A big-data approach to understanding metabolic rate and response to obesity in laboratory mice

  1. June K Corrigan
  2. Deepti Ramachandran
  3. Yuchen He
  4. Colin J Palmer
  5. Michael J Jurczak
  6. Rui Chen
  7. Bingshan Li
  8. Randall H Friedline
  9. Jason K Kim
  10. Jon J Ramsey
  11. Louise Lantier
  12. Owen P McGuinness
  13. Mouse Metabolic Phenotyping Center Energy Balance Working Group
  14. Alexander S Banks  Is a corresponding author
  1. Beth Israel Deaconess Medical Center and Harvard Medical School, United States
  2. University of Pittsburgh School of Medicine, United States
  3. Vanderbilt University School of Medicine, United States
  4. University of Massachusetts Medical School, United States
  5. University of California, Davis, United States

Abstract

Maintaining a healthy body weight requires an exquisite balance between energy intake and energy expenditure. To understand the genetic and environmental factors that contribute to the regulation of body weight, an important first step is to establish the normal range of metabolic values and primary sources contributing to variability. Energy metabolism is measured by powerful and sensitive indirect calorimetry devices. Analysis of nearly 10,000 wild-type mice from two large-scale experiments revealed that the largest variation in energy expenditure is due to body composition, ambient temperature, and institutional site of experimentation. We also analyze variation in 2,329 knockout strains and establish a reference for the magnitude of metabolic changes. Based on these findings, we provide suggestions for how best to design and conduct energy balance experiments in rodents. These recommendations will move us closer to the goal of a centralized physiological repository to foster transparency, rigor and reproducibility in metabolic physiology experimentation.

Data availability

All data and code can be found at https://github.com/banks-lab/Cal-Repository. Repository data includes complete indirect calorimetry data for MMPC experiments including CalR files for 4 sites at 0, 4, and 11 week trials, our MMPC analysis database, corrected IMPC database, and additional data for Figures 5 and 7. The R code to reproduce all figures is also included.

Article and author information

Author details

  1. June K Corrigan

    Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Deepti Ramachandran

    Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1113-1295
  3. Yuchen He

    Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Colin J Palmer

    Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Michael J Jurczak

    Division of Endocrinology and Metabolism, University of Pittsburgh School of Medicine, Pittsburgh, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Rui Chen

    Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Bingshan Li

    Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Randall H Friedline

    Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Jason K Kim

    Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Jon J Ramsey

    Department of Molecular Biosciences, University of California, Davis, Davis, United States
    Competing interests
    The authors declare that no competing interests exist.
  11. Louise Lantier

    Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6620-4976
  12. Owen P McGuinness

    Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1778-3203
  13. Mouse Metabolic Phenotyping Center Energy Balance Working Group

  14. Alexander S Banks

    Division of Endocrinology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, United States
    For correspondence
    asbanks@bidmc.harvard.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1787-6925

Funding

National Institutes of Health (R01DK107717)

  • Alexander S Banks

National Institutes of Health (U24-DK092993)

  • Jon J Ramsey

National Institutes of Health (U24-DK059635)

  • Michael J Jurczak

National Institutes of Health (U24-DK076174)

  • Owen P McGuinness

National Institutes of Health (U24-DK059637)

  • Owen P McGuinness

National Institutes of Health (U24-DK059630)

  • Owen P McGuinness

National Institutes of Health (U24-DK093000)

  • Jason K Kim

National Institutes of Health (U24-DK076169)

  • Alexander S Banks

Swiss National Science Foundation (Postdoc mobility grant)

  • Deepti Ramachandran

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

Reviewing Editor

  1. Joel K Elmquist, University of Texas Southwestern Medical Center, United States

Ethics

Animal experimentation: These studies were performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (IACUC) at the site where they were performed.

Version history

  1. Received: November 13, 2019
  2. Accepted: April 30, 2020
  3. Accepted Manuscript published: May 1, 2020 (version 1)
  4. Accepted Manuscript updated: May 4, 2020 (version 2)
  5. Version of Record published: June 5, 2020 (version 3)

Copyright

© 2020, Corrigan 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. June K Corrigan
  2. Deepti Ramachandran
  3. Yuchen He
  4. Colin J Palmer
  5. Michael J Jurczak
  6. Rui Chen
  7. Bingshan Li
  8. Randall H Friedline
  9. Jason K Kim
  10. Jon J Ramsey
  11. Louise Lantier
  12. Owen P McGuinness
  13. Mouse Metabolic Phenotyping Center Energy Balance Working Group
  14. Alexander S Banks
(2020)
A big-data approach to understanding metabolic rate and response to obesity in laboratory mice
eLife 9:e53560.
https://doi.org/10.7554/eLife.53560

Share this article

https://doi.org/10.7554/eLife.53560

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