Abstract
Abscisic acid (ABA) is a major phytohormone that regulates various processes in plants (e.g., seed dormancy/germination, abiotic/biotic stress responses). As protein phosphorylation is involved in the major pathways of ABA signaling, it is necessary to elucidate the phosphosignaling pathway involved in the ABA response. Phosphoproteomics enables determination of the proteins phosphorylated in vivo, and recent studies have applied a comparative phosphoproteomic approach to analyze ABA signaling in plants. For example, ABA-responsive phosphoproteins were identified in barley embryos. Furthermore, a phosphoproteomic approach is useful for screening protein kinase substrates by comparative analysis using kinase knockout mutants. Here, some technical points regarding phosphoproteomic analyses of ABA responses in plants are described.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cutler SR, Rodriguez PL, Finkelstein RR, Abrams SR (2010) Abscisic acid: emergence of a core signaling network. Annu Rev Plant Biol 61:651–679. https://doi.org/10.1146/annurev-arplant-042809-112122
Umezawa T, Nakashima K, Miyakawa T et al (2010) Molecular basis of the core regulatory network in abscisic acid responses: sensing, signaling, and transport. Plant Cell Physiol 51:1821–1839. https://doi.org/10.1093/pcp/pcq156
Umezawa T, Sugiyama N, Mizoguchi M et al (2009) Type 2C protein phosphatases directly regulate abscisic acid-activated protein kinases in Arabidopsis. Proc Natl Acad Sci U S A 106:17588–17593. https://doi.org/10.1073/pnas.0907095106
Umezawa T, Sugiyama N, Takahashi F et al (2013) Genetics and phosphoproteomics reveal a protein phosphorylation network in the abscisic acid signaling pathway in Arabidopsis thaliana. Sci Signal 6:rs8. https://doi.org/10.1126/scisignal.2003509
Wang P, Xue L, Batelli G et al (2013) Quantitative phosphoproteomics identifies SnRK2 protein kinase substrates and reveals the effectors of abscisic acid action. Proc Natl Acad Sci 110:11205–11210. https://doi.org/10.1073/pnas.1308974110
Macek B, Mann M, Olsen JV (2009) Global and site-specific quantitative phosphoproteomics: principles and applications. Annu Rev Pharmacol Toxicol 49:199–221. https://doi.org/10.1146/annurev.pharmtox.011008.145606
de la Fuente van Bentem S, Hirt H (2007) Using phosphoproteomics to reveal signalling dynamics in plants. Trends Plant Sci 12:404–411. https://doi.org/10.1016/j.tplants.2007.08.007
Peck SC (2006) Phosphoproteomics in Arabidopsis: moving from empirical to predictive science. J Exp Bot 57:1523–1527. https://doi.org/10.1093/jxb/erj126
Koenig T, Menze BH, Kirchner M et al (2008) Robust prediction of the MASCOT score for an improved quality assessment in mass spectrometric proteomics. J Proteome Res 7:3708–3717. https://doi.org/10.1021/pr700859x
Eng JK, McCormack AL, Yates JR (1994) An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J Am Soc Mass Spectrom 5:976–989. https://doi.org/10.1016/1044-0305(94)80016-2
Cox J, Neuhauser N, Michalski A et al (2011) Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res 10:1794–1805. https://doi.org/10.1021/pr101065j
Kim S, Pevzner PA (2014) MS-GF+ makes progress towards a universal database search tool for proteomics. Nat Commun 5:5277. https://doi.org/10.1038/ncomms6277
Eng JK, Jahan TA, Hoopmann MR (2013) Comet: an open-source MS/MS sequence database search tool. Proteomics 13:22–24. https://doi.org/10.1002/pmic.201200439
Craig R, Beavis RC (2004) TANDEM: matching proteins with tandem mass spectra. Bioinformatics 20:1466–1467. https://doi.org/10.1093/bioinformatics/bth092
Kong AT, Leprevost FV, Avtonomov DM et al (2017) MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry–based proteomics. Nat Methods 14:513–520. https://doi.org/10.1038/nmeth.4256
Cox J, Hein MY, Luber CA et al (2014) Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol Cell Proteom 13:2513–2526. https://doi.org/10.1074/mcp.M113.031591
Schilling B, Rardin MJ, MacLean BX et al (2012) Platform-independent and label-free quantitation of proteomic data using MS1 extracted ion chromatograms in skyline application to protein acetylation and phosphorylation. Mol Cell Proteomics 11:202–214. https://doi.org/10.1074/mcp.M112.017707
Weisser H, Choudhary JS (2017) Targeted feature detection for data-dependent shotgun proteomics. J Proteome Res 16:2964–2974. https://doi.org/10.1021/acs.jproteome.7b00248
Griffin NM, Yu J, Long F et al (2010) Label-free, normalized quantification of complex mass spectrometry data for proteomic analysis. Nat Biotechnol 28:83–89. https://doi.org/10.1038/nbt.1592
Ishihama Y, Oda Y, Tabata T et al (2005) Exponentially modified protein abundance index (emPAI) for estimation of absolute protein amount in proteomics by the number of sequenced peptides per protein*S. Mol Cell Proteomics 4:1265–1272. https://doi.org/10.1074/mcp.M500061-MCP200
Zybailov B, Mosley AL, Sardiu ME et al (2006) Statistical analysis of membrane proteome expression changes in Saccharomyces cerevisiae. J Proteome Res 5:2339–2347. https://doi.org/10.1021/pr060161n
Röst HL, Sachsenberg T, Aiche S et al (2016) OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat Methods 13:741–748. https://doi.org/10.1038/nmeth.3959
Ishikawa S, Barrero JM, Takahashi F et al (2019) Comparative phosphoproteomic analysis reveals a decay of ABA signaling in barley embryos during after-ripening. Plant Cell Physiol 60:2758–2768. https://doi.org/10.1093/pcp/pcz163
Ma K, Vitek O, Nesvizhskii AI (2012) A statistical model-building perspective to identification of MS/MS spectra with PeptideProphet. BMC Bioinformatics 13:S1. https://doi.org/10.1186/1471-2105-13-S16-S1
Shteynberg DD, Deutsch EW, Campbell DS et al (2019) PTMProphet: fast and accurate mass modification localization for the trans-proteomic pipeline. J Proteome Res 18:4262–4272. https://doi.org/10.1021/acs.jproteome.9b00205
Acknowledgments
This work was supported by the Japan Science and Technology Agency program PRESTO and Grants-in-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science and Technology, Japan.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Yamashita, K., Umezawa, T. (2022). Phosphoproteomic Approaches to Evaluate ABA Signaling. In: Yoshida, T. (eds) Abscisic Acid. Methods in Molecular Biology, vol 2462. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2156-1_13
Download citation
DOI: https://doi.org/10.1007/978-1-0716-2156-1_13
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-2155-4
Online ISBN: 978-1-0716-2156-1
eBook Packages: Springer Protocols