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      From a melt of rings to chromosome territories: the role of topological constraints in genome folding

      , , ,
      Reports on Progress in Physics
      IOP Publishing

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          Architectural protein subclasses shape 3D organization of genomes during lineage commitment.

          Understanding the topological configurations of chromatin may reveal valuable insights into how the genome and epigenome act in concert to control cell fate during development. Here, we generate high-resolution architecture maps across seven genomic loci in embryonic stem cells and neural progenitor cells. We observe a hierarchy of 3D interactions that undergo marked reorganization at the submegabase scale during differentiation. Distinct combinations of CCCTC-binding factor (CTCF), Mediator, and cohesin show widespread enrichment in chromatin interactions at different length scales. CTCF/cohesin anchor long-range constitutive interactions that might form the topological basis for invariant subdomains. Conversely, Mediator/cohesin bridge short-range enhancer-promoter interactions within and between larger subdomains. Knockdown of Smc1 or Med12 in embryonic stem cells results in disruption of spatial architecture and downregulation of genes found in cohesin-mediated interactions. We conclude that cell-type-specific chromatin organization occurs at the submegabase scale and that architectural proteins shape the genome in hierarchical length scales. Copyright © 2013 Elsevier Inc. All rights reserved.
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            A genomic code for nucleosome positioning.

            Eukaryotic genomes are packaged into nucleosome particles that occlude the DNA from interacting with most DNA binding proteins. Nucleosomes have higher affinity for particular DNA sequences, reflecting the ability of the sequence to bend sharply, as required by the nucleosome structure. However, it is not known whether these sequence preferences have a significant influence on nucleosome position in vivo, and thus regulate the access of other proteins to DNA. Here we isolated nucleosome-bound sequences at high resolution from yeast and used these sequences in a new computational approach to construct and validate experimentally a nucleosome-DNA interaction model, and to predict the genome-wide organization of nucleosomes. Our results demonstrate that genomes encode an intrinsic nucleosome organization and that this intrinsic organization can explain approximately 50% of the in vivo nucleosome positions. This nucleosome positioning code may facilitate specific chromosome functions including transcription factor binding, transcription initiation, and even remodelling of the nucleosomes themselves.
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              Long-range correlations in nucleotide sequences.

              DNA sequences have been analysed using models, such as an n-step Markov chain, that incorporate the possibility of short-range nucleotide correlations. We propose here a method for studying the stochastic properties of nucleotide sequences by constructing a 1:1 map of the nucleotide sequence onto a walk, which we term a 'DNA walk'. We then use the mapping to provide a quantitative measure of the correlation between nucleotides over long distances along the DNA chain. Thus we uncover in the nucleotide sequence a remarkably long-range power law correlation that implies a new scale-invariant property of DNA. We find such long-range correlations in intron-containing genes and in nontranscribed regulatory DNA sequences, but not in complementary DNA sequences or intron-less genes.
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                Author and article information

                Journal
                Reports on Progress in Physics
                Rep. Prog. Phys.
                IOP Publishing
                0034-4885
                1361-6633
                February 01 2014
                February 01 2014
                January 28 2014
                : 77
                : 2
                : 022601
                Article
                10.1088/0034-4885/77/2/022601
                24472896
                03052990-8a7f-4d19-92e4-01988f059337
                © 2014

                http://iopscience.iop.org/info/page/text-and-data-mining

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