Abstract
Recent results comparing the temporal program of genome replication of yeast species belonging to the Lachancea clade support the scenario that the evolution of the replication timing program could be mainly driven by correlated acquisition and loss events of active replication origins. Using these results as a benchmark, we develop an evolutionary model defined as birth-death process for replication origins and use it to identify the evolutionary biases that shape the replication timing profiles. Comparing different evolutionary models with data, we find that replication origin birth and death events are mainly driven by two evolutionary pressures, the first imposes that events leading to higher double-stall probability of replication forks are penalized, while the second makes less efficient origins more prone to evolutionary loss. This analysis provides an empirically grounded predictive framework for quantitative evolutionary studies of the replication timing program.
Highlights
Eukaryotes, from yeast to mammals, rely on pre-defined “replication origins” along the genome to initiate replication [1,2,3,4], but we still ignore most of the evolutionary principles shaping the biological properties of these objects
Experimental data motivate an evolutionary model for origins turnover
We summarize the main results of that study, and present additional considerations on the same data, which motivate the evolutionary model framework used in the following
Summary
Eukaryotes, from yeast to mammals, rely on pre-defined “replication origins” along the genome to initiate replication [1,2,3,4], but we still ignore most of the evolutionary principles shaping the biological properties of these objects. Initiation at origins is stochastic, so that different cells of the same population undergoing genome replication in S-phase will typically initiate replication from different origins [7, 8]. Initiation from a single origin can be described by intrinsic rates and/or licensing events [9]. Recent techniques allow to measure replication progression at the single-cell level [12, 13]. The estimation of key origin parameters from data requires minimal mathematical models describing stochastic origin initiation and fork progression [10, 14,15,16]. One can extract from the data origin positions, as well as estimated origin-intrinsic characteristic firing times or rates. Knowledge of origins positions and rates makes it possible to estimate the “efficiency” of an origin, i.e
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