Abstract
In this chapter we describe the workflow used in our laboratory to analyze rice leaf samples using label-free shotgun proteomics based on SDS-PAGE fractionation of proteins. Rice proteomics has benefitted substantially from successful execution of shotgun proteomics techniques. We describe steps on how to proceed starting from rice protein extraction, SDS-PAGE, in-gel protein digestion with trypsin, nanoLC-MS/MS, and database searching using the GPM. Data from these experiments can be used for spectral counting, where simultaneous quantitation of several thousand proteins can be obtained.
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Abbreviations
- 2-DE:
-
Two-dimensional electrophoresis
- ACN:
-
Acetonitrile
- BCA:
-
Bicinchoninic acid
- BSA:
-
Bovine serum albumin
- DTT:
-
Dithiothreitol
- FDR:
-
False discovery rate
- GO:
-
Gene ontology
- GPM:
-
Global proteome machine
- IAA:
-
Iodoacetamide
- MS:
-
Mass spectrometry
- MS/MS:
-
Tandem mass spectrometry
- MudPIT:
-
Multidimensional protein identification technology
- NSAF:
-
Normalized spectral abundance factor
- RP:
-
Reversed phase
- SDS-PAGE:
-
Sodium dodecyl sulfate-polyacrylamide gel electrophoresis
- TCA:
-
Trichloroacetic acid
- WEGO:
-
Web gene ontology annotation plot
References
Weinberger KM, Easdown WJ, Yang R et al (2009) Food crisis in the Asia-Pacific region. Asia Pac J Clin Nutr 18:507–515
Goff SA, Ricke D, Lan TH et al (2002) A draft sequence of the rice genome (Oryza sativa L. ssp. japonica). Science 296:92–100
Rabbani MA, Maruyama K, Abe H et al (2003) Monitoring expression profiles of rice genes under cold, drought, and high-salinity stresses and abscisic acid application using cDNA microarray and RNA gel-blot analyses. Plant Physiol 133:1755–1767
Koller A, Washburn MP, Lange B et al (2002) Proteomic survey of metabolic pathways in rice. Proc Natl Acad Sci U S A 99: 11969–11974
Link AJ, Eng J, Schieltz DM et al (1999) Direct analysis of protein complexes using mass spectrometry. Nat Biotechnol 17:676–682
Washburn MP, Wolters D, Yates JR (2001) Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat Biotechnol 19:242–247
Wolters DA, Washburn MP, Yates JR 3rd (2001) An automated multidimensional protein identification technology for shotgun proteomics. Anal Chem 73:5683–5690
Helmy M, Tomita M, Ishihama Y (2011) OryzaPG-DB: rice proteome database based on shotgun proteogenomics. BMC Plant Biol 11:63
Liu L, Bai L, Luo C et al (2011) Systematic annotation and bioinformatics analyses of large-scale Oryza sativa proteome. Curr Protein Pept Sci 12:621–630
Nakagami H, Sugiyama N, Mochida K et al (2010) Large-scale comparative phosphoproteomics identifies conserved phosphorylation sites in plants. Plant Physiol 153:1161–1174
Ong SE, Foster LJ, Mann M (2003) Mass spectrometric-based approaches in quantitative proteomics. Methods 29:124–130
Ong SE, Mann M (2005) Mass spectrometry-based proteomics turns quantitative. Nat Chem Biol 1:252–262
Steen H, Pandey A (2002) Proteomics goes quantitative: measuring protein abundance. Trends Biotechnol 20:361–364
Agrawal GK, Rakwal R (2011) Rice proteomics: a move toward expanded proteome coverage to comparative and functional proteomics uncovers the mysteries of rice and plant biology. Proteomics 11:1630–1649
Gammulla CG, Pascovici D, Atwell BJ et al (2010) Differential metabolic response of cultured rice (Oryza sativa) cells exposed to high- and low-temperature stress. Proteomics 10:3001–3019
Gammulla CG, Pascovici D, Atwell BJ et al (2011) Differential proteomic response of rice (Oryza sativa) leaves exposed to high- and low-temperature stress. Proteomics 11:2839–2850
Mirzaei M, Pascovici D, Atwell BJ et al (2012) Differential regulation of aquaporins, small GTPases and V-ATPases proteins in rice leaves subjected to drought stress and recovery. Proteomics 12:864–877
Mirzaei M, Soltani N, Sarhadi E et al (2012) Shotgun proteomic analysis of long-distance drought signaling in rice roots. J Proteome Res 11:348–358
Hamamoto K, Aki T, Shigyo M et al (2012) Proteomic characterization of the greening process in rice seedlings using the MS spectral intensity-based label free method. J Proteome Res 11:331–347
He D, Han C, Yao J et al (2011) Constructing the metabolic and regulatory pathways in germinating rice seeds through proteomic approach. Proteomics 11:2693–2713
Lee J, Jiang W, Qiao Y et al (2011) Shotgun proteomic analysis for detecting differentially expressed proteins in the reduced culm number rice. Proteomics 11:455–468
Schirle M, Heurtier MA, Kuster B (2003) Profiling core proteomes of human cell lines by one-dimensional PAGE and liquid chromatography-tandem mass spectrometry. Mol Cell Proteomics 2:1297–1305
Simpson RJ, Connolly LM, Eddes JS et al (2000) Proteomic analysis of the human colon carcinoma cell line (LIM 1215): development of a membrane protein database. Electrophoresis 21:1707–1732
Pascovici D, Keighley T, Mirzaei M et al (2012) PloGO: plotting gene ontology annotation and abundance in multi-condition proteomics experiments. Proteomics 12:406–410
Shevchenko A, Wilm M, Vorm O et al (1996) A strategy for identifying gel-separated proteins in sequence databases by MS alone. Biochem Soc Trans 24:893–896
Craig R, Beavis RC (2004) TANDEM: matching proteins with tandem mass spectra. Bioinformatics 20:1466–1467
Neilson KA, Keighley T, Pascovici D et al (Submitted May 2012) Label-free quantitative shotgun proteomics using normalized spectral abundance factors. Meth Mol Biol
Neilson KA, Ali NA, Muralidharan S et al (2011) Less label, more free: approaches in label-free quantitative mass spectrometry. Proteomics 11:535–553
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
Colaert N, Vandekerckhove J, Gevaert K et al (2011) A comparison of MS2-based label-free quantitative proteomic techniques with regards to accuracy and precision. Proteomics 11:1110–1113
Gokce E, Shuford CM, Franck WL et al (2011) Evaluation of normalization methods on GeLC-MS/MS label-free spectral counting data to correct for variation during proteomic workflows. J Am Soc Mass Spectrom 22:2199–2208
Pavelka N, Fournier ML, Swanson SK et al (2008) Statistical similarities between transcriptomics and quantitative shotgun proteomics data. Mol Cell Proteomics 7:631–644
Neilson KA, Mariani M, Haynes PA (2011) Quantitative proteomic analysis of cold-responsive proteins in rice. Proteomics 11:1696–1706
Ashburner M, Ball CA, Blake JA et al (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25:25–29
Ye J, Fang L, Zheng H et al (2006) WEGO: a web tool for plotting GO annotations. Nucleic Acids Res 34:W293–W297
Acknowledgements
KAN, ISG, and SM acknowledge support in the form of iMQRES awards. SJE acknowledges support in the form of an APA scholarship. PAH acknowledges funding support from the Australian Research Council and wishes to thank Gayani Gammulla for providing images.
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Neilson, K.A., George, I.S., Emery, S.J., Muralidharan, S., Mirzaei, M., Haynes, P.A. (2014). Analysis of Rice Proteins Using SDS-PAGE Shotgun Proteomics. In: Jorrin-Novo, J., Komatsu, S., Weckwerth, W., Wienkoop, S. (eds) Plant Proteomics. Methods in Molecular Biology, vol 1072. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-631-3_21
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DOI: https://doi.org/10.1007/978-1-62703-631-3_21
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