Experimental and Analytical Approaches to Characterize Plant Kinases Using Protein Microarrays

  • Elizabeth K. Brauer
  • Sorina C. PopescuEmail author
  • George V. PopescuEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1171)


Comprehensive analysis of protein kinases and cellular signaling pathways requires the identification of kinase substrates and interaction partners using large-scale amenable approaches. Here, we describe our methods for producing plant protein microarrays (PMAs) and discuss various parameters critical to the quality of PMAs. Next, we describe methods for detecting protein-protein interactions and kinase activity including auto-phosphorylation and substrate phosphorylation. We have provided a short video demonstrating how to conduct an interaction assay and how to properly handle a protein microarray. Finally, a set of analytical methods are presented as a bioinformatics pipeline for the acquisition of PMA data and for selecting PMA candidates using statistical testing. The experimental and analytical protocols described here outline the steps to produce and utilize PMAs to analyze signaling networks.

Key words

Protein microarrays Kinase substrates Protein interaction Phosphorylation assays Statistical decision 



We are grateful to Claire Smith and Kent Loeffler from the Photo Laboratory in the Department of Plant Pathology and Plant Microbe Interactions (Cornell University) for the help with producing the movie accompanying this manuscript. This work was supported by the National Science and Engineering Research Council of Canada (post-graduate fellowship to E. K. Brauer), the National Science Foundation (project IOS-1025642 to S. C. Popescu) and National Research Council-Executive Agency for Higher Education, Research, Development and Innovation Funding (projects PN-II-PT-PCCA-2011-3.1-1350 and PN-II-CT-RO-FR-2012-1-709 to G. V. Popescu).

Supplementary material

(MOV 766212 kb)


  1. 1.
    Popescu SC, Popescu GV, Bachan S, Zhang Z, Seay M, Gerstein M, Snyder M, Dinesh-Kumar SP (2007) Differential binding of calmodulin-related proteins to their targets revealed through high-density Arabidopsis protein microarrays. Proc Natl Acad Sci U S A 104: 4730–4735PubMedCentralPubMedCrossRefGoogle Scholar
  2. 2.
    Popescu S, Michael S, Dinesh-Kumar S (2007) Arabidopsis protein microarrays for the high-throughput identification of protein–protein interactions. Plant Signal Behav 2:415–419CrossRefGoogle Scholar
  3. 3.
    Popescu S, Popescu G, Bachan S, Zhang Z, Gerstein M, Snyder M, Dinesh-Kumar S (2009) MAPK target networks in Arabidopsis thaliana revealed using functional protein microarrays. Genes Dev 23:80–92PubMedCentralPubMedCrossRefGoogle Scholar
  4. 4.
    Popescu SC, Popescu GV, Snyder M, Dinesh-Kumar SP (2009) Integrated analysis of co-expressed MAP kinase substrates in Arabidopsis thaliana. Plant Signal Behav 4:524–527PubMedCentralPubMedCrossRefGoogle Scholar
  5. 5.
    Wolf-Yadlin A, Sevecka M, MacBeath G (2009) Dissecting protein function and signaling using protein microarrays. Curr Opin Chem Biol 13: 398–405PubMedCentralPubMedCrossRefGoogle Scholar
  6. 6.
    MacBeath G, Schreiber SL (2000) Printing proteins as microarrays for high-throughput function determination. Science 289:1760–1763PubMedGoogle Scholar
  7. 7.
    Zhu H, Klemic JF, Chang S, Bertone P, Casamayor A, Klemic KG, Smith D, Gerstein M, Reed MA, Snyder M (2000) Analysis of yeast protein kinases using protein chips. Nat Genet 26:283–289PubMedCrossRefGoogle Scholar
  8. 8.
    MacBeath G (2002) Protein microarrays and proteomics. Nat Genet 32:526–532PubMedCrossRefGoogle Scholar
  9. 9.
    Chen R, Snyder M (2010) Yeast proteomics and protein microarrays. J Proteomics 73: 2147–2157PubMedCentralPubMedCrossRefGoogle Scholar
  10. 10.
    Ptacek J, Snyder M (2007) Yeast gene analysis – second edition. Academic Press, Waltham, MA, pp 303–329, 704–705CrossRefGoogle Scholar
  11. 11.
    Zhu H, Snyder M (2003) Protein chip technology. Curr Opin Chem Biol 7:55–63PubMedCrossRefGoogle Scholar
  12. 12.
    Invitrogen Catalog/Catalog no. PA012106Google Scholar
  13. 13.
    Jones RB, Gordus A, Krall JA, MacBeath G (2006) A quantitative protein interaction network for the ErbB receptors using protein microarrays. Nature 439:168–174PubMedCrossRefGoogle Scholar
  14. 14.
    Mok J, Im H, Snyder M (2009) Global identification of protein kinase substrates by protein microarray analysis. Nat Protoc 4:1820–1827PubMedCentralPubMedCrossRefGoogle Scholar
  15. 15.
    Popescu G, Popescu S (2011) Complexity and modularity of MAPK signaling networks. In: Limin Angela Liu, Dongqing Wei, Yixue Li (eds) Handbook of research on computational and systems biology: interdisciplinary applications, vol 1. IGI Global, Hershey, pp 355–68Google Scholar
  16. 16.
    Lee HY, Bowen CH, Popescu GV, Kang H-G, Kato N, Ma S, Dinesh-Kumar S, Snyder M, Popescu SC (2011) Arabidopsis RTNLB1 and RTNLB2 Reticulon-like proteins regulate intracellular trafficking and activity of the FLS2 immune receptor. Plant Cell 23:3374–3391PubMedCentralPubMedCrossRefGoogle Scholar
  17. 17.
    Adams JA (2001) Kinetic and catalytic mechanisms of protein kinases. Chem Rev 101: 2271–2290PubMedCrossRefGoogle Scholar
  18. 18.
    Ratcliff F, Martin-Hernandez AM, Baulcombe DC (2001) Technical Advance. Tobacco rattle virus as a vector for analysis of gene function by silencing. Plant J 25:237–245PubMedCrossRefGoogle Scholar
  19. 19.
    Zhu X, Gerstein M, Snyder M (2006) ProCAT: a data analysis approach for protein microarrays. Genome Biol 7:R110PubMedCentralPubMedCrossRefGoogle Scholar
  20. 20.
    White AM, Daly DS, Varnum SM, Anderson KK, Bollinger N, Zangar RC (2006) ProMAT: protein microarray analysis tool. Bioinformatics 22:1278–1279PubMedCrossRefGoogle Scholar
  21. 21.
    Box GE, Hunter WG, Hunter JS (1978) Statistics for experimenters. Wiley, New YorkGoogle Scholar
  22. 22.
    Everitt BS, Hothorn T (2006) A handbook of statistical analyses using R. Chapman and Hall/CRC, LondonCrossRefGoogle Scholar
  23. 23.
    Quinn GGP, Keough MJ (2002) Experimental design and data analysis for biologists. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  24. 24.
    Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4:249–264PubMedCrossRefGoogle Scholar
  25. 25.
    Quackenbush J (2002) Microarray data normalization and transformation. Nat Genet 32:496–501PubMedCrossRefGoogle Scholar
  26. 26.
    Huber W, Von Heydebreck A, Sültmann H, Poustka A, Vingron M (2002) Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 18:S96–S104PubMedCrossRefGoogle Scholar
  27. 27.
    Smyth GK (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3:3Google Scholar
  28. 28.
    Stark C, Breitkreutz B-J, Reguly T, Boucher L, Breitkreutz A, Tyers M (2006) BioGRID: a general repository for interaction datasets. Nucleic Acids Res 34:D535–D539PubMedCentralPubMedCrossRefGoogle Scholar
  29. 29.
    Stark C, Breitkreutz B-J, Chatr-Aryamontri A, Boucher L, Oughtred R, Livstone MS, Nixon J, Van Auken K, Wang X, Shi X (2011) The BioGRID Interaction Database: 2011 update. Nucleic Acids Res 39:D698–D704PubMedCentralPubMedCrossRefGoogle Scholar
  30. 30.
    Kerrien S, Aranda B, Breuza L, Bridge A, Broackes-Carter F, Chen C, Duesbury M, Dumousseau M, Feuermann M, Hinz U, Jandrasits C, Jimenez RC, Khadake J, Mahadevan U, Masson P, Pedruzzi I, Pfeiffenberger E, Porras P, Raghunath A, Roechert B, Orchard S, Hermjakob H (2012) The IntAct molecular interaction database in 2012. Nucleic Acids Res 40: D841–D846PubMedCentralPubMedCrossRefGoogle Scholar
  31. 31.
    Storey JD (2002) A direct approach to false discovery rates. J R Stat Soc 64:479–498CrossRefGoogle Scholar
  32. 32.
    Benjamini Y, Hochberg Y (1995) Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J R Stat Soc 57:289–300Google Scholar
  33. 33.
    Singh DK, Calvino M, Brauer EK, Fernendez-Pozo N, Strickler S, Yalamanchili R, Suzuki H, Aoki K, Shibata D, Stratmann JW, Popescu GV, Mueller L, Popescu SC (2014) The tomato kinome and the TOKN ORFeome: resources for the study of kinases and signal transduction in tomato and Solanaceae. Molec Plant Microbe Interaction 27:7–17Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.The Boyce Thompson Institute for Plant ResearchIthacaUSA
  2. 2.Department of Plant Pathology and Plant Microbe BiologyCornell UniversityIthacaUSA
  3. 3.National Institute for Laser, Plasma & Radiation PhysicsBucharestRomania

Personalised recommendations