An automated feeding system for the African killifish reveals effects of dietary restriction on lifespan and allows scalable assessment of associative learning

Abstract

The African turquoise killifish is an exciting new vertebrate model for aging studies. A significant challenge for any model organism is the control over its diet in space and time. To address this challenge, we created an automated and networked fish feeding system. Our automated feeder is designed to be open-source, easily transferable, and built from widely available components. Compared to manual feeding, our automated system is highly precise and flexible. As a proof-of-concept for the feeding flexibility of these automated feeders, we define a favorable regimen for growth and fertility for the African killifish and a dietary restriction regimen where both feeding time and quantity are reduced. We show that this dietary restriction regimen extends lifespan in males (but not in females) and impacts the transcriptomes of killifish livers in a sex-specific manner. Moreover, combining our automated feeding system with a video camera, we establish a quantitative associative learning assay to provide an integrative measure of cognitive performance for the killifish. The ability to precisely control food delivery in the killifish opens new areas to assess lifespan and cognitive behavior dynamics and to screen for dietary interventions and drugs in a scalable manner previously impossible with traditional vertebrate model organisms.

Data availability

This study's data are included in the submitted manuscript and supporting files. Source data have been provided as a compressed directory of supporting tables that correspond to figures as indicated in figure legends. All the scripts for analyzing the RNA-seq datasets and the behavioral assay can be accessed on GitHub. RNA-seq data have been deposited in GEO (accession number: GSE216369)..

The following data sets were generated

Article and author information

Author details

  1. Andrew McKay

    Department of Genetics, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9179-5018
  2. Emma K Costa

    Department of Neurology and Neurological Sciences, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Jingxun Chen

    Department of Genetics, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Chi-Kuo Hu

    Department of Genetics, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Xiaoshan Chen

    Department of Genetics, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Claire Nicole Bedbrook

    Department of Genetics, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Rishad C Khondker

    Department of Genetics, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Mike Thielvoldt

    Thielvoldt Engineering, Albany, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Param Priya Singh

    Department of Genetics, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Tony Wyss-Coray

    Department of Neurology and Neurological Sciences, Stanford University, Stanford, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5893-0831
  11. Anne Brunet

    Department of Genetics, Stanford University, Stanford, United States
    For correspondence
    abrunet1@stanford.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4608-6845

Funding

Stanford Brain Rejuvenation Program

  • Tony Wyss-Coray
  • Anne Brunet

Stanford Graduate Fellowship

  • Andrew McKay

Helen Hay Whitney Fellowship

  • Claire Nicole Bedbrook

National Institutes of Health (RF1AG057334)

  • Anne Brunet

National Institutes of Health (R01AG063418)

  • Anne Brunet

Jane Coffin Childs Memorial Fund for Medical Research (61-1762)

  • Jingxun Chen

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 animals were housed within the Stanford Research Animal Facility and treated in accordance with protocols approved by the Stanford Administrative Panel on Laboratory Animal Care (protocol # APLAC- 13645).

Reviewing Editor

  1. Jan Gruber, Yale-NUS College, Singapore

Publication history

  1. Received: March 31, 2021
  2. Accepted: November 9, 2022
  3. Accepted Manuscript published: November 10, 2022 (version 1)

Copyright

© 2022, McKay 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. Andrew McKay
  2. Emma K Costa
  3. Jingxun Chen
  4. Chi-Kuo Hu
  5. Xiaoshan Chen
  6. Claire Nicole Bedbrook
  7. Rishad C Khondker
  8. Mike Thielvoldt
  9. Param Priya Singh
  10. Tony Wyss-Coray
  11. Anne Brunet
(2022)
An automated feeding system for the African killifish reveals effects of dietary restriction on lifespan and allows scalable assessment of associative learning
eLife 11:e69008.
https://doi.org/10.7554/eLife.69008

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