Shaping the Genome via Lengthwise Compaction, Phase Separation, and Lamina Adhesion

Abstract

The link between genomic structure and biological function is yet to be consolidated, it is, however, clear that physical manipulation of the genome, driven by the activity of a variety of proteins, is a crucial step. To understand the consequences of the physical forces underlying genome organization, we build a coarse-grained polymer model of the genome, featuring three fundamentally distinct classes of interactions: lengthwise compaction, i.e., compaction of chromosomes along its contour, self-adhesion among epigenetically similar genomic segments, and adhesion of chromosome segments to the nuclear envelope or lamina. We postulate that these three types of interactions sufficiently represent the concerted action of the different proteins organizing the genome architecture and show that an interplay among these interactions can recapitulate the architectural variants observed across the tree of life. The model elucidates how an interplay of forces arising from the three classes of genomic interactions can drive drastic, yet predictable, changes in the global genome architecture, and makes testable predictions. We posit that precise control over these interactions in vivo is key to the regulation of genome architecture.

Structural Reorganization and Relaxation Dynamics of Axially Stressed Chromosomes

“…In this work, we have tested the mechanical properties of a bead-spring polymer model of chromosomes with data-driven force fields [25] by subjecting it to external stretching, mimicking tweezers-style experiments [5, 14]. The data-driven force field comprises of three components: first, the homopolymer potential contains terms such as the nearest neighbor bonds and inter-monomer self-avoidance; second, the phase separation potential that drives compartmental segregation; and third, lengthwise compaction, encoded by the ideal chromosome potential, that crumples the polymer resembling the steady-state loop-extrusion activity of SMC complexes[30]. Note that the prometaphase chromosomes of chicken DT40 cells show a non-monotonic trend in the scaling of contact probability with genomic distance, suggesting an increase in contact frequency between genomic segments that are ∼ 4 Mb apart [23].…”

“…The type-type potential encodes interactions that depend on the epigenetic character of the participating segments of chromatin, resulting in phase-separation of chromatin into compartments. The ideal chromosome term encodes genomic-distance-dependent interactions which lead to lengthwise compaction of the chromosome, capturing the effect of loop-extruding motors such as cohesin and condensins [30]. We use parameters for the model that best agree with the experimental Hi-C maps of chicken DT40 cells (Fig.…”

“…MiChroM has been shown to quantitatively recapitulate the rich patterns of contact observed in Hi-C experiments. Structural ensembles generated by MiChroM provided new insights into interphase chromosome organization through the interplay between phase separation of biochemically distinct segments of chromatin (compartmentalization) and the genomic distance-dependent lengthwise compaction attributed to the activity of motors such as condensin and cohesin (called the Ideal Chromosome Potential) [25–30]. MiChroM has been shown to be consistent with locus-locus distance distributions determined with fluorescence in situ hybridization (FISH) [25, 31] and the dynamical viscoelasticity of chromosomes observed in experiment [32].…”

Phase Separation and Correlated Motions in Motorized Genome

“…This "ideal" potential approximates the crosslinks produced by Cohesin molecules via their extrusion along chromatin, promoting chromosome territory formation. 24,58,59 Detailed expressions of the energy function can be found in the Supporting Information. Interaction parameters in the ideal and compartment potential were tuned to reproduce various average contact probabilities determined from Hi-C experiments for GM12878 cells using the maximum entropy optimization algorithm.…”