Acute control of the sleep switch in Drosophila reveals a role for gap junctions in regulating behavioral responsiveness

  1. Michael Troup
  2. Melvyn HW Yap
  3. Chelsie Rohrscheib
  4. Martyna J Grabowska
  5. Deniz Ertekin
  6. Roshini Randeniya
  7. Benjamin Kottler
  8. Aoife Larkin
  9. Kelly Munro
  10. Paul Shaw
  11. Bruno van Swinderen  Is a corresponding author
  1. The University of Queensland, Australia
  2. Washington University in St. Louis, United States

Abstract

Sleep is a dynamic process in most animals, involving distinct stages that probably achieve multiple functions for the brain. Before sleep functions can be initiated, it is likely that behavioral responsiveness to the outside world needs to be reduced first, even while animals are still awake. Recent work in Drosophila has uncovered a sleep switch in the dorsal fan-shaped body (dFB) of the fly's central brain, but it is unknown if these sleep-promoting neurons also govern the acute need to ignore salient stimuli in the environment during sleep transitions. We found that optogenetic activation of the sleep switch suppressed behavioral responsiveness to mechanical stimuli, even in awake flies, indicating a broader role for these neurons in regulating arousal. The dFB-mediated suppression mechanism and its associated neural correlates requires innexin6 expression, suggesting that the acute need to reduce sensory perception when flies fall asleep is mediated in part by electrical synapses.

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All data generated or analysed during this study are included in the manuscript and supporting files.

Article and author information

Author details

  1. Michael Troup

    Queensland Brain Institute, The University of Queensland, Brisbane, Australia
    Competing interests
    The authors declare that no competing interests exist.
  2. Melvyn HW Yap

    Queensland Brain Institute, The University of Queensland, Brisbane, Australia
    Competing interests
    The authors declare that no competing interests exist.
  3. Chelsie Rohrscheib

    Queensland Brain Insitute, The University of Queensland, Brisbane, Australia
    Competing interests
    The authors declare that no competing interests exist.
  4. Martyna J Grabowska

    Queensland Brain Institute, The University of Queensland, Brisbane, Australia
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1727-7714
  5. Deniz Ertekin

    Queensland Brain Institute, The University of Queensland, Brisbane, Australia
    Competing interests
    The authors declare that no competing interests exist.
  6. Roshini Randeniya

    Queensland Brain Institute, The University of Queensland, Brisbane, Australia
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1340-750X
  7. Benjamin Kottler

    Queensland Brain Institute, The University of Queensland, Brisbane, Australia
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4551-5791
  8. Aoife Larkin

    Queensland Brain Institute, The University of Queensland, Brisbane, Australia
    Competing interests
    The authors declare that no competing interests exist.
  9. Kelly Munro

    Queensland Brain Institute, The University of Queensland, Brisbane, Australia
    Competing interests
    The authors declare that no competing interests exist.
  10. Paul Shaw

    School of Medicine, Washington University in St. Louis, St Louis, United States
    Competing interests
    The authors declare that no competing interests exist.
  11. Bruno van Swinderen

    Queensland Brain Institute, The University of Queensland, Brisbane, Australia
    For correspondence
    b.vanswinderen@uq.edu.au
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6552-7418

Funding

National Institutes of Health (R01 NS076980)

  • Melvyn HW Yap
  • Paul Shaw
  • Bruno van Swinderen

National Health and Medical Research Council (GNT1065713)

  • Michael Troup
  • Chelsie Rohrscheib
  • Aoife Larkin
  • Bruno van Swinderen

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

Reviewing Editor

  1. Mani Ramaswami, Trinity College Dublin, Ireland

Publication history

  1. Received: March 29, 2018
  2. Accepted: August 14, 2018
  3. Accepted Manuscript published: August 15, 2018 (version 1)
  4. Version of Record published: August 30, 2018 (version 2)

Copyright

© 2018, Troup 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. Michael Troup
  2. Melvyn HW Yap
  3. Chelsie Rohrscheib
  4. Martyna J Grabowska
  5. Deniz Ertekin
  6. Roshini Randeniya
  7. Benjamin Kottler
  8. Aoife Larkin
  9. Kelly Munro
  10. Paul Shaw
  11. Bruno van Swinderen
(2018)
Acute control of the sleep switch in Drosophila reveals a role for gap junctions in regulating behavioral responsiveness
eLife 7:e37105.
https://doi.org/10.7554/eLife.37105

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    Background: The heterogeneity of white matter damage and symptoms in concussion has been identified as a major obstacle to therapeutic innovation. In contrast, most diffusion MRI (dMRI) studies on concussion have traditionally relied on group-comparison approaches that average out heterogeneity. To leverage, rather than average out, concussion heterogeneity, we combined dMRI and multivariate statistics to characterize multi-tract multi-symptom relationships.

    Methods: Using cross-sectional data from 306 previously-concussed children aged 9-10 from the Adolescent Brain Cognitive Development Study, we built connectomes weighted by classical and emerging diffusion measures. These measures were combined into two informative indices, the first representing microstructural complexity, the second representing axonal density. We deployed pattern-learning algorithms to jointly decompose these connectivity features and 19 symptom measures.

    Results: Early multi-tract multi-symptom pairs explained the most covariance and represented broad symptom categories, such as a general problems pair, or a pair representing all cognitive symptoms, and implicated more distributed networks of white matter tracts. Further pairs represented more specific symptom combinations, such as a pair representing attention problems exclusively, and were associated with more localized white matter abnormalities. Symptom representation was not systematically related to tract representation across pairs. Sleep problems were implicated across most pairs, but were related to different connections across these pairs. Expression of multi-tract features was not driven by sociodemographic and injury-related variables, as well as by clinical subgroups defined by the presence of ADHD. Analyses performed on a replication dataset showed consistent results.

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    Funding: financial support for this work from a Vanier Canada Graduate Scholarship from the Canadian Institutes of Health Research (GIG), an Ontario Graduate Scholarship (SS), a Restracomp Research Fellowship provided by the Hospital for Sick Children (SS), an Institutional Research Chair in Neuroinformatics (MD), as well as a Natural Sciences and Engineering Research Council CREATE grant (MD).