1. Physics of Living Systems
Download icon

Live imaging and biophysical modeling support a button-based mechanism of somatic homolog pairing in Drosophila

  1. Myron Barber Child VI
  2. Jack R Bateman  Is a corresponding author
  3. Amir Jahangiri
  4. Armando Reimer
  5. Nicholas C Lammers
  6. Nica Sabouni
  7. Diego Villamarin
  8. Grace C McKenzie-Smith
  9. Justine E Johnson
  10. Daniel Jost  Is a corresponding author
  11. Hernan G Garcia  Is a corresponding author
  1. University of California at Berkeley, United States
  2. Bowdoin College, United States
  3. Grenoble Alpes University, France
  4. University of California, Berkeley, United States
  5. ENS de Lyon, France
Research Article
  • Cited 3
  • Views 877
  • Annotations
Cite this article as: eLife 2021;10:e64412 doi: 10.7554/eLife.64412

Abstract

3D eukaryotic genome organization provides the structural basis for gene regulation. In Drosophila melanogaster, genome folding is characterized by somatic homolog pairing, where homologous chromosomes are intimately paired from end to end; however, how homologs identify one another and pair has remained mysterious. Recently, this process has been proposed to be driven by specifically interacting 'buttons' encoded along chromosomes. Here, we turned this hypothesis into a quantitative biophysical model to demonstrate that a button-based mechanism can lead to chromosome-wide pairing. We tested our model using live-imaging measurements of chromosomal loci tagged with the MS2 and PP7 nascent RNA labeling systems. We show solid agreement between model predictions and experiments in the pairing dynamics of individual homologous loci. Our results strongly support a button-based mechanism of somatic homolog pairing in Drosophila and provide a theoretical framework for revealing the molecular identity and regulation of buttons.

Data availability

Modeling code is available at: https://github.com/physical-biology-of-chromatin/Homologous_pairingCustom Matlab 2019b image analysis scripts can be found at https://github.com/GarciaLab/mRNADynamics/.Raw figure files of relevant plots are available at: https://www.dropbox.com/sh/cwe1t3u5q4v3yos/AACTXXBF6WiOuLuozX0MZRkba?dl=0Samples of generated data used in this study are included in the manuscript and supporting files

Article and author information

Author details

  1. Myron Barber Child VI

    Molecular and Cell Biology, University of California at Berkeley, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8563-0842
  2. Jack R Bateman

    Biology, Bowdoin College, Brunswick, United States
    For correspondence
    jbateman@bowdoin.edu
    Competing interests
    The authors declare that no competing interests exist.
  3. Amir Jahangiri

    TIMC-IMAG, Grenoble Alpes University, La Tronche, France
    Competing interests
    The authors declare that no competing interests exist.
  4. Armando Reimer

    Biophysics Graduate Group, University of California, Berkeley, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Nicholas C Lammers

    Biophysics Graduate Group, University of California, Berkeley, Oakland, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6832-6152
  6. Nica Sabouni

    Molecular and Cell Biology, University of California at Berkeley, Berkeley, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Diego Villamarin

    Biology, Bowdoin College, Brunswick, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3265-1740
  8. Grace C McKenzie-Smith

    Biology, Bowdoin College, Brunswick, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Justine E Johnson

    Biology, Bowdoin College, Brunswick, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Daniel Jost

    Laboratory of Biology and Modelling of the Cell, ENS de Lyon, Lyon, France
    For correspondence
    daniel.jost@ens-lyon.fr
    Competing interests
    The authors declare that no competing interests exist.
  11. Hernan G Garcia

    Molecular and Cell Biology, Physics, University of California, Berkeley, Berkeley, United States
    For correspondence
    hggarcia@berkeley.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5212-3649

Funding

Burroughs Wellcome Fund (Career Award at the Scientific Interface)

  • Hernan G Garcia

ITMO Cancer (BIO2015-08)

  • Daniel Jost

National Institutes of Health (P20 GM0103423,R15 GM132896-01)

  • Jack R Bateman

National Science Foundation (CAREER Award 1349779)

  • Jack R Bateman

Alfred P. Sloan Foundation (Sloan Research Fellowship)

  • Hernan G Garcia

Human Frontier Science Program

  • Hernan G Garcia

Searle Scholars Program

  • Hernan G Garcia

Shurl and Kay Curci Foundation

  • Hernan G Garcia

Hellman Foundation

  • Hernan G Garcia

National Institutes of Health (Director's New Innovator Award,DP2 OD024541-01)

  • Hernan G Garcia

National Science Foundation (CAREER Award,1652236)

  • Hernan G Garcia

Agence Nationale pour la Recherche (ANR-18-CE12-0006-03,ANR-18-CE45-0022-01)

  • Daniel Jost

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

Reviewing Editor

  1. Pierre Sens, Institut Curie, PSL Research University, CNRS, France

Publication history

  1. Received: October 28, 2020
  2. Accepted: June 7, 2021
  3. Accepted Manuscript published: June 8, 2021 (version 1)
  4. Version of Record published: July 21, 2021 (version 2)

Copyright

© 2021, Child 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

  • 877
    Page views
  • 123
    Downloads
  • 3
    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)

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

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

Further reading

    1. Cell Biology
    2. Physics of Living Systems
    Larisa Venkova et al.
    Research Article Updated

    Mechanics has been a central focus of physical biology in the past decade. In comparison, how cells manage their size is less understood. Here, we show that a parameter central to both the physics and the physiology of the cell, its volume, depends on a mechano-osmotic coupling. We found that cells change their volume depending on the rate at which they change shape, when they spontaneously spread or when they are externally deformed. Cells undergo slow deformation at constant volume, while fast deformation leads to volume loss. We propose a mechanosensitive pump and leak model to explain this phenomenon. Our model and experiments suggest that volume modulation depends on the state of the actin cortex and the coupling of ion fluxes to membrane tension. This mechano-osmotic coupling defines a membrane tension homeostasis module constantly at work in cells, causing volume fluctuations associated with fast cell shape changes, with potential consequences on cellular physiology.

    1. Computational and Systems Biology
    2. Physics of Living Systems
    Wataru Toyokawa, Wolfgang Gaissmaier
    Research Article

    Given the ubiquity of potentially adverse behavioural bias owing to myopic trial-and-error learning, it seems paradoxical that improvements in decision-making performance through conformist social learning, a process widely considered to be bias amplification, still prevail in animal collective behaviour. Here we show, through model analyses and large-scale interactive behavioural experiments with 585 human subjects, that conformist influence can indeed promote favourable risk taking in repeated experience-based decision making, even though many individuals are systematically biased towards adverse risk aversion. Although strong positive feedback conferred by copying the majority’s behaviour could result in unfavourable informational cascades, our differential equation model of collective behavioural dynamics identified a key role for increasing exploration by negative feedback arising when a weak minority influence undermines the inherent behavioural bias. This ‘collective behavioural rescue’, emerging through coordination of positive and negative feedback, highlights a benefit of collective learning in a broader range of environmental conditions than previously assumed and resolves the ostensible paradox of adaptive collective behavioural flexibility under conformist influences.