current LC-MS-based shotgun proteomics approaches select for the higher abundant non-phosphorylated peptides for analysis from whole cell or tissue lysate. Because of this, the much lower abundance phosphopeptides usually escape identification. In addition, the negative charges of the phospho-group also have a negative repression effect that further reduces the signals of phosphopeptides in MS [2–4] and makes them more difficult to be analyzed. Therefore, it is necessary to

Xu Na Wu (ed.), Plant Phosphoproteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2358, https://doi.org/10.1007/978-1-0716-1625-3_10, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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enrich phosphopeptides from a complex peptide mixture before LC-MS analysis to ensure a high coverage identification of the phosphoproteome. Different strategies have been developed for phosphopeptide enrichment. These mainly include immunoprecipitation using specific antibodies (e.g., PY100, 4G10), particularly for tyrosine phosphorylated peptides [5], interaction with chelated metal ions (immobilized metal affinity chromatography, IMAC) [6], covalent metal oxides (metal oxide affinity chromatography, MOAC) such as TiO2, and chromatographic fractionation methods (hydrophilic interaction chromatography, HILIC) [7–9]. Among these methods, IMAC and TiO2 methods are the two most widely used affinity enrichment techniques for phosphopeptide enrichment. The advantage of IMAC is the high-affinity interaction between the phospho-group and metals. Fe3+-IMAC is the most commonly used variant [4], and a variety of IMAC materials are commercially available [10]. TiO2 was the first metal oxide to be used for phosphopeptide enrichment [11]. These methods can enrich subsets of phosphopeptides that partially overlap with each other. Therefore, it is necessary to combine multiple approaches to analyze a phosphoproteome if a higher coverage identification is desired or required [12]. The study of plant phosphoproteomics has lagged behind compared with that of animals in terms of depth of identification and the coverage of phosphorylation events. For samples of animal origin, more than 30,000 unique phosphosites can be identified from whole cell lysate in a single study even back in the year 2012 [13], while only a few thousand phosphosites could be identified from Arabidopsis seedlings around the same time [14]. The major technical challenge in plant phoshoproteomics lies on the sample preparation, i.e., extraction and solubilization of proteins from the tissues with rigid cell wall, and the removal of secondary metabolites and chlorophylls that interferes with protein digestion and enrichment of phosphopeptides [15]. In plants, phosphopeptides are usually in low abundance in a complex peptide mixture from a whole tissue lysate. In addition, the proteins in plant-specific membranous organelles such as chloroplast are refractory to solubilization with aqueous solutions compatible for LC-MS. Nevertheless, the pace of technical development has been accelerated in recent years. Methods that are more efficient in preparation of the phosphopeptide samples compatible for LC-MS analysis have been developed [15–17]. Currently, phosphoproteomic studies typically can identify 5000–15,000 distinct phosphosites from a single plant tissue lysate depending on the origin of the samples and the difference of the methods used for protein extraction and LC-MS. More recently, Mergner et al. reported a total of 43,903 phosphosites from 30 tissues, which is one of the most comprehensive single Arabidopsis phosphoproteomes published to date [17]. So far, the

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PhosPhAt 4.0 database has collectively included 79,334 total unique phosphorylation sites that were identified by 42 Arabidopsis phosphoproteome studies [18, 19], and the number will definitely increase if the newly identified phosphosites by Mergner et al. are incorporated [17]. Here we describe a protocol for plant protein extraction and plant phosphopeptide enrichment by Fe3+-IMAC. To increase protein solubilization and the coverage by subsequent proteomic analysis, the strong detergent SDS is added to the extraction buffer and is subsequently removed along with the secondary metabolites and chlorophylls by protein precipitation. The tryptic peptides are prefractionated before phosphopeptide enrichment to increase the depth and coverage of phosphopeptide identification by LC-MS.

2 2.1

Materials Plant Sample

2.2 Protein Extraction

21-day-old Arabidopsis seedlings growing in half-strength Murashige and Skoog (MS) medium containing 0.5% (w/v) sucrose and 0.8% (w/v) agar. 1. Liquid nitrogen. 2. Mortar (Φ 9 cm). 3. Protein extraction buffer: 4% SDS, 290 mM sucrose, 250 mM Tris–HCl (pH 8.0), 1 mM sodium orthovanadate, 1 mM sodium fluoride, 1 Complete EDTA-free Protease Inhibitor Cocktail Tablet, and 1 Phosphatase Inhibitor Cocktail Tablet PhosSTOP. 4. Precipitation buffer: 10% trichloroacetic acid in acetone. 5. 80% acetone and 100% acetone.

2.3 Resuspension, Reduction, Alkylation, and Digestion of Plant Proteins

1. Resuspension buffer: 50 mM ammonium bicarbonate, pH ~8.3, 8 M urea, 1 mM sodium orthovanadate, 1 mM sodium fluoride, 1 mM β-glycerophosphate, 1 Complete EDTA-free Protease Inhibitor Cocktail Tablet, and 1 Phosphatase Inhibitor Cocktail Tablet PhosSTOP. 2. 1 M dithiothreitol (DTT). 3. 0.55 M iodoacetamide (IAA). 4. 1 μg/μL modified trypsin.

2.4 Desalting of Tryptic Peptides

1. Oasis® HLB 3 cc (60 mg) Extraction Cartridges. 2. 2 mL syringe needle. 3. Methanol. 4. 0.1% formic acid and 70% acetonitrile.

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5. 0.1% formic acid. 6. Vacuum manifold. 2.5 Prefractionation of Tryptic Peptides by SCX-HPLC

1. Waters 2695 HPLC. 2. PolyLC Poly SULFOETHYL A™ 5 μm, 200-A, 200  4.6mm. 3. 0.45 μm centrifugal filter. 4. Buffer A: 7 mM KH2PO4, 30% acetonitrile, pH ~ 2.7 (adjust pH with H3PO4). 5. Buffer B: 7 mM KH2PO4, 350 mM KCl, 30% acetonitrile, pH ~ 2.7 (adjust pH with H3PO4).

2.6 Immobilized Metal Ion Affinity Chromatography

1. PHOS-Select™ Iron Affinity Gel, stored at

20  C.

2. Wash/equilibration solution: 250 mM acetic acid, 30% acetonitrile, HPLC grade. 3. Elution solution: 150 mM ammonium hydroxide and 25% acetonitrile. 4. Rotor.

2.7 Desalting of Phosphopeptides by StageTips

1. Octadecyl (C18)-bonded silica Empore extraction disk (3M, 2215). 2. Kel-F hub (KF), point style 3, gauge 16 (Hamilton, 90516). 3. Plunger assembly N, RN, LT, LTN for model 1702 (25 mL) (Hamilton, 1122-01). 4. Methanol. 5. Buffer 1: 0.5% acetic acid. 6. Buffer 2: 0.5% acetic acid and 80% acetonitrile.

2.8 Mass Spectrometry

1. High-resolution/high-mass-accuracy mass spectrometer (e.g., Orbitrap-based mass spectrometer) coupled to a nanoHPLC with a 75–150 μm i.d. reverse phase (RP) capillary column for highly sensitive online peptide separation. 2. Software for searching MS raw data against the proteome sequence databases, such as Proteome Discoverer, MaxQuant, and so on.

2.9

Other Materials

1. pH meter. 2. Centrifuge. 3. Vortex. 4. Constant temperature water bath. 5. Ultrasonic water bath. 6. Bradford assay kit or reagents.

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Methods The goal of the protocol shown in this chapter is to prepare highquality proteins from Arabidopsis seedlings, and enrich the phosphopeptides from the total tryptic peptides by IMAC. The protein extraction was performed using the previously described methods with slight modifications [15, 20].

3.1 Protein Extraction

1. Freeze the seedlings (~2 g) by pouring liquid nitrogen in the mortar, and grinding the sample to a fine powder. 2. Collect the powder into a 15 mL centrifuge tube, and add 5 mL protein extraction buffer to the sample. Mix the powder and extraction buffer immediately by inverting the tube gently. Incubate for 10 min at room temperature. 3. Boil the mixture at 95  C for 5 min, keep the lysate at room temperature for 10 min, then sonicate the mixture for 5 min, and heat it again at 95  C for 5 min. 4. Centrifuge with 1,700  g for 10 min at room temperature. Transfer the supernatant to a new 50 mL centrifuge tube, and add 5 volume of cooled precipitation buffer. Mix well by inversion. 5. Incubate at 20  C overnight (or at least 4 h) to precipitate the proteins. Centrifuge at 1,700  g for 10 min at 4  C and discard the supernatant (see Note 1). 6. Resuspend the pellet with 2 mL cooled 80% acetone (see Note 2). Incubate at 20  C for 2 h, then centrifuge at 1,700  g for 10 min at 4  C, and discard the supernatant. 7. Resuspend the pellet in 1 mL cooled 100% acetone, and remove the suspension to a new 2 mL centrifuge tube. Centrifuge at 1,700  g for 10 min at 4  C and discard the supernatant. 8. Dry the pellet by SpeedVac for about 5 min (see Note 3).

3.2 Resuspension, Reduction, Alkylation, and Digestion of Plant Proteins

1. Add 1 mL resuspension buffer to the protein pellet, then sonicate the pellet for 10 min. Break up the pellet by pipetting to facilitate solubilization of proteins (see Note 4). Centrifuge at 15,000  g for 10 min at room temperature and transfer the supernatant into a new tube (see Note 5). 2. Measure the protein concentration using the Bradford protein assay. Take 5 mg protein for further analysis. 3. Add DTT to a final concentration 10 mM and incubate the sample at 37  C for 1 h to reduce disulfide bonds (see Note 6).

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4. Add iodoacetamide to a final concentration of 55 mM. Incubate for 45 min at room temperature in the dark to alkylate the –SH group of cysteine residues. 5. Dilute the protein mixture by adding 5 volume of 50 mM ammonium bicarbonate (see Note 7). 6. Add trypsin at a 1:50 enzyme:substrate ratio, and incubate overnight at 37  C. 3.3 Desalting of Tryptic Peptides

1. We use Oasis® HLB 3 cc (60 mg) Extraction Cartridges to desalt the tryptic peptides before SCX fractionation. Set up the Extraction Cartridges to the vacuum manifold through a 2 mL syringe needle (see Note 8). 2. Wash and condition the cartridge using 1 mL of methanol followed by 1 mL of 70% acetonitrile in 0.1% formic acid. 3. Equilibrate the cartridge with 1 mL of 0.1% formic acid. 4. Acidify the tryptic peptides with TFA to a final concentration of 0.5% (see Note 9). Centrifuge at 15,000  g for 10 min and discard the pellet. 5. Load sample into the Extraction Cartridges, and allow it to flow-through by gravity (see Note 10). 6. Wash twice with 1 mL of 0.1% formic acid. 7. Transfer the Extraction Cartridges into a new centrifuge tube, elute the peptides from the C18 with 1 mL methanol. 8. Freeze-dry the eluates using a SpeedVac.

3.4 Prefractionation of Tryptic Peptides by SCX-HPLC

1. Set up the following gradient described in Table 1 for peptide separation [21]. Run a blank gradient to equilibrate the SCX HPLC system. 2. Resuspend the desalted peptides with 500 μL buffer A and filter with a 0.45 μm centrifugal filter. Fractionate the peptides with the SCX gradient described using a HPLC system. Collect five fractions (10 min/fraction) from 1 to 50 min (see Note 11). 3. Freeze-dry all fractions with a SpeedVac and desalt them as Subheading 3.3.

3.5 Immobilized Metal Ion Affinity Chromatography (Fig. 1)

1. Wash/equilibration of the affinity gel: Carefully mix the PHOS-Select Iron Affinity Gel beads until they are completely and uniformly suspended. Immediately add 20 μL of the 50% slurry (10 μL of gel) into a 1.5 mL tube (for each fraction). Add 500 μL of wash/equilibration solution to the gel beads, and pellet the gel beads with a microcentrifuge at 8,200  g for 30 s. Discard the supernatant. Repeat the wash/equilibration step twice. 2. Sample loading:

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Table 1 The gradient used for separation of tryptic peptides by SCX chromatography Time (min)

Flow rate (mL/min)

Buffer A (%)

Buffer B (%)

0.00

0.80

100.0

0.0

2.00

0.80

100.0

0.0

35.00

0.80

75.0

25.0

38.00

0.80

0.0

100.0

43.00

0.80

0.0

100.0

44.00

0.80

100.0

0.0

50.00

0.80

100.0

0.0

Fig. 1 Diagram representation of the strategy for phosphopeptide enrichment by Fe3+-IMAC. The tryptic peptides from plant whole tissue lysates were resuspended with wash/equilibration solution, and mixed and incubated with Fe3+-IMAC beads for 1 h at RT. The Fe3+-IMAC beads were then pelleted and washed with wash/equilibration solution. The phosphopeptides were eluted from the Fe3+-IMAC by elution solution

Add 500 μL of wash/equilibration solution to the dry sample, vortex to completely resuspend the peptides, and centrifuge at 15,000  g for 1 min to collect the sample. Add the resuspended sample to the gel beads, and incubate the sample by constantly inverting the tube at room temperature for 1 h. 3. Wash of affinity gel:

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Collect the gel by centrifuging at 8,200  g for 30 s, and discard the supernatant. Add 500 μL of wash/equilibration solution to resuspend the gel, and centrifuge in a microcentrifuge for 30 s at 8,200  g. Discard the supernatant. Repeat this step twice. Wash the gel once with 200 μL of ddH2O, centrifuge for 30 s at 8,200  g, and discard the supernatant. Remove any residual wash/equilibration solution prior to elution (see Note 12). 4. Sample elution: Add 100/200 μL of elution solution, and incubate by inverting the sample at room temperature for 10 min. After incubation, centrifuge at 8,200  g for 30 s to pellet the gel beads. Collect the supernatant to a new tube. Repeat this step twice. Add TFA to final concentration at ~1% (see Note 13). 5. Freeze-dry the collected samples by SpeedVac. 3.6 Desalting of Phosphopeptides by StageTips [22]

1. Place 3  C18 Empore extraction disk in a yellow pipette tip to make a StageTip. Punch a hole in the lid of a 1.5 mL microcentrifuge tube, and place the StageTip tip through the hole. The microcentrifuge tube serves as the fluid collecting device. 2. Add 20 μL ethanol to the upper chamber of the tip, and centrifuge at 150  g for 3 min (see Note 14). 3. Add 20 μL buffer 2 to upper chamber of the tip, centrifuge at 150  g for 3 min. 4. Add 20 μL buffer 1 to upper chamber of the tip, centrifuge at 150  g for 3 min. 5. Resuspend the dry sample in 60 μL buffer 1, vortex until the sample is completely dissolved. Centrifuge at 15,000  g for 1 min to collect the sample. 6. Load the sample into the upper chamber of the StageTip, centrifuge at 150  g until all of the sample is loaded onto the extraction disk. 7. Add 20 μL buffer 1 to the upper chamber of the StageTip, and centrifuge at 150  g for 3 min. 8. Transfer the StageTip to a new 1.5 mL microcentrifuge tube, add 10 μL buffer 2, and centrifuge at 150  g for 3 min to collect the eluates. Repeat twice and combine the eluates. 9. Freeze-dry the combined eluates with a SpeedVac.

3.7 Mass Spectrometry

For LC-MS/MS analysis of enriched phosphopeptides, a highresolution/high-mass-accuracy mass spectrometer coupled online to a nanoLC was used. The latter was equipped with a 150 μm (ID)  25 cm (length) analytical column packed with 1.9 μm

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Fig. 2 Comparison of the protein extraction efficiency between the GdnHCL extraction method and the currently described SDS/sucrose extraction method. (a) SDS-PAGE of the proteins extracted from Arabidopsis seedlings by the two methods. The proteins were visualized with Coomassie blue staining. (b) The bar graph shows the number of proteins identified by LC-MS from the samples in (a). (c) The Venn diagram shows the number of proteins uniquely or commonlyidentified from the samples in (a)

porous C18 resin. The long analytical column packed with small size of chromatographic particles can enhance the resolution of nanoLC separation, and hence increase the efficiency of phosphopeptide identification. 1. The peptides were dissolved in 10 μL 0.1% formic acid, and vortexed to completely dissolve the peptides. Centrifuge at 12,000 rpm for 1 min to collect the sample. 2. Load 2 μL of each sample onto the LC-MS/MS system. 3. The peptides were analyzed by LTQ Orbitrap Elite mass spectrometer (Thermo Fisher Scientific) coupled online to an EasynLC 1000 (Thermo Fisher Scientific) in the data-dependent mode. The LC was run with mobile phases containing buffer A (0.1% FA) and buffer B (100% ACN, 0.1% FA). The peptides were separated in a 90-min nonlinear gradient (3–8% B for 10 min, 8–20% B for 60 min, 20–30% B for 8 min, 30–100% B for 2 min, and 100% B for 10 min) with a flow rate of 600 nL/min. All MS measurements were performed in the positive ion mode. Precursor ions were measured in the Orbitrap analyzer with a resolution 240,000 (at 400 m/z) and a target value of 106 ions. The 20 most intense ions from each MS scan were isolated, fragmented, and measured in the linear

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Fig. 3 Identification of phosphosites in Arabidopsis seedlings using the currently described method. (a) The bar graph shows the numbers of peptide spectral matches (PSMs) for the identified phosphopeptides, phosphopeptides, and phosphosites. (b) Percentage distribution of the number of phosphates on identified phosphopeptides. (c) Percentage distribution of phosphorylation on serine (S), threonine (T), and tyrosine (Y) residues

ion trap. Multiple stage activation (MSA) was enabled and neutral loss values were set at m/z 98, 49, and 32.6. The normalized collision energy was set to 35 for CID. For comparison, proteins were also extracted side by side from Arabidopsis seedlings using the buffer containing guanidine hydrochloride (GdnHCL) as previously described [15]. The profiles of the proteins extracted by the two methods are very similar as displayed by the SDS-PAGE gel (Fig. 2a). Nevertheless, samples prepared with the current method resulted in more protein identifications by LC-MS (Fig. 2b), and the majority of the proteins can be identified by both methods (Fig. 2c). The phosphopeptides enriched by Fe3+-IMAC from the samples prepared by the current SDS/sucrose extraction method were also identified by LC-MS (Fig. 3a). The percentage distributions of the number of

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phospho-group on the identified phosphopeptides and phosphorylation on serine (S), threonine (T), and tyrosine (Y) are shown (Fig. 3b, c).

4

Notes 1. Do not centrifuge at high speed, or it may be difficult to break up the pellet. 2. Break up the pellet as completely as possible to remove the pigments and SDS. 3. Dry out the acetone completely, but do not over-dry the pellet, or it may be difficult to dissolve completely. 4. Keep the final volume as small as possible. 5. If there are many debris remaining, add new resuspension buffer to dissolve the protein as completely as possible. 6. Avoid temperatures higher than 60  C where urea-based carbamylation of lysines and protein N-termini can occur. 7. Reduce the concentration of urea to 1.5 M. The dilution volume is dependent on the enzymatic tolerance of trypsin to urea. 8. Seal up the interface of cartridges, syringe needle, and the vacuum manifold by using parafilm. 9. Verify that the pH is 10

3

1.96

Carbohydrate metabolism

Identified >10

3.1.2

1.39

Carbohydrate metabolism.sucrose metabolism.synthesis

Identified >10

11

1.50

Phytohormones

Identified >10

11.2.2

1.77

Phytohormones.auxin.perception and signal transduction

Identified >10

11.3

2.73

Phytohormones.brassinosteroid

Identified >10

11.3.2

3.05

Phytohormones.brassinosteroid. perception and signal transduction

Identified >10

13.1

1.92

Cell cycle.regulation

Identified >10

13.1.2

2.66

Cell cycle.regulation.cyclin-dependent kinase complex

Identified >10

15.3.2

1.65

RNA biosynthesis.RNA polymerase II-dependent transcription.pre-initiation complex

Identified >10

16

1.96

RNA processing

Identified >10

16.4

3.05

RNA processing.RNA splicing

Identified >10

18

2.36

Protein modification

Identified >10

18.9

2.33

Protein modification.tyrosine sulfation

Identified >10

20.5

2.66

Cytoskeleton.cp-actin-dependent plastid movement

Identified >10

22

3.79

Vesicle trafficking

Identified >10

22.1

2.94

Vesicle trafficking.clathrin coated vesicle (CCV) machinery

Identified >10

24.1.1.1

1.57

Solute transport.primary active transport.V-type ATPase complex.membrane V0 subcomplex

a

Overrepresentation tool: Mercator::Mapman/Mapman Bin Enrichment: https://plabipd.de/portal/bin-enrichment

phosphorylation motifs. The mode of search by motif was newly added [21], in order to provide more flexibility to knowledge about phosphorylation motifs. Under the mode of kinase–targets searching, the network view was linked to each result, showing the relationship between the kinase and its target along with cellular sublocalization information (SUBA4), pathway information (MapMan3.6 and MapMan4), and the corresponding phosphorylation sites (Fig. 2a). The kinase–target database in PhosPhAt 4.0 now includes information from 709 publications covering 3162 kinase–

Fig. 2 Screenshots of PhosPhAt 4.0 updated modules. (a) Entry screen with searching modes and example of p-sites visualization. (b) Overview for “Kinase-target” searching modes and the query result. (c) An example of network view for kinase-target pairs result. (d) Pie charts from “Family search” giving an overview of the kinase families

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target pairs from 58 protein families [28, 40]. The expansion and reorganization of the PhosPhAt database has made database queries much more inclusive, thereby providing more detailed and relevant output data.

2

Search of the Phosphorylation Sites with PhosPhAt 4.0 1. Start the PhosPhAt 4.0 application from the website phosphat. uni-hohenheim.de (see Note 1). 2. Go to the “Experimental data”—“Basic search” tab in the left frame. Enter one or multiple Arabidopsis gene identifiers (AGI code, e.g., AT4G29810.1 or AT5G11510). Alternatively, a tryptic peptide sequence (e.g., KDEPAEESDGDLGF), protein description (e.g., kinase), or UniProt code (e.g., Q9LES3) can be searched in the database (see Note 2). 3. A list of experimentally identified peptides will be shown, grouped by protein. Multiple identifications of the same peptide sequence will be collapsed to display only the peptide sequence with the highest score (see Note 3). 4. Selecting a protein of interest and then choosing “protein prediction” on the pull-down menu, in the menu bar will open a display of the protein sequence with detailed view of experimental and predicted phosphorylation sites (see Note 4). 5. In case where an experimental mass spectrum has been submitted to PhosPhAt 4.0, a respective icon is displayed in the same row as the peptide sequence is listed. Upon double-clicking on that icon, the fragment spectrum of this ion, experimental origin, and available quantitative information are displayed (see Note 5). 6. On “Result” pages, selection of “export” in the top right corner gives users the possibility to export lists of tryptic peptides that cover the identified phosphorylation sites. In the list of experimental data, phosphorylation sites are marked as defined if the precise location of the phosphorylated amino acid has been unambiguously determined by mass spectrometric analysis (see Note 6).

3

Phosphorylation Site Prediction by PhosPhAt 4.0 1. Go to the “Prediction” field in the left tab. 2. Enter either an AGI code or a peptide sequence into the respective filed in the query and submit information. Here the multiple entries submission is allowed.

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3. The protein prediction tab will open and show the protein sequence with highlighted phosphorylation sites. Mouse-over will give the respective prediction score. Scores >0 indicate positive prediction (see Note 4).

4

Motif Search by PhosPhAt 4.0 1. Go to the “Motif search” tab in the left frame. The pull-down menu contains 58 motifs in database. Select one of the listed motifs or enter one motif of your interest, then submit the information. 2. A list of the experimentally identified peptides will be shown, grouped by protein. Multiple identifications of the same peptide sequence will be collapsed to display. 3. Selecting resulted protein(s) of interest and then choosing “protein prediction” on the top of this panel, in the menu bar will open a display of the protein sequence. The motif location was marked with red-bold residue; when mouse-over, the detailed view of experimental and predicted phosphorylation sites will be shown below (see Note 7).

5

Kinase–Target Relationships Retrieval by PhosPhAt 4.0 1. Go to the “Kinase-target: Basic search” tab in the left frame. 2. Enter one or multiple AGI codes into the search field. 3. A list of the experimentally identified targets of the entry will display in the column “target” (see Note 8). For each kinase– target pair, the known relationship is defined and then linked to the original publication (see Note 9). 4. Select one or several kinase–target pair(s), go to the menu tab “Prediction,” a pull-down menu gives the options to retrieve the phosphorylation result by either “kinase” or “target” protein. 5. Double-clicking one of the kinase–targets pair at the row will bring to another result window: the network presenting view (Fig. 2b, c) (see Note 10).

6

Retrieval of Kinase–Substrate Relationships by “Family Search” Mode 1. Go to the “Family search” tab in the left frame. 2. Entre an AGI code of interest in the query field, and the collapsed list below will show the protein status in the clustered families. Clicking the protein AGI code, a list of the

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experimentally identified targets of this protein will be shown (see Note 11). 3. Double-clicking one of the rows will bring to the network presenting view for this kinase–target pair (Fig. 2b, c) (see Note 10). 4. At the left panel, at the bottom of “Family search,” a pull-down tab “Chart kinase” allows to present the overview of kinase– targets data in pie charts by family or by interaction types (Fig. 2d).

7

Conclusion Arabidopsis is a model plant whose genome is relatively small with little redundancy. Since the completion of the Arabidopsis genome in 2000, the gene and protein functions have been well annotated [43]. With the aim of accumulating and organizing Arabidopsis large-scale phosphorylation data, PhosPhAt 4.0 database has been a distinct and valuable resource to the plant research community. The up-to-date PhosPhAt 4.0 allows researchers to retrieve the function of the phosphorylation sites or proteins of their interest. It also presents a platform to share the known knowledge about the experimental phosphorylation data, which provides researchers valuable information in future planning on detailed functional studies at a large scale [21, 44].

8

Notes 1. Recommended browsers are Firefox, Opera, Safari, and Google Chrome. 2. It is also possible to perform an “advanced search” by selection of parameters describing the experimental treatments, cell compartment, tissue, phosphopeptide enrichment method, mass-spectrometry instruments, publication, genotype, MapMan bin numbers (MapMan3.6), and motifs. Thereby, dragand-drop is used to select the parameters of interest. Several parameters can be combined using Boolean terms “and,” “or,” as well as “not.” 3. On all displayed pages, displayed lists can be customized by clicking on the column title and selecting desired parameters for display. A complete tab-delimited table of all database contents can be downloaded from the PhosPhAt 4.0 main page. 4. The protein prediction window contains the following information for each protein (Fig. 2a). The top right corner of this protein tab contains links to various other resources: SUBA,

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TAIR, eFP, ATTED, Aramemnon, and GabiPD. Below the protein ID, its functional description and the MapMan bin classification are listed. The middle panel presents the phosphorylation site prediction. Here, the amino acids from experimentally identified peptides are underlined, and predicted phosphorylated amino acids are marked with a green background. Amino acids that were experimentally confirmed to be phosphorylated are shown in bold. Experimentally verified phosphorylation site from UniProt data were marked in blue color. Hovering with the mouse over one of those will display the details for this identification or prediction just below this sequence. For predicted sites, positive score values indicate positive prediction. The higher the prediction value, the more confidence of the phosphorylation at this residue. Sequences predicted to be the phosphorylation hot spots [45] are highlighted in light-green color. Predicted Pfam domain structures are mapped onto the protein sequence and displayed in a yellow background. Below the sequence display, a list of experimentally identified phosphopeptides is available with icons indicating MS the spectrum availability, and quantitative information. 5. The mass list of each particular peptide ion can be exported as peak list (.csv format). Also at the level of primary query result, custom information can be exported as tab delimited tables, Mascot compatible .mgd format, or in Motif-X format [46]. 6. Phosphorylation site annotation in PhosPhAt 4.0 is as follows: these phosphorylation sites clearly assigned to a specific residue are marked with brackets and a lowercase p, such as (pS), (pT), and (pY). Phosphorylation sites that could not be clearly positioned with the peptide sequence based on the mass spectrum are considered as ambiguous. These sites are marked as lowercase letters in brackets, e.g., (s), (t), (y). The sites with ambiguous identification are usually putatively phosphorylated amino acids in close proximity. In PhosPhAt 4.0, the remark “site undetermined” on the modified tryptic peptide is to mark those phosphopeptides, for which no statements could be made on the location of the phosphorylation site based on the mass spectrum. These are mainly phosphopeptides identified by older generation of mass spectrometers. 7. Hovering with the mouse over the red-bold–marked residue will display the details for this motif. 8. So far, PhosPhAt 4.0 contains 325 kinases for which substrates and/or the respective phosphorylation sites have been experimentally described. In addition to that, information of 45 phosphatases has been added, for which de-phosphorylation targets are known.

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9. For each kinase–target pair, the relationship is defined based on the 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. 10. In the network view, the node represents protein and the edges indicate the relationships. The shape of the node demonstrates annotation of subcellular location by SUBA4. Arrow of the edge shows the phosphorylation direction. The different color of the edges represents annotation of different pathways. It is also possible to perform an “advanced search” by selection of parameters describing kinase, targets, family, interaction type, pathway, and the publications. Thereby, drag-and-drop is used to select the parameters of interest. Several parameters can be combined using Boolean terms “and,” “or,” as well as “not.” 11. Multiple search is supported by clicking one the families in the collapsed list at left panel. Then the list of target proteins and the experimental information for the whole family kinases or phosphatases will display (Fig. 2b). References 1. Schulze WX, Yao Q, Xu D (2015) Databases for plant phosphoproteomics. Methods Mol Biol 1306:207–216 2. Yao Q, Xu D (2017) Bioinformatics analysis of protein phosphorylation in plant systems biology using P3DB. Methods Mol Biol 1558:127–138 3. Humphrey SJ, James DE, Mann M (2015) Protein phosphorylation: a major switch mechanism for metabolic regulation. Trends Endocrinol Metab 26(12):676–687 4. Champion A, Kreis M, Mockaitis K, Picaud A, Henry Y (2004) Arabidopsis kinome: after the casting. Funct Integr Genomics 4(3):163–187 5. Wu XN, Xi L, Pertl-Obermeyer H, Li Z, Chu LC, Schulze WX (2017) Highly efficient single-step enrichment of low abundance phosphopeptides from plant membrane preparations. Front Plant Sci 8:1673 6. Schweighofer A, Meskiene I (2015) Phosphatases in plants. Methods Mol Biol 1306:25–46 7. Liu Q, Wang Q, Deng W, Wang X, Piao M, Cai D, Li Y, Barshop WD, Yu X, Zhou T, Liu B, Oka Y, Wohlschlegel J, Zuo Z, Lin C (2017) Molecular basis for blue lightdependent phosphorylation of Arabidopsis cryptochrome 2. Nat Commun 8:15234

8. Perraki A, DeFalco TA, Derbyshire P, Avila J, Sere D, Sklenar J, Qi X, Stransfeld L, Schwessinger B, Kadota Y, Macho AP, Jiang S, Couto D, Torii KU, Menke FLH, Zipfel C (2018) Phosphocode-dependent functional dichotomy of a common co-receptor in plant signalling. Nature 561 (7722):248–252 9. Ding Y, Jia Y, Shi Y, Zhang X, Song C, Gong Z, Yang S (2018) OST1-mediated BTF3L phosphorylation positively regulates CBFs during plant cold responses. EMBO J 37(8) 10. Barbosa ICR, Hammes UZ, Schwechheimer C (2018) Activation and polarity control of PIN-FORMED auxin transporters by phosphorylation. Trends Plant Sci 23(6):523–538 11. Li Z, Wang Y, Huang J, Ahsan N, Biener G, Paprocki J, Thelen JJ, Raicu V, Zhao D (2017) Two SERK receptor-like kinases interact with EMS1 to control anther cell fate determination. Plant Physiol 173(1):326–337 12. Hu C, Zhu Y, Cui Y, Cheng K, Liang W, Wei Z, Zhu M, Yin H, Zeng L, Xiao Y, Lv M, Yi J, Hou S, He K, Li J, Gou X (2018) A group of receptor kinases are essential for CLAVATA signalling to maintain stem cell homeostasis. Nat Plants 4(4):205–211

PhosPhAt 4.0: An Updated Arabidopsis Database for Searching. . . 13. Luo X, Wu W, Liang Y, Xu N, Wang Z, Zou H, Liu J (2020) Tyrosine phosphorylation of the lectin receptor-like kinase LORE regulates plant immunity. EMBO J 39(4):e102856 14. Sugano S, Maeda S, Hayashi N, Kajiwara H, Inoue H, Jiang CJ, Takatsuji H, Mori M (2018) Tyrosine phosphorylation of a receptor-like cytoplasmic kinase, BSR1, plays a crucial role in resistance to multiple pathogens in rice. Plant J 96(6):1137–1147 15. Eisenach C, Baetz U, Huck NV, Zhang J, De Angeli A, Beckers GJM, Martinoia E (2017) ABA-induced stomatal closure involves ALMT4, a phosphorylation-dependent vacuolar anion channel of Arabidopsis. Plant Cell 29 (10):2552–2569 16. Trotta A, Bajwa AA, Mancini I, Paakkarinen V, Pribil M, Aro EM (2019) The role of phosphorylation dynamics of CURVATURE THYLAKOID 1B in plant thylakoid membranes. Plant Physiol 181(4):1615–1631 17. Chan A, Carianopol C, Tsai AY, Varatharajah K, Chiu RS, Gazzarrini S (2017) SnRK1 phosphorylation of FUSCA3 positively regulates embryogenesis, seed yield, and plant growth at high temperature in Arabidopsis. J Exp Bot 68(15):4219–4231 18. Erwig J, Ghareeb H, Kopischke M, Hacke R, Matei A, Petutschnig E, Lipka V (2017) Chitin-induced and CHITIN ELICITOR RECEPTOR KINASE1 (CERK1) phosphorylation-dependent endocytosis of Arabidopsis thaliana LYSIN MOTIFCONTAINING RECEPTOR-LIKE KINASE5 (LYK5). New Phytol 215 (1):382–396 19. Kimura S, Hunter K, Vaahtera L, Tran HC, Citterico M, Vaattovaara A, Rokka A, Stolze SC, Harzen A, Meissner L, Wilkens MMT, Hamann T, Toyota M, Nakagami H, Wrzaczek M (2020) CRK2 and C-terminal phosphorylation of NADPH oxidase RBOHD regulate reactive oxygen species production in Arabidopsis. Plant Cell 32(4):1063–1080 20. Van Leene J, Han C, Gadeyne A, Eeckhout D, Matthijs C, Cannoot B, De Winne N, Persiau G, Van De Slijke E, Van de Cotte B, Stes E, Van Bel M, Storme V, Impens F, Gevaert K, Vandepoele K, De Smet I, De Jaeger G (2019) Capturing the phosphorylation and protein interaction landscape of the plant TOR kinase. Nat Plants 5(3):316–327 21. 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

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22. Zhang T, Schneider JD, Lin C, Geng S, Ma T, Lawrence SR, Dufresne CP, Harmon AC, Chen S (2019) MPK4 phosphorylation dynamics and interacting proteins in plant immunity. J Proteome Res 18(3):826–840 23. Wong MM, Bhaskara GB, Wen TN, Lin WD, Nguyen TT, Chong GL, Verslues PE (2019) Phosphoproteomics of Arabidopsis highly ABA-Induced1 identifies AT-hook-Like10 phosphorylation required for stress growth regulation. Proc Natl Acad Sci U S A 116 (6):2354–2363 24. Waterworth WM, Wilson M, Wang D, Nuhse T, Warward S, Selley J, West CE (2019) Phosphoproteomic analysis reveals plant DNA damage signalling pathways with a functional role for histone H2AX phosphorylation in plant growth under genotoxic stress. Plant J 100(5):1007–1021 25. Cheng H, Deng W, Wang Y, Ren J, Liu Z, Xue Y (2014) dbPPT: a comprehensive database of protein phosphorylation in plants. Database 2014:bau121 26. Willems P, Horne A, Van Parys T, Goormachtig S, De Smet I, Botzki A, Van Breusegem F, Gevaert K (2019) The plant PTM viewer, a central resource for exploring plant protein modifications. Plant J 99 (4):752–762 27. 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(Database issue):D1015–D1021 28. 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(Database issue): D828–D834 29. 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(Database issue):D1176–D1184 30. Ross KE, Huang H, Ren J, Arighi CN, Li G, Tudor CO, Lv M, Lee JY, Chen SC, VijayShanker K, Wu CH (2017) iPTMnet: integrative bioinformatics for studying PTM networks. Methods Mol Biol 1558:333–353 31. Schonberg A, Bergner E, Helm S, Agne B, Dunschede B, Schunemann D, Schutkowski M, Baginsky S (2014) The peptide microarray “ChloroPhos1.0” identifies new phosphorylation targets of plastid casein kinase II (pCKII) in Arabidopsis thaliana. PLoS One 9(10):e108344

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32. Dai X, Li J, Liu T, Zhao PX (2016) HRGRN: a graph search-empowered integrative database of Arabidopsis signaling transduction, metabolism and gene regulation networks. Plant Cell Physiol 57(1):e12 33. Zhang Y, Shi Y, Zhao L, Wei F, Feng Z, Feng H (2019) Phosphoproteomics profiling of cotton (Gossypium hirsutum L.) roots in response to verticillium dahliae inoculation. ACS Omega 4(19):18434–18443 34. Haj Ahmad F, Wu XN, Stintzi A, Schaller A, Schulze WX (2019) The systemin signaling cascade as derived from time course analyses of the systemin-responsive phosphoproteome. Mol Cell Proteomics 18(8):1526–1542 35. Gao J, Zhang S, He WD, Shao XH, Li CY, Wei YR, Deng GM, Kuang RB, Hu CH, Yi GJ, Yang QS (2017) Comparative phosphoproteomics reveals an important role of MKK2 in Banana (Musa spp.) cold signal network. Sci Rep 7:40852 36. Pi E, Qu L, Hu J, Huang Y, Qiu L, Lu H, Jiang B, Liu C, Peng T, Zhao Y, Wang H, Tsai SN, Ngai S, Du L (2016) Mechanisms of soybean roots’ tolerances to salinity revealed by proteomic and phosphoproteomic comparisons between two cultivars. Mol Cell Proteomics 15(1):266–288 37. Verkest A, Byzova M, Martens C, Willems P, Verwulgen T, Slabbinck B, Rombaut D, Van de Velde J, Vandepoele K, Standaert E, Peeters M, Van Lijsebettens M, Van Breusegem F, De Block M (2015) Selection for improved energy use efficiency and drought tolerance in canola results in distinct transcriptome and epigenome changes. Plant Physiol 168(4):1338–1350 38. Schwacke R, Ponce-Soto GY, Krause K, Bolger AM, Arsova B, Hallab A, Gruden K, Stitt M, Bolger ME, Usadel B (2019) MapMan4: a refined protein classification and annotation framework applicable to multi-omics data analysis. Mol Plant 12(6):879–892 39. Thimm O, Blasing O, Gibon Y, Nagel A, Meyer S, Kruger P, Selbig J, Muller LA, Rhee SY, Stitt M (2004) MAPMAN: a user-driven

tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J 37(6):914–939 40. Reiland S, Messerli G, Baerenfaller 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 41. Szick K, Springer M, Bailey-Serres J (1998) Evolutionary analyses of the 12-kDa acidic ribosomal P-proteins reveal a distinct protein of higher plant ribosomes. Proc Natl Acad Sci U S A 95(5):2378–2383 42. Turkina MV, Klang Arstrand H, Vener AV (2011) Differential phosphorylation of ribosomal proteins in Arabidopsis thaliana plants during day and night. PLoS One 6(12):e29307 43. Rodiger A, Agne B, Baerenfaller K, Baginsky S (2014) Arabidopsis proteomics: a simple and standardizable workflow for quantitative proteome characterization. Methods Mol Biol 1072:275–288 44. Mergner J, Frejno M, List M, Papacek M, Chen X, Chaudhary A, Samaras P, Richter S, Shikata H, Messerer M, Lang D, Altmann S, Cyprys P, Zolg DP, Mathieson T, Bantscheff M, Hazarika RR, Schmidt T, Dawid C, Dunkel A, Hofmann T, Sprunck S, Falter-Braun P, Johannes F, Mayer KFX, Ju¨rgens G, Wilhelm M, Baumbach J, Grill E, Schneitz K, Schwechheimer C, Kuster B (2020) Mass-spectrometry-based draft of the Arabidopsis proteome. Nature 579 (7799):409–414 45. Christian JO, Braginets R, Schulze WX, Walther D (2012) Characterization and prediction of protein phosphorylation hotspots in Arabidopsis thaliana. Front Plant Sci 3:207 46. 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 15 Computational Phosphorylation Network Reconstruction: An Update on Methods and Resources Min Zhang and Guangyou Duan Abstract Most proteins undergo some form of modification after translation, and phosphorylation is one of the most relevant and ubiquitous post-translational modifications. The succession of protein phosphorylation and dephosphorylation catalyzed by protein kinase and phosphatase, respectively, constitutes a key mechanism of molecular information flow in cellular systems. The protein interactions of kinases, phosphatases, and their regulatory subunits and substrates are the main part of phosphorylation networks. To elucidate the landscape of phosphorylation events has been a central goal pursued by both experimental and computational approaches. Substrate specificity (e.g., sequence, structure) or the phosphoproteome has been utilized in an array of different statistical learning methods to infer phosphorylation networks. In this chapter, different computational phosphorylation network inference-related methods and resources are summarized and discussed. Keywords Phosphorylation network, Kinase–substrate relationship, Phosphatase–substrate relationship, Protein–protein interaction, Network reconstruction, Posttranslational modification

1

Introduction Posttranslational modifications (PTMs) of proteins extend the range of possible protein functions by the covalent attachment of various chemical moieties through enzymatic modification of proteins after translation. More than 600 different types of PTMs have been identified that affect many aspects of cellular functionalities, such as metabolism [1], signal transduction [2], and protein stability [3]. PTMs of proteins represent a major level of regulation, from being a very fast and reversible to a slow or irreversible process. These modifications include phosphorylation, glycosylation, methylation, acetylation, and many other types, see http://www.uniprot. org/docs/ptmlist for a more detailed controlled vocabulary of PTMs curated by UniProt [4]. PTMs are not independent, and in the past few years, evidence for crosstalk between PTMs has accumulated [5].

Xu Na Wu (ed.), Plant Phosphoproteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2358, https://doi.org/10.1007/978-1-0716-1625-3_15, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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As one of the most ubiquitous posttranslational modifications, protein phosphorylation is a reversible PTM of proteins in which an amino acid residue is phosphorylated by a protein kinase (PK) by adding a covalently bound phosphate group [6]. Dephosphorylation is the reverse reaction of phosphorylation catalyzed by protein phosphatases (PPs) [7]. Protein kinases and phosphatases work coordinately to regulate the function of proteins [8], and protein phosphorylation or dephosphorylation can form the basis of many critical processes, including enzyme activation or inactivation, protein localization, or protein degradation. Compared with PKs, the study of PPs has been less emphasized due to past difficulties and unsuccessful attempts to target them; however, PPs have regained strong attention in clinical trials [9]. With the advancement of genomics and functional genomics, comprehensive and exact classification of protein kinases [10] and phosphatases [11] provides a solid starting point for comprehensive analysis of protein phosphorylation events in cellular systems. One key question from the study of phosphorylation events is to deduce the kinase–substrate relationships (KSRs) or phosphatase–substrate relationships (PSRs). KSRs and PSRs are the elementary processes in forming the molecular phosphorylation network. Once substrates phosphorylated, a series of phosphorylation events will be initiated. Phosphorylation-mediated protein interaction is also one important component of the phosphorylation network. The phosphorylation network can be treated as a special type of protein interaction network and describes the set of proteins undergoing phosphorylation/dephosphorylation events and the interactions among them. With the advancement of experimental techniques such as mass spectrometry (MS)-based phosphoproteomics, thousands of proteins in different species have been found to undergo phosphorylation [12]. Different experimental methods have been developed to reconstruct the phosphorylation network including in vivo kinase assays and protein microarrays [13]. Other experimental perturbation methods like genetic ablation can also be used to assist in the inference of phosphorylation networks [14], but with high time and cost requirements. Although large amount of phosphorylation sites has been detected experimentally, unfortunately, it is rarely known which kinase(s) phosphorylates those sites. Many computational methods employing an array of different statistical learning methods have been developed to infer phosphorylation networks based on different types of molecular data sets such as protein sequence, protein structure, or phosphoproteomics data. With the development of phosphoproteomics, especially quantitative phosphoproteomics techniques, more condition-specific and temporal phosphorylation information has become available, which can be utilized to further improve the phosphorylation network reconstruction performance

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[15, 16]. In the following sections, different types of phosphorylation network reconstruction methods and resources will be introduced.

2

Basis of Phosphorylation Network Reconstruction

2.1 (De-) Phosphorylation Substrate Specificity

Kinase or phosphatase substrate specificity is the key evidence that makes computational phosphorylation network reconstruction possible. There is a widely accepted assumption that kinase–substrate interactions are accomplished with kinase–domain interactions, and most of the (de-)phosphorylation sites and adjacent regions are conserved to some degree. Table 1 shows some manually curated short linear motifs (SLiMs) related to phosphorylation sites from the ELM resource [17], and those phosphorylation sites motifs could be shown with regular expression. A further comprehensive compendium of curated human phosphorylation-based substrate and binding motifs can be accessible from the HPRD database (http://www.hprd.org/PhosphoMotif_finder) [18]. Only sequence-level conservation is insufficient to elucidate substrate specificity, and spatial context of phosphorylation sites has also been explored [19]. Characteristic spatial distributions of amino acid residue types around phosphorylation sites have been successfully applied to predict kinase-specific substrates [19]. These substrate specificities could be quantified by regular expression, position-specific scoring matrix (PSSM), hidden Markov models (HMMs), or other metrics. Elucidation of phosphatase substrate specificity has also been pursued in the PPs community [11], mainly by experimental studies which are time-consuming. Like kinase substrate specificity, most of the phosphatase substrate specificities are discernable too, but there is much more space for the study of phosphatase substrates. One basic or naı¨ve assumption would be that each phosphorylated site catalyzed by PKs should also be dephosphorylated by PPs to balance the related phosphorylation events. In order to pursue a clear picture of PSRs, both experimental and computational approaches would be appreciated.

2.2 Protein–Protein Interaction Network

Protein–protein interactions (PPIs) play a fundamental role in biological processes. Huge numbers of experimental or predicted PPIs have been published, which provide valuable information to explore protein functions. Phosphorylation can create binding sites for specific protein-interaction domains, and it plays an important role in the modulation of PPIs [20]. Many PPIs database resources exist (Table 2) and some of them can be conveniently queried by the integrated query system such as PSICQUIC (http://www.ebi.ac.uk/Tools/webservices/psicquic/ view/main.xhtml) [21] or iRefWeb (http://wodaklab.org/

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Table 1 Phosphorylation sites-related ELM motifs ELM identifier

Functional site name

Regular expression

MOD_CDK_SPK_2

CDK phosphorylation site

. . .([ST])P[RK]

MOD_CDK_SPxK_1

CDK phosphorylation site

. . .([ST])P.[KR]

MOD_CDK_SPxxK_3 CDK phosphorylation site

. . .([ST])P..[RK]

MOD_CK1_1

Casein kinase 1 (CK1) phosphorylation site

S..([ST]). . .

MOD_CK2_1

Casein kinase 2 (CK2) phosphorylation site

. . .([ST])..E

MOD_GSK3_1

GSK3 phosphorylation site

. . .([ST]). . .[ST]

MOD_NEK2_1

NEK2 phosphorylation site

[FLM][^P][^P]([ST])[^DEP][^DE]

MOD_NEK2_2

NEK2 phosphorylation site

[WYPCAG][^P][^P]([ST])[IFCVML] [KRHYF]

MOD_PIKK_1

PIKK phosphorylation site

. . .([ST])Q..

MOD_PK_1

PK phosphorylation site

[RK]..(S)[VI]..

MOD_PKA_1

PKA phosphorylation site

[RK][RK].([ST])[^P]..

MOD_PKA_2

PKA phosphorylation site

.R.([ST])[^P]..

MOD_PKB_1

PKB phosphorylation site

R.R..([ST])[^P]..

MOD_ProDKin_1

MAPK phosphorylation site

. . .([ST])P..

MOD_TYR_CSK

TYR phosphorylation site

[TAD][EA].Q(Y)[QE].[GQA][PEDLS]

MOD_TYR_DYR

TYR phosphorylation site

..[RKTC][IVL]Y[TQHS](Y)[IL]QSR

Table 2 List of selected PPIs databases Name

URL

Type

STRING [68]

http://string.embl.de/

Experimental/predicted

BioGRID [69]

http://thebiogrid.org/

Experimental

IntAct [70]

http://www.ebi.ac.uk/intact/

Experimental

DIP [71]

http://dip.doe-mbi.ucla.edu/dip/

Experimental

IMEx [72]

http://www.imexconsortium.org/

Experimental

iRefWeb/search/index) [22]. Table 2 only lists several commonly used PPI databases. There exist many other databases, containing large-scale studies or with prediction services, especially for speciesspecific and protein family-specific PPIs resources [23–26]. These

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high-quality and comprehensive PPI databases are useful for the exploration of phosphorylation events. 2.3 Quantitative Phosphorylation Level Profiling

3

High-throughput OMICs techniques, performed by measuring many molecules in parallel, have resulted in large quantitative combinational datasets of mRNAs [27], proteins [28], or metabolites [29]. Phosphoproteomic techniques have generated a large volume of protein phosphorylation level datasets [12]. These OMICsderived datasets necessitated the development and application of network inference methods to better understand the complex relationships among molecules. Different types of network reconstruction methods have been developed and applied to infer correlational or causal relationships between molecules [30]. Network reconstruction approaches can be categorized into either bottom-up or top-down strategies, depending on if the network was treated as a whole or not [31]. The correlationbased bottom-up approach is most widely used as it is simple and intuitively best suited for the task of biological network reconstruction [15]. Model-based top-down approaches such as Bayesian network (BN) [32] and ordinary differential equations (ODEs) [33] have also been widely used to infer molecular networks, especially when a large number of samples are available. Several selected biological network reconstruction packages available for the popular statistical programming language R (https://www.r-project.org) are listed in Table 3. They can be used directly or customized depending on the users’ exact requirements.

(De-)Phosphorylation Substrate Prediction With the broad application of the phosphoproteomics technique, many phosphorylation sites and phosphorylated proteins have been identified across different species. Specialized phosphorylation sites databases have been developed to capture this generated information (see Table 4). Compared with the increasingly accumulated phosphorylation site information, de-phosphorylation site information is still rare [34] which requires further community effort and input. Identification of phosphorylation site is just the basic starting point for the study of phosphorylation events, but because of experimental limitations only a small group of phosphorylated proteins have their corresponding PKs or PPs identified. Computational methods are needed to fill this gap [35] as summarized in the following paragraphs. The (de-)phosphorylation substrate specificity provides solid evidence to these kinds of substrate prediction methods. There are also other (de-)phosphorylation substrates, other than proteins such as lipids or saccharides, but this chapter will only focus on protein substrates.

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Table 3 List of available R packages for molecular network reconstruction Name

Method

URL

WGCNA [73]

Correlation

https://horvath.genetics.ucla.edu/html/ CoexpressionNetwork/Rpackages/WGCNA/

Minet [74]

MI

http://www.bioconductor.org/packages/release/bioc/ html/minet.html

Inferelatora Regression [75]

Available upon request from authors

Cascade [76]

Regression

https://cran.r-project.org/web/packages/Cascade/index. html

Gemonet [77]

Regression + BN

http://www.stat.nthu.edu.tw/~wphsieh/causalinference. html

G1DBN [78]

DBN

http://cran.r-project.org/web/packages/G1DBN/index. html

ARTIVA [79]

DBN

http://cran.r-project.org/web/packages/ARTIVA/index. html

TSCGM [80]

Graphical Gaussian Model (GGM) + DBN

http://www.math.rug.nl/stat/uploads/Main/CGsparse_ 1.0.zip

DELDBN [81]

ODE + DBN

File 3 in supplementary material of the original paper

Only the key methods from different packages are listed, some packages may contain other kinds of methods inherent. DBN dynamic Bayesian network, GGM graphical Gaussian model, BN Bayesian network, MI mutual information, ODE ordinary differential equation a Python version is available: https://github.com/flatironinstitute/inferelator

3.1 Kinase-Generic Phosphorylation Site Prediction

Whether or not a protein can be phosphorylated is quite often a basic question for most researchers, especially when the information about the related PKs is insufficient. Some kinase-generic phosphorylation site prediction methods or tools have been developed to provide a low-cost and fast alternative to performing experiments. Phosphorylation site-related features such as sequence, structure, and physicochemical attributes have been utilized across different methods (mainly machine learning-based methods) to predict potential phosphorylation sites. Table 5 summarize some of the available kinase-generic phosphorylation site prediction services. A kinase-generic phosphorylation site predictor could be treated as a special kind of kinase-specific phosphorylation substrate predictor without distinguishing PKs.

3.2 Kinase-Specific Phosphorylation Site Prediction

Kinase–substrate relationships (KSRs) are important component of phosphorylation networks. With more kinase substrates experimentally verified, many kinase-specific phosphorylation site predictors have been developed for many protein kinase families. Like

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Table 4 List of selected available protein phosphorylation site information databases Name

Species

URL

PTM type

Evidence

UniProt [82]

All

http://www.uniprot.org

All

Experimental/ predicted

dbPTM [83]

All

http://dbptm.mbc.nctu.edu. tw/

All

Experimental/ predicted

PHOSIDA [84]

All

http://www.phosida.com/

Phosphorylation Experimetnal

PhosPhAt [85]

Arabidopsis thaliana

http://phosphat.unihohenheim.de/

Phosphorylation Experimental

FAT-PTM [86]

Arabidopsis thaliana

https://bioinformatics.cse.unr. All edu/fat-ptm/

Phospho.ELM [87]

Eukaryotes

http://phospho.elm.eu.org/

Phosphorylation Experimental

HRPD [88]

Human

http://www.hprd.org/

All

Experimental

PhosphoSitePlusa Human/ [89] mouse/rat

http://www.phosphosite.org/

All

Experimental

dbPPT [90]

http://dbppt.biocuckoo.org/

Phosphorylation Experimental

Plants

Experimental

Some of them also provide the corresponding kinase information a Commercial resource

Table 5 List of selected kinase-generic phosphorylation site predictors Name

Species

URL

Method

NetPhos 2.0 [91]

Eukaryotes

http://www.cbs.dtu.dk/services/NetPhos-2.0

NN

http://www.dabi.temple.edu/disphos/

Linear

Bacteria

https://services.healthtech.dtu.dk/service.php? NetPhosBac-1.0

NN

NetPhosYeast [93]

Yeast

https://services.healthtech.dtu.dk/service.php? NetPhosYeast-1.0

NN

PhosphoRice [94]

Rice

https://github.com/PEHGP/PhosphoRice

Meta

DIPHOS [92] All predictor NetPhosBac

predictor PhosTryp [95] Trypanosomatidae http://phostryp.bio.uniroma2.it/

SVM

SVM support vector machine, NN neural network

kinase-generic phosphorylation site prediction, kinase-specific phosphorylation site predictors were developed based on phosphopeptide sequence conservation and different classification methods

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Table 6 List of selected kinase-specific phosphorylation site predictors Name

URL

Method

Phos3D [19]

https://phos3d.mpimp-golm.mpg.de/

SVM + structure

SubPhos [65]

http://bioinfo.ncu.edu.cn/SubPhos.aspx

SVM + filter

KinasePhos [96]

http://kinasephos2.mbc.nctu.edu.tw/

SVM

NetworKIN [38]

http://networkin.info/

Scoring system + filter

iGPS [39]

http://igps.biocuckoo.org/

Scoring system + filter

PKPred [97]

http://bioinfo.ncu.edu.cn/PKPred_Home.aspx

Scoring system

GPS [98]

http://gps.biocuckoo.cn/

Scoring system

PhoScan [99]

http://bioinfo.au.tsinghua.edu.cn/phoscan/

Scoring system

ScanSite [100]

https://scansite4.mit.edu/4.0/

Scoring system

PREDIKIN [101]

http://predikin.biosci.uq.edu.au/

Scoring system

PostMod [102]

http://pbil.kaist.ac.kr/PostMod/

Scoring system

PhophoMotif Finder http://www.hprd.org/PhosphoMotif_finder [18]

Scoring system

PhosphoNET [103] http://www.phosphonet.ca/

Scoring system

NetPhosK [104]

http://www.cbs.dtu.dk/services/NetPhosK

NN

Musite [105]

http://musite.sourceforge.net/

Meta predictor

PhosD [106]

http://comp-sysbio.org/phosd/

Domain based probabilistic model

NetPhospan [107]

https://services.healthtech.dtu.dk/service.php? NetPhospan-1.0

CNN

PPSP [108]

http://ppsp.biocuckoo.org/

Bayesian decision theory

SVM support vector machine, CNN convolutional neural network, NN neural network

(e.g., position-specific scoring matrix, hidden Markov model, support vector machine). Meanwhile, prediction methods of protein kinases for experimentally discovered protein phosphorylation sites have also been developed [36]. Table 6 shows several kinase-specific phosphorylation substrate inference services. Despite best efforts, false-positive predictions of phosphorylation site cannot be avoided based solely on phosphopeptide sequence or structural information [37]. Additional orthogonal information types such as information on protein–protein interactions or protein subcellular localization have been integrated to filter out potential false-positive predictions [38, 39]. Kinasespecific phosphorylation site predictions are even more challenging when phosphorylation sites are targeted by more than one kinase

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and possibly in a condition-specific way, and quantitative proteomic data may help to address this challenge. 3.3 Phosphatase– Substrate Relationships (PSRs) Prediction

4

As another important component of phosphorylation networks, the systematic computational studies of PSRs are still very rare. After comprehensive analysis of the protein sequences surrounding sites of dephosphorylation in humans [11], similar common substrate recognition mechanisms as PKs were suggested for a subset of PPs. A sequence-based substrate prediction method with biologically meaningful filters and molecular docking for one protein tyrosine phosphatase PTP1B was developed and showed its potential to identify novel peptide substrates [40]. Phosphorylation and de-phosphorylation is one inherent pair of protein regulation mechanism, but former studies were biased to PKs or phosphorylation apparently and there is still a long journey to deduce a clear landscape of PKs-substrate-PPs. Experimental techniques still need to evolve to better study PPs in the near future.

Phosphorylation-Mediated Protein Interaction Network Reconstruction Protein substrate modifications by PPs or PKs are one kind of initial phosphorylation events, which could further influence PPIs by changes of protein structural conformation. Text-mining techniques have been applied to extract these kind of phosphorylation events by connecting phosphorylated proteins with kinases and interaction partners from scientific literature like the eFIP system [41]. Manual curation assisted with text mining techniques will still be valuable to extract (de-)phosphorylation information from dispersed literature [34]. In this section, utilization of PPI and quantitative proteomics data in phosphorylation network reconstruction are introduced.

4.1 Protein–Protein Interactions (PPI) Mapping

Protein phosphorylation plays an important role in the modulation of protein–protein interactions [20, 42]. As described in [16, 43], phosphoproteins in yeast and Arabidopsis thaliana have significantly higher interaction degree than average proteins, which implies that they play a central role in cellular processes including protein–protein interactions. As mentioned in Subheading 3.2, PPIs have also been used to filter out false-positive KSRs. The integration of PPI in the context of protein phosphorylation has been applied to the study of the DNA damage response (DDR) [44], the downstream signaling of EGFRvIII (a truncated extracellular mutant of the EGF receptor) [45], and many others. As introduced in Subheading 2.2, PPIs have been accumulated through a lot of data across different species, but the detailed roles of phosphorylation in PPIs still need to be clarified.

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4.2 Inference Methods Exploiting Quantitative Proteomics Data

Substrate specificity-based phosphorylation network reconstruction introduced above cannot capture the dynamics of phosphorylation events. With the development of quantitative phosphoproteomics [46], especially time-course quantitative phosphoproteomics [47], phosphorylation networks could be reconstructed with higher accuracy and specificity, under temporal resolution. In general, network reconstruction introduced in Subheading 2.2 could be applied using quantitative phosphoproteomics data. Although different efforts were made to infer complex networks using quantitative phosphoproteomics data, intuitive and linear correlation-based network reconstruction methods have been used most frequently for the purpose of large-scale phosphorylation network reconstruction considering the obvious limitations of the quantitative phosphoproteomics technique (e.g., limited sample numbers). Pearson correlation was applied to the reconstruction of the phosphorylation network of the epidermal growth factor (EGF)-stimulated HeLa cells [15], nutrient-induced phosphorylation networks in Arabidopsis [16], and some others [48]. As one information-based method, maximum entropy was employed to reconstruct a tyrosine signaling pathway [49]. Regression-based approaches have also been applied to infer the phosphorylation network in some studies [50], especially for small-scale phosphorylation networks. Specifically, multiple linear regression (MLR) was used to study the phosphorylation networks in primary hepatocytes and hepatocellular carcinoma cell lines, and partial least square regression (PLSR) was applied to study mammalian cell apoptotic response processes [51, 52]. Although the application of model-based approaches was hampered by the limitation of low number of time points, there were still some efforts in this direction. As one canonical model-based method, Bayesian network [53] was used to infer the downstream signaling of CD3, CD28, and LFA-1 activation in human primary CD4+ T cells [54] and the signaling downstream of EGF stimulation [55]. Ordinary differential equations, which are employed quite often in mathematical modeling, were applied to study ErbB-activated pathways [33]. Logic modeling including Boolean logic or constrained fuzzy logic modeling was also applied to study signaling networks in hepatocellular carcinoma cells [56, 57]. Modular response analysis, which aims to detect network topology responses after different stimulations, was employed to infer MAPK networks in response to NGF or EGF stimulation [58]. Finally, multiple-inputs-multiple-outputs (MIMO) models that predict the quantitative outcomes of combinatorial perturbations were utilized to the study of EGFR/MAPK and PI3K/AKT pathways in a breast cancer cell line [59].

Computational Phosphorylation Network Reconstruction: An Update on Methods. . .

4.3 Heterogeneous Data-Based Integration Method

5

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As mentioned in Subheading 3.2, protein–protein interactions or protein subcellular localization information have been utilized to filter out false-positive KSRs, such as NetworKIN [38], iGPS [39], and PhosphoPICK [60]. With the increasing accumulation of prior biological knowledge, integration methods based on heterogeneous data sources have been developed. A Bayesian framework that can integrate different kinds of information was used to reconstruct phosphorylation networks in stimulated cells by integrating different types of data in addition to quantitative phosphoproteomics data, such as peptide sequence data, genomic context data (e.g., gene fusion, gene neighborhood, phylogenetic profiles), primary experimental evidence (e.g., physical protein interaction, gene co-expression), known pathway databases, and literature mining [61]. Rule-based methods have also been employed to reconstruct phosphorylation networks by integrating different types of prior information and knowledge [62, 63]. The mRNA expression level, which may be treated as a proxy for protein activity, was integrated in PhosphoChain to predict a phosphorylation network in yeast with a motif detection algorithm and optional prior information [64]. Compared with transcriptional regulatory networks, prediction of protein phosphorylation networks remains a significant challenge and there is no one-stop solution in general. Most of the successful phosphorylation network reconstruction studies needed customization depending on the inherent biological questions and prior knowledge. Obviously, other experimental perturbation methods such as genetic ablation could further assist in the generation of phosphorylation networks [64].

Summary and Future Challenges Reconstruction of phosphorylation networks bears great potential to investigate the corresponding cellular signaling. As summarized and discussed in this chapter, many different computational methods have been developed to infer phosphorylation networks based on different strategies and data types, which constitute an important step for the following network modelling approaches such as exploring more detailed kinetic information for the corresponding phosphorylation signaling cascades. Most of the published studies for phosphorylation networks generation have been based on the data from organ, tissue, or cell lines; however, phosphorylation events are also cell type-specific and cellular compartment-dependent [65, 66]. With the advancement of single-cell experimental techniques, reconstruction of cell or subcellular localization specific phosphorylation network would be another important direction for future research.

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Chapter 16 The Application of an R Language-Based Platform cRacker for Phosphoproteomics Data Analysis Mingjie He and Zhi Li Abstract Phosphoproteomics has drawn great attention of biologist since phosphorylation is proven to play an important role in regulation of proteins. Mass spectrometry technology has helped with the development of phosphoproteomics due to its ability in generating large amount of detailed information after analyzing the phosphoproteome samples. However, interpreting the phosphoproteome data deprived from mass spectrometry can be time-consuming. Here, we introduced a free R language-based platform which can be used in accelerating phosphoproteome data analysis. This platform has integrated different functions and methods which are popularly used in phosphoproteome data analysis, so users can customize their analysis according to their demands. Keywords Mass spectrometry, Phosphoproteomics data analysis, R language, cRacker

1

Introduction Phosphorylation is one of numerous protein posttranslational modifications. Protein phosphorylation has been proven critical in regulating protein activity, protein–protein interaction, and for proper protein localization [1–3]. With the help of liquid chromatography-coupled mass spectrometry (LC-MS), it is possible to identify and quantify thousands of phosphopeptides in a few runs from a complex sample peptide mixture. Therefore, LC-MSbased phosphoproteomics has become a valuable tool in studying phosphorylation-involved regulatory mechanisms of different organisms [4–7]. To date, several protein identification and quantification platforms have been widely used to help with interpreting MS-based phosphoproteome raw data, such as MaxQuant [8], MSQuant [9], and Thermo Scientific™ Proteome Discoverer™ Software. However, these tools only provide a list of quantified peptide ions. In order to get biologically meaningful results, several time-consuming processes still need to be accomplished. To

Xu Na Wu (ed.), Plant Phosphoproteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2358, https://doi.org/10.1007/978-1-0716-1625-3_16, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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facilitate these processes, various computer programs have been developed. In 2012, cRacker, an R language-based platform for proteomic data analysis, was used to process a preliminary proteome dataset derived from MaxQuant [10]. Here we would like to further introduce the application of cRacker for phosphoproteomic data analysis (PDA).

2

Materials cRacker is written in the R language, in principle cRacker can run on all machines that can execute R. Therefore, a computer operation system with preinstalled R is required. cRacker can be freely downloaded under the URL http://cracker.mpimp-golm.mpg.de. An R source package is distributed under http://r-forge.r-project. org. There is no need of installation for cRacker. Once downloaded and decompressed, the program can be started through doubleclicking on cRacker-windows.exe [10].

3 3.1

Methods Loading the Data

1. Once cRacker has been started, select a folder containing the raw peptide list (Fig. 1) (see Note 1). 2. The quantitation software from which the raw peptide list was deprived can be chosen in the pop-up window. 3. A specific parameter or data can be used by loading parameters. Rdata file or import-binary.Rdata from a previous experiment.

3.2 Parameters and Settings

There are two different quantitation methods in cRacker, one is emPAI mode [10, 11] the other is ion intensities mode. For PDA, ion intensities mode is required (Fig. 2).

3.2.1 The “Main” Tab

This tab used to deal with peptide intensities between replicate samples and duplicate identifications of a peptide species in each sample. 1. In the Main tab, choosing “Average Replicates” means peptide intensities between replicate samples would be averaged to a protein intensity. 2. If “Average Replicates” was chosen, then one average method should be selected through clicking the “Averaging Method” pop-up box. In this box, there are three options available, including “mean,” “median,” and “sum.” 3. The “Peptide Duplicates” controls how to proceed with duplicate identifications of a peptide species in each sample. cRacker

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Fig. 1 Start window of cRacker

Fig. 2 Tab “Main” in parameter window

contains five options for this function including “mean,” “sum,” “max,” “min,” and “exclude.”

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4. “Exclude Contaminants” can remove specific accessions matching the regular expression (strings that define contaminants) in the “import-config.csv” file. 3.2.2 The “Peptides!Proteins” Tab 3.2.3 The “Extra” Tab

In a PDA, this tab is not applicable.

1. In “Phospho Peptides” pop-up tab, user can use “peptidebased analysis” or “protein-based analysis.” For PDA, “peptide-based analysis” should be used (Fig. 3). 2. “Outlier Exclusion” choosing none.

function

should

be

disabled

by

3. “Zero Handling” can replace intensities of value 0 with a number user defined or “NA” by default. 3.2.4 The “Quantitation Mode” Tab

Three quantitation strategies are available in cRacker, including “Label Free,” “Reference Protein Normalization,” and “Labelled Normalization” (Fig. 4). However, “Reference Protein Normalization” is not suitable for PDA. 1. In “Label Free” normalization mode, there are three options offered, user can choose “fraction of total (fot),” “fot plus n correction,” or “no normalization.” 2. “Labelled Normalization” can be activated if isotopic-labeled peptides were used for quantitation. If this function was activated, user can also choose to enable log2 transformation of ratios between labeled peptide intensities and unlabeled peptide intensities by clicking “log2” box. And log2 ratios can be corrected through activating “correct log2 ratios.” Moreover, log2 ratios can be corrected to an expected ratio which was taken from the entry in the “expected ratio labelled: unlabelled” box. 3. The function “Reference Protein Normalization” is not activated (see Note 2).

3.2.5 The “Statistics” Tab

In a PDA, this tab is not applicable.

3.2.6 The “Plotting” Tab

In a PDA, this tab is not applicable.

3.2.7 The “Paths” Tab

1. In “experimental design” box, user can create an experimental design (ED) file by clicking “create” button (see Fig. 5). Then a tab delimited template called “experimental-design-cRacker. tab” is created in the data folder (see Table 1). User can define this ED file according to the experiment.

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Fig. 3 Tab “Extra” in parameter window

2. In the ED file, the column “Name” defines the name of the sample. These names must not be changed. 3. The column “Experiment” defines the replicates. Samples with the same Experiment name are defined as replicates. 4. The column “Group” specifies groups of samples of experiments. If “group scaling” is enabled (in tab “Paths”), this entry will be additionally used for group-specific scaling of peptide/ protein intensities. Each group will be scaled independently. As default, all samples belong to the same group. 5. The “Group.Filter” column can be used to filter peptides according to their occurrence frequency in defined groups. To use this option, “group shaping” must be activated. 6. The “Alternative.name” column defines aliases of sample names which will be used in the output. 7. The “Order” column is not applicable in a PDA, leave it as default.

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Fig. 4 Tab “Quantitation mode” in parameter window

8. The “Time” column is not applicable in a PDA, leave it as default. 9. In the “Include” column, those experiments with value “1” will be included for calculation, those experiments with value “0” will be excluded for calculation. 10. The function “group scaling” can be activated to scale peptides of different groups independently. 11. The function “group filter” can be activated to filter peptides according to their occurrence in the specified groups and not the whole dataset (see Note 3). 3.3

Output

The results will be written into a newly created folder. This folder contains the results in the form of “csv” and “pdf” or “eps” files. The “peptidlist.csv” file contains phosphopeptides with identifications and values calculated according to parameters that the user has set, and this file can be used for further PDA (see Note 4).

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Fig. 5 Tab “Paths” in parameter window Table 1 Experimental-design-cRacker.tab Name

Experiment

Group

Group.Filter

Alternative.name

Order

Time

Include

Treatment1

Treatment

1

1

Stimuli1

1

1

1

Treatment2

Treatment

1

1

Stimuli2

2

1

0

Control1

Control

2

1

Con1

3

1

1

Control2

Control

2

1

Con2

4

1

0

4

Notes 1. If you used MaxQuant to identify peptide list, you can load the “evidence.txt” directly. 2. “Reference Protein Normalization” function is used for proteome data analysis, that is why this function should not be activated for phosphoproteome data analysis.

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3. cRacker may report some errors during data processing, but most of these error problems can be solved through updating the R language packages used by cRacker. 4. cRacker can only parse phosphoproteomics data by outputting a phosphopeptide list which was calculated according to parameters that the user has set, and it cannot perform further analysis or plotting since cRacker was initially designed to do proteome data analysis. More functions for PDA are currently under development. However, using cRacker can still save a lot of time for PDA users. References 1. Lee SC, Lan W-Z, Kim B-G et al (2007) A protein phosphorylation/dephosphorylation network regulates a plant potassium channel. Proc Natl Acad Sci U S A 104 (40):15959–15964 2. Moorhead G, Douglas P, Cotelle V et al (1999) Phosphorylation-dependent interactions between enzymes of plant metabolism and 14-3-3 proteins. Plant J 18(1):1–12 3. Schwessinger B, Roux M, Kadota Y et al (2011) Phosphorylation-dependent differential regulation of plant growth, cell death, and innate immunity by the regulatory receptor-like kinase BAK1. PLoS Genet 7(4):e1002046 4. Bodenmiller B, Mueller LN, Mueller M et al (2007) Reproducible isolation of distinct, overlapping segments of the phosphoproteome. Nat Methods 4(3):231–237 5. Ficarro SB, McCleland ML, Stukenberg PT et al (2002) Phosphoproteome analysis by mass spectrometry and its application to Saccharomyces cerevisiae. Nat Biotechnol 20 (3):301–305

6. Mann M, Ong S-E, Grønborg M et al (2002) Analysis of protein phosphorylation using mass spectrometry: deciphering the phosphoproteome. Trends Biotechnol 20(6):261–268 7. Zhai B, Ville´n J, Beausoleil SA et al (2008) Phosphoproteome analysis of Drosophila melanogaster embryos. J Proteome Res 7 (4):1675–1682 8. Griffin NM, Yu J, Long F et al (2010) Labelfree, normalized quantification of complex mass spectrometry data for proteomic analysis. Nat Biotechnol 28(1):83–89 9. Journet E-P, Bligny R, Douce R (1986) Biochemical changes during sucrose deprivation in higher plant cells. J Biol Chem 261 (7):3193–3199 10. Zauber H, Schulze WX (2012) Proteomics wants cRacker: automated standardized data analysis of LC–MS derived proteomic data. J Proteome Res 11(11):5548–5555 11. Mortensen P, Gouw JW, Olsen JV et al (2010) MSQuant, an open source platform for mass spectrometry-based quantitative proteomics. J Proteome Res 9(1):393–403

Chapter 17 Kinase Activity Assay Using Unspecific Substrate or Specific Synthetic Peptides Jiahui Wang, Xiaolin Yang, Lin Xi, and Xu Na Wu Abstract Phosphorylation of a substrate by protein kinases leads to the activation or inactivation of numerous signaling pathways and metabolic processes. The assessment of kinase activity by using a specific or generic substrate plays a crucial role in characterization of kinase specificity and activity. Here we describe a protocol using either a synthetic peptide as a specific substrate or using myelin basic protein (MBP) as a generic substrate for the kinase activity assay. The kinase of interest is fused with a GFP (green fluorescent protein) tag and can be purified by GFP magnetic beads. Kinase–GFP complexes are then incubated with ATP, substrate, and coordinated reaction reagent for the kinase reaction. The assay is then quantified through mass spectrometry or enzymatic luminescence. Keywords Kinase substrates, Magnetic Kinase-GFP purification, Phosphorylation detection, Enzymatic luminescence, Mass spectrometry

1

Introduction The protein kinases belong to a large family that is responsible for the mechanism of phosphorylation [1]. They catalyze the transfer of a phosphate group (PO4) from ATP to the side chain of specific polar amino acids on target proteins. The majority of amino acids which are phosphorylated are serine (Ser), threonine (Thr), and tyrosine (Tyr). In Arabidopsis, the relative frequency of phosphorylation is typically 80–85% for Ser, 10–15% for Thr, and 0–5% for Tyr [2]. Sometimes, phosphorylation of histidine (His) and aspartate residues (Asp) can also occur, but these moieties are usually more transient than the others [3, 4]. These phosphorylation events are vital for proper cellular processes such as the regulation of metabolism, protein subcellular trafficking, and gene transcription [1]. Therefore, knowing the specific targets of the kinases and kinase activity are essential in the study of cell signaling mechanisms.

Xu Na Wu (ed.), Plant Phosphoproteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2358, https://doi.org/10.1007/978-1-0716-1625-3_17, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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The most common method for measuring kinase activity is to incubate a purified protein kinase with its target protein(s) or peptide substrate and radiolabeled [γ -32P]-ATP [5–8]. The subsequently phosphorylated substrate proteins or peptides are then analyzed by SDS-page and autoradiography. For a substrate containing a known phosphorylation site, the phosphorylation state of that specific residue can also be analyzed by phospho-specific antibodies, if available [9–12]. As well, many non-radiometric fluorescence methods were created to measure the activity of protein kinases such as homogeneous time-resolved fluorescence/lanthanide chelate excitation (HTRF/LANCE) and fluorescence polarization (FP) [13–16]. Both methods require unlabeled antibodies, and the distance between the donor and acceptor fluorophores (for HTRF/LANCE) or the length of the peptide substrate (for FP) needs to be considered [17]. Although these methods depend on a phospho-specific antibody, the fluorescence/luminescence assays allow the researcher to measure the kinase activity of virtually any kinase. As all kinases utilize ATP as a cofactor, another method for kinase activity assay is to quantify the amount of unreacted ATP using luminescent detection coupled to a luciferase assay to monitor the kinase reaction. In this method, the readout of the luminescence signal is proportional to the amount of ATP in the solution and is inversely correlated with kinase activity [17]. As quantitative mass spectrometry-based proteomic methods became more popular and accessible, combining mass spectrometry with other biochemistry techniques permits us to analyze kinase– substrate relationships in a large scale. An example of this is to use phospho-specific antibodies to enrich phosphorylated proteins for the identification of kinase substrates [18]. In addition, a purified kinase, or its kinase complex, can be incubated with the potential substrate proteins, or a substrate peptide mixture, and the resulting phosphorylation event can be detected by mass spectrometry. Such mass spectrometry-based kinase assays, using synthetic peptides as the substrate, is extremely useful for studying a kinase activity with a known substrate. In general, for kinases with known substrates, peptide-based kinase assays against immobilized peptides (microarrays) are frequently utilized. As well, kinase assays of peptides in solution can be used in combination with mass spectrometry for detection and quantitation of substrate phosphorylation. In the case, where an endogenous substrate(s) is not known, the generic alternative kinase substrates can also be used to test kinase activity in many instances [19]. Myelin basic protein (MBP) is one of the most successfully applied generic substrates. It has been used to detect the kinase activity of mitogen-activated protein kinases (MAPKs) in different biological systems [20–25]. Recently,

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its use has been expanded to measuring the activity of receptor-like kinases and other cytosolic kinases [26–29]. Here, we describe a protocol from isolation and purification of a receptor-like kinase–GFP fusion protein complex, conduction of a kinase assay in solution using either synthetic peptides or MBP as a substrate, finally to analyze the reaction products by mass spectrometry or enzymatic luminesce.

2

Materials

2.1 Purifying Kinase–GFP Complex

1. GFP-Trap® _MA beads for immunoprecipitation of GFP fusion proteins. 2. Magnetic tube rack for magnetically separating GFP beads. 3. Wash buffer I: 10 mM Tris/Cl, pH 7.5, 150 mM NaCl, and 0.5 mM EDTA. Store at 4
C. 4. Wash buffer II: 10 mM Tris/Cl, pH 7.5, 300 mM NaCl, and 0.5 mM EDTA. Store at 4
C.

2.2 Collecting the (Phosphorylated) Peptides from the Kinase Reaction

1. C8 and C18 StageTip: 200 μl tips and two discs containing C8 or C18 materials (see Note 1). 2. Solution A for StageTip: 0.5% acetic acid. 3. Solution B for StageTip: 80% acetonitrile and 0.5% acetic acid. 4. 2% trifluoracetic acid.

2.3

Kinase Assay

1. 1 mM ATP solution. 2. 1 mM ADP solution. 3. 1 mg/mL myelin basic protein (MBP): As unspecific substrate for kinase activity assay. 4. Synthetic peptides (target peptides): As target peptides, can be obtained commercially from various companies (see Note 2). 5. Kinase reaction buffer: 50 mM HEPES/KOH, pH 7.4, 10 mM MgCl2, 10 mM MnCl2, and 0.1 mg bovine serum albumin (BSA). Store at 20
C. Freshly add dithiothreitol (DTT) to a final concentration of 1 mM. 6. ADP-Glo™ Reagent (obtained from ADP-Glo™ Kinase Assay Kit). 7. Kinase Detection Buffer (anti-light, obtained from ADP-Glo™ Kinase Assay Kit). 8. Solid white multi-well plate. 9. Luminometer capable of reading multi-well plates. 10. Plate shaker.

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Methods

3.1 Purification of Kinase–GFP Complex

1. Before distributing the beads, prepare a 2 ml tube (see Note 3) for each pull-down reaction. Give the stock GFP-Trap® _MA beads a short vortex for even distribution before putting the beads into the reaction tubes. 2. Equilibrate GFP-Trap® _MA beads in dilution buffer (wash buffer I). For this, resuspend 15 μl beads in 250 μl ice-cold dilution buffer and spin down at 2500  g. Use the magnetic tube rack to fix the beads, and discard the supernatant. Wash beads twice more with 250 μl ice-cold dilution buffer. 3. Add prepared proteins solution (see Note 4) to equilibrated GFP-Trap® _MA beads and incubate the sample for 2 h at 4
C under constant mixing. 4. Spin tube at 2500  g and use magnetic tube rack to fix the beads, and discard the supernatant. 5. Wash beads one time with 250 μl ice-cold wash buffer I, and spin tube at 2500  g. Use the magnetic tube rack to fix the beads, and discard the supernatant. 6. Wash beads one time with 300 μl ice-cold wash buffer II, and spin tube at 2500  g. Use the magnetic tube rack to fix the beads, and discard the supernatant. 7. Wash beads one time with 250 μl ice-cold wash buffer I, and spin tube at 2500  g. Use the magnetic tube rack to fix the beads, and discard the supernatant.

3.2 In Vitro Peptide Phosphorylation Assay (See Note 5)

1. After purification of the kinase–GFP complex, wash beads two times with 300 μl ice-cold kinase reaction buffer, and spin tube at 2500  g. Use the magnetic tube rack to fix the beads, and discard the supernatant. 2. Add 1 pmol target/synthetic peptide and 5 μl 1 mM ATP into 50 μl kinase reaction buffer, mix well. 3. Add reaction mixture (kinase reaction buffer + ATP + target peptides, mixture from step 2) into the kinase–GFP complex tube (bead with kinase–GFP complex) (see Note 6). Then incubate mixture at room temperature for 1 h. 4. After the incubation, the reaction mixture is then purified over a C8 StageTip to remove GFP-tagged protein and collect the target peptides. C8 StageTip is conditioned by placing 50 μl of Solution B into the StageTip, and the StageTip is centrifuged through to dryness (see Note 7). 5. Wash the C8 StageTip two times with 100 μl of Solution A, centrifuge the StageTip through to dryness (see Note 7).

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6. Load the reaction mixture into the C8 StageTip and centrifuge the sample through to dryness. The flow-through containing the peptides mixture from the kinase reaction needs to be collected into a new tube (see Note 8). 7. After purification over the C8 StageTip, the collected peptide mixture needs to be acidified to pH 2.0 with TFA (add approximately 1/10 volume of 2% TFA). 8. The acidified peptide mixtures are then desalted over a C18 column prior to mass spectrometric analysis. C18 StageTip is conditioned by placing 50 μl of Solution B into the StageTip and centrifuge the StageTip through to dryness (see Note 7). 9. Wash the C18 StageTip two times with 100 μl Solution A, centrifuge the StageTip through to dryness (see Note 7). 10. Load the peptide mixture into the C18 StageTip and centrifuge the sample through to dryness (see Note 7). 11. Wash the C18 StageTip (including peptide mixture) two times with 100 μl Solution A and centrifuge the sample through to dryness (see Note 7). 12. Elute the peptides mixture from the C18 StageTip by adding 20 μl of Solution B and centrifuge the sample through to dryness (see Note 7). Collect the eluate into a fresh tube. 13. Repeat the elution with 20 μl more of Solution B and collect the eluate in the same tube as used previously. Immediately dry the elutes in a speed-vacuum. 14. Confirm the phosphorylation state of target peptides via mass spectrometry. 3.3 Kinase Activity Assay by Using Unspecific Substrate

1. After purification of the kinase–GFP complex, wash beads two times with 300 μl ice-cold kinase reaction buffer, and spin tube at 2500  g. Use the magnetic tube rack to fix the beads, and discard the supernatant. 2. Prepare kinase reaction buffer + ATP + MBP mixture as described in Table 1 (see Note 9). 3. Resuspend beads in 25 μl ice-cold kinase reaction mixture, mix well, and incubate for 1 h at room temperature under constant mixing. 4. After the reaction, use the magnetic tube rack to fix the beads and uptake the supernatant into a white plate for further reaction and luminescence detection. 5. Add 25 μl ADP-GLO Reagent (see Note 10) to stop the kinase reaction and deplete the unconsumed ATP, leaving only ADP and a very low background of ATP. Incubate mixture at room temperature for 40 min.

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Table 1 Preparation of kinase reaction buffer Stock

25 μl per sample (Ratio of each buffer added)

2 kinase reaction buffer

12.5 μl

1 mM ATP

2.5 μl (to a final concentration of 100 μM)

dd H2O

1.75 μl

Substrate (MBP, 1 mg/ml)

8 μl

(Freshly add) 100 mM DTT

0.25 μl (to a final concentration of 1 mM)

Table 2 Generation of standard curve for conversion of ATP to ADP A1

A2

A3

A4

A5

A6

A7

A8

A9

A10

A11

A12

1 mM ADP (μl)

100

80

60

40

20

10

5

4

3

2

1

0

1 mM ATP (μl)

0

20

40

60

80

90

95

96

97

98

99

100

B1

B2

B3

B4

B5

B6

B7

B8

B9

B10

B11

B12

90

90

90

90

90

90

90

90

90

90

90

90

C1

C2

C3

C4

C5

C6

C7

C8

C9

C10

C11

C12

90

90

90

90

90

90

90

90

90

90

90

90

D1

D2

D3

D4

D5

D6

D7

D8

D9

D10

D11

D12

90

90

90

90

90

90

90

90

90

90

90

90

Kinase reaction buffer (μl)

Kinase reaction buffer (μl)

Kinase reaction buffer (μl)

6. Add 50 μl kinase detection reagent (light-sensitive) (see Note 10) to convert ADP to ATP and introduce luciferase to detect ATP, incubate mixture at room temperature for 40 min (see Note 11). 7. Prior to performing ADP-Glo™ Kinase Assay: Generate a standard curve for conversion of ATP to ADP as described in Table 2 (see Note 12); Take 1 ml of 1 mM ATP and 500 μl of 1 mM ADP, and add them into plate as below. 8. Dilute the samples in wells A1–A12 by transferring 10 μl of the sample in well A1 to well B1, 10 μl from well A2 to well B2, etc. Mix well. This is the 100 μM series. 9. Dilute the samples in wells B1–B12 by transferring 10 μl of the sample in well B1 to well C1, 10 μl from well B2 to well C2, etc. Mix well. This is the 10 μM series. 10. Dilute the samples in wells C1–C12 by transferring 10 μl of the sample in well C1 to well D1, 10 μl from well C2 to well D2, etc. Mix well. This is the 1 μM series.

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11. Transfer 25 μl from the desired ATP + ADP series into separate wells of the new assay plate or the assay plate where the kinase reactions are present. 12. Add 25 μl of ADP-Glo™ Reagent to stop the kinase reaction and deplete the unconsumed ATP, leaving only ADP and a very low background of ATP. Incubate mixture at room temperature for 40 min. 13. Add 50 μl of kinase detection reagent to convert ADP to ATP and introduce luciferase and luciferin to detect ATP. 14. Measure the luminescence. 15. Make standard curves (1 μM series, 10 μM series, 100 μM series, 1 mM series).

4

Notes 1. The Empore Disks of C8 or C18 material (Life Technologies) can be placed on a flat, clean surface. Punch out a small disk by using a blunt-tipped hypodermic needle, and the disk will stick in the needle and can then be transferred into a 200 μl pipette tip. Details on StageTip manufacturing are described elsewhere [30]. 2. Each peptide is supplied in the assay at a concentration of 1 pmol. 3. A 2 ml tube is preferable to use in this case since it provides an adequate space at the bottom of the tube for protein binding to beads. 4. 100–150 μg microsomal fraction protein in membrane resuspension buffer (see MF isolation protocol in Chapter 4) is sufficient for the kinase assay. For other soluble kinase–GFP extract, the proper application should be found individually. 5. The same amount of kinase preparations should be used in the activity assays with all substrate peptides. 6. The target peptides were selected based on experimentally identified phosphopeptides. 7. The speed of centrifugation duration maybe longer or shorter to pass the buffers completely through. 8. The purification over C8 StageTip is necessary to remove the undigested kinase from the peptide mixture. Alternatively, the phospho-peptide enrichment assay (described in Chapter 4) can be performed to remove the non-phosphorylated peptides and to specifically collect the phosphorylated peptides.

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9. Before preparation, calculate the number of your samples and mix the solution together in one tube (i.e., master-mix), to reduce the variance caused by experimental manipulation. 10. Kinase reaction volume: ADP-Glo™ Reagent volume:kinase detection reagent volume is 1:1:2 ratio. 11. Incubation time depending on the ATP concentration produced in the kinase reaction as below: Incubation time to convert ADP to ATP. ATP concentration

10–100 μM

100–500 μM

500–1000 μM

Time

30 min

40 min

60 min

12. A standard curve for conversion of ATP to ADP is highly recommended [31]. References 1. Zulawski M, Schulze G, Braginets R, Hartmann S, Schulze WX (2014) The Arabidopsis Kinome: phylogeny and evolutionary insights into functional diversification. BMC Genomics 15:548 2. 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 3. Ardito F, Giuliani M, Perrone D, Troiano G, Lo Muzio L (2017) The crucial role of protein phosphorylation in cell signaling and its use as targeted therapy (Review). Int J Mol Med 40 (2):271–280 4. 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 13(8):1953–1964 5. Elkhadragy L, Long W (2019) A radioactive in vitro ERK3 kinase assay. Bio Protoc 9(16): e3332 6. Kim HS, Bian X, Lee CJ, Kim SE, Park SC, Xie Y, Guo X, Kwak SS (2019) IbMPK3/ IbMPK6-mediated IbSPF1 phosphorylation promotes tolerance to bacterial pathogen in sweetpotato. Plant Cell Rep 38 (11):1403–1415 7. Lee HY, Yoon GM (2017) Kinase assay for CONSTITUTIVE TRIPLE RESPONSE 1 (CTR1) in Arabidopsis thaliana. Methods Mol Biol 1573:133–140 8. Tan S, Abas M, Verstraeten I, Glanc M, Molnar G, Hajny J, Lasak P, Petrik I,

Russinova E, Petrasek J, Novak O, Pospisil J, Friml J (2020) Salicylic acid targets protein phosphatase 2A to attenuate growth in plants. Curr Biol 30(3):381–395.e388 9. Kersten B, Agrawal GK, Iwahashi H, Rakwal R (2006) Plant phosphoproteomics: a long road ahead. Proteomics 6(20):5517–5528 10. Willmann R, Haischer DJ, Gust AA (2014) Analysis of MAPK activities using MAPKspecific antibodies. Methods Mol Biol 1171:27–37 11. Wurzinger B, Mair A, Fischer-Schrader K, Nukarinen E, Roustan V, Weckwerth W, Teige M (2017) Redox state-dependent modulation of plant SnRK1 kinase activity differs from AMPK regulation in animals. FEBS Lett 591 (21):3625–3636 12. Zhang M, Su J, Zhang Y, Xu J, Zhang S (2018) Conveying endogenous and exogenous signals: MAPK cascades in plant growth and defense. Curr Opin Plant Biol 45(Pt A):1–10 13. Hemmila II (1999) LANCEtrade mark: homogeneous assay platform for HTS. J Biomol Screen 4(6):303–308 14. Mathis G (1995) Probing molecular interactions with homogeneous techniques based on rare earth cryptates and fluorescence energy transfer. Clin Chem 41(9):1391–1397 15. Seethala R, Menzel R (1997) A homogeneous, fluorescence polarization assay for src-family tyrosine kinases. Anal Biochem 253 (2):210–218 16. Wu JJ (2002) Comparison of SPA, FRET, and FP for kinase assays. Methods Mol Biol 190:65–85

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Correction to: Plant Phosphopeptide Identification and Label-Free Quantification by MaxQuant and Proteome Discoverer Software Shalan Li, Haitao Zan, Zhe Zhu, Dandan Lu, and Leonard Krall

Correction to: Chapter 13 in: Xu Na Wu (ed.), Plant Phosphoproteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2358, https://doi.org/10.1007/978-1-0716-1625-3_13 In the original version of this book, the title of chapter 13 was published with a typographical error. It has now been rectified in the revised version of this book.

The updated online version of this chapter can be found at https://doi.org/10.1007/978-1-0716-1625-3_13 Xu Na Wu (ed.), Plant Phosphoproteomics: Methods and Protocols, Methods in Molecular Biology, vol. 2358, https://doi.org/10.1007/978-1-0716-1625-3_18, © Springer Science+Business Media, LLC, part of Springer Nature 2021

C1

INDEX A Abiotic stresses ................................... 2–5, 17–35, 46–48, 53, 54, 57, 59–61 Abscisic acid (ABA) signalling ........................3, 4, 27–29, 34, 49, 51–53, 56–58, 60–62, 93 ACQUITY nano-LC system ........................................ 124 Al(OH)3-based MOAC enrichment of phosphoproteins ....................................... 107, 109 Ammopiptanthus mongolicus .......................................... 12 AQUIP ................................................................ 116, 117, 128, 130, 133 Arabidopsis ........................................ 1, 2, 4, 5, 9, 12, 19, 21–23, 25–27, 29, 31, 33–35, 45, 47–50, 52, 54–56, 58–64, 73, 74, 76, 83, 93, 94, 106, 108, 110, 114–116, 118, 120, 125–127, 130, 132, 138, 143, 146, 147, 149, 153, 154, 159–161, 189–200, 209, 212, 229 Auxin signalling.................................................. 48, 49, 60

B BAK1 .............................................. 12, 31, 33, 50, 61, 73 Barley .........................................................................12, 58 Basic leucine zipper (bZIP) transcription factors.............................................................28, 29 Bayesian network (BN)............................... 206, 208, 212 Brassinosteroids signalling .............................................. 33 BRI1 ............................................... 11, 31, 33, 47, 50, 73

C C8 ......................................................... 79, 139, 142–144, 231–233, 235 C18 .............................................. 75, 77–80, 96, 98–100, 102, 108, 110, 122–124, 128, 148, 150, 151, 153, 231, 233, 234 C18-AQ beads ....................................................... 96, 102 Calcium-dependent protein kinases (CDPKs) ............... 3, 25, 26, 54, 56, 57, 60, 61, 64 Calcium sensors............................................................... 22 Casein-like kinases (CKLs) ........................................... 3, 4 Chlamydomonas reinhardtii................................. 169–176 CLAVATA3 insensitive receptor kinases (CIKs) ............. 2 Cold stress signaling .................................................62, 63

cRacker software.............................................78, 221–228 CyDye labelling ........................................... 173, 174, 176 Cytokinin signaling ............................................ 46, 50, 52 Cytosolic kinases ....................................... 2, 4, 7, 45, 231

D dbPTM (protein modification database) ............ 114, 209 Desalting................................................... 79, 96, 98, 101, 142, 143, 147, 148, 150, 151 Dimethyl labeling................................................... 94, 100 Double in vivo substrate and kinase assay (DISKA).......................................... 116, 127–129, 132, 133 Drought stress signaling .................................... 61, 63, 64

E Ethylene signaling .....................................................50, 60

F Fe+3IMAC bead affinity enrichment ................... 123, 124 Filter aided sample preparation (FASP)...................85, 87

G Gibberellic acids (GAs) signalling .......................... 29, 31, 32, 47, 50 Glycosylation .......................................................... 19, 203 Green Fluorescent Protein (GFP)...............................231, 232, 234 Guanidine hydrochloride (GdnHCl) ..................... 94, 95, 153, 154

H H+-ATPases (AHA1/2/3) .......................................22, 23 Heat stress signaling .................................................21, 62 Heavy labelled nitrogen (15N) ....................................100, 116, 132 Heterotrimeric G-protein phosphorylation................... 34 High-affinity K+ transporters (HKTs) ........................... 22 High pH reverse phase (Hp-RP) fractionation............................................ 94–96, 99 HISTIDINE KINASEs (AHK2/3/4) ....................47, 50

Xu Na Wu (ed.), Plant Phosphoproteomics: Methods and Protocols , Methods in Molecular Biology, vol. 2358, https://doi.org/10.1007/978-1-0716-1625-3, © Springer Science+Business Media, LLC, part of Springer Nature 2021

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PLANT PHOSPHOPROTEOMICS: METHODS AND PROTOCOLS

240 Index I

Immobilized metal affinity chromatography (IMAC) ................................................84, 90, 123, 128, 145–156 Immunoprecipitation (IP) .............................84, 146, 231 Ion stress signaling.......................................................... 21 iTRAQ labeling .........................................................83–90

J Jasmonic acid (JA) signalling.......................................... 47

K Kinase activity assay....................................................... 233 Kinase families ................................................. 3–7, 10–12, 34, 48, 60, 208 Kinases ....................................................2, 19, 45, 73, 83, 93, 116, 137, 169, 179, 189, 204, 229 Kinase substrates .......................................... 7, 12, 74, 94, 205, 208, 230 Kinome .............................................................................. 2

L Label-free quantitation ................................................... 74 Luminescence .............................................. 230, 233, 235 Lys-C digestion .................................................75, 76, 94, 95, 98, 107, 109

M MapMan program ...................................... 6, 8, 190, 191, 194, 198, 199 Mass spectrometry (MS).................................1, 5, 20, 21, 23, 28, 74, 76, 78, 79, 83–86, 88, 93, 94, 100–102, 114–117, 125, 128, 131, 138, 145, 147, 148, 151, 153, 155, 159, 160, 162, 163, 166, 170, 174, 181, 199, 204, 221, 230, 231, 233 Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS)................................. 159, 160, 166, 168, 170, 174 MaxQuant program ................................. 78, 80, 97, 100, 110, 148, 179–186, 221, 222, 227 Membrane fluidity/membrane fluidity changes ........... 54 Membrane fraction (MF) ......................... 76, 79, 80, 234 Metal-dependent protein phosphatases (PPM) ............ 48 Metal oxide affinity chromatography (MOAC) .... 84, 90, 106–110, 146 Microsomal pellet............................................................ 76 Mitogen-activated protein kinase family (MAPK kinases) 3, 27, 46, 63 Moss Physcomitrella patens ............................................. 12

Motifs...................................................... 1–12, 25, 28, 29, 89, 116, 126, 127, 131, 192, 194, 197–199, 205, 206, 213 MSQuant ....................................................................... 221 Myelin basic protein (MBP) ............................... 230, 231, 233, 234

N Nanoflow Easy-nLC 1000 HPLC ................................. 76 Network analysis/network reconstruction ......... 203–214 Ni-NTA agarose ............................................................ 123 N-myristoylation (MYR) ................................................ 19 15 N-stable isotope labelling in Arabidopsis (SILIA)...................................................... 113–133

O Orbitrap ........................................................ 76, 108, 110, 124, 140, 153 Osmotic stress signaling ...........................................21, 62

P Peptide desalting .......................................................77–78 Perseus ..................................................... 78, 97, 100, 111 PhosPhAt 4.0 database ........................................ 146, 198 Phosphocodes.................................................................. 12 Phosphoprotein phosphatases (PPPs) ........................... 48 Phosphorous signalling................................................... 23 Phosphorylation motifs ....................................2, 5, 7, 11, 24, 46, 126, 190, 192, 194 Phosphorylation site predictions........................ 190, 199, 208–210 Phospho-specific antibodies ......................................... 230 Plant roots ......................................................89, 137–144 p-motifs........................................................................ 5–12 PolyMAC-Ti enrichment................................................ 94 Potassium signaling..........................................23, 25, 107 Prefractionation.................................................... 148, 150 Pro-Q diamond stain ..........................159, 165, 167, 175 14-3-3 proteins ........................................... 22, 56, 57, 63 Protein tyrosine phosphatases (PTPs) ........................... 48 Proteome Discoverer software ............................ 180, 183 ProteoWizard software ................................................. 125

Q Q-Exactive plus ............................................................... 76 QSK1 ............................................................................... 73 Quantitative analysis .......................................... 84, 88, 90

R Reactive oxygen species (ROS) ........................ 21, 25–27, 30, 53, 54, 58, 62

PLANT PHOSPHOPROTEOMICS: METHODS Receptor-like kinases (RLKs) ................................. 2, 3, 6, 31, 45–48, 54, 231 RESID database ............................................................ 114 R programming language ............................................. 207

S Salicylic acid (SA) signaling ........................ 47, 52, 53, 61 Salt stress signaling/SOS pathway ................... 21–23, 29, 55, 57, 60 SCX-HPLC .......................................................... 148, 150 Short linear motifs (SLiMs).......................................... 205 ShortPhos method .......................................................... 74 Shotgun proteomics...................................................... 145 SILIA-based 4C quantitative PTM proteomics (S4Quap) .................................................. 113–133 Siliamass software ........................................ 117, 125, 126 Silique-N software....................................... 117, 125, 126 SIRK1 ........................................................................73, 74 Sodium dodecyl sulfate (SDS)........................20, 85, 118, 147, 153, 154, 161, 162, 170, 174, 176, 183 Soybean (Glycine max) ............................. 86, 88, 89, 190 Stomatal closure ........................................................52, 58 String ..........................................127, 131, 132, 206, 224 Strong cation exchange (SCX) ............................. 84, 119, 124, 131, 150, 151, 153

AND

PROTOCOLS Index 241

Substrates................................................ 1–12, 27, 30, 46, 50, 59, 64, 73, 74, 78, 116, 127, 128, 132, 150, 199, 204–206, 208–210, 212, 229–236 Sucrose non-fermenting receptor kinase (SnRK) .......... 3, 6, 7, 11, 19, 27, 28, 30, 48, 50, 52, 54, 61, 63, 74

T Tandem metal oxide affinity chromatography (Tandem MOAC)..................................... 105–112 Target of rapamycin (TOR) kinase ................................ 50 TiO2 phosphopeptide enrichment ................................. 77 Tomato ......................................................................55, 94 Trypsin digestion ..................................80, 121, 128, 160 2-D difference gel electrophoresis (DIGE) ........ 169–176 Two-Dimensional gel electrophoresis (2-DE) ..................................... 159–168, 172–175

U Ultrasonication.............................................................. 171 UNIMOD database ...................................................... 114 Urea based denaturing solutions.................................. 117

W With-No-Lysine Kinases (WNKs)................................ 3, 4