1. Neuroscience
Download icon

Enhanced insulin signalling ameliorates C9orf72 hexanucleotide repeat expansion toxicity in Drosophila

  1. Magda Luciana Atilano
  2. Sebastian Grönke
  3. Teresa Niccoli
  4. Liam Kempthorne
  5. Oliver Hahn
  6. Javier Morón-Oset
  7. Oliver Hendrich
  8. Miranda Dyson
  9. Mirjam Lisette Adams
  10. Alexander Hull
  11. Marie-Therese Salcher-Konrad
  12. Amy Monaghan
  13. Magda Bictash
  14. Idoia Glaria
  15. Adrian M Isaacs  Is a corresponding author
  16. Linda Partridge  Is a corresponding author
  1. University College London, United Kingdom
  2. Max Planck Institute for Biology of Ageing, Germany
  3. University College of London, United Kingdom
Research Article
  • Cited 0
  • Views 1,396
  • Annotations
Cite this article as: eLife 2021;10:e58565 doi: 10.7554/eLife.58565

Abstract

G4C2 repeat expansions within the C9orf72 gene are the most common genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). The repeats undergo repeat-associated non-ATG translation to generate toxic dipeptide repeat proteins. Here, we show that insulin/Igf signalling is reduced in fly models of C9orf72 repeat expansion using RNA-sequencing of adult brain. We further demonstrate that activation of insulin/Igf signalling can mitigate multiple neurodegenerative phenotypes in flies expressing either expanded G4C2 repeats or the toxic dipeptide repeat protein poly-GR. Levels of poly-GR are reduced when components of the insulin/Igf signalling pathway are genetically activated in the diseased flies, suggesting a mechanism of rescue. Modulating insulin signalling in mammalian cells also lowers poly-GR levels. Remarkably, systemic injection of insulin improves the survival of flies expressing G4C2 repeats. Overall, our data suggest that modulation of insulin/Igf signalling could be an effective therapeutic approach against C9orf72 ALS/FTD.

Data availability

Sequencing data have been deposited in GEO under accession codes GSE151826. All data generated or analysed during this study are included in the manuscript.

Article and author information

Author details

  1. Magda Luciana Atilano

    Genetics, Evolution & Environment, Institute of Healthy Ageing, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3819-2023

  2. Sebastian Grönke

    Max Planck Institute for Biology of Ageing, Cologne, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1539-5346

  3. Teresa Niccoli

    Genetics, Evolution & Environment, Institute of Healthy Ageing, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  4. Liam Kempthorne

    UK Dementia Research Institute, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  5. Oliver Hahn

    Max Planck Institute for Biology of Ageing, Cologne, Germany
    Competing interests
    The authors declare that no competing interests exist.

  6. Javier Morón-Oset

    Max Planck Institute for Biology of Ageing, Cologne, Germany
    Competing interests
    The authors declare that no competing interests exist.

  7. Oliver Hendrich

    Max Planck Institute for Biology of Ageing, Cologne, Germany
    Competing interests
    The authors declare that no competing interests exist.

  8. Miranda Dyson

    Genetics, Evolution & Environment, Institute of Healthy Ageing, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  9. Mirjam Lisette Adams

    UK Dementia Research Institute, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  10. Alexander Hull

    Genetics, Evolution & Environment, Institute of Healthy Ageing, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  11. Marie-Therese Salcher-Konrad

    UK Dementia Research Institute, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  12. Amy Monaghan

    Alzheimer's Research UK UCL Drug Discovery Institute, University College of London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  13. Magda Bictash

    Alzheimer's Research UK UCL Drug Discovery Institute, University College of London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

  14. Idoia Glaria

    UK Dementia Research Institute, University College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4556-489X

  15. Adrian M Isaacs

    UK Dementia Research Institute, University College London, London, United Kingdom
    For correspondence
    a.isaacs@ucl.ac.uk
    Competing interests
    The authors declare that no competing interests exist.

  16. Linda Partridge

    Max Planck Institute for Biology of Ageing, Cologne, Germany
    For correspondence
    Linda.Partridge@age.mpg.de
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9615-0094

Funding

Alzheimer's Research UK (ARUK-PG2016A-6)

  • Adrian M Isaacs

Wellcome Trust

  • Linda Partridge

Max-Planck-Gesellschaft (Open-access funding)

  • Linda Partridge

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: May 4, 2020
  2. Accepted: March 9, 2021
  3. Accepted Manuscript published: March 19, 2021 (version 1)
  4. Version of Record published: March 29, 2021 (version 2)

Copyright

© 2021, Atilano 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

  • 1,396
    Page views
  • 218
    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)

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)

Categories and tags

Research organism

Further reading

    1. Developmental Biology
    2. Neuroscience

    The development of active binocular vision under normal and alternate rearing conditions

    Lukas Klimmasch et al.
    Research Article Updated

    The development of binocular vision is an active learning process comprising the development of disparity tuned neurons in visual cortex and the establishment of precise vergence control of the eyes. We present a computational model for the learning and self-calibration of active binocular vision based on the Active Efficient Coding framework, an extension of classic efficient coding ideas to active perception. Under normal rearing conditions with naturalistic input, the model develops disparity tuned neurons and precise vergence control, allowing it to correctly interpret random dot stereograms. Under altered rearing conditions modeled after neurophysiological experiments, the model qualitatively reproduces key experimental findings on changes in binocularity and disparity tuning. Furthermore, the model makes testable predictions regarding how altered rearing conditions impede the learning of precise vergence control. Finally, the model predicts a surprising new effect that impaired vergence control affects the statistics of orientation tuning in visual cortical neurons.

    1. Neuroscience

    The BigBrainWarp toolbox for integration of BigBrain 3D histology with multimodal neuroimaging

    Casey Paquola et al.
    Tools and Resources Updated

    Neuroimaging stands to benefit from emerging ultrahigh-resolution 3D histological atlases of the human brain; the first of which is ‘BigBrain’. Here, we review recent methodological advances for the integration of BigBrain with multi-modal neuroimaging and introduce a toolbox, ’BigBrainWarp’, that combines these developments. The aim of BigBrainWarp is to simplify workflows and support the adoption of best practices. This is accomplished with a simple wrapper function that allows users to easily map data between BigBrain and standard MRI spaces. The function automatically pulls specialised transformation procedures, based on ongoing research from a wide collaborative network of researchers. Additionally, the toolbox improves accessibility of histological information through dissemination of ready-to-use cytoarchitectural features. Finally, we demonstrate the utility of BigBrainWarp with three tutorials and discuss the potential of the toolbox to support multi-scale investigations of brain organisation.

    1. Neuroscience

    Classification and genetic targeting of cell types in the primary taste and premotor center of the adult Drosophila brain

    Gabriella R Sterne et al.
    Tools and Resources Updated

    Neural circuits carry out complex computations that allow animals to evaluate food, select mates, move toward attractive stimuli, and move away from threats. In insects, the subesophageal zone (SEZ) is a brain region that receives gustatory, pheromonal, and mechanosensory inputs and contributes to the control of diverse behaviors, including feeding, grooming, and locomotion. Despite its importance in sensorimotor transformations, the study of SEZ circuits has been hindered by limited knowledge of the underlying diversity of SEZ neurons. Here, we generate a collection of split-GAL4 lines that provides precise genetic targeting of 138 different SEZ cell types in adult Drosophila melanogaster, comprising approximately one third of all SEZ neurons. We characterize the single-cell anatomy of these neurons and find that they cluster by morphology into six supergroups that organize the SEZ into discrete anatomical domains. We find that the majority of local SEZ interneurons are not classically polarized, suggesting rich local processing, whereas SEZ projection neurons tend to be classically polarized, conveying information to a limited number of higher brain regions. This study provides insight into the anatomical organization of the SEZ and generates resources that will facilitate further study of SEZ neurons and their contributions to sensory processing and behavior.