An evolutionary model identifies the main evolutionary biases for the evolution of genome-replication profiles

Abstract
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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 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.

Data availability

The code used to run the simulations, together with a readme file, was shared as a Mendeley data repository (https://data.mendeley.com/datasets/vg3r5355bj/2). The link is available in the methods section.

The following previously published data sets were used

(2018) The evolution of the temporal program of genome replication
SRA - project number: SRP111158. Accessions ranging from SRR5807795 to SRR5807891.

https://www.ncbi.nlm.nih.gov/sra?term=SRP111158

Article and author information

Author details

Rossana Droghetti

Physics, University of Milan, Milan, Italy

Competing interests
The authors declare that no competing interests exist.
Nicolas Agier

Laboratory of Computational and Quantitative Biology, Sorbonne Universite, CNRS, Institut de Biologie Paris-Seine, Paris, France

Competing interests
The authors declare that no competing interests exist.
Gilles Fischer

Laboratory of Computational and Quantitative Biology, Sorbonne Universite, CNRS, Institut de Biologie Paris-Seine, Paris, France

Competing interests
The authors declare that no competing interests exist.
Marco Gherardi

Physics, University of Milan and INFN, Milan, Italy

Competing interests
The authors declare that no competing interests exist.
Marco Cosentino Lagomarsino

Quantitative Biology and Physics, IFOM Foundation and University of Milan, Milan, Italy

For correspondence
marco.cosentino-lagomarsino@ifom.eu

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-0235-0445

Funding

Associazione Italiana per la Ricerca sul Cancro ((IG REF: 23258))

Marco Cosentino Lagomarsino

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.