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Quantitating the Specificity and Selectivity of Gcn5-Mediated Acetylation of Histone H3

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Title
Quantitating the Specificity and Selectivity of Gcn5-Mediated Acetylation of Histone H3
Published in
PLOS ONE, February 2013
DOI 10.1371/journal.pone.0054896
Pubmed ID
Authors

Yin-Ming Kuo, Andrew J. Andrews

Abstract

Lysine acetyltransferases (KATs) play a unique role in regulating gene transcription as well as maintaining the epigenetic state of the cell. KATs such as Gcn5 and p300/CBP can modify multiple residues on a single histone; however, order and specificity of acetylation can be altered by factors such as histone chaperones, subunit proteins or external stimulus. While the importance of acetylation is well documented, it has been difficult to quantitatively measure the specificity and selectivity of acetylation at different residues within a histone. In this paper, we demonstrate a label-free quantitative high throughput mass spectrometry-based assay capable of quantitatively monitoring all known acetylation sites of H3 simultaneously. Using this assay, we are able to analyze the steady-state enzyme kinetics of Gcn5, an evolutionarily conserved KAT. In doing so, we measured Gcn5-mediated acetylation at six residues (K14>K9 ≈ K23> K18> K27 ≈ K36) and the catalytic efficiency (k(cat)/K(m)) for K9, K14, K18, and K23 as well as the nonenzymatic acetylation rate. We observed selectivity differences of up to -4 kcal/mol between K14 and K18, the highest and lowest measurable k(cat)/K(m). These data provide a first look at quantitating the specificity and selectivity of multiple lysines on a single substrate (H3) by Gcn5.

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Geographical breakdown

Country Count As %
United States 1 1%
France 1 1%
Ireland 1 1%
Unknown 93 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 27%
Researcher 13 14%
Student > Master 10 10%
Student > Doctoral Student 8 8%
Student > Postgraduate 7 7%
Other 19 20%
Unknown 13 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 32 33%
Biochemistry, Genetics and Molecular Biology 26 27%
Chemistry 12 13%
Engineering 2 2%
Neuroscience 2 2%
Other 7 7%
Unknown 15 16%