A Toll-receptor map underlies structural brain plasticity

  1. Guiyi Li
  2. Manuel G Forero
  3. Jill S Wentzell
  4. Ilgim Durmus
  5. Reinhard Wolf
  6. Niki C Anthoney
  7. Mieczyslaw Parker
  8. Ruiying Jiang
  9. Jacob Hasenauer
  10. Nicholas James Strausfeld
  11. Martin Heisenberg
  12. Alicia Hidalgo  Is a corresponding author
  1. University of Birmingham, United Kingdom
  2. Universidad de Ibagué, Colombia
  3. University of Würzburg, Germany
  4. University of Arizona, United States

Abstract

Experience alters brain structure, but the underlying mechanism remained unknown. Structural plasticity reveals that brain function is encoded in generative changes to cells that compete with destructive processes driving neurodegeneration. At an adult critical period, experience increases fiber number and brain size in Drosophila. Here, we asked if Toll receptors are involved. Tolls demarcate a map of brain anatomical domains. Focusing on Toll-2, loss of function caused apoptosis, neurite atrophy and impaired behaviour. Toll-2 gain of function and neuronal activity at the critical period increased cell number. Toll-2 induced cycling of adult progenitor cells via a novel pathway, that antagonized MyD88-dependent quiescence, and engaged Weckle and Yorkie downstream. Constant knock-down of multiple Tolls synergistically reduced brain size. Conditional over-expression of Toll-2 and wek at the adult critical period increased brain size. Through their topographic distribution, Toll receptors regulate neuronal number and brain size, modulating structural plasticity in the adult brain.

Data availability

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

Article and author information

Author details

  1. Guiyi Li

    School of Biosciences, University of Birmingham, Birmingham, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  2. Manuel G Forero

    Facultad de Ingeniería, Universidad de Ibagué, Ibagué, Colombia
    Competing interests
    The authors declare that no competing interests exist.
  3. Jill S Wentzell

    School of Biosciences, University of Birmingham, Birmingham, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  4. Ilgim Durmus

    School of Biosciences, University of Birmingham, Birmingham, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  5. Reinhard Wolf

    Rudolf-Virchow-Center for Experimental Biomedicine, University of Würzburg, Würzburg, Germany
    Competing interests
    The authors declare that no competing interests exist.
  6. Niki C Anthoney

    Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3311-6328
  7. Mieczyslaw Parker

    School of Biosciences, University of Birmingham, Birmingham, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  8. Ruiying Jiang

    School of Biosciences, University of Birmingham, Birmingham, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  9. Jacob Hasenauer

    School of Biosciences, University of Birmingham, Birmingham, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  10. Nicholas James Strausfeld

    Department of Neuroscience, University of Arizona, Tucson, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1115-1774
  11. Martin Heisenberg

    Rudolf-Virchow-Center for Experimental Biomedicine, University of Würzburg, Würzburg, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4462-8655
  12. Alicia Hidalgo

    School of Biosciences, University of Birmingham, Birmingham, United Kingdom
    For correspondence
    a.hidalgo@bham.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8041-5764

Funding

Biotechnology and Biological Sciences Research Council (BB/P004997/1)

  • Alicia Hidalgo

Biotechnology and Biological Sciences Research Council (BB/R017034/1)

  • Alicia Hidalgo

EU Marie Curie-Sklodowska Fellowship (NPN)

  • Jill S Wentzell

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

Copyright

© 2020, Li 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

  • 4,847
    views
  • 743
    downloads
  • 39
    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. Guiyi Li
  2. Manuel G Forero
  3. Jill S Wentzell
  4. Ilgim Durmus
  5. Reinhard Wolf
  6. Niki C Anthoney
  7. Mieczyslaw Parker
  8. Ruiying Jiang
  9. Jacob Hasenauer
  10. Nicholas James Strausfeld
  11. Martin Heisenberg
  12. Alicia Hidalgo
(2020)
A Toll-receptor map underlies structural brain plasticity
eLife 9:e52743.
https://doi.org/10.7554/eLife.52743

Share this article

https://doi.org/10.7554/eLife.52743

Further reading

    1. Neuroscience
    Zachary Fournier, Leandro M Alonso, Eve Marder
    Research Article

    Circuit function results from both intrinsic conductances of network neurons and the synaptic conductances that connect them. In models of neural circuits, different combinations of maximal conductances can give rise to similar activity. We compared the robustness of a neural circuit to changes in their intrinsic versus synaptic conductances. To address this, we performed a sensitivity analysis on a population of conductance-based models of the pyloric network from the crustacean stomatogastric ganglion (STG). The model network consists of three neurons with nine currents: a sodium current (Na), three potassium currents (Kd, KCa, KA), two calcium currents (CaS and CaT), a hyperpolarization-activated current (H), a non-voltage-gated leak current (leak), and a neuromodulatory current (MI). The model cells are connected by seven synapses of two types, glutamatergic and cholinergic. We produced one hundred models of the pyloric network that displayed similar activities with values of maximal conductances distributed over wide ranges. We evaluated the robustness of each model to changes in their maximal conductances. We found that individual models have different sensitivities to changes in their maximal conductances, both in their intrinsic and synaptic conductances. As expected, the models become less robust as the extent of the changes increases. Despite quantitative differences in their robustness, we found that in all cases, the model networks are more sensitive to the perturbation of their intrinsic conductances than their synaptic conductances.

    1. Neuroscience
    Jacob A Miller
    Insight

    When navigating environments with changing rules, human brain circuits flexibly adapt how and where we retain information to help us achieve our immediate goals.