Mammalian chromosomes fold into arrays of megabase‐sized topologically associating domains (TADs), which are arranged into compartments spanning multiple megabases of genomic DNA. TADs have internal substructures that are often cell type specific, but their higher‐order organization remains elusive. Here, we investigate TAD higher‐order interactions with Hi‐C through neuronal differentiation and show that they form a hierarchy of domains‐within‐domains (metaTADs) extending across genomic scales up to the range of entire chromosomes. We find that TAD interactions are well captured by tree‐like, hierarchical structures irrespective of cell type. metaTAD tree structures correlate with genetic, epigenomic and expression features, and structural tree rearrangements during differentiation are linked to transcriptional state changes. Using polymer modelling, we demonstrate that hierarchical folding promotes efficient chromatin packaging without the loss of contact specificity, highlighting a role far beyond the simple need for packing efficiency.

Screening of arrays and libraries of compounds is well-established as a high-throughput method for detecting and analyzing interactions in both biological and chemical systems. Arrays and libraries can be composed from various types of molecules, ranging from small organic compounds to DNA, proteins and peptides. The applications of libraries for detecting and characterizing biological interactions are wide and diverse, including for example epitope mapping, carbohydrate arrays, enzyme binding and protein-protein interactions. Here, we will focus on the use of peptide arrays to study protein-protein interactions. Characterization of protein-protein interactions is crucial for understanding cell functionality. Using peptides, it is possible to map the precise binding sites in such complexes. Peptide array libraries usually contain partly overlapping peptides derived from the sequence of one protein from the complex of interest. The peptides are attached to a solid support using various techniques such as SPOT-synthesis and photolithography. Then, the array is incubated with the partner protein from the complex of interest. Finally, the detection of the protein-bound peptides is carried out by using immunodetection assays. Peptide array screening is semi-quantitative, and quantitative studies with selected peptides in solution are required to validate and complement the screening results. These studies can improve our fundamental understanding of cellular processes by characterizing amino acid patterns of protein-protein interactions, which may even develop into prediction algorithms. The binding peptides can then serve as a basis for the design of drugs that inhibit or activate the target protein-protein interactions. In the current review, we will introduce the recent work on this subject performed in our and in other laboratories. We will discuss the applications, advantages and disadvantages of using peptide arrays as a tool to study protein-protein interactions.

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Motivation: Biological network comparison software largely relies on the concept of alignment where close matches between the nodes of two or more networks are sought. These node matches are based on sequence similarity and/or interaction patterns. However, because of the incomplete and error-prone datasets currently available, such methods have had limited success. Moreover, the results of network alignment are in general not amenable for distance-based evolutionary analysis of sets of networks. In this article, we describe Netdis, a topology-based distance measure between networks, which offers the possibility of network phylogeny reconstruction.Results: We first demonstrate that Netdis is able to correctly separate different random graph model types independent of network size and density. The biological applicability of the method is then shown by its ability to build the correct phylogenetic tree of species based solely on the topology of current protein interaction networks. Our results provide new evidence that the topology of protein interaction networks contains information about evolutionary processes, despite the lack of conservation of individual interactions. As Netdis is applicable to all networks because of its speed and simplicity, we apply it to a large collection of biological and non-biological networks where it clusters diverse networks by type.Availability and implementation: The source code of the program is freely available at http://www.stats.ox.ac.uk/research/proteins/resources.Contact: w.ali@stats.ox.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

Polycomb repression in mouse embryonic stem cells (ESCs) is tightly associated with promoter co‐occupancy of RNA polymerase II (RNAPII) which is thought to prime genes for activation during early development. However, it is unknown whether RNAPII poising is a general feature of Polycomb repression, or is lost during differentiation. Here, we map the genome‐wide occupancy of RNAPII and Polycomb from pluripotent ESCs to non‐dividing functional dopaminergic neurons. We find that poised RNAPII complexes are ubiquitously present at Polycomb‐repressed genes at all stages of neuronal differentiation. We observe both loss and acquisition of RNAPII and Polycomb at specific groups of genes reflecting their silencing or activation. Strikingly, RNAPII remains poised at transcription factor genes which are silenced in neurons through Polycomb repression, and have major roles in specifying other, non‐neuronal lineages. We conclude that RNAPII poising is intrinsically associated with Polycomb repression throughout differentiation. Our work suggests that the tight interplay between RNAPII poising and Polycomb repression not only instructs promoter state transitions, but also may enable promoter plasticity in differentiated cells.

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Motivation: A wealth of protein–protein interaction (PPI) data has recently become available. These data are organized as PPI networks and an efficient and biologically meaningful method to compare such PPI networks is needed. As a first step, we would like to compare observed networks to established network models, under the aspect of small subgraph counts, as these are conjectured to relate to functional modules in the PPI network. We employ the software tool GraphCrunch with the Graphlet Degree Distribution Agreement (GDDA) score to examine the use of such counts for network comparison.Results: Our results show that the GDDA score has a pronounced dependency on the number of edges and vertices of the networks being considered. This should be taken into account when testing the fit of models. We provide a method for assessing the statistical significance of the fit between random graph models and biological networks based on non-parametric tests. Using this method we examine the fit of Erdös–Rényi (ER), ER with fixed degree distribution and geometric (3D) models to PPI networks. Under these rigorous tests none of these models fit to the PPI networks. The GDDA score is not stable in the region of graph density relevant to current PPI networks. We hypothesize that this score instability is due to the networks under consideration having a graph density in the threshold region for the appearance of small subgraphs. This is true for both geometric (3D) and ER random graph models. Such threshold behaviour may be linked to the robustness and efficiency properties of the PPI networks.Contact: tiago@stats.ox.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

Cortical deep projection neurons (DPNs) are implicated in neurodevelopmental disorders. Although recent findings emphasize post-mitotic programs in projection neuron fate selection, the establishment of primate DPN identity during layer formation is not well understood. The subplate lies underneath the developing cortex and is a post-mitotic compartment that is transiently and disproportionately enlarged in primates in the second trimester. The evolutionary significance of subplate expansion, the molecular identity of its neurons, and its contribution to primate corticogenesis remain open questions. By modeling subplate formation with human pluripotent stem cells (hPSCs), we show that all classes of cortical DPNs can be specified from subplate neurons (SPNs). Post-mitotic WNT signaling regulates DPN class selection, and DPNs in the caudal fetal cortex appear to exclusively derive from SPNs. Our findings indicate that SPNs have evolved in primates as an important source of DPNs that contribute to cortical lamination prior to their known role in circuit formation.