A big-data approach to understanding metabolic rate and response to obesity in laboratory mice
AuthorsCorrigan, June K.
Palmer, Colin J.
Jurczak, Michael J.
Friedline, Randall H.
Kim, Jason K.
Ramsey, Jon J.
McGuinness, Owen P.
Banks, Alexander S.
UMass Chan AffiliationsDivision of Endocrinology, Metabolism, and Diabetes, Department of Medicine
Program in Molecular Medicine
Biochemical Phenomena, Metabolism, and Nutrition
Cellular and Molecular Physiology
Nutritional and Metabolic Diseases
Research Methods in Life Sciences
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AbstractMaintaining 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.
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