Nucleosome positioning by genomic excluding-energy barriers

Universitè Claude Bernard Lyon 1, Université de Lyon, F-69000 Lyon, France.
Proceedings of the National Academy of Sciences (Impact Factor: 9.67). 12/2009; 106(52):22257-62. DOI: 10.1073/pnas.0909511106
Source: PubMed


Recent genome-wide nucleosome mappings along with bioinformatics studies have confirmed that the DNA sequence plays a more important role in the collective organization of nucleosomes in vivo than previously thought. Yet in living cells, this organization also results from the action of various external factors like DNA-binding proteins and chromatin remodelers. To decipher the code for intrinsic chromatin organization, there is thus a need for in vitro experiments to bridge the gap between computational models of nucleosome sequence preferences and in vivo nucleosome occupancy data. Here we combine atomic force microscopy in liquid and theoretical modeling to demonstrate that a major sequence signaling in vivo are high-energy barriers that locally inhibit nucleosome formation rather than favorable positioning motifs. We show that these genomic excluding-energy barriers condition the collective assembly of neighboring nucleosomes consistently with equilibrium statistical ordering principles. The analysis of two gene promoter regions in Saccharomyces cerevisiae and the human genome indicates that these genomic barriers direct the intrinsic nucleosome occupancy of regulatory sites, thereby contributing to gene expression regulation.

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    • "Besides those features such as the propeller twist and free energy which overlap with previous computational studies, we also find that the DNA denaturation, DNA-bending stiffness, Stacking energy and Z-DNA are effective in capturing nucleosome occupancy. These structural features capture more accurately in vivo nucleosome occupancy than sequence compositional features, consistent with a previous analysis which indicated that a major sequence signaling in vivo is a high-energy barrier rather than favorable sequence motifs [48]. Furthermore, we proposed a novel computational method, DLaNe, to detect peaks (valleys) of structural profiles to locate nucleosome positions. "
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    ABSTRACT: Nucleosome distribution along chromatin dictates genomic DNA accessibility and thus profoundly influences gene expression. However, the underlying mechanism of nucleosome formation remains elusive. Here, taking a structural perspective, we systematically explored nucleosome formation potential of genomic sequences and the effect on chromatin organization and gene expression in S. cerevisiae. We analyzed twelve structural features related to flexibility, curvature and energy of DNA sequences. The results showed that some structural features such as DNA denaturation, DNA-bending stiffness, Stacking energy, Z-DNA, Propeller twist and free energy, were highly correlated with in vitro and in vivo nucleosome occupancy. Specifically, they can be classified into two classes, one positively and the other negatively correlated with nucleosome occupancy. These two kinds of structural features facilitated nucleosome binding in centromere regions and repressed nucleosome formation in the promoter regions of protein-coding genes to mediate transcriptional regulation. Based on these analyses, we integrated all twelve structural features in a model to predict more accurately nucleosome occupancy in vivo than the existing methods that mainly depend on sequence compositional features. Furthermore, we developed a novel approach, named DLaNe, that located nucleosomes by detecting peaks of structural profiles, and built a meta predictor to integrate information from different structural features. As a comparison, we also constructed a hidden Markov model (HMM) to locate nucleosomes based on the profiles of these structural features. The result showed that the meta DLaNe and HMM-based method performed better than the existing methods, demonstrating the power of these structural features in predicting nucleosome positions. Our analysis revealed that DNA structures significantly contribute to nucleosome organization and influence chromatin structure and gene expression regulation. The results indicated that our proposed methods are effective in predicting nucleosome occupancy and positions and that these structural features are highly predictive of nucleosome organization.The implementation of our DLaNe method based on structural features is available online.
    BMC Bioinformatics 03/2012; 13(1):49. DOI:10.1186/1471-2105-13-49 · 2.58 Impact Factor
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    • "Recently, the studies of DNA crystal structures have brought to light the fact that structural properties of DNA sequences play important roles in different genome functions [13,14]. The second-order structural information encoded in promoter regions could be recognized by RNA polymerase [15]. "
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    ABSTRACT: Promoter prediction is an integrant step for understanding gene regulation and annotating genomes. Traditional promoter analysis is mainly based on sequence compositional features. Recently, many kinds of structural features have been employed in promoter prediction. However, considering the high-dimensionality and overfitting problems, it is unfeasible to utilize all available features for promoter prediction. Thus it is necessary to choose some appropriate features for the prediction task. This paper conducts an extensive comparison study on feature selection of DNA structural properties for promoter prediction. Firstly, to examine whether promoters possess some special structures, we carry out a systematical comparison among the profiles of thirteen structural features on promoter and non-promoter sequences. Secondly, we investigate the correlations between these structural features and promoter sequences. Thirdly, both filter and wrapper methods are utilized to select appropriate feature subsets from thirteen different kinds of structural features for promoter prediction, and the predictive power of the selected feature subsets is evaluated. Finally, we compare the prediction performance of the feature subsets selected in this paper with nine existing promoter prediction approaches. Experimental results show that the structural features are differentially correlated to promoters. Specifically, DNA-bending stiffness, DNA denaturation and energy-related features are highly correlated with promoters. The predictive power for promoter sequences differentiates greatly among different structural features. Selecting the relevant features can significantly improve the accuracy of promoter prediction.
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    • "Zooms on individual DNA fragments, nucleosomes or DNA-TBP-TFIIB complexes were achieved and the DNA molecule path was skeletonized; then topographical analysis and length measurements were carried out (Figure S3). The results enabled a measurement of (i) the height variation along the DNA molecules which allowed us to orient the DNA fragment [19] (Figure S3), (ii) the DNA-protein complexes height (Figure S4) and (iii) the mapping of the nucleosome and TBP-TFIIB positions along the DNA fragments. From the measurement of the length of free DNA outside the nucleosome, namely L+ (resp. "
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