Editorial: Interactions of Plants With Bacteria and Fungi: Molecular and Epigenetic Plasticity of the Host

Editorial on the Research Topic 'Interactions of Plants with Bacteria and Fungi: Molecular and Epigenetic Plasticity of the Host

ZipHiC: a novel Bayesian framework to identify enriched interactions and experimental biases in Hi-C data

Motivation Several computational and statistical methods have been developed to analyse data generated through the 3C-based methods, especially the Hi-C. Most of the existing methods do not account for dependency in Hi-C data. Results Here, we present ZipHiC, a novel statistical method to explore Hi-C data focusing on the detection of enriched contacts. ZipHiC implements a Bayesian method based on a hidden Markov random field (HMRF) model and the Approximate Bayesian Computation (ABC) to detect interactions in two-dimensional space based on a Hi-C contact frequency matrix. ZipHiC uses data on the sources of biases related to the contact frequency matrix, allows borrowing information from neighbours using the Potts model and improves computation speed by using the ABC model. In addition to outperforming existing tools on both simulated and real data, our model also provides insights into different sources of biases that affects Hi-C data. We show that some datasets display higher biases from DNA accessibility or Transposable Elements content. Furthermore, our analysis in D. melanogaster showed that approximately half of the detected significant interactions connect promoters with other parts of the genome indicating a functional biological role. Finally, we found that the micro-C datasets display higher biases from DNA accessibility compared to a similar Hi-C experiment, but this can be corrected by ZipHiC

Physical constraints determine the logic of bacterial promoter architectures

Site-specific transcription factors (TFs) bind to their target sites on the DNA, where they regulate the rate at which genes are transcribed. Bacterial TFs undergo facilitated diffusion (a combination of 3D diffusion around and 1D random walk on the DNA) when searching for their target sites. Using computer simulations of this search process, we show that the organisation of the binding sites, in conjunction with TF copy number and binding site affinity, plays an important role in determining not only the steady state of promoter occupancy, but also the order at which TFs bind. These effects can be captured by facilitated diffusion-based models, but not by standard thermodynamics. We show that the spacing of binding sites encodes complex logic, which can be derived from combinations of three basic building blocks: switches, barriers and clusters, whose response alone and in higher orders of organisation we characterise in detail. Effective promoter organizations are commonly found in the E. coli genome and are highly conserved between strains. This will allow studies of gene regulation at a previously unprecedented level of detail, where our framework can create testable hypothesis of promoter logic

Gene expression dominance in allopolyploids: hypotheses and models.

The classical example of non-additive contributions of the two parents to allopolyploids is nucleolar dominance, which entails silencing of one parental set of ribosomal RNA genes. This has been observed for many other loci. The prevailing explanation for this genome-wide expression disparity is that the two merged genomes differ in their transposable element (TE) complement and in their level of TE-mediated repression of gene expression. Alternatively, and not exclusively, gene-expression dominance may arise from mismatches between trans effectors and their targets. Here, we explore quantitative models of regulatory mismatches leading to gene expression dominance. We also suggest that, when pairs of merged genomes are similar from one allopolyploidization event to another, gene-level and genome dominance patterns should also be similar

Cytosine methylation at CpCpG sites triggers accumulation of non-CpG methylation in gene bodies

Methylation of cytosine is an epigenetic mark involved in the regulation of transcription, usually associated with transcriptional repression. In mammals, methylated cytosines are found predominantly in CpGs but in plants non-CpG methylation (in the CpHpG or CpHpH contexts, where H is A, C or T) is also present and is associated with the transcriptional silencing of transposable elements. In addition, CpG methylation is found in coding regions of active genes. In the absence of the demethylase of lysine 9 of histone 3 (IBM1), a subset of body-methylated genes acquires non-CpG methylation. This was shown to alter their expression and affect plant development. It is not clear why only certain body-methylated genes gain non-CpG methylation in the absence of IBM1 and others do not. Here we describe a link between CpG methylation and the establishment of methylation in the CpHpG context that explains the two classes of body-methylated genes. We provide evidence that external cytosines of CpCpG sites can only be methylated when internal cytosines are methylated. CpCpG sites methylated in both cytosines promote spreading of methylation in the CpHpG context in genes protected by IBM1. In contrast, CpCpG sites remain unmethylated in IBM1-independent genes and do not promote spread of CpHpG methylation.European Research Council [322621]; Gatsby Foundation [AT3273/GLE]. Funding for open access charge: Gatsby Foundation [AT3273/GLE]

The Influence of Transcription Factor Competition on the Relationship between Occupancy and Affinity

Transcription factors (TFs) are proteins that bind to specific sites on the DNA and regulate gene activity. Identifying where TF molecules bind and how much time they spend on their target sites is key to understanding transcriptional regulation. It is usually assumed that the free energy of binding of a TF to the DNA (the affinity of the site) is highly correlated to the amount of time the TF remains bound (the occupancy of the site). However, knowing the binding energy is not sufficient to infer actual binding site occupancy. This mismatch between the occupancy predicted by the affinity and the observed occupancy may be caused by various factors, such as TF abundance, competition between TFs or the arrangement of the sites on the DNA. We investigated the relationship between the affinity of a TF for a set of binding sites and their occupancy. In particular, we considered the case of the transcription factor lac repressor (lacI) in E.coli, and performed stochastic simulations of the TF dynamics on the DNA for various combinations of lacI abundance and competing TFs that contribute to macromolecular crowding. We also investigated the relationship of site occupancy and the information content of position weight matrices (PWMs) used to represent binding sites. Our results showed that for medium and high affinity sites, TF competition does not play a significant role for genomic occupancy except in cases when the abundance of the TF is significantly increased, or when the PWM displays relatively low information content. Nevertheless, for medium and low affinity sites, an increase in TF abundance (for both cognate and non-cognate molecules) leads to an increase in occupancy at several sites. © 2013 Zabet et al

Combination of BMI1 and MAPK/ERK inhibitors is effective in medulloblastoma.

BACKGROUND: Epigenetic changes play a key role in the pathogenesis of medulloblastoma (MB), the most common malignant paediatric brain tumour. METHODS: We explore the therapeutic potential of BMI1 and MAPK/ERK inhibition in BMI1 High;CHD7 Low MB cells and in a pre-clinical xenograft model. RESULTS: We identify a synergistic vulnerability of BMI1 High;CHD7 Low MB cells to a combination treatment with BMI1 and MAPK/ERK inhibitors. Mechanistically, CHD7-dependent binding of BMI1 to MAPK-regulated genes underpins the CHD7-BMI1-MAPK regulatory axis responsible of the anti-tumour effect of the inhibitors in vitro and in a pre-clinical mouse model. Increased ERK1 and ERK2 phosphorylation activity is found in BMI1 High;CHD7 Low G4 MB patients, raising the possibility that they could be amenable to a similar therapy. CONCLUSIONS: The molecular dissection of the CHD7-BMI1-MAPK regulatory axis in BMI1 High;CHD7 Low MB identifies this signature as a proxy to predict MAPK functional activation, which can be effectively drugged in preclinical models, and paves the way for further exploration of combined BMI1 and MAPK targeting in G4 MB patients

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

Gene switching rate determines response to extrinsic perturbations in the self-activation transcriptional network motif

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