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.

Metrics

  • 1,110
    Page views
  • 108
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  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

Further reading

    1. Computational and Systems Biology
    Ricardo Omar Ramirez Flores, Jan David Lanzer ... Julio Saez-Rodriguez
    Research Article

    Biomedical single-cell atlases describe disease at the cellular level. However, analysis of this data commonly focuses on cell-type centric pairwise cross-condition comparisons, disregarding the multicellular nature of disease processes. Here we propose multicellular factor analysis for the unsupervised analysis of samples from cross-condition single-cell atlases and the identification of multicellular programs associated with disease. Our strategy, which repurposes group factor analysis as implemented in multi-omics factor analysis, incorporates the variation of patient samples across cell-types or other tissue-centric features, such as cell compositions or spatial relationships, and enables the joint analysis of multiple patient cohorts, facilitating the integration of atlases. We applied our framework to a collection of acute and chronic human heart failure atlases and described multicellular processes of cardiac remodeling, independent to cellular compositions and their local organization, that were conserved in independent spatial and bulk transcriptomics datasets. In sum, our framework serves as an exploratory tool for unsupervised analysis of cross-condition single-cell atlases and allows for the integration of the measurements of patient cohorts across distinct data modalities.

    1. Cancer Biology
    2. Computational and Systems Biology
    Jessica Xin Hjaltelin, Sif Ingibergsdóttir Novitski ... Søren Brunak
    Research Article

    Pancreatic cancer is one of the deadliest cancer types with poor treatment options. Better detection of early symptoms and relevant disease correlations could improve pancreatic cancer prognosis. In this retrospective study, we used symptom and disease codes (ICD-10) from the Danish National Patient Registry (NPR) encompassing 6.9 million patients from 1994 to 2018,, of whom 23,592 were diagnosed with pancreatic cancer. The Danish cancer registry included 18,523 of these patients. To complement and compare the registry diagnosis codes with deeper clinical data, we used a text mining approach to extract symptoms from free text clinical notes in electronic health records (3078 pancreatic cancer patients and 30,780 controls). We used both data sources to generate and compare symptom disease trajectories to uncover temporal patterns of symptoms prior to pancreatic cancer diagnosis for the same patients. We show that the text mining of the clinical notes was able to complement the registry-based symptoms by capturing more symptoms prior to pancreatic cancer diagnosis. For example, ‘Blood pressure reading without diagnosis’, ‘Abnormalities of heartbeat’, and ‘Intestinal obstruction’ were not found for the registry-based analysis. Chaining symptoms together in trajectories identified two groups of patients with lower median survival (<90 days) following the trajectories ‘Cough→Jaundice→Intestinal obstruction’ and ‘Pain→Jaundice→Abnormal results of function studies’. These results provide a comprehensive comparison of the two types of pancreatic cancer symptom trajectories, which in combination can leverage the full potential of the health data and ultimately provide a fuller picture for detection of early risk factors for pancreatic cancer.