Literature DB >> 18775334

Combined use of RNAi and quantitative proteomics to study gene function in Drosophila.

Tiziana Bonaldi1, Tobias Straub, Jürgen Cox, Chanchal Kumar, Peter B Becker, Matthias Mann.   

Abstract

RNA interference is a powerful way to study gene function and is frequently combined with microarray analysis. Here we introduce a similar technology at the protein level by simultaneously applying Stable Isotope Labeling by Amino acids in Cell culture (SILAC) and RNA interference (RNAi) to Drosophila SL2 cells. After knockdown of ISWI, an ATP-hydrolyzing motor of different chromatin remodeling complexes, we obtained a quantitative proteome of more than 4,000 proteins. ISWI itself was reduced 10-fold as quantified by SILAC. Several hundred proteins were significantly regulated and clustered into distinct functional categories. Acf-1, a direct interaction partner of ISWI, is severely depleted at the protein, but not the transcript, level; this most likely results from reduced protein stability. We found little overall correlation between changes in the transcriptome and proteome with many protein changes unaccompanied by message changes. However, correlation was high for those mRNAs that changed significantly by microarray.

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Year:  2008        PMID: 18775334     DOI: 10.1016/j.molcel.2008.07.018

Source DB:  PubMed          Journal:  Mol Cell        ISSN: 1097-2765            Impact factor:   17.970


  39 in total

1.  Dynamics of the skeletal muscle secretome during myoblast differentiation.

Authors:  Jeanette Henningsen; Kristoffer T G Rigbolt; Blagoy Blagoev; Bente Klarlund Pedersen; Irina Kratchmarova
Journal:  Mol Cell Proteomics       Date:  2010-07-14       Impact factor: 5.911

2.  Quantitative proteomic analyses of influenza virus-infected cultured human lung cells.

Authors:  Kevin M Coombs; Alicia Berard; Wanhong Xu; Oleg Krokhin; Xiaobo Meng; John P Cortens; Darwyn Kobasa; John Wilkins; Earl G Brown
Journal:  J Virol       Date:  2010-08-11       Impact factor: 5.103

3.  Quantitative proteomics analysis reveals molecular networks regulated by epidermal growth factor receptor level in head and neck cancer.

Authors:  Wei Yang; Quan Cai; Vivian W Y Lui; Patrick A Everley; Jayoung Kim; Neil Bhola; Kelly M Quesnelle; Bruce R Zetter; Hanno Steen; Michael R Freeman; Jennifer R Grandis
Journal:  J Proteome Res       Date:  2010-06-04       Impact factor: 4.466

4.  The SILAC fly allows for accurate protein quantification in vivo.

Authors:  Matthias D Sury; Jia-Xuan Chen; Matthias Selbach
Journal:  Mol Cell Proteomics       Date:  2010-06-05       Impact factor: 5.911

Review 5.  Drosophila RNAi screening in a postgenomic world.

Authors:  Chris Bakal
Journal:  Brief Funct Genomics       Date:  2011-07-12       Impact factor: 4.241

6.  A practical guide to the MaxQuant computational platform for SILAC-based quantitative proteomics.

Authors:  Jürgen Cox; Ivan Matic; Maximiliane Hilger; Nagarjuna Nagaraj; Matthias Selbach; Jesper V Olsen; Matthias Mann
Journal:  Nat Protoc       Date:  2009       Impact factor: 13.491

Review 7.  Applying mass spectrometry-based proteomics to genetics, genomics and network biology.

Authors:  Matthias Gstaiger; Ruedi Aebersold
Journal:  Nat Rev Genet       Date:  2009-09       Impact factor: 53.242

8.  Comparative analysis to guide quality improvements in proteomics.

Authors:  Matthias Mann
Journal:  Nat Methods       Date:  2009-10       Impact factor: 28.547

9.  Proteomics analysis of human skeletal muscle reveals novel abnormalities in obesity and type 2 diabetes.

Authors:  Hyonson Hwang; Benjamin P Bowen; Natalie Lefort; Charles R Flynn; Elena A De Filippis; Christine Roberts; Christopher C Smoke; Christian Meyer; Kurt Højlund; Zhengping Yi; Lawrence J Mandarino
Journal:  Diabetes       Date:  2009-10-15       Impact factor: 9.461

10.  Global effects of kinase inhibitors on signaling networks revealed by quantitative phosphoproteomics.

Authors:  Cuiping Pan; Jesper V Olsen; Henrik Daub; Matthias Mann
Journal:  Mol Cell Proteomics       Date:  2009-08-03       Impact factor: 5.911

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