Research

2025-06-08

Structural-Maintenance-of-Chromosome (SMC) complexes such as condensins are well-known to dictate the folding and entanglement of interphase and mitotic chromosomes. However, their role in modulating the rheology and viscoelasticity of entangled DNA is not fully understood. In this work, we discover that physiological concentrations of yeast condensin increase both the effective viscosity and elasticity of dense solutions of λ-DNA even in absence of ATP. By combining biochemical assays and single-molecule imaging, we discover that yeast condensin can proficiently bind double-stranded DNA through its hinge domain, in addition to its heads. We further discover that presence of ATP fluidifies the entangled solution possibly by activating loop extrusion. Finally, we show that the observed rheology can be understood by modelling SMCs as transient crosslinkers in bottle-brush-like entangled polymers. Our findings help us to understand how SMCs affect the rheology and dynamics of the genome.

2024-08-12

Loop extrusion is one of the main processes shaping chromosome organization across the cell cycle, yet its role in regulating deoxyribonucleic acid (DNA) entanglement and nucleoplasm viscoelasticity remains overlooked. We simulate entangled solutions of linear polymers under the action of generic loop extruding factors (LEFs) with a model that fully accounts for topological constraints and LEF-DNA uncrossability. We discover that extrusion drives the formation of bottlebrushlike structures which significantly lower the entanglement and effective viscosity of the system through an active fluidification mechanism. Interestingly, this fluidification displays an optimum at one LEF every 300–3000 base pairs. In marked contrast with entangled linear chains, the viscosity of extruded chains scales linearly with polymer length, yielding up to 1000-fold fluidification in our system. Our results illuminate how intrachain loop extrusion contributes to actively modulate genome entanglement and viscoelasticity in vivo.

2023-10-20

Knots are deeply entangled with every branch of science. One of the biggest open challenges in knot theory is to formalise a knot invariant that can unambiguously and efficiently distinguish any two knotted curves. Additionally, the conjecture that the geometrical embedding of a curve encodes information on its underlying topology is, albeit physically intuitive, far from proven. Here we attempt to tackle both these outstanding challenges by proposing a neural network (NN) approach that takes as input a geometric representation of a knotted curve and tries to make predictions of the curve's topology. Intriguingly, we discover that NNs trained with a so-called geometrical “local writhe” representation of a knot can distinguish curves that share one or many topological invariants and knot polynomials, such as mutant and composite knots, and can thus classify knotted curves more precisely than some knot polynomials. Additionally, we also show that our approach can be scaled up to classify all prime knots up to 10-crossings with more than 95% accuracy. Finally, we show that our NNs can also be trained to solve knot localisation problems on open and closed curves. Our main discovery is that the pattern of “local writhe” is a potentially unique geometric signature of the underlying topology of a curve. We hope that our results will suggest new methods for quantifying generic entanglements in soft matter and even inform new topological invariants.