Chromosomal dynamics predicted by an elastic network model explains genome-wide accessibility and long-range couplings

Abstract

Understanding the three-dimensional (3D) architecture of chromatin and its relation to gene expression and regulation is fundamental to understanding how the genome functions. Advances in Hi-C technology now permit us to study 3D genome organization, but we still lack an understanding of the structural dynamics of chromosomes. The dynamic couplings between regions separated by large genomic distances (>50 Mb) have yet to be characterized. We adapted a well-established protein-modeling framework, the Gaussian Network Model (GNM), to model chromatin dynamics using Hi-C data. We show that the GNM can identify spatial couplings at multiple scales: it can quantify the correlated fluctuations in the positions of gene loci, find large genomic compartments and smaller topologically-associating domains (TADs) that undergo en bloc movements, and identify dynamically coupled distal regions along the chromosomes. We show that the predictions of the GNM correlate well with genome-wide experimental measurements. We use the GNM to identify novel cross-correlated distal domains (CCDDs) representing pairs of regions distinguished by their long-range dynamic coupling and show that CCDDs are associated with increased gene co-expression. Together, these results show that GNM provides a mathematically well-founded unified framework for modeling chromatin dynamics and assessing the structural basis of genome-wide observations.

Flexibility and rigidity index for chromosome packing, flexibility and dynamics analysis

“…A high correlation coefficient is found between our flexibility index, DNase-seq and ATAC-seq values. Compared with GNM (Sauerwald et al, 2017), our model is slightly more accurate with average 1%-2% increase in accuracy and significantly more efficient in both computational time and resources. Moreover, interchromosome interaction can be incorporated into our FRI model.…”

“…We bin the data into the same resolution as used in the Hi-C data by adding all peak values within each locus. The binned data were then smoothed using moving average with a window size of 200 kb in the same way as GNM (Sauerwald et al, 2017).…”

“…To illustrate their relations, we consider two 5kb resolution Hi-C chromosome data from GM12878. Chromosome packing density is measured by chromosome flexibility index from our FRI. Chromatin accessibility is characterized by DNase-seq and ATAC-seq values (Sauerwald et al, 2017). After the normalization of DNase-seq and ATAC-seq data, flexibility index is linearly fitted with their values.…”

“…In this way, the chromatin accessibility can be characterized by nuclease hypersensitivity, which is directly measured by MNase, DNase, FAIRE, as well as ATAC. Based on the Hi-C data, a mobility profile is defined from GNM and has been found to be highly correlated with DNase-seq and ATAC-seq values (Sauerwald et al, 2017). Essentially, the mobility profile can be viewed as a characterization of flexibility properties of the chromosome structures.…”

Microrheology for Hi-C Data Reveals the Spectrum of the Dynamic 3D Genome Organization

“…Therefore, the physical interactions of all pairs can be described as an N × N matrix K = K i j . The positive values of K stand for elastic forces between two monomers, and the model formally resembles the Gaussian network model (27)(28)(29). Here, the negative values are acceptable as repulsive forces in the polymer network model.…”