Nucleoid clustering drives stepwise expansion and segregation of replicating bacterial chromosomes

Abstract

Bacterial chromosome replication occurs in the absence of a canonical spindle apparatus; yet it reliably produces organised and segregated genomes. While both passive and active mechanisms have been investigated, DNA replication itself is a non-equilibrium process that continuously generates new genetic material and reorganizes the nucleoid. Here, we investigate how replication-driven dynamics, combined with nucleoid-associated protein (NAP) interactions, shape spatiotemporal chromosome organisation using a three-dimensional polymer model that explicitly simulates DNA synthesis. We show that NAP-mediated interactions induce dynamic clustering of DNA, generating density fluctuations in the nucleoid. When coupled to replication, these clusters undergo cycles of stress buildup and release that produce stepwise expansion dynamics consistent with experimental observations. Chromosome segregation occurs naturally in this regime, but only within a finite range of interaction strengths: weak interactions fail to structure the nucleoid, whereas strong interactions hinder replication progression. Within this optimal balance, replication also promotes the spontaneous formation of replication factories. Our results demonstrate that bacterial chromosome organisation can be understood as a non-equilibrium system in which the interplay between replication forces and protein-mediated interactions generates nucleoid mechanics, dynamics, and segregation.

Figures (4)

• Figure 1: A (i) The bacterial DNA is modelled as a coarse-grained polymer ring confined within a cylindrical volume of diameter $D$ and length $L$. (ii) Nucleoid compaction is driven by NAP-mediated bridging, implemented as effective pairwise interactions. Specific ON NAP binding sites (light blue) interact strongly with each other and weakly with non-specific DNA sites (dark blue), whereas non-specific DNA sites interact weakly among themselves. (iii) NAP post-translational modifications are incorporated by allowing NAP binding sites to switch between an ON and an OFF state with rates $k_{\mathrm{on}}$ and $k_{\mathrm{off}}$, respectively. OFF NAP binding sites are energetically equivalent to non-specific sites and are therefore visually indistinguishable from them in the figure. The set of beads comprising both ON and OFF NAP binding sites—referred to as potential NAP binding sites—remains unchanged throughout the simulation. B Replication is implemented following the PolyRep framework introduced in Ref. Forte2024. (i) A single DNA bead represents the replication origin (red) and experiences a moderate attraction to multivalent firing factors (FFs). FFs, in turn, weakly attract both non-specific DNA sites and ON NAP binding sites. (ii) When an FF approaches the replication origin within a threshold distance, replication is initiated. The origin and one of its nearest-neighbour beads are converted into two replication forks, which strongly attract FFs and move in opposite directions whenever an FF is nearby. (iii) The progression of each replication fork converts the template DNA into replicated DNA (dark green) and simultaneously generates a newly synthesized daughter strand (dark purple). Both replicated strands interact weakly with FFs, similarly to the template strand. (iv) The subset of beads corresponding to potential NAP binding sites (ON and OFF states) preserves its identity during replication: when a potential NAP binding site is duplicated, both daughter copies remain potential NAP binding sites. In the panel, replicated ON NAP binding sites are shown as light green and light purple beads. (v–vi) Upon collision at the end of replication, the two forks annihilate each other (panel (v)), resulting in the formation of two independent replicated rings (panel (vi)). (C) (i) The template and the two replicated DNA strands are energetically equivalent: all ON NAP binding sites (light blue, green, and purple) interact strongly with each other and weakly with all non-specific sites (dark blue, green, and purple), while non-specific sites interact weakly among themselves. (ii) Replicated potential NAP binding sites undergo post-translational modifications, switching between an ON state and an OFF state (energetically equivalent to non-specific sites) with rates $k_{\mathrm{on}}$ and $k_{\mathrm{off}}$, respectively. (iii) The cylindrical confinement expands linearly with replication progression, thereby mimicking the growth of the bacterial cell.
• Figure 2: A (i) Time-lapse snapshots of the constrained G1-phase nucleoid prior to replication. Reference axes are indicated on the left. (ii) Corresponding time evolution of nucleoid density for the snapshots shown in panel (i). The colour scale represents the number of DNA beads projected onto the x–z plane, with Gaussian smoothing applied. (iii) Reconstruction of the nucleoid surface for the snapshots in panel (i), illustrating the dynamic nature of the nucleoid structure. B Plots showing the position along the $z$-axis corresponding to the maximum nucleoid density as a function of time. The two panels correspond to independent simulation runs with different values of the attractive energy among NAP binding sites. In both cases, the nucleoid density exhibits fluctuations, but no clear periodicity is observed. C (i) Fraction of the cell volume occupied by nucleoid in G1 phase for the three different NAP binding-site interaction strengths considered. (ii) Variations in the longitudinal ($z$-axis) nucleoid extension for the three interaction strengths. Notably, although the $NAP_{strong}$ configuration produces the highest nucleoid compaction, it also shows the largest fluctuations in nucleoid extension. (iii) Fraction of ON NAP binding sites engaged in clusters as a function of nucleoid length for the three interaction conditions. The $NAP_{strong}$ case generally forms the largest clusters and the most compact nucleoids; however, the nucleoid extension can increase considerably when these clusters break. Data in panels (i–iii) are obtained from $10$ independent simulations for each NAP binding-site interaction strength.
• Figure 3: A (i) Representative time evolution of the nucleoid extension along the $z$-axis, $Ext_{nucl}$ (light and dark blue lines representing raw and smoothed data respectively), for a simulation run with interaction set $NAP_{med}$. The grey region indicates the replicated fraction (right $y$-axis), while grey arrows mark abrupt expansion events superimposed on the overall linear growth. (ii) Deviations of the nucleoid extension from its linear trend, $\delta Ext$ (blue curve, left $y$-axis), together with the fraction of ON NAP binding sites in clusters, $f_{\mathrm{cluster}}$ (orange curve, right $y$-axis), shown as a function of time. B Pearson correlation coefficient between $\delta Ext$ and $f_{\mathrm{cluster}}$ for the three NAP binding-site interaction strengths. C Distributions of the time intervals between consecutive nucleoid extension jumps for the three interaction sets. Panels B and C are computed from $10$ independent simulations for each interaction condition.
• Figure 4: A (i) Kymograph showing the local segregation parameter $\phi$ as a function of $z$ and time for a representative simulation of the $NAP_{med}$ case. (ii) Corresponding time evolution of the global segregation parameter $\Sigma$. The light grey region indicates the replication fraction (refer to the $y$-axis). (iii) Time evolution of the $z$ positions of the two replication forks (light and dark grey curves). The diverging light grey region represents the longitudinal size of the cylindrical cell confinement (refer to the $y$-axis). B Surface reconstructions of the three nucleoid structures—the template (blue) and the two replicated nucleoids (green and purple)—at three different time points corresponding to the simulation shown in panel A. C Replication factory probability versus global segregation parameter $\Sigma$. The factory probability is defined as the probability that the two forks occupy the same NAP binding-site cluster. The plot combines data from all simulations across the three parameter sets. D Distributions of the global segregation parameter $\Sigma$ at three distinct stages of replication for the three NAP binding-site interaction strengths considered. Each box plot is derived from 10 independent simulation runs at the given interaction strength.