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

  1. Rossana Droghetti
  2. Nicolas Agier
  3. Gilles Fischer
  4. Marco Gherardi
  5. Marco Cosentino Lagomarsino  Is a corresponding author
  1. University of Milan, Italy
  2. Sorbonne Universite, CNRS, Institut de Biologie Paris-Seine, France
  3. University of Milan and INFN, Italy
  4. IFOM Foundation and University of Milan, Italy

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

Article and author information

Author details

  1. Rossana Droghetti

    Physics, University of Milan, Milan, Italy
    Competing interests
    The authors declare that no competing interests exist.
  2. 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.
  3. 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.
  4. Marco Gherardi

    Physics, University of Milan and INFN, Milan, Italy
    Competing interests
    The authors declare that no competing interests exist.
  5. 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.
    ORCID icon "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.

Reviewing Editor

  1. Sandeep Krishna, National Centre for Biological Sciences­‐Tata Institute of Fundamental Research, India

Version history

  1. Received: September 28, 2020
  2. Accepted: May 20, 2021
  3. Accepted Manuscript published: May 20, 2021 (version 1)
  4. Version of Record published: June 18, 2021 (version 2)

Copyright

© 2021, Droghetti et al.

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.

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  1. Rossana Droghetti
  2. Nicolas Agier
  3. Gilles Fischer
  4. Marco Gherardi
  5. Marco Cosentino Lagomarsino
(2021)
An evolutionary model identifies the main evolutionary biases for the evolution of genome-replication profiles
eLife 10:e63542.
https://doi.org/10.7554/eLife.63542

Share this article

https://doi.org/10.7554/eLife.63542

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