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Proteomic Analysis Reveals New Cardiac-Specific Dystrophin-Associated Proteins

Overview of attention for article published in PLOS ONE, August 2012
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Title
Proteomic Analysis Reveals New Cardiac-Specific Dystrophin-Associated Proteins
Published in
PLOS ONE, August 2012
DOI 10.1371/journal.pone.0043515
Pubmed ID
Authors

Eric K. Johnson, Liwen Zhang, Marvin E. Adams, Alistair Phillips, Michael A. Freitas, Stanley C. Froehner, Kari B. Green-Church, Federica Montanaro

Abstract

Mutations affecting the expression of dystrophin result in progressive loss of skeletal muscle function and cardiomyopathy leading to early mortality. Interestingly, clinical studies revealed no correlation in disease severity or age of onset between cardiac and skeletal muscles, suggesting that dystrophin may play overlapping yet different roles in these two striated muscles. Since dystrophin serves as a structural and signaling scaffold, functional differences likely arise from tissue-specific protein interactions. To test this, we optimized a proteomics-based approach to purify, identify and compare the interactome of dystrophin between cardiac and skeletal muscles from as little as 50 mg of starting material. We found selective tissue-specific differences in the protein associations of cardiac and skeletal muscle full length dystrophin to syntrophins and dystrobrevins that couple dystrophin to signaling pathways. Importantly, we identified novel cardiac-specific interactions of dystrophin with proteins known to regulate cardiac contraction and to be involved in cardiac disease. Our approach overcomes a major challenge in the muscular dystrophy field of rapidly and consistently identifying bona fide dystrophin-interacting proteins in tissues. In addition, our findings support the existence of cardiac-specific functions of dystrophin and may guide studies into early triggers of cardiac disease in Duchenne and Becker muscular dystrophies.

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The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 1 1%
France 1 1%
Sweden 1 1%
United Kingdom 1 1%
United States 1 1%
Unknown 70 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 32%
Researcher 11 15%
Student > Master 6 8%
Student > Bachelor 6 8%
Professor > Associate Professor 4 5%
Other 15 20%
Unknown 9 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 32 43%
Biochemistry, Genetics and Molecular Biology 12 16%
Medicine and Dentistry 11 15%
Neuroscience 2 3%
Social Sciences 2 3%
Other 7 9%
Unknown 9 12%