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Single-Nucleosome Mapping of Histone Modifications in S. cerevisiae

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2005

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Public Library of Science
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Liu, Chih, Tommy Kaplan, Minkyu Kim, Stephen Buratowski, Stuart L. Schreiber, Nir Friedman, and Oliver J. Randoa. 2005. Single-Nucleosome Mapping of Histone Modifications in S. cerevisiae. PLoS Biology 3(10).

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Abstract

Covalent modification of histone proteins plays a role in virtually every process on eukaryotic DNA, from transcription to DNA repair. Many different residues can be covalently modified, and it has been suggested that these modifications occur in a great number of independent, meaningful combinations. Published low-resolution microarray studies on the combinatorial complexity of histone modification patterns suffer from confounding effects caused by the averaging of modification levels over multiple nucleosomes. To overcome this problem, we used a high-resolution tiled microarray with single-nucleosome resolution to investigate the occurrence of combinations of 12 histone modifications on thousands of nucleosomes in actively growing S. cerevisiae. We found that histone modifications do not occur independently; there are roughly two groups of co-occurring modifications. One group of lysine acetylations shows a sharply defined domain of two hypo-acetylated nucleosomes, adjacent to the transcriptional start site, whose occurrence does not correlate with transcription levels. The other group consists of modifications occurring in gradients through the coding regions of genes in a pattern associated with transcription. We found no evidence for a deterministic code of many discrete states, but instead we saw blended, continuous patterns that distinguish nucleosomes at one location (e.g., promoter nucleosomes) from those at another location (e.g., over the 3′ ends of coding regions). These results are consistent with the idea of a simple, redundant histone code, in which multiple modifications share the same role.

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saccaromyces, systems biology, molecular biology, structural biology, genetics, genomics, gene therapy, cell biology, bioinformatics, computational biology

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