The cytokine GDF15 signals through a population of brainstem cholecystokinin neurons to mediate anorectic signalling

  1. Amy A Worth
  2. Rosemary Shoop
  3. Katie Tye
  4. Claire H Feetham
  5. Giuseppe D'Agostino
  6. Garron T Dodd
  7. Frank Reimann
  8. Fiona M Gribble
  9. Emily C Beebe
  10. James D Dunbar
  11. Jesline T Alexander-Chacko
  12. Dana K Sindelar
  13. Tamer Coskun
  14. Paul J Emmerson
  15. Simon M Luckman  Is a corresponding author
  1. University of Manchester, United Kingdom
  2. University of Melbourne, Australia
  3. University of Cambridge, United Kingdom
  4. Eli Lilly and Company, United States

Abstract

The cytokine, GDF15, is produced in pathological states which cause cellular stress, including cancer. When over expressed, it causes dramatic weight reduction, suggesting a role in disease-related anorexia. Here we demonstrate that the GDF15 receptor, GFRAL, is located in a subset of cholecystokinin neurons which span the area postrema and the nucleus of the tractus solitarius of the mouse. GDF15 activates GFRALAP/NTS neurons and supports conditioned taste and place aversions, while the anorexia it causes can be blocked by a monoclonal antibody directed at GFRAL or by disrupting CCK neuronal signalling. The cancer-therapeutic drug, cisplatin, induces the release of GDF15 and activates GFRALAP/NTS neurons, as well as causing significant reductions in food intake and body weight in mice. These metabolic effects of cisplatin are abolished by pre-treatment with the GFRAL monoclonal antibody. Our results suggest that GFRAL neutralising antibodies or antagonists may provide a co-treatment opportunity for patients undergoing chemotherapy.

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. Amy A Worth

    Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
    Competing interests
    No competing interests declared.
  2. Rosemary Shoop

    Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3617-4358
  3. Katie Tye

    Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
    Competing interests
    No competing interests declared.
  4. Claire H Feetham

    Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
    Competing interests
    No competing interests declared.
  5. Giuseppe D'Agostino

    Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
    Competing interests
    No competing interests declared.
  6. Garron T Dodd

    Department of Physiology, University of Melbourne, Melbourne, Australia
    Competing interests
    No competing interests declared.
  7. Frank Reimann

    Wellcome Trust MRC Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    No competing interests declared.
  8. Fiona M Gribble

    Wellcome Trust MRC Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    No competing interests declared.
  9. Emily C Beebe

    Lilly Research Laboratories, Lilly Corporate Center, Eli Lilly and Company, Indianapolis, United States
    Competing interests
    Emily C Beebe, Paid employee of Eli Lilly..
  10. James D Dunbar

    Lilly Research Laboratories, Lilly Corporate Center, Eli Lilly and Company, Indianapolis, United States
    Competing interests
    James D Dunbar, Paid employee of Eli Lilly..
  11. Jesline T Alexander-Chacko

    Lilly Research Laboratories, Lilly Corporate Center, Eli Lilly and Company, Indianapolis, United States
    Competing interests
    Jesline T Alexander-Chacko, Paid employee of Eli Lilly..
  12. Dana K Sindelar

    Lilly Research Laboratories, Lilly Corporate Center, Eli Lilly and Company, Indianapolis, United States
    Competing interests
    Dana K Sindelar, Paid employee of Eli Lilly..
  13. Tamer Coskun

    Lilly Research Laboratories, Lilly Corporate Center, Eli Lilly and Company, Indianapolis, United States
    Competing interests
    Tamer Coskun, Paid employee of Eli Lilly..
  14. Paul J Emmerson

    Lilly Research Laboratories, Lilly Corporate Center, Eli Lilly and Company, Indianapolis, United States
    Competing interests
    Paul J Emmerson, Paid employee of Eli Lilly..
  15. Simon M Luckman

    Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom
    For correspondence
    simon.luckman@manchester.ac.uk
    Competing interests
    Simon M Luckman, BB/S008098/1 is a BBSRC Industrial Partnership Award between SML and Eli Lilly.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5318-5473

Funding

Biotechnology and Biological Sciences Research Council (BB/M001067/1)

  • Simon M Luckman

Biotechnology and Biological Sciences Research Council (BB/L021129/1)

  • Simon M Luckman

Medical Research Council (MR/R002991/1)

  • Simon M Luckman

Medical Research Council (MR/P009824/2)

  • Giuseppe D'Agostino

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

Ethics

Animal experimentation: All procedures were conducted in accordance with either: the United Kingdom Animals (Scientific Procedures) Act, 1986 (ASPA) and approved by the local animal welfare ethical review body (AWERB); the Eli Lilly Institutional Animal Care and Use Committee (IACUC) in accordance with the National Institutes of Health Guide for Care and Use of Laboratory Animals; or the University of Melbourne Animal Ethics Committee (1914919) and conformed to National Health & 8 Medical Research Council (Australia) guidelines regarding the care and use of experimental animals. Additional guidance from the UK National Centre for 3R's (NC3Rs) was followed where applicable.

Copyright

© 2020, Worth 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

  • 5,018
    views
  • 650
    downloads
  • 52
    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. Amy A Worth
  2. Rosemary Shoop
  3. Katie Tye
  4. Claire H Feetham
  5. Giuseppe D'Agostino
  6. Garron T Dodd
  7. Frank Reimann
  8. Fiona M Gribble
  9. Emily C Beebe
  10. James D Dunbar
  11. Jesline T Alexander-Chacko
  12. Dana K Sindelar
  13. Tamer Coskun
  14. Paul J Emmerson
  15. Simon M Luckman
(2020)
The cytokine GDF15 signals through a population of brainstem cholecystokinin neurons to mediate anorectic signalling
eLife 9:e55164.
https://doi.org/10.7554/eLife.55164

Share this article

https://doi.org/10.7554/eLife.55164

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.