Polymer modelling unveils the roles of heterochromatin and nucleolar organizing regions in shaping 3D genome organization in Arabidopsis thaliana

The 3D genome is characterized by a complex organization made of genomic and epigenomic layers with profound implications on gene regulation and cell function. However, the understanding of the fundamental mechanisms driving the crosstalk between nuclear architecture and (epi)genomic information is still lacking. The plant Arabidopsis thaliana is a powerful model organism to address these questions owing to its compact genome for which we have a rich collection of microscopy, chromosome conformation capture (Hi-C) and ChIP-seq experiments. Using polymer modelling, we investigate the roles of nucleolus formation and epigenomics-driven interactions in shaping the 3D genome of A. thaliana. By validation of several predictions with published data, we demonstrate that self-attracting nucleolar organizing regions and repulsive constitutive heterochromatin are major mechanisms to regulate the organization of chromosomes. Simulations also suggest that interphase chromosomes maintain a partial structural memory of the V-shapes, typical of (sub)metacentric chromosomes in anaphase. Additionally, self-attraction between facultative heterochromatin regions facilitates the formation of Polycomb bodies hosting H3K27me3-enriched gene-clusters. Since nucleolus and heterochromatin are highly-conserved in eukaryotic cells, our findings pave the way for a comprehensive characterization of the generic principles that are likely to shape and regulate the 3D genome in many species.     

Modeling epigenome folding: formation and dynamics of topologically associated chromatin domains

Genomes of eukaryotes are partitioned into domains of functionally distinct chromatin states. These domains are stably inherited across many cell generations and can be remodeled in response to developmental and external cues, hence contributing to the robustness and plasticity of expression patterns and cell phenotypes. Remarkably, recent studies indicate that these 1D epigenomic domains tend to fold into 3D topologically associated domains forming specialized nuclear chromatin compartments. However, the general mechanisms behind such compartmentalization including the contribution of epigenetic regulation remain unclear. Here, we address the question of the coupling between chromatin folding and epigenome. Using polymer physics, we analyze the properties of a block copolymer model that accounts for local epigenomic information. Considering copolymers build from the epigenomic landscape of Drosophila, we observe a very good agreement with the folding patterns observed in chromosome conformation capture experiments. Moreover, this model provides a physical basis for the existence of multistability in epigenome folding at sub-chromosomal scale. We show how experiments are fully consistent with multistable conformations where topologically associated domains of the same epigenomic state interact dynamically with each other. Our approach provides a general framework to improve our understanding of chromatin folding during cell cycle and differentiation and its relation to epigenetics.     

PASTE: a high-throughput method for large DNA insertions

Prime editing (PE) enables precise genome editing at targeted locus without inducing double-stranded breaks (DSBs). Despite its precision, PE lacks the tendency to integrate large DNA fragments into the genome. Recently, Yarnall et al. reported clustered regularly interspaced palindromic repeats (CRISPR)/Cas9 and an integrase-based system that conducts targeted integration of large DNA sequences (~36 kb) into the genome more efficiently.     

Viral vectors as carriers of genome-editing reagents

The presence of a transgene in the genome of plants is a regulatory challenge. Recently, Liu et al. reported an engineered tomato spotted wilt virus (TSWV) that can carry large clustered regularly interspaced palindromic repeats (CRISPR)/Cas reagents for targeted genome editing in various crops without the integration of the transgene into the genome.     

LigBind: Identifying Binding Residues for Over 1000 Ligands with Relation-Aware Graph Neural Networks

Identifying the interactions between proteins and ligands is significant for drug discovery and design. Considering the diverse binding patterns of ligands, the ligand-specific methods are trained per ligand to predict binding residues. However, most of the existing ligand-specific methods ignore shared binding preferences among various ligands and generally only cover a limited number of ligands with a sufficient number of known binding proteins. In this study, we propose a relation-aware framework LigBind with graph-level pre-training to enhance the ligand-specific binding residue predictions for 1159 ligands, which can effectively cover the ligands with a few known binding proteins. LigBind first pre-trains a graph neural network-based feature extractor for ligand-residue pairs and relation-aware classifiers for similar ligands. Then, LigBind is fine-tuned with ligand-specific binding data, where a domain adaptive neural network is designed to automatically leverage the diversity and similarity of various ligand-binding patterns for accurate binding residue prediction. We construct ligand-specific benchmark datasets of 1159 ligands and 16 unseen ligands, which are used to evaluate the effectiveness of LigBind. The results demonstrate the LigBind’s efficacy on large-scale ligand-specific benchmark datasets, and it generalizes well to unseen ligands. LigBind also enables accurate identification of the ligand-binding residues in the main protease, papain-like protease and the RNA-dependent RNA polymerase of SARS-CoV-2. The web server and source codes of LigBind are available at http://www.csbio.sjtu.edu.cn/bioinf/LigBind/ and https://github.com/YYingXia/LigBind/ for academic use.     

Chromatin segmentation based on a probabilistic model for read counts explains a large portion of the epigenome

Chromatin immunoprecipitation followed by sequencing (ChIP-seq) is an increasingly common experimental approach to generate genome-wide maps of histone modifications and to dissect the complexity of the epigenome. Here, we propose EpiCSeg: a novel algorithm that combines several histone modification maps for the segmentation and characterization of cell-type specific epigenomic landscapes. By using an accurate probabilistic model for the read counts, EpiCSeg provides a useful annotation for a considerably larger portion of the genome, shows a stronger association with validation data, and yields more consistent predictions across replicate experiments when compared to existing methods.The software is available at http://github.com/lamortenera/epicseg

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-015-0708-z) contains supplementary material, which is available to authorized users.     

Emerging patterns of epigenomic variation

Fuelled by new sequencing technologies, epigenome mapping projects are revealing epigenomic variation at all levels of biological complexity, from species to cells. Comparisons of methylation profiles among species reveal evolutionary conservation of gene body methylation patterns, pointing to the fundamental role of epigenomes in gene regulation. At the human population level, epigenomic changes provide footprints of the effects of genomic variants within the vast nonprotein-coding fraction of the genome, and comparisons of the epigenomes of parents and their offspring point to quantitative epigenomic parent-of-origin effects confounding classical Mendelian genetics. At the organismal level, comparisons of epigenomes from diverse cell types provide insights into cellular differentiation. Finally, comparisons of epigenomes from monozygotic twins help dissect genetic and environmental influences on human phenotypes and longitudinal comparisons reveal aging-associated epigenomic drift. The development of new bioinformatic frameworks for comparative epigenome analysis is putting epigenome maps within the reach of researchers across a wide spectrum of biological disciplines.     

Reconstructing A/B compartments as revealed by Hi-C using long-range correlations in epigenetic data

Analysis of Hi-C data has shown that the genome can be divided into two compartments called A/B compartments. These compartments are cell-type specific and are associated with open and closed chromatin. We show that A/B compartments can reliably be estimated using epigenetic data from several different platforms: the Illumina 450 k DNA methylation microarray, DNase hypersensitivity sequencing, single-cell ATAC sequencing and single-cell whole-genome bisulfite sequencing. We do this by exploiting that the structure of long-range correlations differs between open and closed compartments. This work makes A/B compartment assignment readily available in a wide variety of cell types, including many human cancers.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-015-0741-y) contains supplementary material, which is available to authorized users.     

Fine-tuning germline mutation rates across evolution

The germline mutation rate (GMR) sets the pace at which mutations, the raw material of evolution, are introduced into the genome. By sequencing a dataset of unprecedently broad phylogenetic scope, Bergeron et al. estimated species-specific GMR, offering numerous insights into how this parameter shapes and is shaped by life-history traits.     

ASHIC: hierarchical Bayesian modeling of diploid chromatin contacts and structures

The recently developed Hi-C technique has been widely applied to map genome-wide chromatin interactions. However, current methods for analyzing diploid Hi-C data cannot fully distinguish between homologous chromosomes. Consequently, the existing diploid Hi-C analyses are based on sparse and inaccurate allele-specific contact matrices, which might lead to incorrect modeling of diploid genome architecture. Here we present ASHIC, a hierarchical Bayesian framework to model allele-specific chromatin organizations in diploid genomes. We developed two models under the Bayesian framework: the Poisson-multinomial (ASHIC-PM) model and the zero-inflated Poisson-multinomial (ASHIC-ZIPM) model. The proposed ASHIC methods impute allele-specific contact maps from diploid Hi-C data and simultaneously infer allelic 3D structures. Through simulation studies, we demonstrated that ASHIC methods outperformed existing approaches, especially under low coverage and low SNP density conditions. Additionally, in the analyses of diploid Hi-C datasets in mouse and human, our ASHIC-ZIPM method produced fine-resolution diploid chromatin maps and 3D structures and provided insights into the allelic chromatin organizations and functions. To summarize, our work provides a statistically rigorous framework for investigating fine-scale allele-specific chromatin conformations. The ASHIC software is publicly available at https://github.com/wmalab/ASHIC.     

Endoreplication controls cell size via mechanochemical signaling

During hypocotyl development, an asymmetric auxin gradient causes differential cell elongation, leading to tissue bending and apical hook formation. Recently, Ma et al. identified a molecular pathway that links auxin with endoreplication and cell size through cell wall integrity sensing, cell wall remodeling, and regulation of cell wall stiffness.     

Clinicopathologic predictors of early relapse in advanced epithelial ovarian cancer: development of prediction models using nationwide data

ObjectiveTo identify clinicopathologic factors predictive of early relapse (platinum-free interval (PFI) of ≤6 months) in advanced epithelial ovarian cancer (EOC) in first-line treatment, and to develop and internally validate risk prediction models for early relapse.MethodsAll consecutive patients diagnosed with advanced stage EOC between 01-01-2008 and 31-12-2015 were identified from the Netherlands Cancer Registry. Patients who underwent cytoreductive surgery and platinum-based chemotherapy as initial EOC treatment were selected. Two prediction models, i.e. pretreatment and postoperative, were developed. Candidate predictors of early relapse were fitted into multivariable logistic regression models. Model performance was assessed on calibration and discrimination. Internal validation was performed through bootstrapping to correct for model optimism.ResultsA total of 4,557 advanced EOC patients were identified, including 1,302 early relapsers and 3,171 late or non-relapsers. Early relapsers were more likely to have FIGO stage IV, mucinous or clear cell type EOC, ascites, >1 cm residual disease, and to have undergone NACT-ICS. The final pretreatment model demonstrated subpar model performance (AUC = 0.64 [95 %-CI 0.62−0.66]). The final postoperative model based on age, FIGO stage, pretreatment CA-125 level, histologic subtype, presence of ascites, treatment approach, and residual disease after debulking, demonstrated adequate model performance (AUC = 0.72 [95 %-CI 0.71−0.74]). Bootstrap validation revealed minimal optimism of the final postoperative model.ConclusionA (postoperative) discriminative model has been developed and presented online that predicts the risk of early relapse in advanced EOC patients. Although external validation is still required, this prediction model can support patient counselling in daily clinical practice.     

Variant-to-gene,gene-to-trait: finding new sleep genes through GWAS

Distilling insomnia genome-wide association study (GWAS) variants, Palermo and colleagues identified several genes that participate in sleep regulation in two different model organisms. This workflow sets off an innovative strategy to extract biological relevance from large human genomic databases.     

Arena: Rapid and Accurate Reconstruction of Full Atomic RNA Structures From Coarse-grained Models

RNA tertiary structures from experiments or computational predictions often contain missing atoms, which prevent analyses requiring full atomic structures. Current programs for RNA reconstruction can be slow, inaccurate, and/or require specific atoms to be present in the input. We present Arena (Atomic Reconstruction of RNA), which reconstructs a full atomic RNA structure from residues that can have as few as one atom. Arena first fills in missing atoms and then iteratively refines their placement to reduce nonideal geometries. We benchmarked Arena on a dataset of 361 RNA structures, where Arena achieves high accuracy and speed compared to other structure reconstruction programs. For example, Arena was used to reconstruct full atomic structures from a single phosphorus atom per nucleotide to, on average, within 3.63 Å RMSD of the experimental structure, while virtually removing all clashes and running in <3 s, which is 353× and 46× faster than state-of-the-art programs PDBFixer and C2A, respectively. The Arena source code is available at https://github.com/pylelab/Arena and the webserver at https://zhanggroup.org/Arena/.     

A multicompartment model of carboxyhemoglobin and carboxymyoglobin responses to inhalation of carbon monoxide.

We have developed a model that predicts the distribution of carbon monoxide (CO) in the body resulting from acute inhalation exposures to CO. The model includes a lung compartment, arterial and venous blood compartments, and muscle and nonmuscle soft tissues with both vascular and nonvascular subcompartments. In the model, CO is allowed to diffuse between the vascular and nonvascular subcompartments of the tissues and to combine with myoglobin in the nonvascular subcompartment of muscle tissue. The oxyhemoglobin dissociation curve is represented by a modified Hill equation whose parameters are functions of the carboxyhemoglobin (HbCO) level. Values for skeletal muscle mass and cardiac output are calculated from prediction formulas based on age, weight, and height of individual subjects. We demonstrate that the model fits data from CO rebreathing studies when diffusion of CO into the muscle compartment is considered. The model also fits responses of HbCO to single or multiple exposures to CO lasting for a few minutes each. In addition, the model reproduces reported differences between arterial and venous HbCO levels and replicates predictions from the Coburn-Forster-Kane equation for CO exposures of a 1- to 83-h duration. In contrast to approaches based on the Coburn-Forster-Kane equation, the present model predicts uptake and distribution of CO in both vascular and tissue compartments during inhalation of either constant or variable levels of CO.     

Unleashing the potential of peptides in agriculture and beyond

Peptides display a broad range of regulatory functions. Ormancey et al. recently identified an important new mechanism – complementary peptides (cPEPs) – that provide a versatile means to control cell functions. We draw a parallel between RNA and peptide biology, and discuss new routes of investigation and industrial applications opened by this work.     

Cryptosporidium secretome sheds new light on host–parasite interactions

Invasive Cryptosporidium sporozoites contain organelles that secrete unique proteins to facilitate invasion and remodeling of the infected cell. By identifying a novel secretory organelle, ‘small granules’, and defining the global content of all the secretory organelles, Guérin et al. set the stage to uncover molecular determinants of virulence at the host cell interface.     

Malaria high-content imaging,where to next?

High-content imaging has produced greater insights into the complexities of cell biology. The ability to characterise specific phenotypes, as demonstrated by Rosenthal and Ng, provides a powerful tool for elucidating mechanisms of action and resistance, illustrating that high-content imaging in malaria research is only limited by our creativity.     

Liver-stage Plasmodium infection tunes clinical outcomes

Chora and colleagues show that infection of the liver by Plasmodium modulates severity of disease in the experimental cerebral malaria (ECM) model by generating gamma delta (ɣδ) T cells that produce IL-17. This work calls into question the long-standing assumption that liver infection does not modulate severity of malaria.