Literature DB >> 17951628

Eight-channel iTRAQ enables comparison of the activity of six leukemogenic tyrosine kinases.

Andrew Pierce1, Richard D Unwin, Caroline A Evans, Stephen Griffiths, Louise Carney, Liqun Zhang, Ewa Jaworska, Chia-Fang Lee, David Blinco, Michal J Okoniewski, Crispin J Miller, Danny A Bitton, Elaine Spooncer, Anthony D Whetton.   

Abstract

There are a number of leukemogenic protein-tyrosine kinases (PTKs) associated with leukemic transformation. Although each is linked with a specific disease their functional activity poses the question whether they have a degree of commonality in their effects upon target cells. Exon array analysis of the effects of six leukemogenic PTKs (BCR/ABL, TEL/PDGFRbeta, FIP1/PDGFRalpha, D816V KIT, NPM/ALK, and FLT3ITD) revealed few common effects on the transcriptome. It is apparent, however, that proteome changes are not directly governed by transcriptome changes. Therefore, we assessed and used a new generation of iTRAQ tagging, enabling eight-channel relative quantification discovery proteomics, to analyze the effects of these six leukemogenic PTKs. Again these were found to have disparate effects on the proteome with few common targets. BCR/ABL had the greatest effect on the proteome and had more effects in common with FIP1/PDGFRalpha. The proteomic effects of the four type III receptor kinases were relatively remotely related. The only protein commonly affected was eosinophil-associated ribonuclease 7. Five of six PTKs affected the motility-related proteins CAPG and vimentin, although this did not correspond to changes in motility. However, correlation of the proteomics data with that from the exon microarray not only showed poor levels of correlation between transcript and protein levels but also revealed alternative patterns of regulation of the CAPG protein by different oncogenes, illustrating the utility of such a combined approach.

Entities:  

Mesh:

Substances:

Year:  2007        PMID: 17951628     DOI: 10.1074/mcp.M700251-MCP200

Source DB:  PubMed          Journal:  Mol Cell Proteomics        ISSN: 1535-9476            Impact factor:   5.911


  83 in total

1.  A robust method for quantitative high-throughput analysis of proteomes by 18O labeling.

Authors:  Elena Bonzon-Kulichenko; Daniel Pérez-Hernández; Estefanía Núñez; Pablo Martínez-Acedo; Pedro Navarro; Marco Trevisan-Herraz; María del Carmen Ramos; Saleta Sierra; Sara Martínez-Martínez; Marisol Ruiz-Meana; Elizabeth Miró-Casas; David García-Dorado; Juan Miguel Redondo; Javier S Burgos; Jesús Vázquez
Journal:  Mol Cell Proteomics       Date:  2010-08-31       Impact factor: 5.911

2.  Simultaneous analysis of relative protein expression levels across multiple samples using iTRAQ isobaric tags with 2D nano LC-MS/MS.

Authors:  Richard D Unwin; John R Griffiths; Anthony D Whetton
Journal:  Nat Protoc       Date:  2010-08-26       Impact factor: 13.491

3.  Relative quantification: characterization of bias, variability and fold changes in mass spectrometry data from iTRAQ-labeled peptides.

Authors:  Douglas W Mahoney; Terry M Therneau; Carrie J Heppelmann; Leeann Higgins; Linda M Benson; Roman M Zenka; Pratik Jagtap; Gary L Nelsestuen; H Robert Bergen; Ann L Oberg
Journal:  J Proteome Res       Date:  2011-08-02       Impact factor: 4.466

4.  Multiplex peptide stable isotope dimethyl labeling for quantitative proteomics.

Authors:  Paul J Boersema; Reinout Raijmakers; Simone Lemeer; Shabaz Mohammed; Albert J R Heck
Journal:  Nat Protoc       Date:  2009       Impact factor: 13.491

5.  Proteomic signatures of acquired letrozole resistance in breast cancer: suppressed estrogen signaling and increased cell motility and invasiveness.

Authors:  Syreeta L Tilghman; Ian Townley; Qiu Zhong; Patrick P Carriere; Jin Zou; Shawn D Llopis; Lynez C Preyan; Christopher C Williams; Elena Skripnikova; Melyssa R Bratton; Qiang Zhang; Guangdi Wang
Journal:  Mol Cell Proteomics       Date:  2013-05-23       Impact factor: 5.911

Review 6.  Liquid chromatography-mass spectrometry-based quantitative proteomics.

Authors:  Fang Xie; Tao Liu; Wei-Jun Qian; Vladislav A Petyuk; Richard D Smith
Journal:  J Biol Chem       Date:  2011-06-01       Impact factor: 5.157

Review 7.  A Review on Quantitative Multiplexed Proteomics.

Authors:  Nishant Pappireddi; Lance Martin; Martin Wühr
Journal:  Chembiochem       Date:  2019-04-18       Impact factor: 3.164

8.  A comparison of the accuracy of iTRAQ quantification by nLC-ESI MSMS and nLC-MALDI MSMS methods.

Authors:  Sally L Shirran; Catherine H Botting
Journal:  J Proteomics       Date:  2010-03-15       Impact factor: 4.044

9.  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

10.  iQuantitator: a tool for protein expression inference using iTRAQ.

Authors:  John H Schwacke; Elizabeth G Hill; Edward L Krug; Susana Comte-Walters; Kevin L Schey
Journal:  BMC Bioinformatics       Date:  2009-10-18       Impact factor: 3.169

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.