Genome structural dynamics: insights from Gaussian network analysis of Hi-C data

TL;DR

This paper explores how chromatin structure dynamics influence gene regulation using a Gaussian network model on Hi-C data.

Contribution

The study introduces a novel application of the Gaussian network model to analyze chromatin dynamics at multiple hierarchical resolutions.

Findings

GNM analysis reveals conserved global chromatin movements across different cell types.

Localized genomic interactions are linked to cell differentiation and gene expression.

Mobility profiles of gene loci correlate with cell-specific gene expression patterns.

Abstract

Characterization of the spatiotemporal properties of the chromatin is essential to gaining insights into the physical bases of gene co-expression, transcriptional regulation and epigenetic modifications. The Gaussian network model (GNM) has proven in recent work to serve as a useful tool for modeling chromatin structural dynamics, using as input high-throughput chromosome conformation capture data. We focus here on the exploration of the collective dynamics of chromosomal structures at hierarchical levels of resolution, from single gene loci to topologically associating domains or entire chromosomes. The GNM permits us to identify long-range interactions between gene loci, shedding light on the role of cross-correlations between distal regions of the chromosomes in regulating gene expression. Notably, GNM analysis performed across diverse cell lines highlights the conservation of the global/cooperative movements of the chromatin across different types of cells. Variations driven by localized couplings between genomic loci, on the other hand, underlie cell differentiation, underscoring the significance of the four-dimensional properties of the genome in defining cellular identity. Finally, we demonstrate the close relation between the cell type–dependent mobility profiles of gene loci and their gene expression patterns, providing a clear demonstration of the role of chromosomal 4D features in defining cell-specific differential expression of genes.

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