Goldar A et al. (2008), A dynamic stochastic model for DNA replication ...

XB-ART-38168

PLoS One 2008 Aug 06;38:e2919. doi: 10.1371/journal.pone.0002919.

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A dynamic stochastic model for DNA replication initiation in early embryos.

Goldar A , Labit H , Marheineke K , Hyrien O .

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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. 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. 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.

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	Figure 1. Replication initiation rate, I(t), as a function of time.The open circles are the data points and the two dashed lines are linear fits presented in Figure 10 b in [31].
	Figure 2. Computed I(t) for stationary scenarios.Open circles are numerical simulation data points. (A) Particle recycling scenario: NT = 104;. P = 10−3 kb−1 s−1. (B) Particle abundance scenario: NT = 105; P = 10−4 kb−1 s−1. Blue and red curves represent the simulated replicated fraction and the fork density, respectively.
	Figure 3. Fitting of the experimental I(t) (open circles) using an increased particle availability scenario.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 (N0 = 3100, J = 1 s−1 and P = 0.9×10−4 kb−1 s−1; χ2 = 2.6×10−8). Blue and red curves represent the simulated replicated fraction and the fork density, respectively.
	Figure 4. Computed I(t) for increased affinity scenarios.Open circles are numerical simulation data points. (A) Limiting particles scenario: P(t) = 10−3+10−4t; NT = 104. (B) Abundant particles scenario: P(t) = 10−4+10−5t; NT = 105. Blue and red curves represent the simulated replicated fraction and the fork density, respectively.
	Figure 5. Computed I(t) for fork-density dependent affinity scenarios: P(NB(t)) = P0+P1[1−exp(−NB(t)/Nc)].Open circles are numerical simulation data points. (A) Limiting particles scenario: P0 = 10−3 kb−1 s−1, P1 = 10−3 kb−1 s−1, NT = 104 and NC = 7×103. (B) Abundant particles scenario: P0 = 10−4 kb−1 s−1, P1 = 10−3 kb−1 s−1, NT = 105 and NC = 7×103. Blue and red curves represent the simulated replicated fraction and the fork density, respectively.
	Figure 6. Fitting of the experimental I(t) (open circles) using a scenario that combines increased particle availability (J>0) and fork-density dependent affinity: P(NB(t)) = P0+P1[1−exp(−NB(t)/Nc)] and NC = 7×103.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 (P0 = 10−4 kb−1 s−1, P1 = 2×10−3 kb−1 s−1, J = 5 s−1and N0 = 1000; χ2 = 10−9). Blue and red curves represent the simulated replicated fraction and the fork density, respectively.
	Figure 7. Analysis of a novel set of experimental data using the model shown on Figure 6.(A) Comparison of the novel data points (open circles, see text) with a rescaled fit to the previous data set (Figure 6). (B) Normalised distributions of fibre length in increasing (circles) and decreasing (triangles) parts of the data. The solid black and grey lines represent the smoothed distributions (using a 5 points Fourier filter) of the increasing and decreasing parts of the data, respectively.
	Figure 8. Temporal variation of fork density.(A) Comparison of the simulated fork density profile (solid red curve) and simulated I(t) (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).
	Figure 9. Model for regulation of replication initiation in Xenopus egg extracts.The bimolecular interaction of a trans-acting factor (particle) with an origin gives rise to initiation with a probability P(t) that depends on the density of already existing forks. The number (NT) of particles increases during S phase at a rate J from an initial N0 value, due to recruitment by nuclear import or any analogous process. Initiation events result in a number of forks (Nf) that merge at a frequency 2v/<g> (where v is the fork velocity and <g> the mean size of gaps at a given replication extent) and release particles that can be reused for initiation.

References [+] :

Alexandrow, Chromatin decondensation in S-phase involves recruitment of Cdk2 by Cdc45 and histone H1 phosphorylation. 2005, Pubmed