A Dynamic Stochastic Model for DNA Replication Initiation in Early Embryos

Background: Eukaryotic cells seem unable to monitor replication completion during normal S phase, yet must ensure a reliable replication completion time. This is an acute problem in early Xenopus embryos since DNA replication origins are located and activated stochastically, leading to the random completion problem. DNA combing, kinetic modelling and other studies using Xenopus egg extracts have suggested that potential origins are much more abundant than actual initiation events and that the time-dependent rate of initiation, I(t), markedly increases through S phase to ensure the rapid completion of unreplicated gaps and a narrow distribution of completion times. However, the molecular mechanism that underlies this increase has remained obscure.Methodology/Principal Findings: Using both previous and novel DNA combing data we have confirmed that I(t) increases through S phase but have also established that it progressively decreases before the end of S phase. To explore plausible biochemical scenarios that might explain these features, we have performed comparisons between numerical simulations and DNA combing data. Several simple models were tested: i) recycling of a limiting replication fork component from completed replicons; ii) time-dependent increase in origin efficiency; iii) time-dependent increase in availability of an initially limiting factor, e. g. by nuclear import. None of these potential mechanisms could on its own account for the data. We propose a model that combines time-dependent changes in availability of a replication factor and a fork-density dependent affinity of this factor for potential origins. This novel model quantitatively and robustly accounted for the observed changes in initiation rate and fork density.Conclusions/Significance: This work provides a refined temporal profile of replication initiation rates and a robust, dynamic model that quantitatively explains replication origin usage during early embryonic S phase. These results have significant implications for the organisation of replication origins in higher eukaryotes

Régulation de l'initiation de la réplication chez les vertèbrés (analyse du programme temporel d'activation des origines de réplication dans les extraits d'oeufs de xénope)

PARIS7-Bibliothèque centrale (751132105) / SudocSudocFranceF

Fitting of the experimental <i>I(t)</i> (open circles) using an increased particle availability scenario.

<p>The solid black line is the best fit to the increasing part of the data using a Levenberg-Marquardt algorithm coupled with a dynamic Monte Carlo method (<i>N₀</i> = 3100, <i>J</i> = 1 s⁻¹ and <i>P</i> = 0.9×10⁻⁴ kb⁻¹ s⁻¹; <i>χ²</i> = 2.6×10⁻⁸). Blue and red curves represent the simulated replicated fraction and the fork density, respectively.</p

Temporal variation of fork density.

<p>(A) Comparison of the simulated fork density profile (solid red curve) and simulated <i>I(t)</i> (circles). Blue curve, simulated replicated fraction. (B) Comparison of the rescaled simulated fork density profile (solid black curve) and the experimentally determined fork density (circles).</p

Computed <i>I(t)</i> for stationary scenarios.

<p>Open circles are numerical simulation data points. (A) Particle recycling scenario: <i>N_T</i> = 10⁴;. <i>P</i> = 10⁻³ kb⁻¹ s⁻¹. (B) Particle abundance scenario: <i>N_T</i> = 10⁵; <i>P</i> = 10⁻⁴ kb⁻¹ s⁻¹. Blue and red curves represent the simulated replicated fraction and the fork density, respectively.</p

Model for regulation of replication initiation in <i>Xenopus</i> egg extracts.

<p>The bimolecular interaction of a trans-acting factor (particle) with an origin gives rise to initiation with a probability <i>P(t)</i> that depends on the density of already existing forks. The number (<i>N_T</i>) of particles increases during S phase at a rate <i>J</i> from an initial <i>N₀</i> value, due to recruitment by nuclear import or any analogous process. Initiation events result in a number of forks (<i>N_f</i>) that merge at a frequency 2<i>v/</i> (where <i>v</i> is the fork velocity and <i></i> the mean size of gaps at a given replication extent) and release particles that can be reused for initiation.</p

Replication initiation rate, <i>I(t)</i>, as a function of time.

<p>The open circles are the data points and the two dashed lines are linear fits presented in Figure 10 b in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0002919#pone.0002919-Zhang1" target="_blank">[31]</a>.</p

Computed <i>I(t)</i> for fork-density dependent affinity scenarios: <i>P(N_B(t))</i> = <i>P₀</i>+<i>P₁</i>[1−exp(−<i>N_B(t)/N_c</i>)].

<p>Open circles are numerical simulation data points. (A) Limiting particles scenario: <i>P₀</i> = 10⁻³ kb⁻¹ s⁻¹, <i>P₁</i> = 10⁻³ kb⁻¹ s⁻¹, <i>N_T</i> = 10⁴ and <i>N_C</i> = 7×10³. (B) Abundant particles scenario: <i>P₀</i> = 10⁻⁴ kb⁻¹ s⁻¹, <i>P₁</i> = 10⁻³ kb⁻¹ s⁻¹, <i>N_T</i> = 10⁵ and <i>N_C</i> = 7×10³. Blue and red curves represent the simulated replicated fraction and the fork density, respectively.</p