A big-data approach to understanding metabolic rate and response to obesity in laboratory mice
- Beth Israel Deaconess Medical Center and Harvard Medical School, United States
- University of Pittsburgh School of Medicine, United States
- Vanderbilt University School of Medicine, United States
- University of Massachusetts Medical School, United States
- 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
Deepti Ramachandran
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
- 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
- Received: November 13, 2019
- Accepted: April 30, 2020
- Accepted Manuscript published: May 1, 2020 (version 1)
- Accepted Manuscript updated: May 4, 2020 (version 2)
- 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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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
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