Control of DNA replication by anomalous reaction-diffusion kinetics

DNA replication timing: Biochemical mechanisms and biological significance

The regulation of DNA replication is a fascinating biological problem both from a mechanistic angle-How is replication timing regulated?-and from an evolutionary one-Why is replication timing regulated? Recent work has provided significant insight into the first question. Detailed biochemical understanding of the mechanism and regulation of replication initiation has made possible robust hypotheses for how replication timing is regulated. Moreover, technical progress, including high-throughput, single-molecule mapping of replication initiation and single-cell assays of replication timing, has allowed for direct testing of these hypotheses in mammalian cells. This work has consolidated the conclusion that differential replication timing is a consequence of the varying probability of replication origin initiation. The second question is more difficult to directly address experimentally. Nonetheless, plausible hypotheses can be made and one-that replication timing contributes to the regulation of chromatin structure-has received new experimental support.

Organization of DNA Replication Origin Firing in Xenopus Egg Extracts: The Role of Intra-S Checkpoint

During cell division, the duplication of the genome starts at multiple positions called replication origins. Origin firing requires the interaction of rate-limiting factors with potential origins during the S(ynthesis)-phase of the cell cycle. Origins fire as synchronous clusters which is proposed to be regulated by the intra-S checkpoint. By modelling the unchallenged, the checkpoint-inhibited and the checkpoint protein Chk1 over-expressed replication pattern of single DNA molecules from Xenopus sperm chromatin replicated in egg extracts, we demonstrate that the quantitative modelling of data requires: (1) a segmentation of the genome into regions of low and high probability of origin firing; (2) that regions with high probability of origin firing escape intra-S checkpoint regulation and (3) the variability of the rate of DNA synthesis close to replication forks is a necessary ingredient that should be taken in to account in order to describe the dynamic of replication origin firing. This model implies that the observed origin clustering emerges from the apparent synchrony of origin firing in regions with high probability of origin firing and challenge the assumption that the intra-S checkpoint is the main regulator of origin clustering.

In silico analysis of DNA re-replication across a complete genome reveals cell-to-cell heterogeneity and genome plasticity

DNA replication is a complex and remarkably robust process: despite its inherent uncertainty, manifested through stochastic replication timing at a single-cell level, multiple control mechanisms ensure its accurate and timely completion across a population. Disruptions in these mechanisms lead to DNA re-replication, closely connected to genomic instability and oncogenesis. Here, we present a stochastic hybrid model of DNA re-replication that accurately portrays the interplay between discrete dynamics, continuous dynamics and uncertainty. Using experimental data on the fission yeast genome, model simulations show how different regions respond to re-replication and permit insight into the key mechanisms affecting re-replication dynamics. Simulated and experimental population-level profiles exhibit a good correlation along the genome, robust to model parameters, validating our approach. At a single-cell level, copy numbers of individual loci are affected by intrinsic properties of each locus, in cis effects from adjoining loci and in trans effects from distant loci. In silico analysis and single-cell imaging reveal that cell-to-cell heterogeneity is inherent in re-replication and can lead to genome plasticity and a plethora of genotypic variations.

On the Interplay of the DNA Replication Program and the Intra-S Phase Checkpoint Pathway

DNA replication in eukaryotes is achieved by the activation of multiple replication origins which needs to be precisely coordinated in space and time. This spatio-temporal replication program is regulated by many factors to maintain genome stability, which is frequently threatened through stresses of exogenous or endogenous origin. Intra-S phase checkpoints monitor the integrity of DNA synthesis and are activated when replication forks are stalled. Their activation leads to the stabilization of forks, to the delay of the replication program by the inhibition of late firing origins, and the delay of G2/M phase entry. In some cell cycles during early development these mechanisms are less efficient in order to allow rapid cell divisions. In this article, we will review our current knowledge of how the intra-S phase checkpoint regulates the replication program in budding yeast and metazoan models, including early embryos with rapid S phases. We sum up current models on how the checkpoint can inhibit origin firing in some genomic regions, but allow dormant origin activation in other regions. Finally, we discuss how numerical and theoretical models can be used to connect the multiple different actors into a global process and to extract general rules.

The eukaryotic bell-shaped temporal rate of DNA replication origin firing emanates from a balance between origin activation and passivation

The time-dependent rate I(t) of origin firing per length of unreplicated DNA presents a universal bell shape in eukaryotes that has been interpreted as the result of a complex time-evolving interaction between origins and limiting firing factors. Here, we show that a normal diffusion of replication fork components towards localized potential replication origins (p-oris) can more simply account for the I(t) universal bell shape, as a consequence of a competition between the origin firing time and the time needed to replicate DNA separating two neighboring p-oris. We predict the I(t) maximal value to be the product of the replication fork speed with the squared p-ori density. We show that this relation is robustly observed in simulations and in experimental data for several eukaryotes. Our work underlines that fork-component recycling and potential origins localization are sufficient spatial ingredients to explain the universality of DNA replication kinetics.

Before a cell can divide, it must duplicate its DNA. In eukaryotes – organisms such as animals and fungi, which store their DNA in the cell’s nucleus – DNA replication starts at specific sites in the genome called replication origins. At each origin sits a protein complex that will activate when it randomly captures an activating protein that diffuses within the nucleus. Once a replication origin activates or “fires”, the complex then splits into two new complexes that move away from each other as they duplicate the DNA. If an active complex collides with an inactive one at another origin, the latter is inactivated – a phenomenon known as origin passivation. When two active complexes meet, they release the activating proteins, which diffuse away and eventually activate other origins in unreplicated DNA.

The number of origins that activate each minute divided by the length of unreplicated DNA is referred to as the “rate of origin firing”. In all eukaryotes, this rate – also known as I(t) – follows the same pattern. First, it increases until more than half of the DNA is duplicated. Then it decreases until everything is duplicated. This means that, if plotted out, the graph of origin firing rate would always be a bell-shaped curve, even for organisms with genomes of different sizes that have different numbers of origins. The reason for this universal shape remained unclear.

Scientists had tried to create numerical simulations that model the rate of origin firing. However, for these simulations to reproduce the bell-shape curve, a number of untested assumptions had to be made about how DNA replication takes place. In addition, these models ignored the fact that it takes time to replicate the DNA between origins.

To take this time into account, Arbona et al. instead decided to model the replication origins as discrete and distinct entities. This way of building the mathematical model succeeded in reproducing the universal bell curve shape without additional assumptions. With this simulation, the balance between origin activation and passivation is enough to achieve the observed pattern.

The new model also predicts that the maximum rate of origin firing is determined by the speed of DNA replication and the density of origins in the genome. Arbona et al. verified this prediction in yeast, fly, frog and human cells – organisms with different sized genomes that take between 20 minutes and 8 hours to replicate their DNA. Lastly, the prediction also held true in yeast treated with hydroxyurea, an anticancer drug that slows DNA replication.

A better understanding of DNA replication can help scientists to understand how this process is perturbed in cancers and how drugs that target DNA replication can treat these diseases. Future work will explore how the 3D organization of the genome affects the diffusion of activating proteins within the cell nucleus.

Model-based analysis of DNA replication profiles: predicting replication fork velocity and initiation rate by profiling free-cycling cells

Eukaryotic cells initiate DNA synthesis by sequential firing of hundreds of origins. This ordered replication is described by replication profiles, which measure the DNA content within a cell population. Here, we show that replication dynamics can be deduced from replication profiles of free-cycling cells. While such profiles lack explicit temporal information, they are sensitive to fork velocity and initiation capacity through the passive replication pattern, namely the replication of origins by forks emanating elsewhere. We apply our model-based approach to a compendium of profiles that include most viable budding yeast mutants implicated in replication. Predicted changes in fork velocity or initiation capacity are verified by profiling synchronously replicating cells. Notably, most mutants implicated in late (or early) origin effects are explained by global modulation of fork velocity or initiation capacity. Our approach provides a rigorous framework for analyzing DNA replication profiles of free-cycling cells.

Deciphering DNA replication dynamics in eukaryotic cell populations in relation with their averaged chromatin conformations

We propose a non-local model of DNA replication that takes into account the observed uncertainty on the position and time of replication initiation in eukaryote cell populations. By picturing replication initiation as a two-state system and considering all possible transition configurations, and by taking into account the chromatin’s fractal dimension, we derive an analytical expression for the rate of replication initiation. This model predicts with no free parameter the temporal profiles of initiation rate, replication fork density and fraction of replicated DNA, in quantitative agreement with corresponding experimental data from both S. cerevisiae and human cells and provides a quantitative estimate of initiation site redundancy. This study shows that, to a large extent, the program that regulates the dynamics of eukaryotic DNA replication is a collective phenomenon that emerges from the stochastic nature of replication origins initiation.

DNA replication timing: Coordinating genome stability with genome regulation on the X chromosome and beyond

Recent studies based on next-generation DNA sequencing have revealed that the female inactive X chromosome is replicated in a rapid, unorganized manner, and undergoes increased rates of mutation. These observations link the organization of DNA replication timing to gene regulation on one hand, and to the generation of mutations on the other hand. More generally, the exceptional biology of the inactive X chromosome highlights general principles of genome replication. Cells may control replication timing by a combination of intrinsic replication origin properties, local chromatin states and global levels of replication factors, leading to a functional separation between the activity of genes and their mutation.

The three most important things about origins: location, location, location

The reasons why some DNA replication origins fire earlier than others have remained elusive. New work by Gindin et al suggests that the distribution of replication origins, not their timing per se, is the major determinant of the timing of genome replication in human cells.

A chromatin structure-based model accurately predicts DNA replication timing in human cells

The metazoan genome is replicated in precise cell lineage‐specific temporal order. However, the mechanism controlling this orchestrated process is poorly understood as no molecular mechanisms have been identified that actively regulate the firing sequence of genome replication. Here, we develop a mechanistic model of genome replication capable of predicting, with accuracy rivaling experimental repeats, observed empirical replication timing program in humans. In our model, replication is initiated in an uncoordinated (time‐stochastic) manner at well‐defined sites. The model contains, in addition to the choice of the genomic landmark that localizes initiation, only a single adjustable parameter of direct biological relevance: the number of replication forks. We find that DNase‐hypersensitive sites are optimal and independent determinants of DNA replication initiation. We demonstrate that the DNA replication timing program in human cells is a robust emergent phenomenon that, by its very nature, does not require a regulatory mechanism determining a proper replication initiation firing sequence.

DNA replication timing

Patterns of replication within eukaryotic genomes correlate with gene expression, chromatin structure, and genome evolution. Recent advances in genome-scale mapping of replication kinetics have allowed these correlations to be explored in many species, cell types, and growth conditions, and these large data sets have allowed quantitative and computational analyses. One striking new correlation to emerge from these analyses is between replication timing and the three-dimensional structure of chromosomes. This correlation, which is significantly stronger than with any single histone modification or chromosome-binding protein, suggests that replication timing is controlled at the level of chromosomal domains. This conclusion dovetails with parallel work on the heterogeneity of origin firing and the competition between origins for limiting activators to suggest a model in which the stochastic probability of individual origin firing is modulated by chromosomal domain structure to produce patterns of replication. Whether these patterns have inherent biological functions or simply reflect higher-order genome structure is an open question.

Statistical analysis of molecular signal recording

A molecular device that records time-varying signals would enable new approaches in neuroscience. We have recently proposed such a device, termed a “molecular ticker tape”, in which an engineered DNA polymerase (DNAP) writes time-varying signals into DNA in the form of nucleotide misincorporation patterns. Here, we define a theoretical framework quantifying the expected capabilities of molecular ticker tapes as a function of experimental parameters. We present a decoding algorithm for estimating time-dependent input signals, and DNAP kinetic parameters, directly from misincorporation rates as determined by sequencing. We explore the requirements for accurate signal decoding, particularly the constraints on (1) the polymerase biochemical parameters, and (2) the amplitude, temporal resolution, and duration of the time-varying input signals. Our results suggest that molecular recording devices with kinetic properties similar to natural polymerases could be used to perform experiments in which neural activity is compared across several experimental conditions, and that devices engineered by combining favorable biochemical properties from multiple known polymerases could potentially measure faster phenomena such as slow synchronization of neuronal oscillations. Sophisticated engineering of DNAPs is likely required to achieve molecular recording of neuronal activity with single-spike temporal resolution over experimentally relevant timescales.

Recording of physiological signals from inaccessible microenvironments is often hampered by the macroscopic sizes of current recording devices. A signal-recording device constructed on a molecular scale could advance biology by enabling the simultaneous recording from millions or billions of cells. We recently proposed a molecular device for recording time-varying ion concentration signals: DNA polymerases (DNAPs) copy known template DNA strands with an error rate dependent on the local ion concentration. The resulting DNA polymers could then be sequenced, and with the help of statistical techniques, used to estimate the time-varying ion concentration signal experienced by the polymerase. We develop a statistical framework to treat this inverse problem and describe a technique to decode the ion concentration signals from DNA sequencing data. We also provide a novel method for estimating properties of DNAP dynamics, such as polymerization rate and pause frequency, directly from sequencing data. We use this framework to explore potential application scenarios for molecular recording devices, achievable via molecular engineering within the biochemical parameter ranges of known polymerases. We find that accurate recording of neural firing rate responses across several experimental conditions would likely be feasible using molecular recording devices with kinetic properties similar to those of known polymerases.

Quantitative, genome-wide analysis of eukaryotic replication initiation and termination

Many fundamental aspects of DNA replication, such as the exact locations where DNA synthesis is initiated and terminated, how frequently origins are used, and how fork progression is influenced by transcription, are poorly understood. Via the deep sequencing of Okazaki fragments, we comprehensively document replication fork directionality throughout the S. cerevisiae genome, which permits the systematic analysis of initiation, origin efficiency, fork progression, and termination. We show that leading-strand initiation preferentially occurs within a nucleosome-free region at replication origins. Using a strain in which late origins can be induced to fire early, we show that replication termination is a largely passive phenomenon that does not rely on cis-acting sequences or replication fork pausing. The replication profile is predominantly determined by the kinetics of origin firing, allowing us to reconstruct chromosome-wide timing profiles from an asynchronous culture.

Inferring where and when replication initiates from genome-wide replication timing data

Based on an analogy between DNA replication and one dimensional nucleation-and-growth processes, various attempts to infer the local initiation rate I(x,t) of DNA replication origins from replication timing data have been developed in the framework of phase transition kinetics theories. These works have all used curve-fit strategies to estimate I(x,t) from genome-wide replication timing data. Here, we show how to invert analytically the Kolmogorov-Johnson-Mehl-Avrami model and extract I(x,t) directly. Tests on both simulated and experimental budding-yeast data confirm the location and firing-time distribution of replication origins.

Replication timing and its emergence from stochastic processes

The temporal organization of DNA replication has puzzled cell biologists since before the mechanism of replication was understood. The realization that replication timing correlates with important features, such as transcription, chromatin structure and genome evolution, and is misregulated in cancer and aging has only deepened the fascination. Many ideas about replication timing have been proposed, but most have been short on mechanistic detail. However, recent work has begun to elucidate basic principles of replication timing. In particular, mathematical modeling of replication kinetics in several systems has shown that the reproducible replication timing patterns seen in population studies can be explained by stochastic origin firing at the single-cell level. This work suggests that replication timing need not be controlled by a hierarchical mechanism that imposes replication timing from a central regulator, but instead results from simple rules that affect individual origins.

Modeling inhomogeneous DNA replication kinetics

In eukaryotic organisms, DNA replication is initiated at a series of chromosomal locations called origins, where replication forks are assembled proceeding bidirectionally to replicate the genome. The distribution and firing rate of these origins, in conjunction with the velocity at which forks progress, dictate the program of the replication process. Previous attempts at modeling DNA replication in eukaryotes have focused on cases where the firing rate and the velocity of replication forks are homogeneous, or uniform, across the genome. However, it is now known that there are large variations in origin activity along the genome and variations in fork velocities can also take place. Here, we generalize previous approaches to modeling replication, to allow for arbitrary spatial variation of initiation rates and fork velocities. We derive rate equations for left- and right-moving forks and for replication probability over time that can be solved numerically to obtain the mean-field replication program. This method accurately reproduces the results of DNA replication simulation. We also successfully adapted our approach to the inverse problem of fitting measurements of DNA replication performed on single DNA molecules. Since such measurements are performed on specified portion of the genome, the examined DNA molecules may be replicated by forks that originate either within the studied molecule or outside of it. This problem was solved by using an effective flux of incoming replication forks at the model boundaries to represent the origin activity outside the studied region. Using this approach, we show that reliable inferences can be made about the replication of specific portions of the genome even if the amount of data that can be obtained from single-molecule experiments is generally limited.

Do replication forks control late origin firing in Saccharomyces cerevisiae?

Recent studies of eukaryotic DNA replication timing profiles suggest that the time-dependent rate of origin firing, I(t), has a universal shape, which ensures a reproducible replication completion time. However, measurements of I(t) are based on population averages, which may bias the shape of the I(t) because of imperfect cell synchrony and cell-to-cell variability. Here, we measure the population-averaged I(t) profile from synchronized Saccharomyces cerevisiae cells using DNA combing and we extract the single-cell I(t) profile using numerical deconvolution. The single cell I(t) and the population-averaged I(t) extracted from DNA combing and replication timing profiles are similar, indicating a genome scale invariance of the replication process, and excluding cell-to-cell variability in replication time as an explanation for the shape of I(t). The single cell I(t) correlates with fork density in wild-type cells, which is specifically loosened in late S phase in the clb5Δ mutant. A previously proposed numerical model that reproduces the wild-type I(t) profile, could also describe the clb5Δ mutant I(t) once modified to incorporate the decline in CDK activity and the looser dependency of initiation on fork density in the absence of Clb5p. Overall, these results suggest that the replication forks emanating from early fired origins facilitate origin firing in later-replicating regions.

Modeling genome-wide replication kinetics reveals a mechanism for regulation of replication timing

We developed analytical models of DNA replication that include probabilistic initiation of origins, fork progression, passive replication, and asynchrony.

We fit the model to budding yeast genome-wide microarray data probing the replication fraction and found that initiation times correlate with the precision of timing.

We extracted intrinsic origin properties, such as potential origin efficiency and firing-time distribution, which cannot be done using phenomenological approaches.

We propose that origin timing is controlled by stochastically activated initiators bound to origin sites rather than explicit time-measuring mechanisms.

The kinetics of DNA replication must be controlled for cells to develop properly. Although the biochemical mechanisms of origin initiations are increasingly well understood, the organization of initiation timing as a genome-wide program is still a mystery. With the advance of technology, researchers have been able to generate large amounts of data revealing aspects of replication kinetics. In particular, the use of microarrays to probe the replication fraction of budding yeast genome wide has been a successful first step towards unraveling the details of the replication program (Raghuraman et al, 2001; Alvino et al, 2007; McCune et al, 2008). On the surface, the microarray data shows apparent patterns of early and late replicating regions and seems to support the prevailing picture of eukaryotic replication—origins are positioned at defined sites and initiated at defined, preprogrammed times (Donaldson, 2005). Molecular combing, a single-molecule technique, however, showed that the initiation of origins is stochastic (Czajkowsky et al, 2008). Motivated by these conflicting viewpoints, we developed a model that is flexible enough to describe both deterministic and stochastic initiation.

We modeled origin initiation as probabilistic events. We first propose a model where each origin is allowed to have its distinct ‘firing-time distribution.' Origins that have well-determined initiation times have narrow distributions, whereas more stochastic origins have wider distributions. Similar models based on simulations have previously been proposed (Lygeros et al, 2008; Blow and Ge, 2009; de Moura et al, 2010); however, our model is novel in that it is analytic. It is much faster than simulations and allowed us, for the first time, to fit genome-wide microarray data and extract parameters that describe the replication program in unprecedented detail (Figure 2).

Our main result is this: origins that fire early, on average, have precisely defined initiation times, whereas origins that fire late, on average, do not have a well-defined initiation time and initiate throughout S phase. What kind of global controlling mechanism can account for this trend? We propose a second model where an origin is composed of multiple initiators, each of which fires independently and identically. A good candidate for the initiator is the minichromosome maintenance (MCM) complex, as it is found to be associated with origin firing and loaded in abundance (Hyrien et al, 2003). We show that the aforementioned relationship can be explained quantitatively if the earlier-firing origins have more MCM complexes. This model offers a new view of replication: controlled origin timing can emerge from stochastic firing and does not need an explicit time-measuring mechanism, a ‘clock.' This model provides a new, detailed, plausible, and testable mechanism for replication timing control.

Our models also capture the effects of passive replication, which is often neglected in phenomenological approaches (Eshaghi et al, 2007). There are two ways an origin site can be replicated. The site can be replicated by the origin binding to it but can also be passively replicated by neighboring origins. This complication makes it difficult to extract the intrinsic properties of origins. By modeling passive replication, we can separate the contribution from each origin and extract the potential efficiency of origins, i.e., the efficiency of the origin given that there is no passive replication. We found that while most origins are potentially highly efficient, their observed efficiency varies greatly. This implies that many origins, though capable of initiating, are often passively replicated and appear dormant. Such a design makes the replication process robust against replication stress such as fork stalling (Blow and Ge, 2009). If two approaching forks stall, normally dormant origins in the region, not being passively replicated, will initiate to replicate the gap.

With the advance of the microarray and molecular-combing technology, experiments have been done to probe many different types of cells, and large amounts of replication fraction data have been generated. Our model can be applied to spatiotemporally resolved replication fraction data for any organism, as the model is flexible enough to capture a wide range of replication kinetics. The analytical model is also much faster than simulation-based models. For these reasons, we believe that the model is a powerful tool for analyzing these large datasets. This work opens the possibility for understanding the replication program across species in more rigor and detail (Goldar et al, 2009).

Microarrays are powerful tools to probe genome-wide replication kinetics. The rich data sets that result contain more information than has been extracted by current methods of analysis. In this paper, we present an analytical model that incorporates probabilistic initiation of origins and passive replication. Using the model, we performed least-squares fits to a set of recently published time course microarray data on Saccharomyces cerevisiae. We extracted the distribution of firing times for each origin and found that the later an origin fires on average, the greater the variation in firing times. To explain this trend, we propose a model where earlier-firing origins have more initiator complexes loaded and a more accessible chromatin environment. The model demonstrates how initiation can be stochastic and yet occur at defined times during S phase, without an explicit timing program. Furthermore, we hypothesize that the initiators in this model correspond to loaded minichromosome maintenance complexes. This model is the first to suggest a detailed, testable, biochemically plausible mechanism for the regulation of replication timing in eukaryotes.

Mathematical modelling of eukaryotic DNA replication

Eukaryotic DNA replication is a complex process. Replication starts at thousand origins that are activated at different times in S phase and terminates when converging replication forks meet. Potential origins are much more abundant than actually fire within a given S phase. The choice of replication origins and their time of activation is never exactly the same in any two cells. Individual origins show different efficiencies and different firing time probability distributions, conferring stochasticity to the DNA replication process. High-throughput microarray and sequencing techniques are providing increasingly huge datasets on the population-averaged spatiotemporal patterns of DNA replication in several organisms. On the other hand, single-molecule replication mapping techniques such as DNA combing provide unique information about cell-to-cell variability in DNA replication patterns. Mathematical modelling is required to fully comprehend the complexity of the chromosome replication process and to correctly interpret these data. Mathematical analysis and computer simulations have been recently used to model and interpret genome-wide replication data in the yeast Saccharomyces cerevisiae and Schizosaccharomyces pombe, in Xenopus egg extracts and in mammalian cells. These works reveal how stochasticity in origin usage confers robustness and reliability to the DNA replication process.

Reconciling stochastic origin firing with defined replication timing

Eukaryotic chromosomes replicate with defined timing patterns. However, the mechanism that regulates the timing of replication is unknown. In particular, there is an apparent conflict between population experiments, which show defined average replication times, and single-molecule experiments, which show that origins fire stochastically. Here, we provide a simple simulation that demonstrates that stochastic origin firing can produce defined average patterns of replication firing if two criteria are met. The first is that origins must have different relative firing probabilities, with origins that have relatively high firing probability being likely to fire in early S phase and origins with relatively low firing probability being unlikely to fire in early S phase. The second is that the firing probability of all origins must increase during S phase to ensure that origins with relatively low firing probability, which are unlikely to fire in early S phase, become likely to fire in late S phase. In addition, we propose biochemically plausible mechanisms for these criteria and point out how stochastic and defined origin firing can be experimentally distinguished in population experiments.

Defects and DNA replication

We introduce a rate-equation formalism to study DNA replication kinetics in the presence of defects resulting from DNA damage and find a crossover between two regimes: a normal regime, where the influence of defects is local, and an initiation-limited regime. In the latter, defects have a global impact on replication, whose progress is set by the rate at which origins of replication are activated, or initiated. Normal, healthy cells have defect densities in the normal regime. Our model can explain an observed correlation between interorigin separation and rate of DNA replication.

Mathematical modelling of whole chromosome replication

All chromosomes must be completely replicated prior to cell division, a requirement that demands the activation of a sufficient number of appropriately distributed DNA replication origins. Here we investigate how the activity of multiple origins on each chromosome is coordinated to ensure successful replication. We present a stochastic model for whole chromosome replication where the dynamics are based upon the parameters of individual origins. Using this model we demonstrate that mean replication time at any given chromosome position is determined collectively by the parameters of all origins. Combining parameter estimation with extensive simulations we show that there is a range of model parameters consistent with mean replication data, emphasising the need for caution in interpreting such data. In contrast, the replicated-fraction at time points through S phase contains more information than mean replication time data and allowed us to use our model to uniquely estimate many origin parameters. These estimated parameters enable us to make a number of predictions that showed agreement with independent experimental data, confirming that our model has predictive power. In summary, we demonstrate that a stochastic model can recapitulate experimental observations, including those that might be interpreted as deterministic such as ordered origin activation times.

Universal temporal profile of replication origin activation in eukaryotes

Although replication proteins are conserved among eukaryotes, the sequence requirements for replication initiation differ between species. In all species, however, replication origins fire asynchronously throughout S phase. The temporal program of origin firing is reproducible in cell populations but largely probabilistic at the single-cell level. The mechanisms and the significance of this program are unclear. Replication timing has been correlated with gene activity in metazoans but not in yeast. One potential role for a temporal regulation of origin firing is to minimize fluctuations in replication end time and avoid persistence of unreplicated DNA in mitosis. Here, we have extracted the population-averaged temporal profiles of replication initiation rates for S. cerevisiae, S. pombe, D. melanogaster, X. laevis and H. sapiens from genome-wide replication timing and DNA combing data. All the profiles have a strikingly similar shape, increasing during the first half of S phase then decreasing before its end. A previously proposed minimal model of stochastic initiation modulated by accumulation of a recyclable, limiting replication-fork factor and fork-promoted initiation of new origins, quantitatively described the observed profiles without requiring new implementations.

The selective pressure for timely completion of genome replication and optimal usage of replication proteins that must be imported into the cell nucleus can explain the generic shape of the profiles. We have identified a universal behavior of eukaryotic replication initiation that transcends the mechanisms of origin specification. The population-averaged efficiency of replication origin usage changes during S phase in a strikingly similar manner in a highly diverse set of eukaryotes. The quantitative model previously proposed for origin activation in X. laevis can be generalized to explain this evolutionary conservation.