Regulation of the nucleosome repeat length in vivo by the DNA sequence, protein concentrations and long-range interactions.

The nucleosome repeat length (NRL) is an integral chromatin property important for its biological functions. Recent experiments revealed several conflicting trends of the NRL dependence on the concentrations of histones and other architectural chromatin proteins, both in vitro and in vivo, but a systematic theoretical description of NRL as a function of DNA sequence and epigenetic determinants is currently lacking. To address this problem, we have performed an integrative biophysical and bioinformatics analysis in species ranging from yeast to frog to mouse where NRL was studied as a function of various parameters. We show that in simple eukaryotes such as yeast, a lower limit for the NRL value exists, determined by internucleosome interactions and remodeler action. For higher eukaryotes, also the upper limit exists since NRL is an increasing but saturating function of the linker histone concentration. Counterintuitively, smaller H1 variants or non-histone architectural proteins can initiate larger effects on the NRL due to entropic reasons. Furthermore, we demonstrate that different regimes of the NRL dependence on histone concentrations exist depending on whether DNA sequence-specific effects dominate over boundary effects or vice versa. We consider several classes of genomic regions with apparently different regimes of the NRL variation. As one extreme, our analysis reveals that the period of oscillations of the nucleosome density around bound RNA polymerase coincides with the period of oscillations of positioning sites of the corresponding DNA sequence. At another extreme, we show that although mouse major satellite repeats intrinsically encode well-defined nucleosome preferences, they have no unique nucleosome arrangement and can undergo a switch between two distinct types of nucleosome positioning

Taking into account nucleosomes for predicting gene expression

The eukaryotic genome is organized in a chain of nucleosomes that consist of 145-147. bp of DNA wrapped around a histone octamer protein core. Binding of transcription factors (TF) to nucleosomal DNA is frequently impeded, which makes it a challenging task to calculate TF occupancy at a given regulatory genomic site for predicting gene expression. Here, we review methods to calculate TF binding to DNA in the presence of nucleosomes. The main theoretical problems are (i) the computation speed that is becoming a bottleneck when partial unwrapping of DNA from the nucleosome is considered, (ii) the perturbation of the binding equilibrium by the activity of ATP-dependent chromatin remodelers, which translocate nucleosomes along the DNA, and (iii) the model parameterization from high-throughput sequencing data and fluorescence microscopy experiments in living cells. We discuss strategies that address these issues to efficiently compute transcription factor binding in chromatin. © 2013 Elsevier Inc

Measuring Self-Association of Pt Complexes by 195 Pt NMR

A highly soluble neutral cyclometallated Pt complex of C N N type was prepared and its self-aggregation in CDCl3 solution was studied by NMR dilution method. It was shown that 195Pt NMR can be used to quantify aggregation and therefore be potentially applied to many other Pt complexes. Two-dimensional HMBC 1H-195Pt technique increases sensitivity of experiment and allowed recording 195Pt chemical shift values at concentration as low as 1 mM and it was observed that opposite to shielding of aromatic protons, Pt nucleus is deshielded upon increase of concentration

Nucleosome repositioning links DNA (de)methylation and differential CTCF binding during stem cell development

During differentiation of embryonic stem cells, chromatin reorganizes to establish cell type-specific expression programs. Here, we have dissected the linkages between DNA methylation (5mC), hydroxymethylation (5hmC), nucleosome repositioning, and binding of the transcription factor CTCF during this process. By integrating MNase-seq and ChIP-seq experiments in mouse embryonic stem cells (ESC) and their differentiated counterparts with biophysical modeling, we found that the interplay between these factors depends on their genomic context. The mostly unmethylated CpG islands have reduced nucleosome occupancy and are enriched in cell type-independent binding sites for CTCF. The few remaining methylated CpG dinucleotides are preferentially associated with nucleosomes. In contrast, outside of CpG islands most CpGs are methylated, and the average methylation density oscillates so that it is highest in the linker region between nucleosomes. Outside CpG islands, binding of TET1, an enzyme that converts 5mC to 5hmC, is associated with labile, MNase-sensitive nucleosomes. Such nucleosomes are poised for eviction in ESCs and become stably bound in differentiated cells where the TET1 and 5hmC levels go down. This process regulates a class of CTCF binding sites outside CpG islands that are occupied by CTCF in ESCs but lose the protein during differentiation. We rationalize this cell type-dependent targeting of CTCF with a quantitative biophysical model of competitive binding with the histone octamer, depending on the TET1, 5hmC, and 5mC state

Poly(ADP-ribosyl)ation associated changes in CTCF-chromatin binding and gene expression in breast cells

CTCF is an evolutionarily conserved and ubiquitously expressed architectural protein regulating a plethora of cellular functions via different molecular mechanisms. CTCF can undergo a number of post-translational modifications which change its properties and functions. One such modifications linked to cancer is poly(ADP-ribosyl)ation (PARylation). The highly PARylated CTCF form has an apparent molecular mass of 180 kDa (referred to as CTCF180), which can be distinguished from hypo- and non-PARylated CTCF with the apparent molecular mass of 130 kDa (referred to as CTCF130). The existing data accumulated so far have been mainly related to CTCF130. However, the properties of CTCF180 are not well understood despite its abundance in a number of primary tissues. In this study we performed ChIP-seq and RNA-seq analyses in human breast cells 226LDM, which display predominantly CTCF130 when proliferating, but CTCF180 upon cell cycle arrest. We observed that in the arrested cells the majority of sites lost CTCF, whereas fewer sites gained CTCF or remain bound (i.e. common sites). The classical CTCF binding motif was found in the lost and common, but not in the gained sites. The changes in CTCF occupancies in the lost and common sites were associated with increased chromatin densities and altered expression from the neighboring genes. Based on these results we propose a model integrating the CTCF130/180 transition with CTCF-DNA binding and gene expression changes. This study also issues an important cautionary note concerning the design and interpretation of any experiments using cells and tissues where CTCF180 may be present

NucTools: analysis of chromatin feature occupancy profiles from high-throughput sequencing data

Background: Biomedical applications of high-throughput sequencing methods generate a vast amount of data in which numerous chromatin features are mapped along the genome. The results are frequently analysed by creating binary data sets that link the presence/absence of a given feature to specific genomic loci. However, the nucleosome occupancy or chromatin accessibility landscape is essentially continuous. It is currently a challenge in the field to cope with continuous distributions of deep sequencing chromatin readouts and to integrate the different types of discrete chromatin features to reveal linkages between them. Results: Here we introduce the NucTools suite of Perl scripts as well as MATLAB- and R-based visualization programs for a nucleosome-centred downstream analysis of deep sequencing data. NucTools accounts for the continuous distribution of nucleosome occupancy. It allows calculations of nucleosome occupancy profiles averaged over several replicates, comparisons of nucleosome occupancy landscapes between different experimental conditions, and the estimation of the changes of integral chromatin properties such as the nucleosome repeat length. Furthermore, NucTools facilitates the annotation of nucleosome occupancy with other chromatin features like binding of transcription factors or architectural proteins, and epigenetic marks like histone modifications or DNA methylation. The applications of NucTools are demonstrated for the comparison of several datasets for nucleosome occupancy in mouse embryonic stem cells (ESCs) and mouse embryonic fibroblasts (MEFs). Conclusions: The typical workflows of data processing and integrative analysis with NucTools reveal information on the interplay of nucleosome positioning with other features such as for example binding of a transcription factor CTCF, regions with stable and unstable nucleosomes, and domains of large organized chromatin K9me2 modifications (LOCKs). As potential limitations and problems we discuss how inter-replicate variability of MNase-seq experiments can be addressed

Chromatin and epigenetics: current biophysical views

Recent advances in high-throughput sequencing experiments and their theoretical descriptions have determined fast dynamics of the "chromatin and epigenetics" field, with new concepts appearing at high rate. This field includes but is not limited to the study of DNA-protein-RNA interactions, chromatin packing properties at different scales, regulation of gene expression and protein trafficking in the cell nucleus, binding site search in the crowded chromatin environment and modulation of physical interactions by covalent chemical modifications of the binding partners. The current special issue does not pretend for the full coverage of the field, but it rather aims to capture its development and provide a snapshot of the most recent concepts and approaches. Eighteen open-access articles comprising this issue provide a delicate balance between current theoretical and experimental biophysical approaches to uncover chromatin structure and understand epigenetic regulation, allowing free flow of new ideas and preliminary results

CTCF-dependent chromatin boundaries formed by asymmetric nucleosome arrays with decreased linker length

The CCCTC-binding factor (CTCF) organises the genome in 3D through DNA loops and in 1D by setting boundaries isolating different chromatin states, but these processes are not well understood. Here we focus on the relationship between CTCF binding and the decrease of the Nucleosome Repeat Length (NRL) for ∼20 adjacent nucleosomes, affecting up to 10% of the mouse genome. We found that the chromatin boundary near CTCF is created by the nucleosome-depleted region (NDR) asymmetrically located >40 nucleotides 5’-upstream from the centre of CTCF motif. The strength of CTCF binding to DNA is correlated with the decrease of NRL near CTCF and anti-correlated with the level of asymmetry of the nucleosome array. Individual chromatin remodellers have different contributions, with Snf2h having the strongest effect on the NRL decrease near CTCF and Chd4 playing a major role in the symmetry breaking. Upon differentiation of embryonic stem cells to neural progenitor cells and embryonic fibroblasts, a subset of common CTCF sites preserved in all three cell types maintains a relatively small local NRL despite genome-wide NRL increase. The sites which lost CTCF upon differentiation are characterised by nucleosome rearrangement 3’-downstream, but the boundary defined by the NDR 5’-upstream of CTCF motif remains

Related Topics of a Novel TCR-based Cancer Detection Approach

AbstractWe developed a novel algorithm (DeepCAT) to perform de novo detection of cancer associated TCRs, which is based on a convolutional neural network (CNN) model. In this manuscript, we compared its performance with a similar non-deep learning approach, TCRboost, and demonstrated that DeepCAT achieved better prediction accuracy when used to distinguish cancer from non-cancer individuals. Further, although DeepCAT was trained for CDR3s with different lengths, we showed that the combined outcome does not bias the prediction accuracy. Finally, human immune repertoire is affected by many common inflammatory conditions, and our analysis demonstrated that DeepCAT predictions are minimally affected by these factors.</jats:p