3DPolyS-LE: an accessible simulation framework to model the interplay between chromatin and loop extrusion

Tuning cohesin trajectories enables differential readout of the Pcdhα cluster across neurons

Expression of Protocadherin (Pcdh) genes is critical to the generation of neuron identity and wiring of the nervous system. Pcdhα genes are arranged in clusters and exhibit a range of expression profiles, from stochastic to deterministic. Because Pcdhα promoters have high sequence identity and share distal enhancers, how distinct neurons choose which gene to express remains unclear. We show that the interplay between multiple enhancers, epigenetics, and genome folding orchestrates differential readouts of the locus across neurons. The probability of Pcdhα promoter choice depends on enhancer/promoter encounters catalyzed by cohesin, whose extrusion trajectories determine the likelihood that an individual promoter can "escape" heterochromatin-mediated silencing. We propose that tunable locus-specific regulatory elements and cell type-specific cohesin activity underlie the generation of cellular diversity by Pcdh genes.

LoopSage: An energy-based Monte Carlo approach for the loop extrusion modeling of chromatin

The connection between the patterns observed in 3C-type experiments and the modeling of polymers remains unresolved. This paper presents a simulation pipeline that generates thermodynamic ensembles of 3D structures for topologically associated domain (TAD) regions by loop extrusion model (LEM). The simulations consist of two main components: a stochastic simulation phase, employing a Monte Carlo approach to simulate the binding positions of cohesins, and a dynamical simulation phase, utilizing these cohesins' positions to create 3D structures. In this approach, the system's total energy is the combined result of the Monte Carlo energy and the molecular simulation energy, which are iteratively updated. The structural maintenance of chromosomes (SMC) protein complexes are represented as loop extruders, while the CCCTC-binding factor (CTCF) locations on DNA sequence are modeled as energy minima on the Monte Carlo energy landscape. Finally, the spatial distances between DNA segments from ChIA-PET experiments are compared with the computer simulations, and we observe significant Pearson correlations between predictions and the real data. LoopSage model offers a fresh perspective on chromatin loop dynamics, allowing us to observe phase transition between sparse and condensed states in chromatin.

Computational methods for analysing multiscale 3D genome organization

Recent progress in whole-genome mapping and imaging technologies has enabled the characterization of the spatial organization and folding of the genome in the nucleus. In parallel, advanced computational methods have been developed to leverage these mapping data to reveal multiscale three-dimensional (3D) genome features and to provide a more complete view of genome structure and its connections to genome functions such as transcription. Here, we discuss how recently developed computational tools, including machine-learning-based methods and integrative structure-modelling frameworks, have led to a systematic, multiscale delineation of the connections among different scales of 3D genome organization, genomic and epigenomic features, functional nuclear components and genome function. However, approaches that more comprehensively integrate a wide variety of genomic and imaging datasets are still needed to uncover the functional role of 3D genome structure in defining cellular phenotypes in health and disease.