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.