Program in Molecular Medicine; Division of Endocrinology, Metabolism, and Diabetes, Department of Medicine
Biochemical Phenomena, Metabolism, and Nutrition | Bioinformatics | Cellular and Molecular Physiology | Nutritional and Metabolic Diseases | Physiological Processes | Research Methods in Life Sciences
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 2329 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.
energy expenditure, food intake, genetics, human biology, medicine, metabolic rate, mouse, neuroscience, obesity, thermogenesis
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Copyright © 2020, Corrigan et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
DOI of Published Version
Corrigan JK, Ramachandran D, He Y, Palmer CJ, Jurczak MJ, Chen R, Li B, Friedline RH, Kim JK, Ramsey JJ, Lantier L, McGuinness OP; Mouse Metabolic Phenotyping Center Energy Balance Working Group, Banks AS. A big-data approach to understanding metabolic rate and response to obesity in laboratory mice. Elife. 2020 May 1;9:e53560. doi: 10.7554/eLife.53560. PMID: 32356724; PMCID: PMC7274785. Link to article on publisher's site
Corrigan JK, Ramachandran D, He Y, Palmer CJ, Jurczak MJ, Chen R, Li B, Friedline RH, Kim JK, Ramsey JJ, Lantier L, McGuinness OP, Banks AS. (2020). A big-data approach to understanding metabolic rate and response to obesity in laboratory mice. Open Access Articles. https://doi.org/10.7554/eLife.53560. Retrieved from https://escholarship.umassmed.edu/oapubs/4241
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