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Epigenome Microarray Platform for Proteome-Wide Dissection of Chromatin-Signaling Networks

Overview of attention for article published in PLOS ONE, August 2009
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Title
Epigenome Microarray Platform for Proteome-Wide Dissection of Chromatin-Signaling Networks
Published in
PLOS ONE, August 2009
DOI 10.1371/journal.pone.0006789
Pubmed ID
Authors

Dennis J. Bua, Alex J. Kuo, Peggie Cheung, Chih Long Liu, Valentina Migliori, Alexsandra Espejo, Fabio Casadio, Christian Bassi, Bruno Amati, Mark T. Bedford, Ernesto Guccione, Or Gozani

Abstract

Knowledge of protein domains that function as the biological effectors for diverse post-translational modifications of histones is critical for understanding how nuclear and epigenetic programs are established. Indeed, mutations of chromatin effector domains found within several proteins are associated with multiple human pathologies, including cancer and immunodeficiency syndromes. To date, relatively few effector domains have been identified in comparison to the number of modifications present on histone and non-histone proteins. Here we describe the generation and application of human modified peptide microarrays as a platform for high-throughput discovery of chromatin effectors and for epitope-specificity analysis of antibodies commonly utilized in chromatin research. Screening with a library containing a majority of the Royal Family domains present in the human proteome led to the discovery of TDRD7, JMJ2C, and MPP8 as three new modified histone-binding proteins. Thus, we propose that peptide microarray methodologies are a powerful new tool for elucidating molecular interactions at chromatin.

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Mendeley readers

The data shown below were compiled from readership statistics for 88 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 2%
United Kingdom 2 2%
Turkey 1 1%
Italy 1 1%
Japan 1 1%
Korea, Republic of 1 1%
Unknown 80 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 32 36%
Researcher 19 22%
Professor 7 8%
Other 5 6%
Student > Master 4 5%
Other 9 10%
Unknown 12 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 47 53%
Biochemistry, Genetics and Molecular Biology 20 23%
Chemistry 4 5%
Medicine and Dentistry 3 3%
Unspecified 1 1%
Other 1 1%
Unknown 12 14%