Literature DB >> 16850419

Analysis of the defence phosphoproteome of Arabidopsis thaliana using differential mass tagging.

Alexandra M E Jones1, Mark H Bennett, John W Mansfield, Murray Grant.   

Abstract

Despite recent advances in proteomic technologies, quantitative analysis of the proteome remains a challenging task. Phosphorylation of proteins is central to signal transduction pathways and plays an important role in plant defence against pathogens, although the immediate targets of kinases remain elusive. Determining changes in the phosphoproteome during the defence response is a major goal in molecular plant pathology. In this first description of the novel mass tagging strategy (iTRAQ Applied Biosystems) applied to plant pathogen interactions, we describe early changes to the phosphoproteome of Arabidopsis thaliana during the defence response to Pseudomonas syringae pv. tomato DC3000. We identified five proteins which showed reproducible differences between a control and three different bacterial challenges, thus identifying proteins potentially phosphorylated as part of a plant basal defence response. Four of the five proteins a dehydrin, a putative p23 co-chaperone, heat shock protein 81 and a plastid-associated protein (PAP)/fibrillin, are known to be phosphorylated or have potential phosphorylation sites. One further protein, the large subunit of Rubisco, showed a significant difference between tissue undergoing the hypersensitive response and a basal defence response. We document the reproducibility, utility and problems associated with this approach.

Entities:  

Mesh:

Substances:

Year:  2006        PMID: 16850419     DOI: 10.1002/pmic.200500172

Source DB:  PubMed          Journal:  Proteomics        ISSN: 1615-9853            Impact factor:   3.984


  34 in total

1.  Accurate mass spectrometry based protein quantification via shared peptides.

Authors:  Banu Dost; Nuno Bandeira; Xiangqian Li; Zhouxin Shen; Steven P Briggs; Vineet Bafna
Journal:  J Comput Biol       Date:  2012-03-13       Impact factor: 1.479

2.  Quantitative proteomics in plants: choices in abundance.

Authors:  Jay J Thelen; Scott C Peck
Journal:  Plant Cell       Date:  2007-11-30       Impact factor: 11.277

3.  Identification of differentially expressed proteins in experimental autoimmune encephalomyelitis (EAE) by proteomic analysis of the spinal cord.

Authors:  Tong Liu; K Christian Donahue; Jun Hu; Michael P Kurnellas; Jennifer E Grant; Hong Li; Stella Elkabes
Journal:  J Proteome Res       Date:  2007-06-16       Impact factor: 4.466

Review 4.  Application of proteomics to investigate stress-induced proteins for improvement in crop protection.

Authors:  Amber Afroz; Ghulam Muhammad Ali; Asif Mir; Setsuko Komatsu
Journal:  Plant Cell Rep       Date:  2011-02-02       Impact factor: 4.570

Review 5.  Application of Proteomics Technologies in Oil Palm Research.

Authors:  Benjamin Yii Chung Lau; Abrizah Othman; Umi Salamah Ramli
Journal:  Protein J       Date:  2018-12       Impact factor: 2.371

6.  Regulatory subunit B'gamma of protein phosphatase 2A prevents unnecessary defense reactions under low light in Arabidopsis.

Authors:  Andrea Trotta; Michael Wrzaczek; Judith Scharte; Mikko Tikkanen; Grzegorz Konert; Moona Rahikainen; Maija Holmström; Hanna-Maija Hiltunen; Stephan Rips; Nina Sipari; Paula Mulo; Engelbert Weis; Antje von Schaewen; Eva-Mari Aro; Saijaliisa Kangasjärvi
Journal:  Plant Physiol       Date:  2011-05-12       Impact factor: 8.340

7.  Microscopy and proteomic analysis of the non-host resistance of Oryza sativa to the wheat leaf rust fungus, Puccinia triticina f. sp. tritici.

Authors:  Hongbing Li; Paul H Goodwin; Qingmei Han; Lili Huang; Zhensheng Kang
Journal:  Plant Cell Rep       Date:  2011-10-30       Impact factor: 4.570

8.  Quantitative proteomics of tomato defense against Pseudomonas syringae infection.

Authors:  Jennifer Parker; Jin Koh; Mi-Jeong Yoo; Ning Zhu; Michelle Feole; Sarah Yi; Sixue Chen
Journal:  Proteomics       Date:  2013-04-27       Impact factor: 3.984

9.  Genomics of fungal disease resistance in tomato.

Authors:  Dilip R Panthee; Feng Chen
Journal:  Curr Genomics       Date:  2010-03       Impact factor: 2.236

10.  Generation of a predicted protein database from EST data and application to iTRAQ analyses in grape (Vitis vinifera cv. Cabernet Sauvignon) berries at ripening initiation.

Authors:  Joost Lücker; Mario Laszczak; Derek Smith; Steven T Lund
Journal:  BMC Genomics       Date:  2009-01-26       Impact factor: 3.969

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.