Replication Origins and Timing of Temporal Replication in Budding Yeast: How to Solve the Conundrum?

Similarly to metazoans, the budding yeast Saccharomyces cereviasiae replicates its genome with a defined timing. In this organism, well-defined, site-specific origins, are efficient and fire in almost every round of DNA replication. However, this strategy is neither conserved in the fission yeast Saccharomyces pombe, nor in Xenopus or Drosophila embryos, nor in higher eukaryotes, in which DNA replication initiates asynchronously throughout S phase at random sites. Temporal and spatial controls can contribute to the timing of replication such as Cdk activity, origin localization, epigenetic status or gene expression. However, a debate is going on to answer the question how individual origins are selected to fire in budding yeast. Two opposing theories were proposed: the “replicon paradigm” or “temporal program” vs. the “stochastic firing”. Recent data support the temporal regulation of origin activation, clustering origins into temporal blocks of early and late replication. Contrarily, strong evidences suggest that stochastic processes acting on origins can generate the observed kinetics of replication without requiring a temporal order. In mammalian cells, a spatiotemporal model that accounts for a partially deterministic and partially stochastic order of DNA replication has been proposed. Is this strategy the solution to reconcile the conundrum of having both organized replication timing and stochastic origin firing also for budding yeast? In this review we discuss this possibility in the light of our recent study on the origin activation, suggesting that there might be a stochastic component in the temporal activation of the replication origins, especially under perturbed conditions

What Influences DNA Replication Rate in Budding Yeast?

BACKGROUND: DNA replication begins at specific locations called replication origins, where helicase and polymerase act in concert to unwind and process the single DNA filaments. The sites of active DNA synthesis are called replication forks. The density of initiation events is low when replication forks travel fast, and is high when forks travel slowly. Despite the potential involvement of epigenetic factors, transcriptional regulation and nucleotide availability, the causes of differences in replication times during DNA synthesis have not been established satisfactorily, yet. METHODOLOGY/PRINCIPAL FINDINGS: Here, we aimed at quantifying to which extent sequence properties contribute to the DNA replication time in budding yeast. We interpreted the movement of the replication machinery along the DNA template as a directed random walk, decomposing influences on DNA replication time into sequence-specific and sequence-independent components. We found that for a large part of the genome the elongation time can be well described by a global average replication rate, thus by a single parameter. However, we also showed that there are regions within the genomic landscape of budding yeast with highly specific replication rates, which cannot be explained by global properties of the replication machinery. CONCLUSION/SIGNIFICANCE: Computational models of DNA replication in budding yeast that can predict replication dynamics have rarely been developed yet. We show here that even beyond the level of initiation there are effects governing the replication time that can not be explained by the movement of the polymerase along the DNA template alone. This allows us to characterize genomic regions with significantly altered elongation characteristics, independent of initiation times or sequence composition

A yeast cell cycle model integrating stress, signaling, and physiology

The cell division cycle in eukaryotic cells is a series of highly coordinated molecular interactions that ensure that cell growth, duplication of genetic material, and actual cell division are precisely orchestrated to give rise to two viable progeny cells. Moreover, the cell cycle machinery is responsible for incorporating information about external cues or internal processes that the cell must keep track of to ensure a coordinated, timely progression of all related processes. This is most pronounced in multicellular organisms, but also a cardinal feature in model organisms such as baker's yeast. The complex and integrative behavior is difficult to grasp and requires mathematical modeling to fully understand the quantitative interplay of the single components within the entire system. Here, we present a self-oscillating mathematical model of the yeast cell cycle that comprises all major cyclins and their main regulators. Furthermore, it accounts for the regulation of the cell cycle machinery by a series of external stimuli such as mating pheromones and changes in osmotic pressure or nutrient quality. We demonstrate how the external perturbations modify the dynamics of cell cycle components and how the cell cycle resumes after adaptation to or relief from stress.Peer Reviewe

Bud-Localization of CLB2 mRNA Can Constitute a Growth Rate Dependent Daughter Sizer.

Maintenance of cellular size is a fundamental systems level process that requires balancing of cell growth with proliferation. This is achieved via the cell division cycle, which is driven by the sequential accumulation and destruction of cyclins. The regulatory network around these cyclins, particularly in G1, has been interpreted as a size control network in budding yeast, and cell size as being decisive for the START transition. However, it is not clear why disruptions in the G1 network may lead to altered size rather than loss of size control, or why the S-G2-M duration also depends on nutrients. With a mathematical population model comprised of individually growing cells, we show that cyclin translation would suffice to explain the observed growth rate dependence of cell volume at START. Moreover, we assess the impact of the observed bud-localisation of the G2 cyclin CLB2 mRNA, and find that localised cyclin translation could provide an efficient mechanism for measuring the biosynthetic capacity in specific compartments: The mother in G1, and the growing bud in G2. Hence, iteration of the same principle can ensure that the mother cell is strong enough to grow a bud, and that the bud is strong enough for independent life. Cell sizes emerge in the model, which predicts that a single CDK-cyclin pair per growth phase suffices for size control in budding yeast, despite the necessity of the cell cycle network around the cyclins to integrate other cues. Size control seems to be exerted twice, where the G2/M control affects bud size through bud-localized translation of CLB2 mRNA, explaining the dependence of the S-G2-M duration on nutrients. Taken together, our findings suggest that cell size is an emergent rather than a regulatory property of the network linking growth and proliferation

A bud sizer is required to predict the <i>CLN</i> overexpression mutant.

<p>(A) Cell size distributions are shown for the fast growing wild type (WT, solid lines) and <i>CLN</i> over producing mutant (<i>OE-CLN</i>, dashed lines) simulated with Model-1 (red) and Model-2 (blue), corresponding to a <i>CLN3</i> overexpression mutant <i>in vivo</i>. (B) Average length of G₁, S-G₂-M and average cell volume are shown for the wild type (solid bars) and <i>OE-CLN</i> cells (dashed bars) simulated with Model-1 (red) and Model-2 (blue).</p

Model equations.

<p>Equation 4.1 is implemented in Model-1, equation 4.2 is implemented in Model-2.</p><p>Model equations.</p

Model parameters.

<p>The parameter values for the best fit of Model-1 and Model-2 are displayed. The parameters <i>growth</i>, <i>k</i>_<i>p</i>1, <i>k</i>_<i>p</i>2, <i>k</i>_<i>R</i>(G₁) and <i>k</i>_<i>R</i>(S-G₂-M) differ for the two model versions, whereas the other parameters have the same value in both models. Parameter boundaries are shown for estimated parameters. Units of parameters without specification are dimensionless.</p><p>Model parameters.</p

Cell size statistics.

<p>(A) Cell size distributions are shown for <i>in silico</i> cultures simulated with Model-2 in glucose (GLC—light blue), galactose (GAL—green), raffinose (RAF—yellow) and ethanol (EtOH—brown). (B-C) Relative variability in cell size (B) and average cell size (C) is shown for <i>in silico</i> cultures obtained from simulations with Model-1 (red) and Model-2 (blue) in same conditions as in (A). (D) Cell size distributions density estimates of Model-1 (dashed lines) and Model-2 (solid lines) on a linear scale. Cell size is log-normally distributed [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004223#pcbi.1004223.ref032" target="_blank">32</a>], hence size average in (B) and relative variability in (C) are computed on log-values.</p

A growth rate dependent bud sizer tunes mitotic entry to maintain size ratios at division.

<p>(A) Deviation around the mean duration of the budded phase (S-G₂-M) is shown for fast growing cells (glucose) simulated with Model-1 (red) and Model-2 (blue). The mean of the data (black line) and the coefficient of variation (CV) are also shown. (B) Volume fraction of cell and bud at division is shown as a function of replicative age for data from experiments [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004223#pcbi.1004223.ref041" target="_blank">41</a>] (black dots) and from simulations with Model-1 (red squares) and Model-2 (blue squares).</p