Neurexin and Neuroligin-based adhesion complexes drive axonal arborisation growth independent of synaptic activity

  1. William D Constance
  2. Amrita Mukherjee
  3. Yvette E Fisher
  4. Sinziana Pop
  5. Eric Blanc
  6. Yusuke Toyama
  7. Darren W Williams  Is a corresponding author
  1. King's College London, United Kingdom
  2. Harvard Medical School, United States
  3. Berlin Institute of Health, Germany
  4. National University of Singapore, Singapore

Abstract

Building arborisations of the right size and shape is fundamental for neural network function. Live imaging in vertebrate brains strongly suggests that nascent synapses are critical for branch growth during development. The molecular mechanisms underlying this are largely unknown. Here we present a novel system in Drosophila for studying the development of complex arborisations live, in vivo during metamorphosis. In growing arborisations we see branch dynamics and localisations of presynaptic proteins very similar to the 'synaptotropic growth' described in fish/frogs. These accumulations of presynaptic proteins do not appear to be presynaptic release sites and are not paired with neurotransmitter receptors. Knockdowns of either evoked or spontaneous neurotransmission do not impact arbor growth. Instead, we find that axonal branch growth is regulated by dynamic, focal localisations of Neurexin and Neuroligin. These adhesion complexes provide stability for filopodia by a 'stick-and-grow' based mechanism wholly independent of synaptic activity.

Article and author information

Author details

  1. William D Constance

    Centre for Developmental Neurobiology, King's College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  2. Amrita Mukherjee

    Centre for Developmental Neurobiology, King's College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  3. Yvette E Fisher

    Department of Neurobiology, Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Sinziana Pop

    Centre for Developmental Neurobiology, King's College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  5. Eric Blanc

    Berlin Institute of Health, Berlin, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4369-0254
  6. Yusuke Toyama

    Department of Biological Sciences, National University of Singapore, Singapore, Singapore
    Competing interests
    The authors declare that no competing interests exist.
  7. Darren W Williams

    Centre for Developmental Neurobiology, King's College London, London, United Kingdom
    For correspondence
    darren.williams@kcl.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5917-4935

Funding

Biotechnology and Biological Sciences Research Council (BB/L022672/1)

  • William D Constance
  • Amrita Mukherjee
  • Yvette E Fisher
  • Sinziana Pop
  • Eric Blanc

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

Copyright

© 2018, Constance 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

  • 6,130
    views
  • 788
    downloads
  • 35
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

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. William D Constance
  2. Amrita Mukherjee
  3. Yvette E Fisher
  4. Sinziana Pop
  5. Eric Blanc
  6. Yusuke Toyama
  7. Darren W Williams
(2018)
Neurexin and Neuroligin-based adhesion complexes drive axonal arborisation growth independent of synaptic activity
eLife 7:e31659.
https://doi.org/10.7554/eLife.31659

Share this article

https://doi.org/10.7554/eLife.31659

Further reading

    1. Neuroscience
    Proloy Das, Mingjian He, Patrick L Purdon
    Tools and Resources

    Modern neurophysiological recordings are performed using multichannel sensor arrays that are able to record activity in an increasingly high number of channels numbering in the 100s to 1000s. Often, underlying lower-dimensional patterns of activity are responsible for the observed dynamics, but these representations are difficult to reliably identify using existing methods that attempt to summarize multivariate relationships in a post hoc manner from univariate analyses or using current blind source separation methods. While such methods can reveal appealing patterns of activity, determining the number of components to include, assessing their statistical significance, and interpreting them requires extensive manual intervention and subjective judgment in practice. These difficulties with component selection and interpretation occur in large part because these methods lack a generative model for the underlying spatio-temporal dynamics. Here, we describe a novel component analysis method anchored by a generative model where each source is described by a bio-physically inspired state-space representation. The parameters governing this representation readily capture the oscillatory temporal dynamics of the components, so we refer to it as oscillation component analysis. These parameters – the oscillatory properties, the component mixing weights at the sensors, and the number of oscillations – all are inferred in a data-driven fashion within a Bayesian framework employing an instance of the expectation maximization algorithm. We analyze high-dimensional electroencephalography and magnetoencephalography recordings from human studies to illustrate the potential utility of this method for neuroscience data.

    1. Neuroscience
    Sihan Yang, Anastasia Kiyonaga
    Insight

    A neural signature of serial dependence has been found, which mirrors the attractive bias of visual information seen in behavioral experiments.